CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Patent Application Ser. No. 62/673,516, filed on May 18, 2018, and claims the benefit of U.S. Patent Application Ser. No. 62/795,900, filed on Jan. 23, 2019. The disclosure of the prior applications are considered part of (and are incorporated by reference in) the disclosure of this application.
STATEMENT REGARDING FEDERAL FUNDING This invention was made with U.S. government support under grant No. CA121113 from the National Institutes of Health. The U.S. government has certain rights in the invention.
BACKGROUND I. Technical Field This document relates to methods and materials for assessing and/or treating mammals (e.g., humans) having cancer. For example, this document provides methods and materials for identifying a mammal as having cancer (e.g., a localized cancer). For example, this document provides methods and materials for monitoring and/or treating a mammal having cancer.
2. Background Information Much of the morbidity and mortality of human cancers world-wide is a result of the late diagnosis of these diseases, where treatments are less effective (Torre et al., 2015 CA Cancer J Clin 65:87; and World Health Organization, 2017 Guide to Cancer Early Diagnosis). Unfortunately, clinically proven biomarkers that can be used to broadly diagnose and treat patients are not widely available (Mazzucchelli, 2000 Advances in clinical pathology 4:111; Ruibal Morell, 1992 The International journal of biological markers 7:160; Galli et al., 2013 Clinical chemistry and laboratory medicine 51:1369; Sikaris, 2011 Heart, lung &circulation 20:634; Lin et al., 2016 in Screening for Colorectal Cancer: A Systematic Review for the U.S. Preventive Services Task Force. (Rockville, Md.); Wanebo et al., 1978 N Engl J Med 299:448; and Zauber, 2015 Dig Dis Sci 60:681).
SUMMARY Recent analyses of cell-free DNA suggests that such approaches may provide new avenues for early diagnosis (Phallen et al., 2017 Sci Transl Med 9; Cohen et al., 2018 Science 359:926; Alix-Panabieres et al., 2016 Cancer discovery 6:479; Siravegna et al., 2017 Nature reviews. Clinical oncology 14:531; Haber et al., 2014 Cancer discovery 4:650; Husain et al., 2017 JAMA 318:1272; and Wan et al., 2017 Nat Rev Cancer 17:223).
This document provides methods and materials for determining a cell free DNA (cfDNA) fragmentation profile in a mammal (e.g., in a sample obtained from a mammal). In some cases, determining a cfDNA fragmentation profile in a mammal can be used for identifying a mammal as having cancer. For example, cfDNA fragments obtained from a mammal (e.g., from a sample obtained from a mammal) can be subjected to low coverage whole-genome sequencing, and the sequenced fragments can be mapped to the genome (e.g., in non-overlapping windows) and assessed to determine a cfDNA fragmentation profile. This document also provides methods and materials for assessing and/or treating mammals (e.g., humans) having, or suspected of having, cancer. In some cases, this document provides methods and materials for identifying a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile. In some cases, this document provides methods and materials for monitoring and/or treating a mammal having cancer. For example, one or more cancer treatments can be administered to a mammal identified as having cancer (e.g., based, at least in part, on a cfDNA fragmentation profile) to treat the mammal.
Described herein is a non-invasive method for the early detection and localization of cancer. cfDNA in the blood can provide a non-invasive diagnostic avenue for patients with cancer. As demonstrated herein, DNA Evaluation of Fragments for early Interception (DELFI) was developed and used to evaluate genome-wide fragmentation patterns of cfDNA of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancers as well as 245 healthy individuals. These analyses revealed that cfDNA profiles of healthy individuals reflected nucleosomal fragmentation patterns of white blood cells, while patients with cancer had altered fragmentation profiles. DELFI had sensitivities of detection ranging from 57% to >99% among the seven cancer types at 98% specificity and identified the tissue of origin of the cancers to a limited number of sites in 75% of cases. Assessing cfDNA (e.g., using DELFI) can provide a screening approach for early detection of cancer, which can increase the chance for successful treatment of a patient having cancer. Assessing cfDNA (e.g., using DELFI) can also provide an approach for monitoring cancer, which can increase the chance for successful treatment and improved outcome of a patient having cancer. In addition, a cfDNA fragmentation profile can be obtained from limited amounts of cfDNA and using inexpensive reagents and/or instruments.
In general, one aspect of this document features methods for determining a cfDNA fragmentation profile of a mammal. The methods can include, or consist essentially of, processing cfDNA fragments obtained from a sample obtained from the mammal into sequencing libraries, subjecting the sequencing libraries to whole genome sequencing (e.g., low-coverage whole genome sequencing) to obtain sequenced fragments, mapping the sequenced fragments to a genome to obtain windows of mapped sequences, and analyzing the windows of mapped sequences to determine cfDNA fragment lengths. The mapped sequences can include tens to thousands of windows. The windows of mapped sequences can be non-overlapping windows. The windows of mapped sequences can each include about 5 million base pairs. The cfDNA fragmentation profile can be determined within each window. The cfDNA fragmentation profile can include a median fragment size. The cfDNA fragmentation profile can include a fragment size distribution. The cfDNA fragmentation profile can include a ratio of small cfDNA fragments to large cfDNA fragments in the windows of mapped sequences. The cfDNA fragmentation profile can be over the whole genome. The cfDNA fragmentation profile can be over a subgenomic interval (e.g., an interval in a portion of a chromosome).
In another aspect, this document features methods for identifying a mammal as having cancer. The methods can include, or consist essentially of, determining a cfDNA fragmentation profile in a sample obtained from a mammal, comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile, and identifying the mammal as having cancer when the cfDNA fragmentation profile in the sample obtained from the mammal is different from the reference cfDNA fragmentation profile. The reference cfDNA fragmentation profile can be a cfDNA fragmentation profile of a healthy mammal. The reference cfDNA fragmentation profile can be generated by determining a cfDNA fragmentation profile in a sample obtained from the healthy mammal. The reference DNA fragmentation pattern can be a reference nucleosome cfDNA fragmentation profile. The cfDNA fragmentation profiles can include a median fragment size, and a median fragment size of the cfDNA fragmentation profile can be shorter than a median fragment size of the reference cfDNA fragmentation profile. The cfDNA fragmentation profiles can include a fragment size distribution, and a fragment size distribution of the cfDNA fragmentation profile can differ by at least 10 nucleotides as compared to a fragment size distribution of the reference cfDNA fragmentation profile. The cfDNA fragmentation profiles can include position dependent differences in fragmentation patterns, including a ratio of small cfDNA fragments to large cfDNA fragments, where a small cfDNA fragment can be 100 base pairs (bp) to 150 bp in length and a large cfDNA fragments can be 151 bp to 220 bp in length, and where a correlation of fragment ratios in the cfDNA fragmentation profile can be lower than a correlation of fragment ratios of the reference cfDNA fragmentation profile. The cfDNA fragmentation profiles can include sequence coverage of small cfDNA fragments, large cfDNA fragments, or of both small and large cfDNA fragments, across the genome. The cancer can be colorectal cancer, lung cancer, breast cancer, bile duct cancer, pancreatic cancer, gastric cancer, or ovarian cancer. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile in windows across the whole genome. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile over a subgenomic interval (e.g., an interval in a portion of a chromosome). The mammal can have been previously administered a cancer treatment to treat the cancer. The cancer treatment can be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or any combinations thereof. The method also can include administering to the mammal a cancer treatment (e.g., surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or any combinations thereof). The mammal can be monitored for the presence of cancer after administration of the cancer treatment.
In another aspect, this document features methods for treating a mammal having cancer. The methods can include, or consist essentially of, identifying the mammal as having cancer, where the identifying includes determining a cfDNA fragmentation profile in a sample obtained from the mammal, comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile, and identifying the mammal as having cancer when the cfDNA fragmentation profile obtained from the mammal is different from the reference cfDNA fragmentation profile; and administering a cancer treatment to the mammal. The mammal can be a human. The cancer can be colorectal cancer, lung cancer, breast cancer, gastric cancers, pancreatic cancers, bile duct cancers, or ovarian cancer. The cancer treatment can be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or combinations thereof. The reference cfDNA fragmentation profile can be a cfDNA fragmentation profile of a healthy mammal. The reference cfDNA fragmentation profile can be generated by determining a cfDNA fragmentation profile in a sample obtained from a healthy mammal. The reference DNA fragmentation pattern can be a reference nucleosome cfDNA fragmentation profile. The cfDNA fragmentation profile can include a median fragment size, where a median fragment size of the cfDNA fragmentation profile is shorter than a median fragment size of the reference cfDNA fragmentation profile. The cfDNA fragmentation profile can include a fragment size distribution, where a fragment size distribution of the cfDNA fragmentation profile differs by at least 10 nucleotides as compared to a fragment size distribution of the reference cfDNA fragmentation profile. The cfDNA fragmentation profile can include a ratio of small cfDNA fragments to large cfDNA fragments in the windows of mapped sequences, where a small cfDNA fragment is 100 bp to 150 bp in length, where a large cfDNA fragments is 151 bp to 220 bp in length, and where a correlation of fragment ratios in the cfDNA fragmentation profile is lower than a correlation of fragment ratios of the reference cfDNA fragmentation profile. The cfDNA fragmentation profile can include the sequence coverage of small cfDNA fragments in windows across the genome. The cfDNA fragmentation profile can include the sequence coverage of large cfDNA fragments in windows across the genome. The cfDNA fragmentation profile can include the sequence coverage of small and large cfDNA fragments in windows across the genome. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile over the whole genome. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile over a subgenomic interval. The mammal can have previously been administered a cancer treatment to treat the cancer. The cancer treatment can be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or combinations thereof. The method also can include monitoring the mammal for the presence of cancer after administration of the cancer treatment.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used to practice the invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.
The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims.
DESCRIPTION OF THE DRAWINGS FIG. 1. Schematic of an exemplary DELFI approach. Blood is collected from a cohort of healthy individuals and patients with cancer. Nucleosome protected cfDNA is extracted from the plasma fraction, processed into sequencing libraries, examined through whole genome sequencing, mapped to the genome, and analyzed to determine cfDNA fragment profiles in different windows across the genome. Machine learning approaches are used to categorize individuals as healthy or as having cancer and to identify the tumor tissue of origin using genome-wide cfDNA fragmentation patterns.
FIG. 2. Simulations of non-invasive cancer detection based on number of alterations analyzed and tumor-derived cfDNA fragment distributions. Monte Carlo simulations were performed using different numbers of tumor-specific alterations to evaluate the probability of detecting cancer alterations in cfDNA at the indicated fraction of tumor-derived molecules. The simulations were performed assuming an average of 2000 genome equivalents of cfDNA and the requirement of five or more observations of any alteration. These analyses indicate that increasing the number of tumor-specific alterations improves the sensitivity of detection of circulating tumor DNA.
FIG. 3. Tumor-derived cfDNA fragment distributions. Cumulative density functions of cfDNA fragment lengths of 42 loci containing tumor-specific alterations from 30 patients with breast, colorectal, lung, or ovarian cancer are shown with 95% confidence bands (blue). Lengths of mutant cfDNA fragments were significantly different in size compared to wild-type cfDNA fragments (red) at these loci.
FIGS. 4A and 4B. Tumor-derived cfDNA GC content and fragment length. A, GC content was similar for mutated and non-mutated fragments. B, GC content was not correlated to fragment length.
FIG. 5. Germline cfDNA fragment distributions. Cumulative density functions of fragment lengths of 44 loci containing germline alterations (non-tumor derived) from 38 patients with breast, colorectal, lung, or ovarian cancer are shown with 95% confidence bands. Fragments with germline mutations (blue) were comparable in length to wild-type cfDNA fragment lengths (red).
FIG. 6. Hematopoietic cfDNA fragment distributions. Cumulative density functions of fragment lengths of 41 loci containing hematopoietic alterations (non-tumor derived) from 28 patients with breast, colorectal, lung, or ovarian cancer are shown with 95% confidence bands. After correction for multiple testing, there were no significant differences (α=0.05) in the size distributions of mutated hematopoietic ctDNA fragments (blue) and wild-type cfDNA fragments (red).
FIGS. 7A-7F. cfDNA fragmentation profiles in healthy individuals and patients with cancer. A, Genome-wide cfDNA fragmentation profiles (defined as the ratio of short to long fragments) from ˜9× whole genome sequencing are shown in 5 Mb bins for 30 healthy individuals (top) and 8 lung cancer patients (bottom). B, An analysis of healthy cfDNA (top), lung cancer cfDNA (middle), and healthy lymphocyte (bottom) fragmentation profiles and lymphocyte profiles from chromosome 1 at 1 Mb resolution. The healthy lymphocyte profiles were scaled with a standard deviation equal to that of the median healthy cfDNA profiles. Healthy cfDNA patterns closely mirrored those in healthy lymphocytes while lung cancer cfDNA profiles were more varied and differed from both healthy and lymphocyte profiles. C, Smoothed median distances between adjacent nucleosome centered at zero using 100 kb bins from healthy cfDNA (top) and nuclease-digested healthy lymphocytes (middle) are depicted together with the first eigenvector for the genome contact matrix obtained through previously reported Hi-C analyses of lymphoblastoid cells (bottom). Healthy cfDNA nucleosome distances closely mirrored those in nuclease-digested lymphocytes as well as those from lymphoblastoid Hi-C analyses. cfDNA fragmentation profiles from healthy individuals (n=30) had high correlations while patients with lung cancer had lower correlations to median fragmentation profiles of lymphocytes (D), healthy cfDNA (E), and lymphocyte nucleosome (F) distances.
FIG. 8. Density of cfDNA fragment lengths in healthy individuals and patients with lung cancer. cfDNA fragments lengths are shown for healthy individuals (n=30, gray) and patients with lung cancer (n=8, blue).
FIGS. 9A and 9B. Subsampling of whole genome sequence data for analysis of cfDNA fragmentation profiles. A, High coverage (9×) whole-genome sequencing data were subsampled to 2×, 1×, 0.5×, 0.2×, and 0.1× fold coverage. Mean centered genome-wide fragmentation profiles in 5 Mb bins for 30 healthy individuals and 8 patients with lung cancer are depicted for each subsampled fold coverage with median profiles shown in blue. B, Pearson correlation of subsampled profiles to initial profile at 9× coverage for healthy individuals and patients with lung cancer.
FIG. 10. cfDNA fragmentation profiles and sequence alterations during therapy. Detection and monitoring of cancer in serial blood draws from NSCLC patients (n=19) undergoing treatment with targeted tyrosine kinase inhibitors (black arrows) was performed using targeted sequencing (top) and genome-wide fragmentation profiles (bottom). For each case, the vertical axis of the lower panel displays −1 times the correlation of each sample to the median healthy cfDNA fragmentation profile. Error bars depict confidence intervals from binomial tests for mutant allele fractions and confidence intervals calculated using Fisher transformation for genome-wide fragmentation profiles. Although the approaches analyze different aspects of cfDNA (whole genome compared to specific alterations) the targeted sequencing and fragmentation profiles were similar for patients responding to therapy as well as those with stable or progressive disease. As fragmentation profiles reflect both genomic and epigenomic alterations, while mutant allele fractions only reflect individual mutations, mutant allele fractions alone may not reflect the absolute level of correlation of fragmentation profiles to healthy individuals.
FIGS. 11A-11C. cfDNA fragmentation profiles in healthy individuals and patients with cancer. A, Fragmentation profiles (bottom) in the context of tumor copy number changes (top) in a colorectal cancer patient where parallel analyses of tumor tissue were performed. The distribution of segment means and integer copy numbers are shown at top right in the indicated colors. Altered fragmentation profiles were present in regions of the genome that were copy neutral and were further affected in regions with copy number changes. B, GC adjusted fragmentation profiles from 1-2× whole genome sequencing for healthy individuals and patients with cancer are depicted per cancer type using 5 Mb windows. The median healthy profile is indicated in black and the 98% confidence band is shown in gray. For patients with cancer, individual profiles are colored based on their correlation to the healthy median. C, Windows are indicated in orange if more than 10% of the cancer samples had a fragment ratio more than three standard deviations from the median healthy fragment ratio. These analyses highlight the multitude of position dependent alterations across the genome in cfDNA of individuals with cancer.
FIGS. 12A and 12B. Profiles of cfDNA fragment lengths in copy neutral regions in healthy individuals and one patient with colorectal cancer. A, The fragmentation profile in 211 copy neutral windows in chromosomes 1-6 for 25 randomly selected healthy individuals (gray). For a patient with colorectal cancer (CGCRC291) with an estimated mutant allele fraction of 20%, the cancer fragment length profile was diluted to an approximate 10% tumor contribution (blue). A and B, While the marginal densities of the fragment profiles for the healthy samples and cancer patient show substantial overlap (A, right), the fragmentation profiles are different as can be seen visualization of the fragmentation profiles (A, left) and by the separation of the colorectal cancer patient from the healthy samples in a principal component analysis (B).
FIGS. 13A and 13B. Genome-wide GC correction of cfDNA fragments. To estimate and control for the effects of GC content on sequencing coverage, coverage in non-overlapping 100 kb genomic windows was calculated across the autosomes. For each window, the average GC of the aligned fragments was calculated. A, Loess smoothing of raw coverage (top row) for two randomly selected healthy subjects (CGPLH189 and CGPLH380) and two cancer patients (CGPLLU161 and CGPLBR24) with undetectable aneuploidy (PA score <2.35). After subtracting the average coverage predicted by the loess model, the residuals were resealed to the median autosomal coverage (bottom row). As fragment length may also result in coverage biases, this GC correction procedure was performed separately for short (≤150 bp) and long (≥151 bp) fragments. While the 100 kb bins on chromosome 19 (blue points) consistently have less coverage than predicted by the loess model, we did not implement a chromosome-specific correction as such an approach would remove the effects of chromosomal copy number on coverage. B, Overall, a limited correlation was found between short or long fragment coverage and GC content after correction among healthy subjects and cancer patients with a PA score <3.
FIG. 14. Schematic of machine learning model. Gradient tree boosting machine learning was used to examine whether cfDNA can be categorized as having characteristics of a cancer patient or healthy individual. The machine learning model included fragmentation size and coverage characteristics in windows throughout the genome, as well as chromosomal arm and mitochondrial DNA copy numbers. A 10-fold cross validation approach was employed in which each sample is randomly assigned to a fold and 9 of the folds (90% of the data) are used for training and one fold (10% of the data) is used for testing. The prediction accuracy from a single cross validation is an average over the 10 possible combinations of test and training sets. As this prediction accuracy can reflect bias from the initial randomization of patients, the entire procedure was repeat, including the randomization of patients to folds, 10 times. For all cases, feature selection and model estimation were performed on training data and were validated on test data and the test data were never used for feature selection. Ultimately, a DELFI score was obtained that could be used to classify individuals as likely healthy or having cancer.
FIG. 15. Distribution of AUCs across the repeated 10-fold cross-validation. The 25th, 50th, and 75th percentiles of the 100 AUCs for the cohort of 215 healthy individuals and 208 patients with cancer are indicated by dashed lines.
FIGS. 16A and 16B. Whole-genome analyses of chromosomal arm copy number changes and mitochondrial genome representation. A, Z scores for each autosome arm are depicted for healthy individuals (n=215) and patients with cancer (n=208). The vertical axis depicts normal copy at zero with positive and negative values indicating arm gains and losses, respectively. Z scores greater than 50 or less than −50 are thresholded at the indicated values. B, The fraction of reads mapping to the mitochondrial genome is depicted for healthy individuals and patients with cancer.
FIGS. 17A and 17B. Detection of cancer using DELFI. A, Receiver operator characteristics for detection of cancer using cfDNA fragmentation profiles and other genome-wide features in a machine learning approach are depicted for a cohort of 215 healthy individuals and 208 patients with cancer (DELFI, AUC=0.94), with ≥95% specificity shaded in blue. Machine learning analyses of chromosomal arm copy number (Chr copy number (ML)), and mitochondrial genome copy number (mtDNA), are shown in the indicated colors. B, Analyses of individual cancers types using the DELFI-combined approach had AUCs ranging from 0.86 to >0.99.
FIG. 18. DELFI detection of cancer by stage. Receiver operator characteristics for detection of cancer using cfDNA fragmentation profiles and other genome-wide features in a machine learning approach are depicted for a cohort of 215 healthy individuals and each stage of 208 patients with cancer with >95% specificity shaded in blue.
FIG. 19. DELFI tissue of origin prediction. Receiver operator characteristics for DELFI tissue prediction of bile duct, breast, colorectal, gastric, lung, ovarian, and pancreatic cancers are depicted. In order to increase sample sizes within cancer type classes, cases detected with a 90% specificity were included, and the lung cancer cohort was supplemented with the addition of baseline cfDNA data from 18 lung cancer patients with prior treatment (see, e.g., Shen et al., 2018 Nature, 563:579-583).
FIG. 20. Detection of cancer using DELFI and mutation-based cfDNA approaches. DELFI (green) and targeted sequencing for mutation identification (blue) were performed independently in a cohort of 126 patients with breast, bile duct, colorectal, gastric, lung, or ovarian cancers. The number of individuals detected by each approach and in combination are indicated for DELFI detection with a specificity of 98%, targeted sequencing specificity at >99%, and a combined specificity of 98%. ND indicates not detected.
DETAILED DESCRIPTION This document provides methods and materials for determining a cfDNA fragmentation profile in a mammal (e.g., in a sample obtained from a mammal). As used herein, the terms “fragmentation profile,” “position dependent differences in fragmentation patterns,” and “differences in fragment size and coverage in a position dependent manner across the genome” are equivalent and can be used interchangeably. In some cases, determining a cfDNA fragmentation profile in a mammal can be used for identifying a mammal as having cancer. For example, cfDNA fragments obtained from a mammal (e.g., from a sample obtained from a mammal) can be subjected to low coverage whole-genome sequencing, and the sequenced fragments can be mapped to the genome (e.g., in non-overlapping windows) and assessed to determine a cfDNA fragmentation profile. As described herein, a cfDNA fragmentation profile of a mammal having cancer is more heterogeneous (e.g., in fragment lengths) than a cfDNA fragmentation profile of a healthy mammal (e.g., a mammal not having cancer). As such, this document also provides methods and materials for assessing, monitoring, and/or treating mammals (e.g., humans) having, or suspected of having, cancer. In some cases, this document provides methods and materials for identifying a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine the presence and, optionally, the tissue of origin of the cancer in the mammal based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for monitoring a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine the presence of the cancer in the mammal based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for identifying a mammal as having cancer, and administering one or more cancer treatments to the mammal to treat the mammal. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile of the mammal, and one or more cancer treatments can be administered to the mammal.
A cfDNA fragmentation profile can include one or more cfDNA fragmentation patterns. A cfDNA fragmentation pattern can include any appropriate cfDNA fragmentation pattern. Examples of cfDNA fragmentation patterns include, without limitation, median fragment size, fragment size distribution, ratio of small cfDNA fragments to large cfDNA fragments, and the coverage of cfDNA fragments. In some cases, a cfDNA fragmentation pattern includes two or more (e.g., two, three, or four) of median fragment size, fragment size distribution, ratio of small cfDNA fragments to large cfDNA fragments, and the coverage of cfDNA fragments. In some cases, cfDNA fragmentation profile can be a genome-wide cfDNA profile (e.g., a genome-wide cfDNA profile in windows across the genome). In some cases, cfDNA fragmentation profile can be a targeted region profile. A targeted region can be any appropriate portion of the genome (e.g., a chromosomal region). Examples of chromosomal regions for which a cfDNA fragmentation profile can be determined as described herein include, without limitation, a portion of a chromosome (e.g., a portion of 2q, 4p, 5p, 6q, 7p, 8q, 9q, 10q, 11q, 12q, and/or 14q) and a chromosomal arm (e.g., a chromosomal arm of 8q, 13q, 11q, and/or 3p). In some cases, a cfDNA fragmentation profile can include two or more targeted region profiles.
In some cases, a cfDNA fragmentation profile can be used to identify changes (e.g., alterations) in cfDNA fragment lengths. An alteration can be a genome-wide alteration or an alteration in one or more targeted regions/loci. A target region can be any region containing one or more cancer-specific alterations. Examples of cancer-specific alterations, and their chromosomal locations, include, without limitation, those shown in Table 3 (Appendix C) and those shown in Table 6 (Appendix F). In some cases, a cfDNA fragmentation profile can be used to identify (e.g., simultaneously identify) from about 10 alterations to about 500 alterations (e.g., from about 25 to about 500, from about 50 to about 500, from about 100 to about 500, from about 200 to about 500, from about 300 to about 500, from about 10 to about 400, from about 10 to about 300, from about 10 to about 200, from about 10 to about 100, from about 10 to about 50, from about 20 to about 400, from about 30 to about 300, from about 40 to about 200, from about 50 to about 100, from about 20 to about 100, from about 25 to about 75, from about 50 to about 250, or from about 100 to about 200, alterations).
In some cases, a cfDNA fragmentation profile can be used to detect tumor-derived DNA. For example, a cfDNA fragmentation profile can be used to detect tumor-derived DNA by comparing a cfDNA fragmentation profile of a mammal having, or suspected of having, cancer to a reference cfDNA fragmentation profile (e.g., a cfDNA fragmentation profile of a healthy mammal and/or a nucleosomal DNA fragmentation profile of healthy cells from the mammal having, or suspected of having, cancer). In some cases, a reference cfDNA fragmentation profile is a previously generated profile from a healthy mammal. For example, methods provided herein can be used to determine a reference cfDNA fragmentation profile in a healthy mammal, and that reference cfDNA fragmentation profile can be stored (e.g., in a computer or other electronic storage medium) for future comparison to a test cfDNA fragmentation profile in mammal having, or suspected of having, cancer. In some cases, a reference cfDNA fragmentation profile (e.g., a stored cfDNA fragmentation profile) of a healthy mammal is determined over the whole genome. In some cases, a reference cfDNA fragmentation profile (e.g., a stored cfDNA fragmentation profile) of a healthy mammal is determined over a subgenomic interval.
In some cases, a cfDNA fragmentation profile can be used to identify a mammal (e.g., a human) as having cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, and/or an ovarian cancer).
A cfDNA fragmentation profile can include a cfDNA fragment size pattern. cfDNA fragments can be any appropriate size. For example, cfDNA fragment can be from about 50 base pairs (bp) to about 400 bp in length. As described herein, a mammal having cancer can have a cfDNA fragment size pattern that contains a shorter median cfDNA fragment size than the median cfDNA fragment size in a healthy mammal. A healthy mammal (e.g., a mammal not having cancer) can have cfDNA fragment sizes having a median cfDNA fragment size from about 166.6 bp to about 167.2 bp (e.g., about 166.9 bp). In some cases, a mammal having cancer can have cfDNA fragment sizes that are, on average, about 1.28 bp to about 2.49 bp (e.g., about 1.88 bp) shorter than cfDNA fragment sizes in a healthy mammal. For example, a mammal having cancer can have cfDNA fragment sizes having a median cfDNA fragment size of about 164.11 bp to about 165.92 bp (e.g., about 165.02 bp).
A cfDNA fragmentation profile can include a cfDNA fragment size distribution. As described herein, a mammal having cancer can have a cfDNA size distribution that is more variable than a cfDNA fragment size distribution in a healthy mammal. In some case, a size distribution can be within a targeted region. A healthy mammal (e.g., a mammal not having cancer) can have a targeted region cfDNA fragment size distribution of about 1 or less than about 1. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution that is longer (e.g., 10, 15, 20, 25, 30, 35, 40, 45, 50 or more bp longer, or any number of base pairs between these numbers) than a targeted region cfDNA fragment size distribution in a healthy mammal. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution that is shorter (e.g., 10, 15, 20, 25, 30, 35, 40, 45, 50 or more bp shorter, or any number of base pairs between these numbers) than a targeted region cfDNA fragment size distribution in a healthy mammal. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution that is about 47 bp smaller to about 30 bp longer than a targeted region cfDNA fragment size distribution in a healthy mammal. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution of, on average, a 10, 11, 12, 13, 14, 15, 15, 17, 18, 19, 20 or more bp difference in lengths of cfDNA fragments. For example, a mammal having cancer can have a targeted region cfDNA fragment size distribution of, on average, about a 13 bp difference in lengths of cfDNA fragments. In some case, a size distribution can be a genome-wide size distribution. A healthy mammal (e.g., a mammal not having cancer) can have very similar distributions of short and long cfDNA fragments genome-wide. In some cases, a mammal having cancer can have, genome-wide, one or more alterations (e.g., increases and decreases) in cfDNA fragment sizes. The one or more alterations can be any appropriate chromosomal region of the genome. For example, an alteration can be in a portion of a chromosome. Examples of portions of chromosomes that can contain one or more alterations in cfDNA fragment sizes include, without limitation, portions of 2q, 4p, 5p, 6q, 7p, 8q, 9q, 10q, 11q, 12q, and 14q. For example, an alteration can be across a chromosome arm (e.g., an entire chromosome arm).
A cfDNA fragmentation profile can include a ratio of small cfDNA fragments to large cfDNA fragments and a correlation of fragment ratios to reference fragment ratios. As used herein, with respect to ratios of small cfDNA fragments to large cfDNA fragments, a small cfDNA fragment can be from about 100 bp in length to about 150 bp in length. As used herein, with respect to ratios of small cfDNA fragments to large cfDNA fragments, a large cfDNA fragment can be from about 151 bp in length to 220 bp in length. As described herein, a mammal having cancer can have a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) that is lower (e.g., 2-fold lower, 3-fold lower, 4-fold lower, 5-fold lower, 6-fold lower, 7-fold lower, 8-fold lower, 9-fold lower, 10-fold lower, or more) than in a healthy mammal. A healthy mammal (e.g., a mammal not having cancer) can have a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) of about 1 (e.g., about 0.96). In some cases, a mammal having cancer can have a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) that is, on average, about 0.19 to about 0.30 (e.g., about 0.25) lower than a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) in a healthy mammal.
A cfDNA fragmentation profile can include coverage of all fragments. Coverage of all fragments can include windows (e.g., non-overlapping windows) of coverage. In some cases, coverage of all fragments can include windows of small fragments (e.g., fragments from about 100 bp to about 150 bp in length). In some cases, coverage of all fragments can include windows of large fragments (e.g., fragments from about 151 bp to about 220 bp in length).
In some cases, a cfDNA fragmentation profile can be used to identify the tissue of origin of a cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, or an ovarian cancer). For example, a cfDNA fragmentation profile can be used to identify a localized cancer. When a cfDNA fragmentation profile includes a targeted region profile, one or more alterations described herein (e.g., in Table 3 (Appendix C) and/or in Table 6 (Appendix F)) can be used to identify the tissue of origin of a cancer. In some cases, one or more alterations in chromosomal regions can be used to identify the tissue of origin of a cancer.
A cfDNA fragmentation profile can be obtained using any appropriate method. In some cases, cfDNA from a mammal (e.g., a mammal having, or suspected of having, cancer) can be processed into sequencing libraries which can be subjected to whole genome sequencing (e.g., low-coverage whole genome sequencing), mapped to the genome, and analyzed to determine cfDNA fragment lengths. Mapped sequences can be analyzed in non-overlapping windows covering the genome. Windows can be any appropriate size. For example, windows can be from thousands to millions of bases in length. As one non-limiting example, a window can be about 5 megabases (Mb) long. Any appropriate number of windows can be mapped. For example, tens to thousands of windows can be mapped in the genome. For example, hundreds to thousands of windows can be mapped in the genome. A cfDNA fragmentation profile can be determined within each window. In some cases, a cfDNA fragmentation profile can be obtained as described in Example 1. In some cases, a cfDNA fragmentation profile can be obtained as shown in FIG. 1.
In some cases, methods and materials described herein also can include machine learning. For example, machine learning can be used for identifying an altered fragmentation profile (e.g., using coverage of cfDNA fragments, fragment size of cfDNA fragments, coverage of chromosomes, and mtDNA).
In some cases, methods and materials described herein can be the sole method used to identify a mammal (e.g., a human) as having cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, and/or an ovarian cancer). For example, determining a cfDNA fragmentation profile can be the sole method used to identify a mammal as having cancer.
In some cases, methods and materials described herein can be used together with one or more additional methods used to identify a mammal (e.g., a human) as having cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, and/or an ovarian cancer). Examples of methods used to identify a mammal as having cancer include, without limitation, identifying one or more cancer-specific sequence alterations, identifying one or more chromosomal alterations (e.g., aneuploidies and rearrangements), and identifying other cfDNA alterations. For example, determining a cfDNA fragmentation profile can be used together with identifying one or more cancer-specific mutations in a mammal's genome to identify a mammal as having cancer. For example, determining a cfDNA fragmentation profile can be used together with identifying one or more aneuploidies in a mammal's genome to identify a mammal as having cancer.
In some aspects, this document also provides methods and materials for assessing, monitoring, and/or treating mammals (e.g., humans) having, or suspected of having, cancer. In some cases, this document provides methods and materials for identifying a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for identifying the location (e.g., the anatomic site or tissue of origin) of a cancer in a mammal. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine the tissue of origin of the cancer in the mammal based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for identifying a mammal as having cancer, and administering one or more cancer treatments to the mammal to treat the mammal. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile of the mammal, and administering one or more cancer treatments to the mammal. In some cases, this document provides methods and materials for treating a mammal having cancer. For example, one or more cancer treatments can be administered to a mammal identified as having cancer (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal) to treat the mammal. In some cases, during or after the course of a cancer treatment (e.g., any of the cancer treatments described herein), a mammal can undergo monitoring (or be selected for increased monitoring) and/or further diagnostic testing. In some cases, monitoring can include assessing mammals having, or suspected of having, cancer by, for example, assessing a sample (e.g., a blood sample) obtained from the mammal to determine the cfDNA fragmentation profile of the mammal as described herein, and changes in the cfDNA fragmentation profiles over time can be used to identify response to treatment and/or identify the mammal as having cancer (e.g., a residual cancer).
Any appropriate mammal can be assessed, monitored, and/or treated as described herein. A mammal can be a mammal having cancer. A mammal can be a mammal suspected of having cancer. Examples of mammals that can be assessed, monitored, and/or treated as described herein include, without limitation, humans, primates such as monkeys, dogs, cats, horses, cows, pigs, sheep, mice, and rats. For example, a human having, or suspected of having, cancer can be assessed to determine a cfDNA fragmentation profiled as described herein and, optionally, can be treated with one or more cancer treatments as described herein.
Any appropriate sample from a mammal can be assessed as described herein (e.g., assessed for a DNA fragmentation pattern). In some cases, a sample can include DNA (e.g., genomic DNA). In some cases, a sample can include cfDNA (e.g., circulating tumor DNA (ctDNA)). In some cases, a sample can be fluid sample (e.g., a liquid biopsy). Examples of samples that can contain DNA and/or polypeptides include, without limitation, blood (e.g., whole blood, serum, or plasma), amnion, tissue, urine, cerebrospinal fluid, saliva, sputum, broncho-alveolar lavage, bile, lymphatic fluid, cyst fluid, stool, ascites, pap smears, breast milk, and exhaled breath condensate. For example, a plasma sample can be assessed to determine a cfDNA fragmentation profiled as described herein.
A sample from a mammal to be assessed as described herein (e.g., assessed for a DNA fragmentation pattern) can include any appropriate amount of cfDNA. In some cases, a sample can include a limited amount of DNA. For example, a cfDNA fragmentation profile can be obtained from a sample that includes less DNA than is typically required for other cfDNA analysis methods, such as those described in, for example, Phallen et al., 2017 Sci Transl Med 9; Cohen et al., 2018 Science 359:926; Newman et al., 2014 Nat Med 20:548; and Newman et al., 2016 Nat Biotechnol 34:547).
In some cases, a sample can be processed (e.g., to isolate and/or purify DNA and/or polypeptides from the sample). For example, DNA isolation and/or purification can include cell lysis (e.g., using detergents and/or surfactants), protein removal (e.g., using a protease), and/or RNA removal (e.g., using an RNase). As another example, polypeptide isolation and/or purification can include cell lysis (e.g., using detergents and/or surfactants), DNA removal (e.g., using a DNase), and/or RNA removal (e.g., using an RNase).
A mammal having, or suspected of having, any appropriate type of cancer can be assessed (e.g., to determine a cfDNA fragmentation profile) and/or treated (e.g., by administering one or more cancer treatments to the mammal) using the methods and materials described herein. A cancer can be any stage cancer. In some cases, a cancer can be an early stage cancer. In some cases, a cancer can be an asymptomatic cancer. In some cases, a cancer can be a residual disease and/or a recurrence (e.g., after surgical resection and/or after cancer therapy). A cancer can be any type of cancer. Examples of types of cancers that can be assessed, monitored, and/or treated as described herein include, without limitation, colorectal cancers, lung cancers, breast cancers, gastric cancers, pancreatic cancers, bile duct cancers, and ovarian cancers.
When treating a mammal having, or suspected of having, cancer as described herein, the mammal can be administered one or more cancer treatments. A cancer treatment can be any appropriate cancer treatment. One or more cancer treatments described herein can be administered to a mammal at any appropriate frequency (e.g., once or multiple times over a period of time ranging from days to weeks). Examples of cancer treatments include, without limitation adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy (e.g., chimeric antigen receptors and/or T cells having wild-type or modified T cell receptors), targeted therapy such as administration of kinase inhibitors (e.g., kinase inhibitors that target a particular genetic lesion, such as a translocation or mutation), (e.g. a kinase inhibitor, an antibody, a bispecific antibody), signal transduction inhibitors, bispecific antibodies or antibody fragments (e.g., BiTEs), monoclonal antibodies, immune checkpoint inhibitors, surgery (e.g., surgical resection), or any combination of the above. In some cases, a cancer treatment can reduce the severity of the cancer, reduce a symptom of the cancer, and/or to reduce the number of cancer cells present within the mammal.
In some cases, a cancer treatment can include an immune checkpoint inhibitor. Non-limiting examples of immune checkpoint inhibitors include nivolumab (Opdivo), pembrolizumab (Keytruda), atezolizumab (tecentriq), avelumab (bavencio), durvalumab (imfinzi), ipilimumab (yervoy). See, e.g., Pardoll (2012) Nat. Rev Cancer 12: 252-264; Sun et al. (2017) Eur Rev Med Pharmacol Sci 21(6): 1198-1205; Hamanishi et al. (2015) J. Clin. Oncol. 33(34): 4015-22; Brahmer et al. (2012) N Engl J Med 366(26): 2455-65; Ricciuti et al. (2017) J. Thorac Oncol. 12(5): e51-e55; Ellis et al. (2017) Clin Lung Cancer pii: S1525-7304(17)30043-8; Zou and Awad (2017) Ann Oncol 28(4): 685-687; Sorscher (2017) N Engl J Med 376(10: 996-7; Hui et al. (2017) Ann Oncol 28(4): 874-881; Vansteenkiste et al. (2017) Expert Opin Biol Ther 17(6): 781-789; Hellmann et al. (2017) Lancet Oncol. 18(1): 31-41, Chen (2017) J. Chin Med Assoc 80(1): 7-14.
In some cases, a cancer treatment can be an adoptive T cell therapy (e.g., chimeric antigen receptors and/or T cells having wild-type or modified T cell receptors). See, e.g., Rosenberg and Restifo (2015) Science 348(6230): 62-68; Chang and Chen (2017) Trends Mol Med 23(5): 430-450; Yee and Lizee (2016) Cancer J. 23(2): 144-148; Chen et al. (2016) Oncoimmunology 6(2): e1273302; US 2016/0194404; US 2014/0050788; US 2014/0271635; U.S. Pat. No. 9,233,125; incorporated by reference in their entirety herein.
In some cases, a cancer treatment can be a chemotherapeutic agent. Non-limiting examples of chemotherapeutic agents include: amsacrine, azacitidine, axathioprine, bevacizumab (or an antigen-binding fragment thereof), bleomycin, busulfan, carboplatin, capecitabine, chlorambucil, cisplatin, cyclophosphamide, cytarabine, dacarbazine, daunorubicin, docetaxel, doxifluridine, doxorubicin, epirubicin, erlotinib hydrochlorides, etoposide, fiudarabine, floxuridine, fludarabine, fluorouracil, gemcitabine, hydroxyurea, idarubicin, ifosfamide, irinotecan, lomustine, mechlorethamine, melphalan, mercaptopurine, methotrxate, mitomycin, mitoxantrone, oxaliplatin, paclitaxel, pemetrexed, procarbazine, all-trans retinoic acid, streptozocin, tafluposide, temozolomide, teniposide, tioguanine, topotecan, uramustine, valrubicin, vinblastine, vincristine, vindesine, vinorelbine, and combinations thereof. Additional examples of anti-cancer therapies are known in the art; see, e.g. the guidelines for therapy from the American Society of Clinical Oncology (ASCO), European Society for Medical Oncology (ESMO), or National Comprehensive Cancer Network (NCCN).
When monitoring a mammal having, or suspected of having, cancer as described herein (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal), the monitoring can be before, during, and/or after the course of a cancer treatment. Methods of monitoring provided herein can be used to determine the efficacy of one or more cancer treatments and/or to select a mammal for increased monitoring. In some cases, the monitoring can include identifying a cfDNA fragmentation profile as described herein. For example, a cfDNA fragmentation profile can be obtained before administering one or more cancer treatments to a mammal having, or suspected or having, cancer, one or more cancer treatments can be administered to the mammal, and one or more cfDNA fragmentation profiles can be obtained during the course of the cancer treatment. In some cases, a cfDNA fragmentation profile can change during the course of cancer treatment (e.g., any of the cancer treatments described herein). For example, a cfDNA fragmentation profile indicative that the mammal has cancer can change to a cfDNA fragmentation profile indicative that the mammal does not have cancer. Such a cfDNA fragmentation profile change can indicate that the cancer treatment is working. Conversely, a cfDNA fragmentation profile can remain static (e.g., the same or approximately the same) during the course of cancer treatment (e.g., any of the cancer treatments described herein). Such a static cfDNA fragmentation profile can indicate that the cancer treatment is not working. In some cases, the monitoring can include conventional techniques capable of monitoring one or more cancer treatments (e.g., the efficacy of one or more cancer treatments). In some cases, a mammal selected for increased monitoring can be administered a diagnostic test (e.g., any of the diagnostic tests disclosed herein) at an increased frequency compared to a mammal that has not been selected for increased monitoring. For example, a mammal selected for increased monitoring can be administered a diagnostic test at a frequency of twice daily, daily, bi-weekly, weekly, bi-monthly, monthly, quarterly, semi-annually, annually, or any at frequency therein. In some cases, a mammal selected for increased monitoring can be administered a one or more additional diagnostic tests compared to a mammal that has not been selected for increased monitoring. For example, a mammal selected for increased monitoring can be administered two diagnostic tests, whereas a mammal that has not been selected for increased monitoring is administered only a single diagnostic test (or no diagnostic tests). In some cases, a mammal that has been selected for increased monitoring can also be selected for further diagnostic testing. Once the presence of a tumor or a cancer (e.g., a cancer cell) has been identified (e.g., by any of the variety of methods disclosed herein), it may be beneficial for the mammal to undergo both increased monitoring (e.g., to assess the progression of the tumor or cancer in the mammal and/or to assess the development of one or more cancer biomarkers such as mutations), and further diagnostic testing (e.g., to determine the size and/or exact location (e.g., tissue of origin) of the tumor or the cancer). In some cases, one or more cancer treatments can be administered to the mammal that is selected for increased monitoring after a cancer biomarker is detected and/or after the cfDNA fragmentation profile of the mammal has not improved or deteriorated. Any of the cancer treatments disclosed herein or known in the art can be administered. For example, a mammal that has been selected for increased monitoring can be further monitored, and a cancer treatment can be administered if the presence of the cancer cell is maintained throughout the increased monitoring period. Additionally or alternatively, a mammal that has been selected for increased monitoring can be administered a cancer treatment, and further monitored as the cancer treatment progresses. In some cases, after a mammal that has been selected for increased monitoring has been administered a cancer treatment, the increased monitoring will reveal one or more cancer biomarkers (e.g., mutations). In some cases, such one or more cancer biomarkers will provide cause to administer a different cancer treatment (e.g., a resistance mutation may arise in a cancer cell during the cancer treatment, which cancer cell harboring the resistance mutation is resistant to the original cancer treatment).
When a mammal is identified as having cancer as described herein (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal), the identifying can be before and/or during the course of a cancer treatment. Methods of identifying a mammal as having cancer provided herein can be used as a first diagnosis to identify the mammal (e.g., as having cancer before any course of treatment) and/or to select the mammal for further diagnostic testing. In some cases, once a mammal has been determined to have cancer, the mammal may be administered further tests and/or selected for further diagnostic testing. In some cases, methods provided herein can be used to select a mammal for further diagnostic testing at a time period prior to the time period when conventional techniques are capable of diagnosing the mammal with an early-stage cancer. For example, methods provided herein for selecting a mammal for further diagnostic testing can be used when a mammal has not been diagnosed with cancer by conventional methods and/or when a mammal is not known to harbor a cancer. In some cases, a mammal selected for further diagnostic testing can be administered a diagnostic test (e.g., any of the diagnostic tests disclosed herein) at an increased frequency compared to a mammal that has not been selected for further diagnostic testing. For example, a mammal selected for further diagnostic testing can be administered a diagnostic test at a frequency of twice daily, daily, bi-weekly, weekly, bi-monthly, monthly, quarterly, semi-annually, annually, or any at frequency therein. In some cases, a mammal selected for further diagnostic testing can be administered a one or more additional diagnostic tests compared to a mammal that has not been selected for further diagnostic testing. For example, a mammal selected for further diagnostic testing can be administered two diagnostic tests, whereas a mammal that has not been selected for further diagnostic testing is administered only a single diagnostic test (or no diagnostic tests). In some cases, the diagnostic testing method can determine the presence of the same type of cancer (e.g., having the same tissue or origin) as the cancer that was originally detected (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal). Additionally or alternatively, the diagnostic testing method can determine the presence of a different type of cancer as the cancer that was original detected. In some cases, the diagnostic testing method is a scan. In some cases, the scan is a computed tomography (CT), a CT angiography (CTA), a esophagram (a Barium swallom), a Barium enema, a magnetic resonance imaging (MRI), a PET scan, an ultrasound (e.g., an endobronchial ultrasound, an endoscopic ultrasound), an X-ray, a DEXA scan. In some cases, the diagnostic testing method is a physical examination, such as an anoscopy, a bronchoscopy (e.g., an autofluorescence bronchoscopy, a white-light bronchoscopy, a navigational bronchoscopy), a colonoscopy, a digital breast tomosynthesis, an endoscopic retrograde cholangiopancreatography (ERCP), an ensophagogastroduodenoscopy, a mammography, a Pap smear, a pelvic exam, a positron emission tomography and computed tomography (PET-CT) scan. In some cases, a mammal that has been selected for further diagnostic testing can also be selected for increased monitoring. Once the presence of a tumor or a cancer (e.g., a cancer cell) has been identified (e.g., by any of the variety of methods disclosed herein), it may be beneficial for the mammal to undergo both increased monitoring (e.g., to assess the progression of the tumor or cancer in the mammal and/or to assess the development of one or more cancer biomarkers such as mutations), and further diagnostic testing (e.g., to determine the size and/or exact location of the tumor or the cancer). In some cases, a cancer treatment is administered to the mammal that is selected for further diagnostic testing after a cancer biomarker is detected and/or after the cfDNA fragmentation profile of the mammal has not improved or deteriorated. Any of the cancer treatments disclosed herein or known in the art can be administered. For example, a mammal that has been selected for further diagnostic testing can be administered a further diagnostic test, and a cancer treatment can be administered if the presence of the tumor or the cancer is confirmed. Additionally or alternatively, a mammal that has been selected for further diagnostic testing can be administered a cancer treatment, and can be further monitored as the cancer treatment progresses. In some cases, after a mammal that has been selected for further diagnostic testing has been administered a cancer treatment, the additional testing will reveal one or more cancer biomarkers (e.g., mutations). In some cases, such one or more cancer biomarkers (e.g., mutations) will provide cause to administer a different cancer treatment (e.g., a resistance mutation may arise in a cancer cell during the cancer treatment, which cancer cell harboring the resistance mutation is resistant to the original cancer treatment).
The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.
EXAMPLES Example 1: Cell-Free DIVA Fragmentation in Patients with Cancer Analyses of cell free DNA have largely focused on targeted sequencing of specific genes. Such studies permit detection of a small number of tumor-specific alterations in patients with cancer and not all patients, especially those with early stage disease, have detectable changes. Whole genome sequencing of cell-free DNA can identify chromosomal abnormalities and rearrangements in cancer patients but detection of such alterations has been challenging in part due to the difficulty in distinguishing a small number of abnormal from normal chromosomal changes (Leary et al., 2010 Sci Transl Med 2:20ra14; and Leary et al., 2012 Sci Transl Med 4:162ra154). Other efforts have suggested nucleosome patterns and chromatin structure may be different between cancer and normal tissues, and that cfDNA in patients with cancer may result in abnormal cfDNA fragment size as well as position (Snyder et al., 2016 Cell 164:57; Jahr et al., 2001 Cancer Res 61:1659; Ivanov et al., 2015 BMC Genomics 16(Suppl 13):S1). However, the amount of sequencing needed for nucleosome footprint analyses of cfDNA is impractical for routine analyses.
The sensitivity of any cell-free DNA approach depends on the number of potential alterations examined as well as the technical and biological limitations of detecting such changes. As a typical blood sample contains ˜2000 genome equivalents of cfDNA per milliliter of plasma (Phallen et al., 2017 Sci Transl Med 9), the theoretical limit of detection of a single alteration can be no better than one in a few thousand mutant to wild-type molecules. An approach that detects a larger number of alterations in the same number of genome equivalents would be more sensitive for detecting cancer in the circulation. Monte Carlo simulations show that increasing the number of potential abnormalities detected from only a few to tens or hundreds can potentially improve the limit of detection by orders of magnitude, similar to recent probability analyses of multiple methylation changes in cfDNA (FIG. 2).
This study presents a novel method called DELFI for detection of cancer and further identification of tissue of origin using whole genome sequencing (FIG. 1). The approach uses cfDNA fragmentation profiles and machine learning to distinguish patterns of healthy blood cell DNA from tumor-derived DNA and to identify the primary tumor tissue. DELFI was used for a retrospective analysis of cfDNA from 245 healthy individuals and 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancers, with most patients exhibiting localized disease. Assuming this approach had sensitivity ≥0.80 for discriminating cancer patients from healthy individuals while maintaining a specificity of 0.95, a study of at least 200 cancer patients would enable estimation of the true sensitivity with a margin of error of 0.06 at the desired specificity of 0.95 or greater.
Materials and Methods Patient and Sample Characteristics Plasma samples from healthy individuals and plasma and tissue samples from patients with breast, lung, ovarian, colorectal, bile duct, or gastric cancer were obtained from ILSBio/Bioreclamation, Aarhus University, Herlev Hospital of the University of Copenhagen, Hvidovre Hospital, the University Medical Center of the University of Utrecht, the Academic Medical Center of the University of Amsterdam, the Netherlands Cancer Institute, and the University of California, San Diego. All samples were obtained under Institutional Review Board approved protocols with informed consent for research use at participating institutions. Plasma samples from healthy individuals were obtained at the time of routine screening, including for colonoscopies or Pap smears. Individuals were considered healthy if they had no previous history of cancer and negative screening results.
Plasma samples from individuals with breast, colorectal, gastric, lung, ovarian, pancreatic, and bile duct cancer were obtained at the time of diagnosis, prior to tumor resection or therapy. Nineteen lung cancer patients analyzed for change in cfDNA fragmentation profiles across multiple time points were undergoing treatment with anti-EGFR or anti-ERBB2 therapy (see, e.g., Phallen et al., 2019 Cancer Research 15, 1204-1213). Clinical data for all patients included in this study are listed in Table 1 (Appendix A). Gender was confirmed through genomic analyses of X and Y chromosome representation. Pathologic staging of gastric cancer patients was performed after neoadjuvant therapy. Samples where the tumor stage was unknown were indicated as stage X or unknown.
Nucleosomal DNA Purification Viably frozen lymphocytes were elutriated from leukocytes obtained from a healthy male (C0618) and female (D0808-L) (Advanced Biotechnologies Inc., Eldersburg, Md.). Aliquots of 1×106 cells were used for nucleosomal DNA purification using EZ Nucleosomal DNA Prep Kit (Zymo Research, Irvine, Calif.). Cells were initially treated with 100 μl of Nuclei Prep Buffer and incubated on ice for 5 minutes. After centrifugation at 200 g for 5 minutes, supernatant was discarded and pelleted nuclei were treated twice with 1000 of Atlantis Digestion Buffer or with 100 μl of micrococcal nuclease (MN) Digestion Buffer. Finally, cellular nucleic DNA was fragmented with 0.5 U of Atlantis dsDNase at 42° C. for 20 minutes or 1.5 U of MNase at 37° C. for 20 minutes. Reactions were stopped using 5×MN Stop Buffer and DNA was purified using Zymo-Spin™ IIC Columns. Concentration and quality of eluted cellular nucleic DNA were analyzed using the Bioanalyzer 2100 (Agilent Technologies, Santa Clara, Calif.).
Sample Preparation and Sequencing of cfDNA
Whole blood was collected in EDTA tubes and processed immediately or within one day after storage at 4° C., or was collected in Streck tubes and processed within two days of collection for three cancer patients who were part of the monitoring analysis. Plasma and cellular components were separated by centrifugation at 800 g for 10 min at 4° C. Plasma was centrifuged a second time at 18,000 g at room temperature to remove any remaining cellular debris and stored at −80° C. until the time of DNA extraction. DNA was isolated from plasma using the Qiagen Circulating Nucleic Acids Kit (Qiagen GmbH) and eluted in LoBind tubes (Eppendorf AG). Concentration and quality of cfDNA were assessed using the Bioanalyzer 2100 (Agilent Technologies).
NGS cfDNA libraries were prepared for whole genome sequencing and targeted sequencing using 5 to 250 ng of cfDNA as described elsewhere (see, e.g., Phallen et al, 2017 Sci Transl Med 9:eaan2415). Briefly, genomic libraries were prepared using the NEBNext DNA Library Prep Kit for Illumina [New England Biolabs (NEB)] with four main modifications to the manufacturer's guidelines: (i) The library purification steps used the on-bead AMPure XP approach to minimize sample loss during elution and tube transfer steps (see, e.g., Fisher et al., 2011 Genome Biol 12:R1); (ii) NEBNext End Repair, A-tailing, and adapter ligation enzyme and buffer volumes were adjusted as appropriate to accommodate the on-bead AMPure XP purification strategy; (iii) a pool of eight unique Illumina dual index adapters with 8-base pair (bp) barcodes was used in the ligation reaction instead of the standard Illumina single or dual index adapters with 6- or 8-bp barcodes, respectively; and (iv) cfDNA libraries were amplified with Phusion Hot Start Polymerase.
Whole genome libraries were sequenced directly. For targeted libraries, capture was performed using Agilent SureSelect reagents and a custom set of hybridization probes targeting 58 genes (see, e.g., Phallen et al., 2017 Sci Transl Med 9:eaan2415) per the manufacturer's guidelines. The captured library was amplified with Phusion Hot Start Polymerase (NEB). Concentration and quality of captured cfDNA libraries were assessed on the Bioanalyzer 2100 using the DNA1000 Kit (Agilent Technologies). Targeted libraries were sequenced using 100-bp paired-end runs on the Illumina HiSeq 2000/2500 (Illumina).
Analyses of Targeted Sequencing Data from cfDNA
Analyses of targeted NGS data for cfDNA samples was performed as described elsewhere (see, e.g., Phallen et al., 2017 Sci Transl Med 9:eaan2415). Briefly, primary processing was completed using Illumina CASAVA (Consensus Assessment of Sequence and Variation) software (version 1.8), including demultiplexing and masking of dual-index adapter sequences. Sequence reads were aligned against the human reference genome (version hg18 or hg19) using NovoAlign with additional realignment of select regions using the Needleman-Wunsch method (see, e.g., Jones et al., 2015 Sci Transl Med 7:283ra53). The positions of the sequence alterations have not been affected by the different genome builds. Candidate mutations, consisting of point mutations, small insertions, and deletions, were identified using VariantDx (see, e.g., Jones et al., 2015 Sci Transl Med 7:283ra53) (Personal Genome Diagnostics, Baltimore, Md.) across the targeted regions of interest.
To analyze the fragment lengths of cfDNA molecules, each read pair from a cfDNA molecule was required to have a Phred quality score ≥30. All duplicate ctDNA fragments, defined as having the same start, end, and index barcode were removed. For each mutation, only fragments for which one or both of the read pairs contained the mutated (or wild-type) base at the given position were included. This analysis was done using the R packages Rsamtools and GenomicAlignments.
For each genomic locus where a somatic mutation was identified, the lengths of fragments containing the mutant allele were compared to the lengths of fragments of the wild-type allele. If more than 100 mutant fragments were identified, Welch's two-sample t-test was used to compare the mean fragment lengths. For loci with fewer than 100 mutant fragments, a bootstrap procedure was implemented. Specifically, replacement N fragments containing the wild-type allele, where N denotes the number of fragments with the mutation, were sampled. For each bootstrap replicate of wild type fragments their median length was computed. The p-value was estimated as the fraction of bootstrap replicates with a median wild-type fragment length as or more extreme than the observed median mutant fragment length.
Analyses of Whole Genome Sequencing Data from cfDNA
Primary processing of whole genome NGS data for cfDNA samples was performed using Illumina CASAVA (Consensus Assessment of Sequence and Variation) software (version 1.8.2), including demultiplexing and masking of dual-index adapter sequences. Sequence reads were aligned against the human reference genome (version hg19) using ELAND.
Read pairs with a MAPQ score below 30 for either read and PCR duplicates were removed. hg19 autosomes were tiled into 26,236 adjacent, non-overlapping 100 kb bins. Regions of low mappability, indicated by the 10% of bins with the lowest coverage, were removed (see, e.g., Fortin et al., 2015 Genome Biol 16:180), as were reads falling in the Duke blacklisted regions (see, e.g., hgdownload.cse.ucsc.edu/goldenpath/hg19/encodeDCC/wgEncodeMapability/). Using this approach, 361 Mb (13%) of the hg19 reference genome was excluded, including centromeric and telomeric regions. Short fragments were defined as having a length between 100 and 150 bp and long fragments were defined has having a length between 151 and 220 bp.
To account for biases in coverage attributable to GC content of the genome, the locally weighted smoother loess with span ¾ was applied to the scatterplot of average fragment GC versus coverage calculated for each 100 kb bin. This loess regression was performed separately for short and long fragments to account for possible differences in GC effects on coverage in plasma by fragment length (see, e.g., Benjamini et al., 2012 Nucleic Acids Res 40:e72). The predictions for short and long coverage explained by GC from the loess model were subtracted, obtaining residuals for short and long that were uncorrelated with GC. The residuals were returned to the original scale by adding back the genome-wide median short and long estimates of coverage. This procedure was repeated for each sample to account for possible differences in GC effects on coverage between samples. To further reduce the feature space and noise, the total GC-adjusted coverage in 5 Mb bins was calculated.
To compare the variability of fragment lengths from healthy subjects to fragments in patients with cancer, the standard deviation of the short to long fragmentation profiles for each individual was calculated. The standard deviations in the two groups were compared by a Wilcoxon rank sum test.
Analyses of Chromosome Arm Copy Number Changes To develop arm-level statistics for copy number changes, an approach for aneuploidy detection in plasma as described elsewhere (see, e.g., Leary et al., 2012 Sci Transl Med 4:162ra154) was adopted. This approach divides the genome into non-overlapping 50 KB bins for which GC-corrected log 2 read depth was obtained after correction by loess with span ¾. This loess-based correction is comparable to the approach outlined above, but is evaluated on a log 2 scale to increase robustness to outliers in the smaller bins and does not stratify by fragment length. To obtain an arm-specific Z-score for copy number changes, the mean GC-adjusted read depth for each arm (GR) was centered and scaled by the average and standard deviation, respectively, of GR scores obtained from an independent set of 50 healthy samples.
Analyses of Mitochondrial-Aligned Reads from cfDNA
Whole genome sequence reads that initially mapped to the mitochondrial genome were extracted from bam files and realigned to the hg19 reference genome in end-to-end mode with Bowtie2 as described elsewhere (see, e.g., Langmead et al., 2012 Nat Methods 9:357-359). The resulting aligned reads were filtered such that both mates aligned to the mitochondrial genome with MAPQ >=30. The number of fragments mapping to the mitochondrial genome was counted and converted to a percentage of the total number of fragments in the original bam files.
Prediction Model for Cancer Classification To distinguish healthy from cancer patients using fragmentation profiles, a stochastic gradient boosting model was used (gbm; see, e.g., Friedman et al., 2001 Ann Stat 29:1189-1232; and Friedman et al., 2002 Comput Stat Data An 38:367-378). GC-corrected total and short fragment coverage for all 504 bins were centered and scaled for each sample to have mean 0 and unit standard deviation. Additional features included Z-scores for each of the 39 autosomal arms and mitochondrial representation (log 10-transformed proportion of reads mapped to the mitochondria). To estimate the prediction error of this approach, 10-fold cross-validation was used as described elsewhere (see, e.g., Efron et al., 1997 J Am Stat Assoc 92, 548-560). Feature selection, performed only on the training data in each cross-validation run, removed bins that were highly correlated (correlation >0.9) or had near zero variance. Stochastic gradient boosted machine learning was implemented using the R package gbm package with parameters n.trees=150, interaction.depth=3, shrinkage=0.1, and n.minobsinside=10. To average over the prediction error from the randomization of patients to folds, the 10-fold cross validation procedure was repeated 10 times. Confidence intervals for sensitivity fixed at 98% and 95% specificity were obtained from 2000 bootstrap replicates.
Prediction Model for Tumor Tissue of Origin Classification For samples correctly classified as cancer patients at 90% specificity (n=174), a separate stochastic gradient boosting model was trained to classify the tissue of origin. To account for the small number of lung samples used for prediction, 18 cfDNA baseline samples from late stage lung cancer patients were included from the monitoring analyses. Performance characteristics of the model were evaluated by 10-fold cross-validation repeated 10 times. This gbm model was trained using the same features as in the cancer classification model. As previously described, features that displayed correlation above 0.9 to each other or had near zero variance were removed within each training dataset during cross-validation. The tissue class probabilities were averaged across the 10 replicates for each patient and the class with the highest probability was taken as the predicted tissue.
Analyses of Nucleosomal DNA from Human Lymphocytes and cfDNA
From the nuclease treated lymphocytes, fragment sizes were analyzed in 5 Mb bins as described for whole genome cfDNA analyses. A genome-wide map of nucleosome positions was constructed from the nuclease treated lymphocyte cell-lines. This approach identified local biases in the coverage of circulating fragments, indicating a region protected from degradation. A “Window positioning score” (WPS) was used to score each base pair in the genome (see, e.g., Snyder et al., 2016 Cell 164:57). Using a sliding window of 60 bp centered around each base, the WPS was calculated as the number of fragments completely spanning the window minus the number of fragments with only one end in the window. Since fragments arising from nucleosomes have a median length of 167 bp, a high WPS indicated a possible nucleosomic position. WPS scores were centered at zero using a running median and smoothed using a Kolmogorov-Zurbenko filter (see, e.g., Zurbenko, The spectral analysis of time series. North-Holland series in statistics and probability; Elsevier, New York, N Y, 1986). For spans of positive WPS between 50 and 450 bp, a nucleosome peak was defined as the set of base pairs with a WPS above the median in that window. The calculation of nucleosome positions for cfDNA from 30 healthy individuals with sequence coverage of 9× was determined in the same manner as for lymphocyte DNA. To ensure that nucleosomes in healthy cfDNA were representative, a consensus track of nucleosomes was defined consisting only of nucleosomes identified in two or more individuals. Median distances between adjacent nucleosomes were calculated from the consensus track.
Monte Carlo Simulation of Detection Sensitivity A Monte Carlo simulation was used to estimate the probability of detecting a molecule with a tumor-derived alteration. Briefly, 1 million molecules were generated from a multinomial distribution. For a simulation with m alterations, wild-type molecules were simulated with probability p and each of the m tumor alterations were simulated with probability (1−p)/m. Next, g*m molecules were sampled randomly with replacement, where g denotes the number of genome equivalents in 1 ml of plasma. If a tumor alteration was sampled s or more times, the sample was classified as cancer-derived. The simulation was repeated 1000 times, estimating the probability that the in silico sample would be correctly classified as cancer by the mean of the cancer indicator. Setting g=2000 and s=5, the number of tumor alterations was varied by powers of 2 from 1 to 256 and the fraction of tumor-derived molecules from 0.0001% to 1%.
Statistical Analyses All statistical analyses were performed using R version 3.4.3. The R packages caret (version 6.0-79) and gbm (version 2.1-4) were used to implement the classification of healthy versus cancer and tissue of origin. Confidence intervals from the model output were obtained with the pROC (version 1.13) R package (see, e.g., Robin et al., 2011 BMC bioinformatics 12:77). Assuming the prevalence of undiagnosed cancer cases in this population is high (1 or 2 cases per 100 healthy), a genomic assay with a specificity of 0.95 and sensitivity of 0.8 would have useful operating characteristics (positive predictive value of 0.25 and negative predictive value near 1). Power calculations suggest that an analysis of more than 200 cancer patients and an approximately equal number of healthy controls, enable an estimation of the sensitivity with a margin of error of 0.06 at the desired specificity of 0.95 or greater.
Data and Code Availability Sequence data utilized in this study have been deposited at the European Genome-phenome Archive under study accession nos. EGAS00001003611 and EGAS00001002577. Code for analyses is available at github.com/Cancer-Genomics/delfi_scripts.
Results DELFI allows simultaneous analysis of a large number of abnormalities in cfDNA through genome-wide analysis of fragmentation patterns. The method is based on low coverage whole genome sequencing and analysis of isolated cfDNA. Mapped sequences are analyzed in non-overlapping windows covering the genome. Conceptually, windows may range in size from thousands to millions of bases, resulting in hundreds to thousands of windows in the genome. 5 Mb windows were used for evaluating cfDNA fragmentation patterns as these would provide over 20,000 reads per window even at a limited amount of 1-2× genome coverage. Within each window, the coverage and size distribution of cfDNA fragments was examined. This approach was used to evaluate the variation of genome-wide fragmentation profiles in healthy and cancer populations (Table 1; Appendix A). The genome-wide pattern from an individual can be compared to reference populations to determine if the pattern is likely healthy or cancer-derived. As genome-wide profiles reveal positional differences associated with specific tissues that may be missed in overall fragment size distributions, these patterns may also indicate the tissue source of cfDNA.
The fragmentation size of cfDNA was focused on as it was found that cancer-derived cfDNA molecules may be more variable in size than cfDNA derived from non-cancer cells. cfDNA fragments from targeted regions that were captured and sequenced at high coverage (43,706 total coverage, 8,044 distinct coverage) from patients with breast, colorectal, lung or ovarian cancer (Table 1 (Appendix A), Table 2 (Appendix B), and Table 3 (Appendix C)) were initially examined. Analyses of loci containing 165 tumor-specific alterations from 81 patients (range of 1-7 alterations per patient) revealed an average absolute difference of 6.5 bp (95% CI, 5.4-7.6 bp) between lengths of median mutant and wild-type cfDNA fragments (FIG. 3, Table 3 (Appendix C)). The median size of mutant cfDNA fragments ranged from 30 bases smaller at chromosome 3 position 41,266,124 to 47 bases larger at chromosome 11 position 108,117,753 than the wild-type sequences at these regions (Table 3; Appendix C). GC content was similar for mutated and non-mutated fragments (FIG. 4a), and there was no correlation between GC content and fragment length (FIG. 4b). Similar analyses of 44 germline alterations from 38 patients identified median cfDNA size differences of less than 1 bp between fragment lengths of different alleles (FIG. 5, Table 3 (Appendix C)). Additionally, 41 alterations related to clonal hematopoiesis were identified through a previous sequence comparison of DNA from plasma, buffy coat, and tumors of the same individuals. Unlike tumor-derived fragments, there were no significant differences between fragments with hematopoietic alterations and wild type fragments (FIG. 6, Table 3 (Appendix C)). Overall, cancer-derived cfDNA fragment lengths were significantly more variable compared to non-cancer cfDNA fragments at certain genomic regions (p<0.001, variance ratio test). It was hypothesized that these differences may be due to changes in higher-order chromatin structure as well as other genomic and epigenomic abnormalities in cancer and that cfDNA fragmentation in a position-specific manner could therefore serve as a unique biomarker for cancer detection.
As targeted sequencing only analyzes a limited number of loci, larger-scale genome-wide analyses to detect additional abnormalities in cfDNA fragmentation were investigated. cfDNA was isolated from ˜4 ml of plasma from 8 lung cancer patients with stage I-III disease, as well as from 30 healthy individuals (Table 1 (Appendix A), Table 4 (Appendix D), and Table 5 (Appendix E)). A high efficiency approach was used to convert cfDNA to next generation sequencing libraries and performed whole genome sequencing at ˜9× coverage (Table 4; Appendix D). Overall cfDNA fragment lengths of healthy individuals were larger, with a median fragment size of 167.3 bp, while patients with cancer had median fragment sizes of 163.8 (p<0.01, Welch's t-test) (Table 5; Appendix E). To examine differences in fragment size and coverage in a position dependent manner across the genome, sequenced fragments were mapped to their genomic origin and fragment lengths were evaluated in 504 windows that were 5 Mb in size, covering ˜2.6 Gb of the genome. For each window, the fraction of small cfDNA fragments (100 to 150 bp in length) to larger cfDNA fragments (151 to 220 bp) as well as overall coverage were determined and used to obtain genome-wide fragmentation profiles for each sample.
Healthy individuals had very similar fragmentation profiles throughout the genome (FIG. 7 and FIG. 8). To examine the origins of fragmentation patterns normally observed in cfDNA, nuclei were isolated from elutriated lymphocytes of two healthy individuals and treated with DNA nucleases to obtain nucleosomal DNA fragments. Analyses of cfDNA patterns in observed healthy individuals revealed a high correlation to lymphocyte nucleosomal DNA fragmentation profiles (FIGS. 7b and 7d) and nucleosome distances (FIGS. 7c and 7f). Median distances between nucleosomes in lymphocytes were correlated to open (A) and closed (B) compartments of lymphoblastoid cells as revealed using the Hi-C method (see, e.g., Lieberman-Aiden et al., 2009 Science 326:289-293; and Fortin et al., 2015 Genome Biol 16:180) for examining the three-dimensional architecture of genomes (FIG. 7c). These analyses suggest that the fragmentation patterns of normal cfDNA are the result of nucleosomal DNA patterns that largely reflect the chromatin structure of normal blood cells.
In contrast to healthy cfDNA, patients with cancer had multiple distinct genomic differences with increases and decreases in fragment sizes at different regions (FIGS. 7a and 7b). Similar to our observations from targeted analyses, there was also greater variation in fragment lengths genome-wide for patients with cancer compared to healthy individuals.
To determine whether cfDNA fragment length patterns could be used to distinguish patients with cancer from healthy individuals, genome-wide correlation analyses were performed of the fraction of short to long cfDNA fragments for each sample compared to the median fragment length profile calculated from healthy individuals (FIGS. 7a, 7b, and 7e). While the profiles of cfDNA fragments were remarkably consistent among healthy individuals (median correlation of 0.99), the median correlation of genome-wide fragment ratios among cancer patients was 0.84 (0.15 lower, 95% CI 0.07-0.50, p<0.001, Wilcoxon rank sum test; Table 5 (Appendix E)). Similar differences were observed when comparing fragmentation profiles of cancer patients to fragmentation profiles or nucleosome distances in healthy lymphocytes (FIGS. 7c, 7d, and 7f). To account for potential biases in the fragmentation profiles attributable to GC content, a locally weighted smoother was applied independently to each sample and found that differences in fragmentation profiles between healthy individuals and cancer patients remained after this adjustment (median correlation of cancer patients to healthy=0.83) (Table 5; Appendix E).
Subsampling analyses of whole genome sequence data was performed at 9× coverage from cfDNA of patients with cancer at ˜2×, ˜1×, ˜0.5×, ˜0.2×, and ˜0.1× genome coverage, and it was determined that altered fragmentation profiles were readily identified even at 0.5× genome coverage (FIG. 9). Based on these observations, whole genome sequencing was performed with coverage of 1-2× to evaluate whether fragmentation profiles may change during the course of targeted therapy in a manner similar to monitoring of sequence alterations. cfDNA from 19 non-small cell lung cancer patients including 5 with partial radiographic response, 8 with stable disease, 4 with progressive disease, and 2 with unmeasurable disease, during the course of anti-EGFR or anti-ERBB2 therapy was evaluated (Table 6; Appendix F). As shown in FIG. 10, the degree of abnormality in the fragmentation profiles during therapy closely matched levels of EGFR or ERBB2 mutant allele fractions as determined using targeted sequencing (Spearman correlation of mutant allele fractions to fragmentation profiles=0.74). This correlation is remarkable as genome-wide and mutation-based methods are orthogonal and examine different cfDNA alterations that may be suppressed in these patients due to prior therapy. Notably all cases that had progression free survival of six or more months displayed a drop of or had extremely low levels of ctDNA after initiation of therapy as determined by fragmentation profiles, while cases with poor clinical outcome had increases in ctDNA. These results demonstrate the feasibility of fragmentation analyses for detecting the presence of tumor-derived cfDNA, and suggests that such analyses may also be useful for quantitative monitoring of cancer patients during treatment.
The fragmentation profiles were examined in the context of known copy number changes in a patient where parallel analyses of tumor tissue were obtained. These analyses demonstrated that altered fragmentation profiles were present in regions of the genome that were copy neutral and that these may be further affected in regions with copy number changes (FIG. 11a and FIG. 12a). Position dependent differences in fragmentation patterns could be used to distinguish cancer-derived cfDNA from healthy cfDNA in these regions (FIG. 12a, b), while overall cfDNA fragment size measurements would have missed such differences (FIG. 12a).
These analyses were extended to an independent cohort of cancer patients and healthy individuals. Whole genome sequencing of cfDNA at 1-2× coverage from a total of 208 patients with cancer, including breast (n=54), colorectal (n=27), lung (n=12), ovarian (n=28), pancreatic (n=34), gastric (n=27), or bile duct cancers (n=26), as well as 215 individuals without cancer was performed (Table 1 (Appendix A) and Table 4 (Appendix D)). All cancer patients were treatment naïve and the majority had resectable disease (n=183). After GC adjustment of short and long cfDNA fragment coverage (FIG. 13a), coverage and size characteristics of fragments in windows throughout the genome were examined (FIG. 11b, Table 4 (Appendix D) and Table 7 (Appendix G)). Genome-wide correlations of coverage to GC content were limited and no differences in these correlations between cancer patients and healthy individuals were observed (FIG. 13b). Healthy individuals had highly concordant fragmentation profiles, while patients with cancer had high variability with decreased correlation to the median healthy profile (Table 7; Appendix G). An analysis of the most commonly altered fragmentation windows in the genome among cancer patients revealed a median of 60 affected windows across the cancer types analyzed, highlighting the multitude of position dependent alterations in fragmentation of cfDNA in individuals with cancer (FIG. 11c).
To determine if position dependent fragmentation changes can be used to detect individuals with cancer, a gradient tree boosting machine learning model was implemented to examine whether cfDNA can be categorized as having characteristics of a cancer patient or healthy individual and estimated performance characteristics of this approach by ten-fold cross validation repeated ten times (FIGS. 14 and 15). The machine learning model included GC-adjusted short and long fragment coverage characteristics in windows throughout the genome. A machine learning classifier for copy number changes from chromosomal arm dependent features rather than a single score was also developed (FIG. 16a and Table 8 (Appendix H)) and mitochondrial copy number changes were also included (FIG. 16b) as these could also help distinguish cancer from healthy individuals. Using this implementation of DELFI, a score was obtained that could be used to classify patients as healthy or having cancer. 152 of the 208 cancer patients were detected (73% sensitivity, 95% CI 67%-79%) while four of the 215 healthy individuals were misclassified (98% specificity) (Table 9). At a threshold of 95% specificity, 80% of patients with cancer were detected (95% CI, 74%-85%), including 79% of resectable (stage I-III) patients (145 of 183) and 82% of metastatic (stage IV) patients (18 out of 22) (Table 9). Receiver operator characteristic analyses for detection of patients with cancer had an AUC of 0.94 (95% CI 0.92-0.96), ranged among cancer types from 0.86 for pancreatic cancer to ≥0.99 for lung and ovarian cancers (FIGS. 17a and 17b), and had AUCs ≥0.92 across all stages (FIG. 18). The DELFI classifier score did not differ with age among either cancer patients or healthy individuals (Table 1; Appendix A).
TABLE 9
DELFI performance for cancer detection.
95% specificity 98% specificity
Individuals Individuals Individuals
analyzed detected Sensitivity 95% Cl detected Sensitivity 95% Cl
Healthy 215 10 — — 4 — —
Cancer 208 166 80% 74%-85% 152 73% 67%-79%
Type Breast 54 38 70% 56%-82% 31 57% 43%-71%
Bile duct 26 23 88% 70%-98% 21 81% 61%-93%
Colorectal 27 22 81% 62%-94% 19 70% 50%-86%
Gastric 27 22 81% 62%-94% 22 81% 62%-94%
Lung 12 12 100% 74%-100% 12 100% 74%-100%
Ovarian 28 25 89% 72%-98% 25 89% 72%-98%
Pancreatic 34 24 71% 53%-85% 22 65% 46%-80%
Stage I 41 30 73% 53%-86% 28 68% 52%-82%
II 109 85 78% 69%-85% 78 72% 62%-80%
III 33 30 91% 76%-98% 26 79% 61%-91%
IV 22 18 82% 60%-95% 17 77% 55%-92%
0, X 3 3 100% 29%-100% 3 100% 29%-100%
To assess the contribution of fragment size and coverage, chromosome arm copy number, or mitochondrial mapping to the predictive accuracy of the model, the repeated 10-fold cross-validation procedure was implemented to assess performance characteristics of these features in isolation. It was observed that fragment coverage features alone (AUC=0.94) were nearly identical to the classifier that combined all features (AUC=0.94) (FIG. 17a). In contrast, analyses of chromosomal copy number changes had lower performance (AUC=0.88) but were still more predictive than copy number changes based on individual scores (AUC=0.78) or mitochondrial mapping (AUC=0.72) (FIG. 17a). These results suggest that fragment coverage is the major contributor to our classifier. Including all features in the prediction model may contribute in a complementary fashion for detection of patients with cancer as they can be obtained from the same genome sequence data.
As fragmentation profiles reveal regional differences in fragmentation that may differ between tissues, a similar machine learning approach was used to examine whether cfDNA patterns could identify the tissue of origin of these tumors. It was found that this approach had a 61% accuracy (95% CI 53%-67%), including 76% for breast, 44% for bile duct, 71% for colorectal, 67% for gastric, 53% for lung, 48% for ovarian, and 50% for pancreatic cancers (FIG. 19, Table 10). The accuracy increased to 75% (95% CI 69%-81%) when considering assigning patients with abnormal cfDNA to one of two sites of origin (Table 10). For all tumor types, the classification of the tissue of origin by DELFI was significantly higher than determined by random assignment (p<0.01, binomial test, Table 10).
TABLE 10
DELFI tissue of origin prediction
Cancer Patients Top Prediction Top Two Predictions Random Assignment
Type Detected* Patients Accuracy (95% Cl) Patients Accuracy (95% Cl) Patients Accuracy
Breast 42 32 76% (61%-88%) 38 91% (77%-97%) 9 22%
Bile Duct 23 10 44% (23%-66%) 15 65% (43%-84%) 3 12%
Colorectal 24 17 71% (49%-87%) 19 79% (58%-93%) 3 12%
Gastric 24 16 67% (45%-84%) 19 79% (58%-93%) 3 12%
Lung 30 16 53% (34%-72%) 23 77% (58%-90%) 2 6%
Ovarian 27 13 48% (29%-68%) 16 59% (38%-78%) 4 14%
Pancreatic 24 12 50% (29%-71%) 16 67% (45%-84%) 3 12%
Total 194 116 61% (53%-67%) 146 75% (69%-81%) 26 13%
*Patients detected are based on DELFI detection at 90% specificity. Lung cohort includes additional lung cancer patients with prior therapy.
As cancer-specific sequence alterations can be used to identify patients with cancer, it was evaluated whether combining DELFI with this approach could increase the sensitivity of cancer detection (FIG. 20). An analysis of cfDNA from a subset of the treatment naïve cancer patients using both DELFI and targeted sequencing revealed that 82% (103 of 126) of patients had fragmentation profile alterations, while 66% (83 of 126) had sequence alterations. Over 89% of cases with mutant allele fractions >1% were detected by DELFI while for cases with mutant allele fractions <1% the fraction detected by DELFI was 80%, including for cases that were undetectable using targeted sequencing (Table 7; Appendix G). When these approaches were used together, the combined sensitivity of detection increased to 91% (115 of 126 patients) with a specificity of 98% (FIG. 20).
Overall, genome-wide cfDNA fragmentation profiles are different between cancer patients and healthy individuals. The variability in fragment lengths and coverage in a position dependent manner throughout the genome may explain the apparently contradictory observations of previous analyses of cfDNA at specific loci or of overall fragment sizes. In patients with cancer, heterogeneous fragmentation patterns in cfDNA appear to be a result of mixtures of nucleosomal DNA from both blood and neoplastic cells. These studies provide a method for simultaneous analysis of tens to potentially hundreds of tumor-specific abnormalities from minute amounts of cfDNA, overcoming a limitation that has precluded the possibility of more sensitive analyses of cfDNA. DELFI analyses detected a higher fraction of cancer patients than previous cfDNA analysis methods that have focused on sequence or overall fragmentation sizes (see, e.g., Phallen et al., 2017 Sci Transl Med 9:eaan2415; Cohen et al., 2018 Science 359:926; Newman et al., 2014 Nat Med 20:548; Bettegowda et al., 2014 Sci Transl Med 6:224ra24; Newman et al., 2016 Nat Biotechnol 34:547). As demonstrated in this Example, combining DELFI with analyses of other cfDNA alterations may further increase the sensitivity of detection. As fragmentation profiles appear related to nucleosomal DNA patterns, DELFI may be used for determining the primary source of tumor-derived cfDNA. The identification of the source of circulating tumor DNA in over half of patients analyzed may be further improved by including clinical characteristics, other biomarkers, including methylation changes, and additional diagnostic approaches (Ruibal Morell, 1992 The International journal of biological markers 7:160; Galli et al., 2013 Clinical chemistry and laboratory medicine 51:1369; Sikaris, 2011 Heart, lung &circulation 20:634; Cohen et al., 2018 Science 359:926). Finally, this approach requires only a small amount of whole genome sequencing, without the need for deep sequencing typical of approaches that focus on specific alterations. The performance characteristics and limited amount of sequencing needed for DELFI suggests that our approach could be broadly applied for screening and management of patients with cancer.
These results demonstrate that genome-wide cfDNA fragmentation profiles are different between cancer patients and healthy individuals. As such, cfDNA fragmentation profiles can have important implications for future research and applications of non-invasive approaches for detection of human cancer.
OTHER EMBODIMENTS It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.
APPENDIX A
Table 1. Summary or patients and samples analyzed
Whole
Age Degree Location of Volume cfDNA Genome Targeted
at Site of Histopa- of Metastases of Ex- cfDNA Fragment Fragment Targeted
Patient Sample Diag- TNM Primary thological Differ- at Plasma tracted Input Profile Profile Mutation
Patient Type Type Timepoint nosis Gender Stage Staging Tumor Diagnosis entiation Diagnosis (ml) (ng/ml) (ng/ml) Analysis Analysis Analysis
CGCRC291 Colorectal cfDNA Preoperative 69 F IV T3N2M1 Coecum Adencarcinoma Moderate Synchronous 7.9 7.80 7.80 Y Y Y
Cancer treatment naive Liver
CGCRC292 Colorectal cfDNA Preoperative 51 M IV T3N2M1 Sigmod Adencarcinoma Moderate Synchronous 7.9 6.73 6.73 Y Y Y
Cancer treatment naive Colon Liver, Lung
CGCRC293 Colorectal cfDNA Preoperative 55 M IV T3N2M1 Rectum Adencarcinoma Moderate Synchronous 7.2 3.83 3.83 Y Y Y
Cancer treatment naive Liver
CGCRC294 Colorectal cfDNA Preoperative 67 F II T3N0M0 Sigmod Adencarcinoma Moderate None 8.4 18.87 18.87 Y Y Y
Cancer treatment naive Colon
CGCRC296 Colorectal cfDNA Preoperative 76 F II T4N0M0 Coecum Adencarcinoma Poor None 4.3 31.24 31.24 Y Y Y
Cancer treatment naive
CGCRC299 Colorectal cfDNA Preoperative 71 M I T1N0M0 Rectum Adencarcinoma Moderate None 8.8 10.18 10.18 Y Y Y
Cancer treatment naive
CGCRC300 Colorectal cfDNA Preoperative 65 M I T2N0M0 Rectum Adencarcinoma Moderate None 4.3 10.48 10.48 Y Y Y
Cancer treatment naive
CGCRC301 Colorectal cfDNA Preoperative 76 F I T2N0M0 Rectum Adencarcinoma Moderate None 4.1 6.51 6.51 Y Y Y
Cancer treatment naive
CGCRC302 Colorectal cfDNA Preoperative 73 M II T3N0M0 Traverse Adencarcinoma Moderate None 4.3 52.13 52.13 Y Y Y
Cancer treatment naive Colon
CGCRC304 Colorectal cfDNA Preoperative 86 F II T3N0M0 Rectum Adencarcinoma Moderate None 4.1 30.19 30.19 Y Y Y
Cancer treatment naive
CGCRC305 Colorectal cfDNA Preoperative 83 F II T3N0M0 Traverse Adencarcinoma Moderate None 8.6 9.10 9.10 Y Y Y
Cancer treatment naive Colon
CGCRC306 Colorectal cfDNA Preoperative 80 F II T4N0M0 Ascending Adencarcinoma Moderate None 4.5 24.31 24.31 Y Y Y
Cancer treatment naive Colon
CGCRC307 Colorectal cfDNA Preoperative 78 F II T3N0M0 Ascending Adencarcinoma Moderate None 8.5 14.26 14.26 Y Y Y
Cancer treatment naive Colon
CGCRC308 Colorectal cfDNA Preoperative 72 F III T4N2M0 Ascending Adencarcinoma Moderate None 4.3 46.37 46.37 Y Y Y
Cancer treatment naive Colon
CGCRC311 Colorectal cfDNA Preoperative 59 M I T2N0M0 Sigmod Adencarcinoma Moderate None 8.5 3.91 3.91 Y Y Y
Cancer treatment naive Colon
CGCRC315 Colorectal cfDNA Preoperative 74 M III T3N1M0 Sigmod Adencarcinoma Moderate None 8.6 9.67 9.67 Y Y Y
Cancer treatment naive Colon
CGCRC316 Colorectal cfDNA Preoperative 80 M III T3N2M0 Traverse Adencarcinoma Moderate None 4.9 52.16 52.16 Y Y Y
Cancer treatment naive Colon
CGCRC317 Colorectal cfDNA Preoperative 74 M III T3N2M0 Descending Adencarcinoma Moderate None 8.8 16.08 16.08 Y Y Y
Cancer treatment naive Colon
CGCRC318 Colorectal cfDNA Preoperative 81 M I T2N0M0 Coecum Adencarcinoma Moderate None 9.8 18.24 18.24 Y Y Y
Cancer treatment naive
CGCRC319 Colorectal cfDNA Preoperative 80 F III T2N1M0 Descending Adencarcinoma Moderate None 4.2 53.84 53.84 Y N Y
Cancer treatment naive Colon
CGCRC320 Colorectal cfDNA Preoperative 73 F I T2N0M0 Ascending Adencarcinoma Moderate None 4.5 30.37 30.37 Y Y Y
Cancer treatment naive Colon
CGCRC321 Colorectal cfDNA Preoperative 68 M I T2N0M0 Rectum Adencarcinoma Moderate None 9.3 4.25 4.25 Y Y Y
Cancer treatment naive
CGCRC333 Colorectal cfDNA Preoperative NA F IV NA Colon/ Adencarcinoma NA Liver 4.0 113.88 113.88 Y Y Y
Cancer treatment naive Rectum
CGCRC336 Colorectal cfDNA Preoperative NA M IV NA Colon/ Adencarcinoma NA Liver 4.4 211.74 211.74 Y Y Y
Cancer treatment naive Rectum
CGCRC338 Colorectal cfDNA Preoperative NA F IV NA Colon/ Adencarcinoma NA Liver 2.3 109.76 109.76 Y Y Y
Cancer treatment naive Rectum
CGCRC341 Colorectal cfDNA Preoperative NA F IV NA Colon/ Adencarcinoma NA Liver 4.6 156.62 156.62 Y N Y
Cancer treatment naive Rectum
CGCRC342 Colorectal cfDNA Preoperative NA M IV NA Colon/ Adencarcinoma NA Liver 3.9 56.09 56.09 Y N Y
Cancer treatment naive Rectum
CGLU316 Lung cfDNA Pre-treatment, 50 F IV T3N2M0 Left Upper Adeno, Poor Lung 5.0 2.38 2.38 Y N Y
Cancer Day 53 Lobe of Lung Squamous,
Small Cell
Carcinoma
CGLU316 Lung cfDNA Pre-treatment, 50 F IV T3N2M0 Left Upper Adeno, Poor Lung 5.0 2.11 2.11 Y N Y
Cancer Day −4 Lobe of Lung Squamous,
Small Cell
Carcinoma
CGLU316 Lung cfDNA Post-treatment, 50 F IV T3N2M0 Left Upper Adeno, Poor Lung 5.0 0.87 1.07 Y N Y
Cancer Day 18 Lobe of Lung Squamous,
Small Cell
Carcinoma
CGLU316 Lung cfDNA Post-treatment, 50 F IV T3N2M0 Left Upper Adeno, Poor Lung 2.0 8.74 8.75 Y N Y
Cancer Day 87 Lobe of Lung Squamous,
Small Cell
Carcinoma
CGLU344 Lung cfDNA Pre-treatment, 65 F IV T2N2M1 Right Upper Adencarcinoma NA Pleura, 5.0 34.77 25.00 Y N Y
Cancer Day −21 Lobe of Lung Liver,
Pentoneum
CGLU344 Lung cfDNA Pre-treatment, 65 F IV T2N2M1 Right Upper Adencarcinoma NA Pleura, 5.0 15.63 15.64 Y N Y
Cancer Day 0 Lobe of Lung Liver,
Pentoneum
CGLU344 Lung cfDNA Post-treatment, 65 F IV T2N2M1 Right Upper Adencarcinoma NA Pleura, 5.0 9.22 9.22 Y N Y
Cancer Day 0.1875 Lobe of Lung Liver,
Pentoneum
CGLU344 Lung cfDNA Post-treatment, 65 F IV T2N2M1 Right Upper Adencarcinoma NA Pleura, 5.0 5.31 5.32 Y N Y
Cancer Day 59 Lobe of Lung Liver,
Pentoneum
CGLU369 Lung cfDNA Pre-treatment, 48 F IV T2NxM1 Right Upper Adencarcinoma NA Brain 2.0 11.28 11.28 Y N Y
Cancer Day −2 Lobe of Lung
CGLU369 Lung cfDNA Post-treatment, 48 F IV T2NxM1 Right Upper Adencarcinoma NA Brain 5.0 10.09 10.09 Y N Y
Cancer Day 12 Lobe of Lung
CGLU369 Lung cfDNA Post-treatment, 48 F IV T2NxM1 Right Upper Adencarcinoma NA Brain 5.0 6.69 6.70 Y N Y
Cancer Day 88 Lobe of Lung
CGLU369 Lung cfDNA Post-treatment, 48 F IV T2NxM1 Right Upper Adencarcinoma NA Brain 5.0 8.41 8.42 Y N Y
Cancer Day 110 Lobe of Lung
CGLU373 Lung cfDNA Pre-treatment, 56 F IV T3N1M0 Right Upper Adencarcinoma Moderate None 5.0 6.35 6.35 Y N Y
Cancer Day −2 Lobe of Lung
CGLU373 Lung cfDNA Post-treatment, 56 F IV T3N1M0 Right Upper Adencarcinoma Moderate None 5.0 6.28 6.28 Y N Y
Cancer Day 0.125 Lobe of Lung
CGLU373 Lung cfDNA Post-treatment, 56 F IV T3N1M0 Right Upper Adencarcinoma Moderate None 5.0 3.82 3.82 Y N Y
Cancer Day 7 Lobe of Lung
CGLU373 Lung cfDNA Post-treatment, 56 F IV T3N1M0 Right Upper Adencarcinoma Moderate None 3.5 5.55 5.55 Y N Y
Cancer Day 47 Lobe of Lung
CGPLBR100 Breast cfDNA Preoperative 44 F III T2N2M0 Left Breast Infiltrating NA None 4.0 4.25 4.25 Y N Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR101 Breast cfDNA Preoperative 46 F II T2N1M0 Left Breast Infiltrating Moderate None 4.0 37.88 37.88 Y N Y
Cancer treatment naive Lobular
Carcinoma
CGPLBR102 Breast cfDNA Preoperative 47 F II T2N1M0 Right Breast Infiltrating Moderate None 3.6 13.67 13.67 Y N Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR103 Breast cfDNA Preoperative 48 F II T2N1M0 Left Breast Infiltrating Moderate None 3.6 7.11 7.11 Y N Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR104 Breast cfDNA Preoperative 68 F II T2N0M0 Right Breast Infiltrating Moderate None 4.7 19.89 19.89 Y N Y
Cancer treatment naive Lobular
Carcinoma
CGPLBR12 Breast cfDNA Preoperative NA F III NA Breast Ductal NA NA 4.3 4.21 4.21 Y N N
Cancer treatment naive Carcinoma
insitu with
Microinvasion
CGPLBR18 Breast cfDNA Preoperative NA F III NA Breast Infiltrating NA NA 4.1 40.39 30.49 Y N N
Cancer treatment naive Lobular
Carcinoma
CGPLBR23 Breast cfDNA Preoperative 53 F II NA Breast Infiltrating NA None 4.7 20.09 20.09 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR24 Breast cfDNA Preoperative 53 F II NA Breast Infiltrating NA None 3.6 58.33 34.72 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR28 Breast cfDNA Preoperative 59 F III NA Breast Infiltrating NA None 4.2 12.86 12.86 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR30 Breast cfDNA Preoperative 61 F II NA Breast Infiltrating NA None 4.1 59.73 30.49 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR31 Breast cfDNA Preoperative 54 F II NA Breast Infiltrating NA None 3.4 23.94 23.94 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR32 Breast cfDNA Preoperative NA F II NA Breast Infiltrating NA None 4.4 71.23 28.41 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR33 Breast cfDNA Preoperative 47 F II NA Breast Infiltrating NA None 4.4 11.00 11.00 Y N N
Cancer treatment naive Lobular
Carcinoma
CGPLBR34 Breast cfDNA Preoperative 60 F II NA Breast Infiltrating NA None 4.4 23.61 23.61 Y N N
Cancer treatment naive Lobular
Carcinoma
CGPLBR35 Breast cfDNA Preoperative 43 F II NA Breast Ductal NA None 4.5 22.58 22.58 Y N N
Cancer treatment naive Carcinoma
insitu with
Microinvasion
CGPLBR36 Breast cfDNA Preoperative 36 F II NA Breast Infiltrating NA None 4.4 17.73 17.73 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR37 Breast cfDNA Preoperative 58 F II NA Breast Infiltrating NA None 4.4 9.39 9.39 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR38 Breast cfDNA Preoperative 54 F I T1N0M0 Left Breast Infiltrating Moderate None 4.0 5.77 5.77 Y Y Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR40 Breast cfDNA Preoperative 66 F III T2N2M0 Left Breast Infiltrating Poor None 4.6 15.69 15.69 Y Y Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR41 Breast cfDNA Preoperative 51 F III T3N1M0 Left Breast Infiltrating Moderate None 4.5 11.56 11.56 Y N Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR45 Breast cfDNA Preoperative 57 F II NA Breast Infiltrating NA None 4.5 20.36 20.36 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR46 Breast cfDNA Preoperative 54 F III NA Breast Infiltrating NA None 3.5 20.17 20.17 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR47 Breast cfDNA Preoperative 54 F I NA Breast Infiltrating NA None 4.5 13.89 13.89 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR48 Breast cfDNA Preoperative 47 F II T2N1M0 Left Breast Infiltrating Poor None 3.9 7.07 7.07 Y Y Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR49 Breast cfDNA Preoperative 37 F II T2N1M0 Left Breast Infiltrating Poor None 4.0 5.74 5.74 Y N Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR50 Breast cfDNA Preoperative 51 F I NA Breast Infiltrating NA None 4.5 45.58 27.78 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR51 Breast cfDNA Preoperative 53 F II NA Breast Infiltrating NA None 4.0 8.83 8.83 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR52 Breast cfDNA Preoperative 68 F III NA Breast Infiltrating NA None 4.5 80.71 27.78 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR55 Breast cfDNA Preoperative 53 F III T3N1M0 Right Breast Infiltrating Poor None 4.3 4.57 4.57 Y Y Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR56 Breast cfDNA Preoperative 56 F II NA Breast Infiltrating NA None 4.5 22.16 22.16 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR57 Breast cfDNA Preoperative 54 F III T2N2M0 Left Breast Infiltrating NA None 4.3 4.02 4.02 Y N Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR59 Breast cfDNA Preoperative 42 F I T1N0M0 Left Breast Infiltrating Moderate None 4.1 8.24 8.24 Y N Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR60 Breast cfDNA Preoperative 61 F II NA Left Breast Infiltrating NA None 4.5 11.09 11.09 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR61 Breast cfDNA Preoperative 67 F II T2N1M0 Left Breast Infiltrating Moderate None 4.1 13.25 13.25 Y N Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR63 Breast cfDNA Preoperative 48 F II T2N1M0 Left Breast Infiltrating Moderate None 4.0 6.19 6.19 Y Y Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR65 Breast cfDNA Preoperative 50 F II NA Left Breast Infiltrating NA None 3.5 41.75 35.71 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR68 Breast cfDNA Preoperative 64 F III T4N1M0 Breast Infiltrating Poor None 3.4 10.41 10.41 Y N Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR69 Breast cfDNA Preoperative 43 F II T2N0M0 Breast Infiltrating Moderate None 4.4 4.07 4.07 Y Y Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR70 Breast cfDNA Preoperative 60 F II T2N1M0 Breast Infiltrating Moderate None 3.4 11.94 11.94 Y Y Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR71 Breast cfDNA Preoperative 65 F II T2N0M0 Breast Infiltrating Poor None 3.1 7.64 7.64 Y Y Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR72 Breast cfDNA Preoperative 67 F II T2N0M0 Breast Infiltrating Well None 3.9 4.43 4.43 Y Y Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR73 Breast cfDNA Preoperative 60 F II T2N1M0 Breast Infiltrating Moderate None 3.3 14.69 14.69 Y Y Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR76 Breast cfDNA Preoperative 53 F II T2N0M0 Right Breast Infiltrating Well None 4.9 8.71 8.71 Y Y Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR81 Breast cfDNA Preoperative 54 F II NA Breast Infiltrating NA None 2.5 83.14 50.00 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR82 Breast cfDNA Preoperative 70 F I T1N0M0 Right Breast Infiltrating Moderate None 4.8 23.39 23.39 Y N Y
Cancer treatment naive Lobular
Carcinoma
CGPLBR83 Breast cfDNA Preoperative 53 F II T2N1M0 Right Breast Infiltrating Moderate None 3.7 100.17 100.17 Y Y Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR84 Breast cfDNA Preoperative NA F III NA Breast Infiltrating NA NA 3.6 16.95 16.95 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR87 Breast cfDNA Preoperative 80 F II T2N1M0 Right Breast Papilary Well None 3.6 277.39 69.44 Y Y Y
Cancer treatment naive Carcinoma
CGPLBR88 Breast cfDNA Preoperative 48 F II T1N1M0 Left Breast Infiltrating Poor None 3.6 49.75 49.75 Y Y Y
Cancer treatment naive Ductal
Carcinoma
CGPLBR90 Breast cfDNA Preoperative 51 F II NA Right Breast Infiltrating NA None 3.0 14.24 14.24 Y N N
Cancer treatment naive Ductal
Carcinoma
CGPLBR91 Breast cfDNA Preoperative 62 F III T2N2M0 Breast Infiltrating Poor None 3.2 22.41 22.41 Y N Y
Cancer treatment naive Lobular
Carcinoma
CGPLBR92 Breast cfDNA Preoperative 58 F II T2N1M0 Breast Infiltrating Poor None 3.1 81.00 81.00 Y Y Y
Cancer treatment naive Meduilary
Carcinoma
CGPLBR93 Breast cfDNA Preoperative 59 F II T1N0M0 Breast Infiltrating Moderate None 3.3 27.94 27.94 Y N Y
Cancer treatment naive Ductal
Carcinoma
CGPLH189 Healthy cfDNA Preoperative 74 M NA NA NA NA NA NA 5.0 5.84 5.84 Y N N
treatment naive
CGPLH190 Healthy cfDNA Preoperative 67 M NA NA NA NA NA NA 4.7 18.07 18.07 Y N N
treatment naive
CGPLH192 Healthy cfDNA Preoperative 74 M NA NA NA NA NA NA 4.7 12.19 12.19 Y N N
treatment naive
CGPLH193 Healthy cfDNA Preoperative 72 F NA NA NA NA NA NA 5.0 5.47 5.47 Y N N
treatment naive
CGPLH194 Healthy cfDNA Preoperative 75 F NA NA NA NA NA NA 5.0 9.98 9.98 Y N N
treatment naive
CGPLH196 Healthy cfDNA Preoperative 64 M NA NA NA NA NA NA 5.0 11.69 11.69 Y N N
treatment naive
CGPLH197 Healthy cfDNA Preoperative 74 M NA NA NA NA NA NA 5.0 5.69 5.69 Y N N
treatment naive
CGPLH198 Healthy cfDNA Preoperative 66 M NA NA NA NA NA NA 5.0 4.36 4.36 Y N N
treatment naive
CGPLH199 Healthy cfDNA Preoperative 75 F NA NA NA NA NA NA 5.0 9.77 9.77 Y N N
treatment naive
CGPLH200 Healthy cfDNA Preoperative 51 M NA NA NA NA NA NA 5.0 5.60 5.60 Y N N
treatment naive
CGPLH201 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 5.0 8.82 8.82 Y N N
treatment naive
CGPLH202 Healthy cfDNA Preoperative 73 M NA NA NA NA NA NA 5.0 5.54 5.54 Y N N
treatment naive
CGPLH203 Healthy cfDNA Preoperative 59 M NA NA NA NA NA NA 5.0 9.03 9.03 Y N N
treatment naive
CGPLH205 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 5.0 4.74 4.74 Y N N
treatment naive
CGPLH208 Healthy cfDNA Preoperative 75 F NA NA NA NA NA NA 5.0 4.67 4.67 Y N N
treatment naive
CGPLH209 Healthy cfDNA Preoperative 74 M NA NA NA NA NA NA 5.0 5.15 5.15 Y N N
treatment naive
CGPLH210 Healthy cfDNA Preoperative 75 M NA NA NA NA NA NA 5.0 5.41 5.41 Y N N
treatment naive
CGPLH211 Healthy cfDNA Preoperative 75 F NA NA NA NA NA NA 5.0 6.24 6.24 Y N N
treatment naive
CGPLH300 Hettithy cfDNA Preoperative 72 F NA NA NA NA NA NA 4.4 6.75 6.75 Y N N
treatment naive
CGPLH307 Healthy cfDNA Preoperative 53 M NA NA NA NA NA NA 4.5 3.50 3.50 Y N N
treatment naive
CGPLH308 Healthy cfDNA Preoperative 60 M NA NA NA NA NA NA 4.5 6.01 6.01 Y N N
treatment naive
CGPLH309 Healthy cfDNA Preoperative 61 F NA NA NA NA NA NA 4.5 5.21 5.21 Y N N
treatment naive
CGPLH310 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA 4.5 15.25 15.25 Y N N
treatment naive
CGPLH311 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.5 4.47 4.47 Y N N
treatment naive
CGPLH314 Healthy cfDNA Preoperative 59 M NA NA NA NA NA NA 4.5 9.62 9.62 Y N N
treatment naive
CGPLH314 Healthy cfDNA, Preoperative 59 M NA NA NA NA NA NA 4.4 16.24 16.24 Y N N
technical treatment naive
replicate
CGPLH315 Healthy cfDNA Preoperative 59 F NA NA NA NA NA NA 4.2 11.55 11.55 Y N N
treatment naive
CGPLH316 Healthy cfDNA Preoperative 64 M NA NA NA NA NA NA 4.5 28.92 27.79 Y N N
treatment naive
CGPLH317 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.5 7.62 7.62 Y N N
treatment naive
CGPLH319 Healthy cfDNA Preoperative 60 F NA NA NA NA NA NA 4.2 4.41 4.41 Y N N
treatment naive
CGPLH320 Healthy cfDNA Preoperative 75 F NA NA NA NA NA NA 4.5 6.93 6.93 Y N N
treatment naive
CGPLH322 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.2 8.17 8.17 Y N N
treatment naive
CGPLH324 Healthy cfDNA Preoperative 59 F NA NA NA NA NA NA 5.0 6.63 6.63 Y N N
treatment naive
CGPLH325 Healthy cfDNA Preoperative 54 M NA NA NA NA NA NA 4.6 4.15 4.15 Y N N
treatment naive
CGPLH326 Healthy cfDNA Preoperative 67 F NA NA NA NA NA NA 4.5 6.06 6.06 Y N N
treatment naive
CGPLH327 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.8 1.24 1.24 Y N N
treatment naive
CGPLH328 Healthy cfDNA, Preoperative 68 F NA NA NA NA NA NA 4.4 3.42 3.42 Y N N
technical treatment naive
replicate
CGPLH328 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 4.9 5.47 5.47 Y N N
treatment naive
CGPLH329 Healthy cfDNA Preoperative 59 M NA NA NA NA NA NA 4.5 5.27 5.27 Y N N
treatment naive
CGPLH330 Healthy cfDNA Preoperative 75 M NA NA NA NA NA NA 4.3 10.21 10.21 Y N N
treatment naive
CGPLH331 Healthy cfDNA Preoperative 55 M NA NA NA NA NA NA 4.6 2.63 2.63 Y N N
treatment naive
CGPLH331 Healthy cfDNA, Preoperative 55 M NA NA NA NA NA NA 4.3 4.15 4.15 Y N N
technical treatment naive
replicate
CGPLH333 Healthy cfDNA Preoperative 60 M NA NA NA NA NA NA 4.7 4.06 4.06 Y N N
treatment naive
CGPLH335 Healthy cfDNA Preoperative 74 M NA NA NA NA NA NA 4.4 9.39 9.39 Y N N
treatment naive
CGPLH336 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.6 6.64 6.64 Y N N
treatment naive
CGPLH337 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.2 4.48 4.48 Y N N
treatment naive
CGPLH338 Healthy cfDNA Preoperative 75 M NA NA NA NA NA NA 4.5 59.44 59.44 Y N N
treatment naive
CGPLH339 Healthy cfDNA Preoperative 70 M NA NA NA NA NA NA 4.5 12.27 12.27 Y N N
treatment naive
CGPLH340 Healthy cfDNA Preoperative 62 M NA NA NA NA NA NA 4.5 4.86 4.86 Y N N
treatment naive
CGPLH341 Healthy cfDNA Preoperative 61 F NA NA NA NA NA NA 4.1 7.62 7.62 Y N N
treatment naive
CGPLH342 Healthy cfDNA Preoperative 49 F NA NA NA NA NA NA 4.2 18.29 18.29 Y N N
treatment naive
CGPLH343 Healthy cfDNA Preoperative 58 M NA NA NA NA NA NA 4.5 3.49 3.49 Y N N
treatment naive
CGPLH344 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.2 8.41 8.41 Y N N
treatment naive
CGPLH345 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA 4.5 9.73 9.73 Y N N
treatment naive
CGPLH346 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 7.86 7.86 Y N N
treatment naive
CGPLH35 Healthy cfDNA Preoperative 48 F NA NA NA NA NA NA 4.0 13.15 13.15 Y N Y
treatment naive
CGPLH350 Healthy cfDNA Preoperative 65 M NA NA NA NA NA NA 3.5 6.09 6.09 Y N N
treatment naive
CGPLH351 Healthy cfDNA Preoperative 71 M NA NA NA NA NA NA 4.0 15.91 15.91 Y N N
treatment naive
CGPLH352 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.2 6.47 6.47 Y N N
treatment naive
CGPLH353 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.2 4.47 4.47 Y N N
treatment naive
CGPLH354 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.2 17.49 17.49 Y N N
treatment naive
CGPLH355 Healthy cfDNA Preoperative 70 M NA NA NA NA NA NA 4.2 11.58 11.58 Y N N
treatment naive
CGPLH356 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 3.84 3.84 Y N N
treatment naive
CGPLH357 Healthy cfDNA Preoperative 52 F NA NA NA NA NA NA 4.2 11.79 11.79 Y N N
treatment naive
CGPLH358 Healthy cfDNA Preoperative 55 M NA NA NA NA NA NA 4.2 21.08 21.08 Y N N
treatment naive
CGPLH36 Healthy cfDNA Preoperative 36 F NA NA NA NA NA NA 4.0 13.00 13.00 Y N Y
treatment naive
CGPLH360 Healthy cfDNA Preoperative 60 M NA NA NA NA NA NA 4.2 3.48 3.48 Y N N
treatment naive
CGPLH361 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.3 6.98 6.98 Y N N
treatment naive
CGPLH362 Healthy cfDNA Preoperative 72 F NA NA NA NA NA NA 4.4 8.49 8.49 Y N N
treatment naive
CGPLH363 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 4.5 4.44 4.44 Y N N
treatment naive
CGPLH364 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 17.31 17.31 Y N N
treatment naive
CGPLH365 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 4.5 0.55 0.55 Y N N
treatment naive
CGPLH366 Healthy cfDNA Preoperative 61 M NA NA NA NA NA NA 4.5 4.88 4.88 Y N N
treatment naive
CGPLH367 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.4 6.48 6.48 Y N N
treatment naive
CGPLH368 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.3 2.53 2.53 Y N N
treatment naive
CGPLH369 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA 4.3 10.18 10.18 Y N N
treatment naive
CGPLH369 Healthy cfDNA, Preoperative 55 F NA NA NA NA NA NA 4.4 10.71 10.71 Y N N
technical treatment naive
replicate
CGPLH37 Healthy cfDNA Preoperative 39 F NA NA NA NA NA NA 4.0 9.73 9.73 Y N Y
treatment naive
CGPLH370 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 7.22 7.22 Y N N
treatment naive
CGPLH371 Healthy cfDNA Preoperative 57 F NA NA NA NA NA NA 4.6 5.62 5.62 Y N N
treatment naive
CGPLH380 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.2 6.61 6.61 Y N N
treatment naive
CGPLH381 Healthy cfDNA Preoperative 56 F NA NA NA NA NA NA 4.2 27.38 27.38 Y N N
treatment naive
CGPLH382 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.5 11.58 11.58 Y N N
treatment naive
CGPLH383 Healthy cfDNA Preoperative 62 F NA NA NA NA NA NA 4.5 25.50 25.50 Y N N
treatment naive
CGPLH384 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.5 15.66 15.66 Y N N
treatment naive
CGPLH385 Healthy cfDNA Preoperative 69 M NA NA NA NA NA NA 4.5 19.35 19.35 Y N N
treatment naive
CGPLH386 Healthy cfDNA Preoperative 62 M NA NA NA NA NA NA 4.5 6.46 6.46 Y N N
treatment naive
CGPLH386 Healthy cfDNA, Preoperative 62 M NA NA NA NA NA NA 4.6 6.54 6.54 Y N N
technical treatment naive
replicate
CGPLH387 Healthy cfDNA Preoperative 71 F NA NA NA NA NA NA 4.5 6.19 6.19 Y N N
treatment naive
CGPLH388 Healthy cfDNA Preoperative 57 F NA NA NA NA NA NA 4.5 6.62 6.62 Y N N
treatment naive
CGPLH389 Healthy cfDNA Preoperative 73 F NA NA NA NA NA NA 4.6 14.78 14.78 Y N N
treatment naive
CGPLH390 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 12.14 12.14 Y N N
treatment naive
CGPLH391 Healthy cfDNA Preoperative 58 M NA NA NA NA NA NA 4.5 8.88 8.88 Y N N
treatment naive
CGPLH391 Healthy cfDNA, Preoperative 58 M NA NA NA NA NA NA 4.5 8.37 8.37 Y N N
technical treatment naive
replicate
CGPLH392 Healthy cfDNA Preoperative 57 F NA NA NA NA NA NA 4.5 8.39 8.39 Y N N
treatment naive
CGPLH393 Healthy cfDNA Preoperative 54 M NA NA NA NA NA NA 4.5 5.27 5.27 Y N N
treatment naive
CGPLH394 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA 4.4 3.79 3.79 Y N N
treatment naive
CGPLH395 Healthy cfDNA Preoperative 56 F NA NA NA NA NA NA 4.4 9.56 9.56 Y N N
treatment naive
CGPLH395 Healthy cfDNA, Preoperative 56 F NA NA NA NA NA NA 4.4 5.40 5.40 Y N N
technical treatment naive
replicate
CGPLH396 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.4 20.31 20.31 Y N N
treatment naive
CGPLH398 Healthy cfDNA Preoperative 68 M NA NA NA NA NA NA 4.3 13.01 13.01 Y N N
treatment naive
CGPLH399 Healthy cfDNA Preoperative 62 F NA NA NA NA NA NA 4.4 4.79 4.79 Y N N
treatment naive
CGPLH400 Healthy cfDNA Preoperative 64 M NA NA NA NA NA NA 4.4 7.70 7.70 Y N N
treatment naive
CGPLH400 Healthy cfDNA, Preoperative 64 M NA NA NA NA NA NA 4.4 6.26 6.26 Y N N
technical treatment naive
replicate
CGPLH401 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.3 13.01 13.01 Y N N
treatment naive
CGPLH401 Healthy cfDNA, Preoperative 50 M NA NA NA NA NA NA 4.4 11.13 11.13 Y N N
technical treatment naive
replicate
CGPLH402 Healthy cfDNA Preoperative 57 F NA NA NA NA NA NA 4.5 2.89 2.89 Y N N
treatment naive
CGPLH403 Healthy cfDNA Preoperative 64 M NA NA NA NA NA NA 4.3 4.41 4.41 Y N N
treatment naive
CGPLH404 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.2 6.38 6.38 Y N N
treatment naive
CGPLH405 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.4 7.28 7.28 Y N N
treatment naive
CGPLH406 Healthy cfDNA Preoperative 57 M NA NA NA NA NA NA 4.2 5.40 5.40 Y N N
treatment naive
CGPLH407 Healthy cfDNA Preoperative 75 F NA NA NA NA NA NA 4.0 13.30 13.30 Y N N
treatment naive
CGPLH408 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.2 5.18 5.18 Y N N
treatment naive
CGPLH409 Healthy cfDNA Preoperative 53 M NA NA NA NA NA NA 3.7 3.98 3.98 Y N N
treatment naive
CGPLH410 Healthy cfDNA Preoperative 52 M NA NA NA NA NA NA 4.1 6.91 6.91 Y N N
treatment naive
CGPLH411 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.1 3.30 3.30 Y N N
treatment naive
CGPLH412 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.1 5.55 5.55 Y N N
treatment naive
CGPLH413 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA 4.5 8.18 8.18 Y N N
treatment naive
CGPLH414 Healthy cfDNA Preoperative 56 M NA NA NA NA NA NA 3.8 5.85 5.85 Y N N
treatment naive
CGPLH415 Healthy cfDNA Preoperative 59 M NA NA NA NA NA NA 4.7 10.20 10.20 Y N N
treatment naive
CGPLH416 Healthy cfDNA Preoperative 58 F NA NA NA NA NA NA 4.5 11.73 11.73 Y N N
treatment naive
CGPLH417 Healthy cfDNA Preoperative 70 M NA NA NA NA NA NA 4.2 10.98 10.98 Y N N
treatment naive
CGPLH418 Healthy cfDNA Preoperative 70 F NA NA NA NA NA NA 4.5 10.96 10.96 Y N N
treatment naive
CGPLH419 Healthy cfDNA Preoperative 65 F NA NA NA NA NA NA 4.5 10.17 10.17 Y N N
treatment naive
CGPLH42 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA 4.0 14.30 14.30 Y N Y
treatment naive
CGPLH420 Healthy cfDNA Preoperative 51 M NA NA NA NA NA NA 4.2 12.32 12.32 Y N N
treatment naive
CGPLH422 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 4.6 5.42 5.42 Y N N
treatment naive
CGPLH423 Healthy cfDNA Preoperative 54 M NA NA NA NA NA NA 4.2 2.85 2.85 Y N N
treatment naive
CGPLH424 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.7 1.66 1.66 Y N N
treatment naive
CGPLH425 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.4 5.98 5.98 Y N N
treatment naive
CGPLH426 Healthy cfDNA Preoperative 68 M NA NA NA NA NA NA 4.4 2.84 2.84 Y N N
treatment naive
CGPLH427 Healthy cfDNA Preoperative 68 M NA NA NA NA NA NA 4.4 10.86 10.86 Y N N
treatment naive
CGPLH428 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 6.27 6.27 Y N N
treatment naive
CGPLH429 Healthy cfDNA Preoperative 63 F NA NA NA NA NA NA 4.5 3.89 3.89 Y N N
treatment naive
CGPLH43 Healthy cfDNA Preoperative 49 F NA NA NA NA NA NA 4.0 8.50 8.50 Y N Y
treatment naive
CGPLH430 Healthy cfDNA Preoperative 69 F NA NA NA NA NA NA 4.2 10.33 10.33 Y N N
treatment naive
CGPLH431 Healthy cfDNA Preoperative 59 F NA NA NA NA NA NA 4.8 12.81 12.81 Y N N
treatment naive
CGPLH432 Healthy cfDNA Preoperative 59 F NA NA NA NA NA NA 4.8 2.42 2.42 Y N N
treatment naive
CGPLH434 Healthy cfDNA Preoperative 59 M NA NA NA NA NA NA 4.6 8.83 8.83 Y N N
treatment naive
CGPLH435 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.5 8.95 8.95 Y N N
treatment naive
CGPLH436 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 4.29 4.29 Y N N
treatment naive
CGPLH437 Healthy cfDNA Preoperative 56 M NA NA NA NA NA NA 4.6 18.07 18.07 Y N N
treatment naive
CGPLH438 Healthy cfDNA Preoperative 69 M NA NA NA NA NA NA 4.8 16.62 16.62 Y N N
treatment naive
CGPLH439 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.7 4.38 4.38 Y N N
treatment naive
CGPLH440 Healthy cfDNA Preoperative 72 M NA NA NA NA NA NA 4.7 4.32 4.32 Y N N
treatment naive
CGPLH441 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.7 7.80 7.80 Y N N
treatment naive
CGPLH442 Healthy cfDNA Preoperative 59 F NA NA NA NA NA NA 4.5 6.15 6.15 Y N N
treatment naive
CGPLH443 Healthy cfDNA Preoperative 52 F NA NA NA NA NA NA 4.4 3.44 3.44 Y N N
treatment naive
CGPLH444 Healthy cfDNA Preoperative 60 F NA NA NA NA NA NA 4.4 4.12 4.12 Y N N
treatment naive
CGPLH445 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.4 4.36 4.36 Y N N
treatment naive
CGPLH446 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA 4.4 2.92 2.92 Y N N
treatment naive
CGPLH447 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.6 3.87 3.87 Y N N
treatment naive
CGPLH448 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA 4.4 5.29 5.29 Y N N
treatment naive
CGPLH449 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA 4.5 3.77 3.77 Y N N
treatment naive
CGPLH45 Healthy cfDNA Preoperative 58 F NA NA NA NA NA NA 4.0 10.85 10.85 Y N Y
treatment naive
CGPLH450 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 5.62 5.62 Y N N
treatment naive
CGPLH451 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA 4.6 7.24 7.24 Y N N
treatment naive
CGPLH452 Healthy cfDNA Preoperative 69 M NA NA NA NA NA NA 4.4 2.54 2.54 Y N N
treatment naive
CGPLH453 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.6 9.11 9.11 Y N N
treatment naive
CGPLH455 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA 4.4 2.64 2.64 Y N N
treatment naive
CGPLH455 Healthy cfDNA, Preoperative 55 F NA NA NA NA NA NA 4.5 2.42 2.42 Y N N
technical treatment naive
replicate
CGPLH456 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA 4.5 3.11 3.11 Y N N
treatment naive
CGPLH457 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA 4.4 5.92 5.92 Y N N
treatment naive
CGPLH458 Healthy cfDNA Preoperative 52 F NA NA NA NA NA NA 4.5 16.04 16.04 Y N N
treatment naive
CGPLH459 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.4 6.52 6.52 Y N N
treatment naive
CGPLH46 Healthy cfDNA Preoperative 35 F NA NA NA NA NA NA 4.0 8.25 8.25 Y N Y
treatment naive
CGPLH460 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.6 5.24 5.24 Y N N
treatment naive
CGPLH463 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.5 22.77 22.77 Y N N
treatment naive
CGPLH464 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.4 2.90 2.90 Y N N
treatment naive
CGPLH465 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 4.76 4.76 Y N N
treatment naive
CGPLH466 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.6 5.68 5.68 Y N N
treatment naive
CGPLH466 Healthy cfDNA, Preoperative 50 F NA NA NA NA NA NA 4.5 6.75 6.75 Y N N
technical treatment naive
replicate
CGPLH467 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 4.59 4.59 Y N N
treatment naive
CGPLH468 Healthy cfDNA Preoperative 53 M NA NA NA NA NA NA 4.5 11.19 11.19 Y N N
treatment naive
CGPLH469 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 3.25 3.25 Y N N
treatment naive
CGPLH47 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.0 7.43 7.43 Y N Y
treatment naive
CGPLH470 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 4.5 13.64 13.64 Y N N
treatment naive
CGPLH471 Healthy cfDNA Preoperative 70 F NA NA NA NA NA NA 4.3 13.00 13.00 Y N N
treatment naive
CGPLH472 Healthy cfDNA Preoperative 69 F NA NA NA NA NA NA 4.2 10.17 10.17 Y N N
treatment naive
CGPLH473 Healthy cfDNA Preoperative 62 M NA NA NA NA NA NA 4.3 2.98 2.98 Y N N
treatment naive
CGPLH474 Healthy cfDNA Preoperative 63 M NA NA NA NA NA NA 4.3 29.15 29.15 Y N N
treatment naive
CGPLH475 Healthy cfDNA Preoperative 67 F NA NA NA NA NA NA 4.0 7.26 7.26 Y N N
treatment naive
CGPLH476 Healthy cfDNA Preoperative 65 F NA NA NA NA NA NA 4.3 6.16 6.16 Y N N
treatment naive
CGPLH477 Healthy cfDNA Preoperative 61 F NA NA NA NA NA NA 4.3 15.21 15.21 Y N N
treatment naive
CGPLH478 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA 4.4 7.29 7.29 Y N N
treatment naive
CGPLH479 Healthy cfDNA Preoperative 52 M NA NA NA NA NA NA 4.5 8.73 8.73 Y N N
treatment naive
CGPLH48 Healthy cfDNA Preoperative 38 F NA NA NA NA NA NA 4.0 6.38 6.38 Y N Y
treatment naive
CGPLH480 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.4 10.62 10.62 Y N N
treatment naive
CGPLH481 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.3 6.75 6.75 Y N N
treatment naive
CGPLH482 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.3 23.58 23.58 Y N N
treatment naive
CGPLH483 Healthy cfDNA Preoperative 66 M NA NA NA NA NA NA 4.4 14.44 14.44 Y N N
treatment naive
CGPLH484 Healthy cfDNA Preoperative 72 M NA NA NA NA NA NA 4.2 14.32 14.32 Y N N
treatment naive
CGPLH485 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.3 9.64 9.64 Y N N
treatment naive
CGPLH486 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.3 10.16 10.16 Y N N
treatment naive
CGPLH487 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.4 6.11 6.11 Y N N
treatment naive
CGPLH488 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 7.88 7.88 Y N N
treatment naive
CGPLH49 Healthy cfDNA Preoperative 39 F NA NA NA NA NA NA 4.0 6.60 6.60 Y N Y
treatment naive
CGPLH490 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 4.18 4.18 Y N N
treatment naive
CGPLH491 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 13.16 13.16 Y N N
treatment naive
CGPLH492 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA 4.5 3.83 3.83 Y N N
treatment naive
CGPLH493 Healthy cfDNA Preoperative 64 M NA NA NA NA NA NA 4.5 25.06 25.06 Y N N
treatment naive
CGPLH494 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.4 5.24 5.24 Y N N
treatment naive
CGPLH495 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.4 5.03 5.03 Y N N
treatment naive
CGPLH496 Healthy cfDNA Preoperative 74 M NA NA NA NA NA NA 4.5 34.01 27.78 Y N N
treatment naive
CGPLH497 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 4.5 8.24 8.24 Y N N
treatment naive
CGPLH497 Healthy cfDNA, Preoperative 68 F NA NA NA NA NA NA 4.4 5.88 5.88 Y N N
technical treatment naive
replicate
CGPLH498 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA 4.4 5.33 5.33 Y N N
treatment naive
CGPLH499 Healthy cfDNA Preoperative 52 F NA NA NA NA NA NA 4.5 7.85 7.85 Y N N
treatment naive
CGPLH50 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA 4.0 7.05 7.05 Y N Y
treatment naive
CGPLH500 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA 4.5 3.49 3.49 Y N N
treatment naive
CGPLH501 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.3 6.29 6.29 Y N N
treatment naive
CGPLH502 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.5 2.24 2.24 Y N N
treatment naive
CGPLH503 Healthy cfDNA Preoperative 67 M NA NA NA NA NA NA 4.5 11.01 11.01 Y N N
treatment naive
CGPLH504 Healthy cfDNA Preoperative 57 F NA NA NA NA NA NA 4.3 6.60 6.60 Y N N
treatment naive
CGPLH504 Healthy cfDNA, Preoperative 57 F NA NA NA NA NA NA 4.2 10.02 10.02 Y N N
technical treatment naive
replicate
CGPLH505 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.1 5.23 5.23 Y N N
treatment naive
CGPLH506 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA 4.5 12.23 12.23 Y N N
treatment naive
CGPLH507 Healthy cfDNA Preoperative 56 F NA NA NA NA NA NA 4.1 9.89 9.89 Y N N
treatment naive
CGPLH508 Healthy cfDNA Preoperative 54 M NA NA NA NA NA NA 4.5 8.66 8.66 Y N N
treatment naive
CGPLH508 Healthy cfDNA, Preoperative 54 F NA NA NA NA NA NA 4.4 9.55 9.55 Y N N
technical treatment naive
replicate
CGPLH509 Healthy cfDNA Preoperative 60 M NA NA NA NA NA NA 4.0 9.79 9.79 Y N N
treatment naive
CGPLH51 Healthy cfDNA Preoperative 48 F NA NA NA NA NA NA 4.0 7.85 7.85 Y N Y
treatment naive
CGPLH510 Healthy cfDNA Preoperative 67 M NA NA NA NA NA NA 4.2 14.20 14.20 Y N N
treatment naive
CGPLH511 Healthy cfDNA Preoperative 75 M NA NA NA NA NA NA 4.5 12.94 12.94 Y N N
treatment naive
CGPLH512 Healthy cfDNA Preoperative 52 M NA NA NA NA NA NA 4.3 8.60 8.60 Y N N
treatment naive
CGPLH513 Healthy cfDNA Preoperative 57 M NA NA NA NA NA NA 4.3 6.54 6.54 Y N N
treatment naive
CGPLH514 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA 4.4 10.94 10.94 Y N N
treatment naive
CGPLH515 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 4.5 8.71 8.71 Y N N
treatment naive
CGPLH516 Healthy cfDNA Preoperative 65 F NA NA NA NA NA NA 4.5 7.32 7.32 Y N N
treatment naive
CGPLH517 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA 4.6 5.16 5.16 Y N N
treatment naive
CGPLH517 Healthy cfDNA, Preoperative 54 F NA NA NA NA NA NA 4.5 9.74 9.74 Y N N
technical treatment naive
replicate
CGPLH518 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.4 5.92 5.92 Y N N
treatment naive
CGPLH519 Healthy cfDNA Preoperative 54 M NA NA NA NA NA NA 4.4 6.96 6.96 Y N N
treatment naive
CGPLH52 Healthy cfDNA Preoperative 40 F NA NA NA NA NA NA 4.0 9.90 9.90 Y N Y
treatment naive
CGPLH520 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA 4.3 8.27 8.27 Y N N
treatment naive
CGPLH54 Healthy cfDNA Preoperative 47 F NA NA NA NA NA NA 4.0 14.18 14.18 Y N Y
treatment naive
CGPLH55 Healthy cfDNA Preoperative 46 F NA NA NA NA NA NA 4.0 7.35 7.35 Y N Y
treatment naive
CGPLH56 Healthy cfDNA Preoperative 42 F NA NA NA NA NA NA 4.0 5.20 5.20 Y N Y
treatment naive
CGPLH57 Healthy cfDNA Preoperative 39 F NA NA NA NA NA NA 4.0 7.15 7.15 Y N Y
treatment naive
CGPLH59 Healthy cfDNA Preoperative 34 F NA NA NA NA NA NA 4.0 6.03 6.03 Y N Y
treatment naive
CGPLH625 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.5 2.64 2.64 Y N N
treatment naive
CGPLH625 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.5 1.69 1.69 Y N N
treatment naive
CGPLH626 Healthy cfDNA, Preoperative 50 F NA NA NA NA NA NA 4.0 11.12 11.12 Y N N
technical treatment naive
replicate
CGPLH63 Healthy cfDNA Preoperative 47 F NA NA NA NA NA NA 4.0 10.10 10.10 Y N Y
treatment naive
CGPLH639 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 2.00 2.00 Y N N
treatment naive
CGPLH64 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA 4.0 8.03 8.03 Y N Y
treatment naive
CGPLH640 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 9.36 9.36 Y N N
treatment naive
CGPLH642 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA 4.5 4.99 4.99 Y N N
treatment naive
CGPLH643 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA 4.4 7.12 7.12 Y N N
treatment naive
CGPLH644 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.4 5.06 5.06 Y N N
treatment naive
CGPLH646 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.4 6.75 6.75 Y N N
treatment naive
CGPLH75 Healthy cfDNA Preoperative 46 F NA NA NA NA NA NA 4.0 3.87 3.87 Y N Y
treatment naive
CGPLH76 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.0 4.03 4.03 Y N Y
treatment naive
CGPLH77 Healthy cfDNA Preoperative 46 F NA NA NA NA NA NA 4.0 5.89 5.89 Y N Y
treatment naive
CGPLH78 Healthy cfDNA Preoperative 34 F NA NA NA NA NA NA 4.0 2.51 2.51 Y N Y
treatment naive
CGPLH79 Healthy cfDNA Preoperative 37 F NA NA NA NA NA NA 4.0 3.68 3.68 Y N Y
treatment naive
CGPLH80 Healthy cfDNA Preoperative 37 F NA NA NA NA NA NA 4.0 1.94 1.94 Y N Y
treatment naive
CGPLH81 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA 4.0 5.16 5.16 Y N Y
treatment naive
CGPLH82 Healthy cfDNA Preoperative 38 F NA NA NA NA NA NA 4.0 3.30 3.30 Y N Y
treatment naive
CGPLH83 Healthy cfDNA Preoperative 60 F NA NA NA NA NA NA 4.0 5.04 5.04 Y N Y
treatment naive
CGPLH84 Healthy cfDNA Preoperative 45 F NA NA NA NA NA NA 4.0 3.33 3.33 Y N Y
treatment naive
CGPLLU13 Lung cfDNA Pre-treatment, 72 F IV T1BN2bM1a Right Adenocarcinoma NA Bone 5.0 7.67 7.67 Y N Y
Cancer Day 2 Lung
CGPLLU13 Lung cfDNA Post-treatment, 72 F IV T1BN2bM1a Right Adenocarcinoma NA Bone 4.5 8.39 8.39 Y N Y
Cancer Day 5 Lung
CGPLLU13 Lung cfDNA Post-treatment, 72 F IV T1BN2bM1a Right Adenocarcinoma NA Bone 3.2 8.66 8.66 Y N Y
Cancer Day 28 Lung
CGPLLU13 Lung cfDNA Post-treatment, 72 F IV T1BN2bM1a Right Adenocarcinoma NA Bone 5.0 5.97 5.97 Y N Y
Cancer Day 91 Lung
CGPLLU14 Lung cfDNA Pre-treatment, 55 F IV T1N1M0 Right Lower Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y
Cancer Day −38 Lobe of Lung
CGPLLU14 Lung cfDNA Pre-treatment, 55 F IV T1N1M0 Right Lower Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y
Cancer Day −16 Lobe of Lung
CGPLLU14 Lung cfDNA Pre-treatment, 55 F IV T1N1M0 Right Lower Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y
Cancer Day −8 Lobe of Lung
CGPLLU14 Lung cfDNA Pre-treatment, 55 F IV T1N1M0 Right Lower Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y
Cancer Day 0 Lobe of Lung
CGPLLU14 Lung cfDNA Post-treatment, 55 F IV T1N1M0 Right Lower Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y
Cancer Day 0.33 Lobe of Lung
CGPLLU14 Lung cfDNA Post-treatment, 55 F IV T1N1M0 Right Lower Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y
Cancer Day 7 Lobe of Lung
CGPLLU144 Lung cfDNA Preoperative 52 M II T2aN1M0 Lung Adenocarcinoma Poor None 3.5 31.51 31.51 Y Y Y
Cancer treatment naive
CGPLLU147 Lung cfDNA Preoperative 60 M III T3N2M0 Lung Adenosquamous Poor None 3.8 6.72 6.72 Y Y Y
Cancer treatment naive Carcinoma
CGPLLU151 Lung cfDNA Preoperative 41 F II T3N2M0 Lung Adenocarcinoma Well None 4.0 83.04 83.04 Y N Y
Cancer treatment naive
CGPLLU162 Lung cfDNA Preoperative 38 M II T1N1M0 Right Adenocarcinoma Moderate None 3.1 40.32 40.32 Y Y Y
Cancer treatment naive Lung
CGPLLU163 Lung cfDNA Preoperative 66 M II T1N1M0 Left Adenocarcinoma Poor None 5.0 54.03 54.03 Y Y Y
Cancer treatment naive Lung
CGPLLU165 Lung cfDNA Preoperative 68 F II T1N1M0 Right Adenocarcinoma Well None 4.5 20.13 20.13 Y Y Y
Cancer treatment naive Lung
CGPLLU168 Lung cfDNA Preoperative 70 F I T2aN0M0 Lung Adenocarcinoma Poor None 4.3 19.38 19.38 Y Y Y
Cancer treatment naive
CGPLLU169 Lung cfDNA Preoperative 64 M I T1bN0M0 Lung Squamous Cel Moderate None 4.2 13.70 13.70 Y N Y
Cancer treatment naive Carcinoma
CGPLLU175 Lung cfDNA Preoperative 47 M I T2N0M0 Lung Squamous Cel Moderate None 4.4 16.84 16.84 Y Y Y
Cancer treatment naive Carcinoma
CGPLLU176 Lung cfDNA Preoperative 58 M I T2N0M0 Lung Adenosquamous Moderate None 3.2 7.86 7.86 Y Y Y
Cancer treatment naive Carcinoma
CGPLLU177 Lung cfDNA Preoperative 45 M II T3N0M0 Right Adenocarcinoma NA None 3.9 19.07 19.07 Y Y Y
Cancer treatment naive Lung
CGPLLU180 Lung cfDNA Preoperative 57 M I T2N0M0 Right Large Cel Poor None 3.2 19.31 19.31 Y Y Y
Cancer treatment naive Lung Carcinoma
CGPLLU198 Lung cfDNA Preoperative 49 F I T2N0M0 Left Adenocarcinoma Moderate None 4.2 14.09 14.09 Y Y Y
Cancer treatment naive Lung
CGPLLU202 Lung cfDNA Preoperative 68 M I T2aN0M0 Right Adenocarcinoma NA None 4.4 24.72 24.72 Y Y Y
Cancer treatment naive Lung
CGPLLU203 Lung cfDNA Preoperative 68 M II T3N0M0 Right Squamous Cel Well None 4.2 26.24 26.24 Y N Y
Cancer treatment naive Lung Carcinoma
CGPLLU205 Lung cfDNA Preoperative 65 M II T3N0M0 Left Adenocarcinoma Poor None 4.0 18.56 18.56 Y Y Y
Cancer treatment naive Lung
CGPLLU206 Lung cfDNA Preoperative 55 M III T3N1M0 Right Squamous Cel Poor None 3.5 18.24 18.24 Y Y Y
Cancer treatment naive Lung Carcinoma
CGPLLU207 Lung cfDNA Preoperative 60 F II T2N1M0 Lung Adenocarcinoma Well None 4.0 17.29 17.29 Y Y Y
Cancer treatment naive
CGPLLU208 Lung cfDNA Preoperative 56 F II T2N1M0 Lung Adenocarcinoma Moderate None 3.0 24.34 24.34 Y Y Y
Cancer treatment naive
CGPLLU209 Lung cfDNA Preoperative 65 M II T2aN0M0 Lung Large Cel Poor None 5.5 53.95 53.95 Y Y Y
Cancer treatment naive Carcinoma
CGPLLU244 Lung cfDNA Pre-treatment, 66 F IV NA Right Upper Adenocarcinoma Moderate/ Liver, Rib, 4.5 17.84 17.84 Y N Y
Cancer Day −7 Lobe of Lung Poor Brain,
Pleura
CGPLLU244 Lung cfDNA Pre-treatment, 66 F IV NA Right Upper Adenocarcinoma Moderate/ Liver, Rib, 4.5 17.84 17.84 Y N Y
Cancer Day −1 Lobe of Lung Poor Brain,
Pleura
CGPLLU244 Lung cfDNA Post-treatment, 66 F IV NA Right Upper Adenocarcinoma Moderate/ Liver, Rib, 4.5 17.84 17.84 Y N Y
Cancer Day 6 Lobe of Lung Poor Brain,
Pleura
CGPLLU244 Lung cfDNA Post-treatment, 66 F IV NA Right Upper Adenocarcinoma Moderate/ Liver, Rib, 4.5 17.84 17.84 Y N Y
Cancer Day 62 Lobe of Lung Poor Brain,
Pleura
CGPLLU245 Lung cfDNA Pre-treatment, 49 M IV T2aN2M1B Left Upper Adenocarcinoma NA Brain 4.7 19.42 19.42 Y N Y
Cancer Day 32 Lobe of Lung
CGPLLU245 Lung cfDNA Pre-treatment, 49 M IV T2aN2M1B Left Upper Adenocarcinoma NA Brain 4.7 19.42 19.42 Y N Y
Cancer Day 0 Lobe of Lung
CGPLLU245 Lung cfDNA Post-treatment, 49 M IV T2aN2M1B Left Upper Adenocarcinoma NA Brain 4.7 19.42 19.42 Y N Y
Cancer Day 7 Lobe of Lung
CGPLLU245 Lung cfDNA Post-treatment, 49 M IV T2aN2M1B Left Upper Adenocarcinoma NA Brain 4.7 19.42 19.42 Y N Y
Cancer Day 21 Lobe of Lung
CGPLLU246 Lung cfDNA Pre-treatment, 65 F IV NA Right Lower Adenocarcinoma Poor Peura 5.5 18.51 18.51 Y N Y
Cancer Day −21 Lobe of Lung
CGPLLU246 Lung cfDNA Pre-treatment, 65 F IV NA Right Lower Adenocarcinoma Poor Peura 5.5 18.51 18.51 Y N Y
Cancer Day 0 Lobe of Lung
CGPLLU246 Lung cfDNA Post-treatment, 65 F IV NA Right Lower Adenocarcinoma Poor Peura 5.5 18.51 18.51 Y N Y
Cancer Day 9 Lobe of Lung
CGPLLU246 Lung cfDNA Post-treatment, 65 F IV NA Right Lower Adenocarcinoma Poor Peura 5.5 18.51 18.51 Y N Y
Cancer Day 42 Lobe of Lung
CGPLLU264 Lung cfDNA Pre-treatment, 84 M IV T4N2BM1 Left Adenocarcinoma NA Lung 4.0 22.97 22.97 Y N Y
Cancer Day −1 Middle Lung
CGPLLU264 Lung cfDNA Post-treatment, 84 M IV T4N2BM1 Left Adenocarcinoma NA Lung 4.5 10.53 10.53 Y N Y
Cancer Day 8 Middle Lung
CGPLLU264 Lung cfDNA Post-treatment, 84 M IV T4N2BM1 Left Adenocarcinoma NA Lung 3.0 7.15 7.15 Y N Y
Cancer Day 27 Middle Lung
CGPLLU264 Lung cfDNA Post-treatment, 84 M IV T4N2BM1 Left Adenocarcinoma NA Lung 4.0 9.60 9.60 Y N Y
Cancer Day 69 Middle Lung
CGPLLU265 Lung cfDNA Pre-treatment, 71 F IV T1N0Mx Left Lower Adenocarcinoma NA None 4.2 7.16 7.16 Y N Y
Cancer Day 0 Lobe of Lung
CGPLLU265 Lung cfDNA Post-treatment, 71 F IV T1N0Mx Left Lower Adenocarcinoma NA None 4.0 8.11 8.11 Y N Y
Cancer Day 3 Lobe of Lung
CGPLLU265 Lung cfDNA Post-treatment, 71 F IV T1N0Mx Left Lower Adenocarcinoma NA None 4.2 7.53 7.53 Y N Y
Cancer Day 7 Lobe of Lung
CGPLLU265 Lung cfDNA Post-treatment, 71 F IV T1N0Mx Left Lower Adenocarcinoma NA None 5.0 16.17 16.17 Y N Y
Cancer Day 84 Lobe of Lung
CGPLLU266 Lung cfDNA Pre-treatment, 78 M IV T2aN1 Left Lower Adenocarcinoma Moderate None 5.0 5.32 5.32 Y N Y
Cancer Day 0 Lobe of Lung
CGPLLU266 Lung cfDNA Post-treatment, 78 M IV T2aN1 Left Lower Adenocarcinoma Moderate None 3.5 6.31 6.31 Y N Y
Cancer Day 16 Lobe of Lung
CGPLLU266 Lung cfDNA Post-treatment, 78 M IV T2aN1 Left Lower Adenocarcinoma Moderate None 5.0 7.64 7.64 Y N Y
Cancer Day 83 Lobe of Lung
CGPLLU266 Lung cfDNA Post-treatment, 78 M IV T2aN1 Left Lower Adenocarcinoma Moderate None 5.0 14.39 14.39 Y N Y
Cancer Day 328 Lobe of Lung
CGPLLU267 Lung cfDNA Pre-treatment, 55 F IV T3NxM1a Right Upper Squamous Cel Poor Lung 4.5 2.87 2.87 Y N Y
Cancer Day −1 Lobe of Lung Carcinoma
CGPLLU267 Lung cfDNA Post-treatment, 55 F IV T3NxM1a Right Upper Squamous Cel Poor Lung 4.5 3.34 3.34 Y N Y
Cancer Day 34 Lobe of Lung Carcinoma
CGPLLU267 Lung cfDNA Post-treatment, 55 F IV T3NxM1a Right Upper Squamous Cel Poor Lung 3.5 3.00 3.00 Y N Y
Cancer Day 90 Lobe of Lung Carcinoma
CGPLLU269 Lung cfDNA Pre-treatment, 52 F IV T1CNxM1C Right Adenocarcinoma NA Brain, Liver, 5.0 11.40 11.40 Y N Y
Cancer Day 0 Paratracheal Bone, Peura
Lesion
CGPLLU269 Lung cfDNA Post-treatment, 52 F IV T1CNxM1C Right Adenocarcinoma NA Brain, Liver, 5.0 8.35 8.35 Y N Y
Cancer Day 9 Paratracheal Bone, Peura
Lesion
CGPLLU269 Lung cfDNA Post-treatment, 52 F IV T1CNxM1C Right Adenocarcinoma NA Brain, Liver, 3.5 17.79 17.79 Y N Y
Cancer Day 28 Paratracheal Bone, Peura
Lesion
CGPLLU271 Lung cfDNA Post-treatment, 73 M IV T1aNxM1 Left Upper Adenocarcinoma Moderate Peura 4.0 4.70 4.70 Y N Y
Cancer Day 259 Lobe of Lung
CGPLLU271 Lung cfDNA Pre-treatment, 73 M IV T1aNxM1 Left Upper Adenocarcinoma Moderate Peura 5.0 18.86 18.86 Y N Y
Cancer Day 0 Lobe of Lung
CGPLLU271 Lung cfDNA Post-treatment, 73 M IV T1aNxM1 Left Upper Adenocarcinoma Moderate Peura 4.5 13.84 13.84 Y N Y
Cancer Day 8 Lobe of Lung
CGPLLU271 Lung cfDNA Post-treatment, 73 M IV T1aNxM1 Left Upper Adenocarcinoma Moderate Peura 3.5 13.46 13.46 Y N Y
Cancer Day 20 Lobe of Lung
CGPLLU271 Lung cfDNA Post-treatment, 73 M IV T1aNxM1 Left Upper Adenocarcinoma Moderate Peura 4.0 13.77 13.77 Y N Y
Cancer Day 104 Lobe of Lung
CGPLLU43 Lung cfDNA Pre-treatment, 57 F IV T1BN0M0 Right Lower Adenocarcinoma Moderate None 4.9 2.17 2.17 Y N Y
Cancer Day −1 Lobe of Lung
CGPLLU43 Lung cfDNA Post-treatment, 57 F IV T1BN0M0 Right Lower Adenocarcinoma Moderate None 3.7 3.26 3.26 Y N Y
Cancer Day 6 Lobe of Lung
CGPLLU43 Lung cfDNA Post-treatment, 57 F IV T1BN0M0 Right Lower Adenocarcinoma Moderate None 4.0 4.12 4.12 Y N Y
Cancer Day 27 Lobe of Lung
CGPLLU43 Lung cfDNA Post-treatment, 57 F IV T1BN0M0 Right Lower Adenocarcinoma Moderate None 3.7 8.20 8.20 Y N Y
Cancer Day 83 Lobe of Lung
CGPLLU86 Lung cfDNA Pre-treatment, 55 M IV NA Left Upper Adenocarcinoma NA Lung 4.0 7.90 7.90 Y N Y
Cancer Day 0 Lobe of Lung
CGPLLU86 Lung cfDNA Post-treatment, 55 M IV NA Left Upper Adenocarcinoma NA Lung 4.0 7.90 7.90 Y N Y
Cancer Day 0.5 Lobe of Lung
CGPLLU86 Lung cfDNA Post-treatment, 55 M IV NA Left Upper Adenocarcinoma NA Lung 4.0 7.90 7.90 Y N Y
Cancer Day 7 Lobe of Lung
CGPLLU86 Lung cfDNA Post-treatment, 55 M IV NA Left Upper Adenocarcinoma NA Lung 4.0 7.90 7.90 Y N Y
Cancer Day 17 Lobe of Lung
CGPLLU88 Lung cfDNA Pre-treatment, 59 M IV NA Right Adenocarcinoma NA None 5.0 27.66 27.66 Y N Y
Cancer Day 0 Middle
Lobe of Lung
CGPLLU88 Lung cfDNA Post-treatment, 59 M IV NA Right Adenocarcinoma NA None 5.0 6.49 6.49 Y N Y
Cancer Day 7 Middle
Lobe of Lung
CGPLLU88 Lung cfDNA Post-treatment, 59 M IV NA Right Adenocarcinoma NA None 4.0 3.04 3.04 Y N Y
Cancer Day 297 Middle
Lobe of Lung
CGPLLU89 Lung cfDNA Pre-treatment, 54 F IV NA Right Upper Adenocarcinoma NA Brain, 8.0 8.43 8.43 Y N Y
Cancer Day 0 Lobe of Lung Bone, Lung
CGPLLU89 Lung cfDNA Post-treatment, 54 F IV NA Right Upper Adenocarcinoma NA Brain, 8.0 8.43 8.43 Y N Y
Cancer Day 7 Lobe of Lung Bone, Lung
CGPLLU89 Lung cfDNA Post-treatment, 54 F IV NA Right Upper Adenocarcinoma NA Brain, 8.0 8.43 8.43 Y N Y
Cancer Day 22 Lobe of Lung Bone, Lung
CGPLOV11 Ovarian cfDNA Preoperative 51 F IV T3cN0M1 Right Endometrioid Moderate Omentum 3.4 17.35 17.35 Y Y Y
Cancer treatment naive Ovary Adenocarcinoma
CGPLOV12 Ovarian cfDNA Preoperative 45 F I T1aN0MX Ovary Endometrioid NA None 3.2 12.44 12.44 Y N Y
Cancer treatment naive Adenocarcinoma
CGPLOV13 Ovarian cfDNA Preoperative 62 F IV T1bN0M1 Right Endometrioid Poor Omentum 3.8 27.00 27.00 Y Y Y
Cancer treatment naive Ovary Adenocarcinoma
CGPLOV15 Ovarian cfDNA Preoperative 54 F III T3N1M0 Ovary Adenocarcinoma Poor None 5.0 4.77 4.77 Y Y Y
Cancer treatment naive
CGPLOV16 Ovarian cfDNA Preoperative 40 F III T3aN0M0 Ovary Serous Moderate None 4.5 27.28 27.28 Y N Y
Cancer treatment naive Adenocarcinoma
CGPLOV19 Ovarian cfDNA Preoperative 52 F II T2aN0M0 Ovary Endometrioid Moderate None 5.0 23.46 23.46 Y Y Y
Cancer treatment naive Adenocarcinoma
CGPLOV20 Ovarian cfDNA Preoperative 52 F II T2aN0M0 Left Endometrioid Poor None 4.2 5.67 5.67 Y Y Y
Cancer treatment naive Ovary Adenocarcinoma
CGPLOV21 Ovarian cfDNA Preoperative 51 F IV TanyN1M1 Ovary Serous Poor Omentum, 4.3 56.32 56.32 Y Y Y
Cancer treatment naive Adenocarcinoma Appendix
CGPLOV22 Ovarian cfDNA Preoperative 64 F III T1cNXMX Left Serous Well None 4.6 17.42 17.42 Y Y Y
Cancer treatment naive Ovary Adenocarcinoma
CGPLOV23 Ovarian cfDNA Preoperative 47 F I T1aN0M0 Ovary Serous Poor None 5.0 26.73 26.73 Y N Y
Cancer treatment naive Adenocarcinoma
CGPLOV24 Ovarian cfDNA Preoperative 14 F I T1aN0M0 Ovary Germ Cell Poor None 4.2 10.71 10.71 Y N Y
Cancer treatment naive Tumor
CGPLOV25 Ovarian cfDNA Preoperative 18 F I T1aN0M0 Ovary Germ Cell Poor None 4.8 6.78 6.78 Y N Y
Cancer treatment naive Tumor
CGPLOV26 Ovarian cfDNA Preoperative 35 F I T1aN0M0 Ovary Germ Cell Poor None 4.5 27.90 27.90 Y N Y
Cancer treatment naive Tumor
CGPLOV28 Ovarian cfDNA Preoperative 63 F I T1aN0M0 Right Serous NA None 3.2 10.74 10.74 Y N Y
Cancer treatment naive Ovary Carcinoma
CGPLOV31 Ovarian cfDNA Preoperative 45 F III T3aNxM0 Right Clear Cell NA None 4.0 14.45 14.45 Y N Y
Cancer treatment naive Ovary adenocarcinoma
CGPLOV32 Ovarian cfDNA Preoperative 53 F I T1aNxM0 Left Mucinous NA None 3.2 27.36 27.36 Y N Y
Cancer treatment naive Ovary Cystadenoma
CGPLOV37 Ovarian cfDNA Preoperative 40 F I T1cN0M0 Ovary Serous NA None 3.2 46.88 46.88 Y N Y
Cancer treatment naive Carcinoma
CGPLOV38 Ovarian cfDNA Preoperative 46 F I T1cN0M0 Ovary Serous NA None 2.4 34.29 34.29 Y N Y
Cancer treatment naive Carcinoma
CGPLOV40 Ovarian cfDNA Preoperative 53 F IV T3N0M1 Ovary Serous NA Omentum, 1.6 193.60 156.25 Y N Y
Cancer treatment naive Carcinoma Uterus,
Appendix
CGPLOV41 Ovarian cfDNA Preoperative 57 F IV T3N0M1 Ovary Serous NA Omentum, 4.4 10.03 10.03 Y N Y
Cancer treatment naive Carcinoma Uterus,
Cervix
CGPLOV42 Ovarian cfDNA Preoperative 52 F I T3aN0M0 Ovary Serous NA None 4.2 49.51 49.51 Y N Y
Cancer treatment naive Carcinoma
CGPLOV43 Ovarian cfDNA Preoperative 30 F I T1aN0M0 Ovary Mucinous NA None 4.4 9.09 9.09 Y N Y
Cancer treatment naive Cyst-
adenocarcinoma
CGPLOV44 Ovarian cfDNA Preoperative 69 F I T1aN0M0 Ovary Mucinous NA None 4.5 8.79 8.79 Y N Y
Cancer treatment naive Adenocarcinoma
CGPLOV46 Ovarian cfDNA Preoperative 58 F I T1bN0M0 Ovary Serous NA None 4.1 8.97 8.97 Y N Y
Cancer treatment naive Carcinoma
CGPLOV47 Ovarian cfDNA Preoperative 41 F I T1aN0M0 Ovary Serous NA None 4.5 19.35 19.35 Y N Y
Cancer treatment naive Adenocarcinoma
CGPLOV48 Ovarian cfDNA Preoperative 52 F I T1bN0M0 Ovary Serous NA None 3.5 22.80 22.80 Y N Y
Cancer treatment naive Carcinoma
CGPLOV49 Ovarian cfDNA Preoperative 68 F III T3bN0M0 Ovary Serous NA None 4.2 16.48 16.48 Y N Y
Cancer treatment naive Carcinoma
CGPLOV50 Ovarian cfDNA Preoperative 30 F III T3cN0M0 Ovary Serous NA None 4.5 8.89 8.89 Y N Y
Cancer treatment naive Carcinoma
CGPLPA112 Pancreatic cfDNA Preoperative 58 M II NA Intra NA NA None 3.5 18.52 18.52 Y N N
Cancer treatment naive Pancreatic
Bile Duct
CGPLPA113 Duodenal cfDNA Preoperative 71 M I NA Intra NA NA None 4.8 8.24 8.24 Y N N
Cancer treatment naive Pancreatic
Bile Duct
CGPLPA114 Bile Duct cfDNA Preoperative NA F II NA Intra NA NA None 4.8 26.43 26.43 Y N N
Cancer treatment naive Pancreatic
Bile Duct
CGPLPA115 Bile Duct cfDNA Preoperative NA M IV NA Intra NA NA NA 5.0 31.41 31.41 Y N N
Cancer treatment naive Hepatic
Bile Duct
CGPLPA117 Bile Duct cfDNA Preoperative NA M II NA Intra NA NA NA 3.4 2.29 2.29 Y N N
Cancer treatment naive Pancreatic
Bile Duct
CGPLPA118 Bile Duct cfDNA Preoperative 68 F I NA Bile Duct Intra- NA None 3.8 9.93 9.93 Y N Y
Cancer treatment naive Ampuliary
Bile Duct
CGPLPA122 Bile Duct cfDNA Preoperative 62 F II NA Bile Duct Intra- NA None 3.8 66.54 32.89 Y N Y
Cancer treatment naive Pancreatic
Bile Duct
CGPLPA124 Bile Duct cfDNA Preoperative 83 F II NA Bile Duct Intra- moderate None 4.6 29.24 27.17 Y N Y
Cancer treatment naive Ampuliary
Bile Duct
CGPLPA125 Bile Duct cfDNA Preoperative 58 M II NA Bile Duct Intra- poor None 2.7 8.31 8.31 Y N N
Cancer treatment naive Pancreatic
Bile Duct
CGPLPA126 Bile Duct cfDNA Preoperative 60 M II NA Bile Duct Intra- NA None 4.2 80.56 29.07 Y N Y
Cancer treatment naive Pancreatic
Bile Duct
CGPLPA127 Bile Duct cfDNA Preoperative 71 F IV NA Bile Duct Extra- NA NA 3.0 20.60 20.60 Y N N
Cancer treatment naive Pancreatic
Bile Duct
CGPLPA128 Bile Duct cfDNA Preoperative 67 M II NA Bile Duct Intra- NA None 3.9 5.91 5.91 Y N Y
Cancer treatment naive Pancreatic
Bile Duct
CGPLPA129 Bile Duct cfDNA Preoperative 56 F II NA Bile Duct Intra- NA None 4.6 27.07 27.07 Y N Y
Cancer treatment naive Pancreatic
Bile Duct
CGPLPA130 Bile Duct cfDNA Preoperative 82 F II NA Bile Duct Intra- well None 4.0 4.34 4.34 Y N Y
Cancer treatment naive Ampuliary
Bile Duct
CGPLPA131 Bile Duct cfDNA Preoperative 71 M II NA Bile Duct Intra- NA None 3.9 68.95 32.05 Y N Y
Cancer treatment naive Pancreatic
Bile Duct
CGPLPA134 Bile Duct cfDNA Preoperative 68 M II NA Bile Duct Intra- NA None 4.1 58.98 30.49 Y N Y
Cancer treatment naive Pancreatic
Bile Duct
CGPLPA135 Bile Duct cfDNA Preoperative 67 F I NA Bile Duct Intra- NA NA 3.9 4.22 4.22 Y N N
Cancer treatment naive Pancreatic
Bile Duct
CGPLPA136 Bile Duct cfDNA Preoperative 69 F II NA Bile Duct Intra- NA None 4.1 20.23 20.23 Y N Y
Cancer treatment naive Pancreatic
Bile Duct
CGPLPA137 Bile Duct cfDNA Preoperative NA M II NA Bile Duct NA NA NA 4.0 5.75 5.75 Y N N
Cancer treatment naive
CGPLPA139 Bile Duct cfDNA Preoperative NA M IV NA Bile Duct NA NA NA 4.0 14.89 14.89 Y N N
Cancer treatment naive
CGPLPA14 Pancreatic cfDNA Preoperative 68 M II NA Pancreas Ductal Poor None 4.0 1.30 1.30 Y N N
Cancer treatment naive Adenocarcinoma
CGPLPA140 Bile Duct cfDNA Preoperative 52 M II NA Extra- Intra- Poor None 4.7 29.34 26.60 Y N Y
Cancer treatment naive Hepatic Pancreatic
Bile Duct Bile Duct
CGPLPA141 Bile Duct cfDNA Preoperative 68 F II NA Extra- Intra- Moderate None 2.8 53.67 44.64 Y N N
Cancer treatment naive Hepatic Pancreatic
Bile Duct Bile Duct
CGPLPA15 Pancreatic cfDNA Preoperative 70 F II NA Pancreas Ductal Well Lymph 4.0 1.92 1.92 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA155 Bile Duct cfDNA Preoperative NA F II NA NA NA NA NA 4.0 25.72 25.72 Y N N
Cancer treatment naive
CGPLPA156 Pancreatic cfDNA Preoperative 73 F II NA Pancreas Ductal Poor Lymph 4.5 7.54 7.54 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA165 Bile Duct cfDNA Preoperative 42 M I NA Bile Duct Intra- well None 3.9 10.48 10.48 Y N N
Cancer treatment naive Pancreatic
Bile Duct with
Meduliary
Features
CGPLPA168 Bile Duct cfDNA Preoperative 58 M II NA Bile Duct NA NA NA 3.0 139.12 34.72 Y N N
Cancer treatment naive
CGPLPA17 Pancreatic cfDNA Preoperative 65 M II NA Pancreas Ductal Well Lymph 4.0 13.08 13.08 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA184 Bile Duct cfDNA Preoperative 75 F II NA Bile Duct Intra- NA None NA NA NA Y N N
Cancer treatment naive Pancreatic
Bile Duct
CGPLPA187 Bile Duct cfDNA Preoperative 67 F II NA Bile Duct Intra- NA None NA NA NA Y N N
Cancer treatment naive Pancreatic
Bile Duct
CGPLPA23 Pancreatic cfDNA Preoperative 58 F II NA Pancreas Ductal Moderate Lymph 4.0 16.62 16.62 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA25 Pancreatic cfDNA Preoperative 69 F II NA Pancreas Ductal Poor Lymph 4.0 8.71 8.71 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA26 Pancreatic cfDNA Preoperative 64 M II NA Pancreas Ductal Well Lymph 4.0 6.97 6.97 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA28 Pancreatic cfDNA Preoperative 79 F II NA Pancreas Ductal Well Lymph 4.0 18.13 18.13 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA33 Pancreatic cfDNA Preoperative 67 F II NA Pancreas Ductal Well Lymph 4.0 1.80 1.80 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA34 Pancreatic cfDNA Preoperative 73 M II NA Pancreas Ductal Well Lymph 4.0 3.36 3.36 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA37 Pancreatic cfDNA Preoperative 67 F II NA Pancreas Ductal NA Lymph 4.0 21.83 21.83 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA38 Pancreatic cfDNA Preoperative 65 M II NA Pancreas Ductal Moderate None 4.0 5.29 5.29 Y N N
Cancer treatment naive Adenocarcinoma
CGPLPA39 Pancreatic cfDNA Preoperative 67 F II NA Pancreas Ductal Well Lymph 4.0 11.73 11.73 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA40 Pancreatic cfDNA Preoperative 64 M II NA Pancreas Ductal Well Lymph 4.0 4.78 4.78 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA42 Pancreatic cfDNA Preoperative 73 M II NA Pancreas Ductal Moderate Lymph 4.0 3.41 3.41 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA46 Pancreatic cfDNA Preoperative 59 F II NA Pancreas Ductal Poor Lymph 4.0 0.74 0.74 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA47 Pancreatic cfDNA Preoperative 67 M II NA Pancreas Ductal Well Lymph 4.0 6.01 6.01 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA48 Pancreatic cfDNA Preoperative 72 F II NA Pancreas Ductal Well None NA NA NA Y N N
Cancer treatment naive Adenocarcinoma
CGPLPA52 Pancreatic cfDNA Preoperative 63 M II NA Pancreas Ductal Moderate None 2.5 9.86 9.86 Y N N
Cancer treatment naive Adenocarcinoma
CGPLPA53 Pancreatic cfDNA Preoperative 46 M I NA Pancreas Ductal Poor Lymph 3.0 14.48 14.48 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA58 Pancreatic cfDNA Preoperative 74 F II NA Pancreas Ductal NA None 3.0 6.87 6.87 Y N N
Cancer treatment naive Adenocarcinoma
CGPLPA59 Pancreatic cfDNA Preoperative 59 F II NA Pancreas Ductal Well Lymph NA NA NA Y N N
Cancer treatment naive Adenocarcinoma Node
or Adenoma
CGPLPA67 Pancreatic cfDNA Preoperative 55 M III NA Pancreas Ductal Well Lymph 3.2 9.72 9.72 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA69 Pancreatic cfDNA Preoperative 70 M I NA Pancreas Ductal Well None 2.0 1.72 1.72 Y N N
Cancer treatment naive Adenocarcinoma
CGPLPA71 Pancreatic cfDNA Preoperative 64 M II NA Pancreas Ductal Well Lymph 2.2 39.07 39.07 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA74 Pancreatic cfDNA Preoperative 71 F II NA Pancreas Ductal Moderate Lymph 2.5 4.99 4.99 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA76 Pancreatic cfDNA Preoperative 69 M II NA Pancreas Ductal Poor None 2.5 23.19 23.19 Y N N
Cancer treatment naive Adenocarcinoma
CGPLPA85 Pancreatic cfDNA Preoperative 77 F II NA Pancreas Ductal Poor Lymph 3.0 152.46 41.67 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA86 Pancreatic cfDNA Preoperative 66 M II NA Pancreas Ductal Moderate Lymph 2.5 11.92 11.92 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA92 Pancreatic cfDNA Preoperative 72 M II NA Pancreas Ductal NA Lymph 2.0 5.34 5.34 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA93 Pancreatic cfDNA Preoperative 48 M II NA Pancreas Ductal Poor None 3.0 96.28 41.67 Y N N
Cancer treatment naive Adenocarcinoma
CGPLPA94 Pancreatic cfDNA Preoperative 72 F II NA Pancreas Ductal NA Lymph 3.0 29.66 29.66 Y N N
Cancer treatment naive Adenocarcinoma Node
CGPLPA95 Pancreatic cfDNA Preoperative 64 F II NA Pancreas Ductal Well Lymph NA NA NA Y N N
Cancer treatment naive Adenocarcinoma Node
CGST102 Gastric cfDNA Preoperative 76 F II T3N0M0 Stomach Tubular Moderate None 4.1 8.03 8.03 Y N Y
Cancer treatment naive Adenocarcinoma
CGST11 Gastric cfDNA Preoperative 49 M IV TXNXM1 Stomach Mixed Moderate None 3.8 3.57 3.57 Y N N
Cancer treatment naive Carcinoma
CGST110 Gastric cfDNA Preoperative 77 M III T4AN3aM0 Stomach Tubular Moderate None 3.8 5.00 5.00 Y N Y
Cancer treatment naive Adenocarcinoma
CGST114 Gastric cfDNA Preoperative 65 M III T4N1M0 Stomach Tubular Poor None 4.4 10.35 10.35 Y N Y
Cancer treatment naive Adenocarcinoma
CGST13 Gastric cfDNA Preoperative 72 F II T1AN2M0 Stomach Signet Ring Poor None 4.4 24.33 24.33 Y N Y
Cancer treatment naive Cell Carcinoma
CGST131 Gastric cfDNA Preoperative 63 M III T2N3aM0 Stomach Signet ring Poor None 4.0 4.28 4.28 Y N N
Cancer treatment naive cell Carcinoma
CGST141 Gastric cfDNA Preoperative 33 F III T3N2M0 Stomach Signet Ring Poor None 4.4 10.84 10.84 Y N Y
Cancer treatment naive Cell Carcinoma
CGST16 Gastric cfDNA Preoperative 78 M III T4AN3aM0 Stomach Tubular Poor None 4.0 40.69 40.69 Y N Y
Cancer treatment naive Adenocarcinoma
CGST18 Gastric cfDNA Preoperative 50 M II T3N0M0 Stomach Mucinous Well None 4.3 9.78 9.78 Y N Y
Cancer treatment naive Adenocarcinoma
CGST21 Gastric cfDNA Preoperative 39 M II T2N1(mi)M0 Stomach Papillary Moderate None 4.0 0.83 0.83 Y N N
Cancer treatment naive Adenocarcinoma
CGST26 Gastric cfDNA Preoperative 51 M IV TXNXM1 Stomach Signet ring Poor None 3.5 5.56 5.56 Y N N
Cancer treatment naive cell Carcinoma
CGST28 Gastric cfDNA Preoperative 55 M X TXNXMX Stomach Undifferentiated Poor None 4.0 5.86 5.86 Y N Y
Cancer treatment naive Carcinoma
CGST30 Gastric cfDNA Preoperative 64 F III T3N2M0 Stomach Signet Ring Poor None 3.0 4.22 4.22 Y N Y
Cancer treatment naive Cell Carcinoma
CGST32 Gastric cfDNA Preoperative 67 M II T3N1M0 Stomach Tubular Moderate None 4.0 11.49 11.49 Y N Y
Cancer treatment naive Adenocarcinoma
CGST33 Gastric cfDNA Preoperative 61 M I T2N0M0 Stomach Tubular Moderate None 3.5 5.71 5.71 Y N Y
Cancer treatment naive Adenocarcinoma
CGST38 Gastric cfDNA Preoperative 71 F 0 T0N0M0 Stomach Mucinous NA None 4.0 NA NA Y N N
Cancer treatment naive Adenocarcinoma
CGST39 Gastric cfDNA Preoperative 51 M IV TXNXM1 Stomach Signet Ring Poor None 3.5 20.69 20.69 Y N Y
Cancer treatment naive Cell Carcinoma
CGST41 Gastric cfDNA Preoperative 66 F IV TXNXM1 Stomach Signet Ring Poor None 3.5 7.83 7.83 Y N Y
Cancer treatment naive Cell Carcinoma
CGST45 Gastric cfDNA Preoperative 41 F II T3N0M0 Stomach Signet Ring Poor None 3.8 7.14 7.14 Y N Y
Cancer treatment naive Cell Carcinoma
CGST47 Gastric cfDNA Preoperative 74 F I T1AN0M0 Stomach Tubular Moderate None 4.0 4.55 4.55 Y N Y
Cancer treatment naive Adenocarcinoma
CGST48 Gastric cfDNA Preoperative 62 M IV TXNXM1 Stomach Tubular Poor None 4.5 8.79 8.79 Y N Y
Cancer treatment naive Adenocarcinoma
CGST53 Gastric cfDNA Preoperative 70 M 0 T0N0M0 Stomach NA NA None 3.8 15.82 15.82 Y N N
Cancer treatment naive
CGST58 Gastric cfDNA Preoperative 58 M III T4AN3bM0 Stomach Signet Ring Poor None 3.8 19.81 19.81 Y N Y
Cancer treatment naive Cell Carcinoma
CGST67 Gastric cfDNA Preoperative 69 M I T1RN0M0 Stomach Tubular Moderate None 3.0 23.01 23.01 Y N N
Cancer treatment naive adenocarcinoma
CGST77 Gastric cfDNA Preoperative 70 M IV TXNXM1 Stomach Tubular Moderate None 4.5 15.09 15.09 Y N N
Cancer treatment naive adenocarcinoma
CGST80 Gastric cfDNA Preoperative 58 M III T3N3aM0 Stomach Mucinous Poor None 4.5 8.56 8.56 Y N Y
Cancer treatment naive Adenocarcinoma
CGST81 Gastric cfDNA Preoperative 64 F I T2N0M1 Stomach Signet Ring Poor None 3.5 37.32 37.32 Y N Y
Cancer treatment naive Cell Carcinoma
CGH14 Healthy Human NA NA M NA NA NA NA NA NA NA NA NA Y N N
Adult
elutriated
lymphoc
CGH15 Healthy Human NA NA F NA NA NA NA NA NA NA NA NA Y N N
Adult
elutriated
lymphoc
*NA denotes data not available or not applicable for healthy individuals.
APPENDIX B
Table 2 Summary of targeted cfDNA analyses
Fragment Profile Mutation Bases in Bases Mapped to Bases Mapped to Percent Mapped to Total Distinct
Patient Patient Type Timepoint Analysis Analysis Read Length Target Region Genome Target Regions Target Regions Coverage Coverage
CGCRC291 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7501485600 3771359756 50% 44345 10359
CGCRC292 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 6736035200 3098886973 46% 36448 8603
CGCRC293 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 6300244000 2818734206 45% 33117 5953
CGCRC294 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7766872600 3911796709 50% 46016 12071
CGCRC295 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 8240660200 3478059753 42% 40787 5826
CGCRC296 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 5718556500 2898549356 51% 33912 10180
CGCRC291 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 7550826100 3717222432 49% 43545 5870
CGCRC298 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 12501036400 6096393764 49% 71196 9617
CGCRC299 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7812602900 4121569690 53% 48098 10338
CGCRC300 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 8648090300 3962285136 46% 46364 5756
CGCRC301 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7538758100 3695480348 49% 43024 6618
CGCRC302 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 8573658300 4349420574 51% 51006 13799
CGCRC303 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 5224046400 2505714343 48% 29365 8372
CGCRC304 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 5762112600 2942170530 51% 34462 10208
CGCRC305 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7213384100 3726953480 52% 43516 8589
CGCRC306 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7075579700 3552441899 50% 41507 7372
CGCRC307 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7572687100 3492191519 46% 40793 9680
CGCRC308 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7945738000 3895908986 49% 45224 11809
CGCRC309 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 8487455800 3921079811 46% 45736 10739
CGCRC310 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 9003580500 4678812441 52% 54713 11139
CGCRC311 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 6528162700 3276653864 50% 38324 6044
CGCRC312 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 7683294300 3316719187 43% 38652 4622
CGCRC313 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 5874099200 2896148722 49% 33821 6506
CGCRC314 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 6883148500 3382767492 49% 39414 6664
CGCRC315 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7497252500 3775556051 50% 44034 8666
CGCRC316 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 10684720400 5533857153 52% 64693 14289
CGCRC317 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7086877600 3669434216 52% 43538 10944
CGCRC318 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 6880041100 3326357413 48% 39077 11571
CGCRC319 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7485342900 3982677483 53% 47327 10502
CGCRC320 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7058703200 3450648135 49% 40888 10198
CGCRC321 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7203625900 3633396892 50% 43065 6499
CGCRC332 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 7202969100 3758323705 52% 44580 3243
CGCRC333 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 8767144700 4199126827 48% 49781 8336
CGCRC334 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 7771869100 3944518280 51% 46518 5014
CGCRC335 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 7972524600 4064901201 51% 48308 6151
CGCRC336 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 8597346400 4333410573 50% 51390 7551
CGCRC337 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 7399611700 3800666199 51% 45083 8092
CGCRC338 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 8029493700 4179383804 52% 49380 5831
CGCRC339 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 7938963500 4095555110 52% 48397 3808
CGCRC340 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 7214889500 3706643098 51% 43805 3014
CGCRC341 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 8803159200 3668208527 42% 43106 11957
CGCRC342 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 8478811500 3425540889 40% 40328 9592
CGCRC344 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6942167800 3098232737 45% 36823 2300
CGCRC345 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 8182868200 2383173431 29% 28233 7973
CGCRC346 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7448272300 3925056341 53% 46679 5582
CGCRC347 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 5804744500 2986809912 51% 35490 4141
CGCRC349 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6943451600 3533145275 51% 41908 5762
CGCRC350 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7434818400 3848923016 52% 45678 4652
CGCRC351 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7306546400 3636910409 50% 43162 5205
CGCRC352 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7864655000 3336939252 42% 39587 4502
CGCRC353 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7501674800 3642919375 49% 43379 4666
CGCRC354 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7938270200 2379068977 30% 28256 4858
CGCRC356 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6013175900 3046754994 51% 36127 3425
CGCRC357 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6013454600 3022035300 50% 35813 4259
CGCRC358 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7227212400 3188723303 44% 37992 5286
CGCRC359 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7818567700 425110101 5% 5040 2566
CGCRC367 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6582043200 3363063597 51% 39844 5839
CGCRC368 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 8042242400 4101646000 51% 48636 11471
CGCRC370 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6940330100 3198954121 46% 38153 4826
CGCRC373 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6587201700 3120088035 47% 37234 5190
CGCRC376 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6727983100 3162416807 47% 37735 3445
CGCRC377 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6716339200 3131415570 47% 37160 4524
CGCRC378 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6523969900 2411096720 37% 28728 3239
CGCRC379 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6996252100 3371081103 48% 39999 2891
CGCRC380 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7097496300 2710244446 38% 32020 3251
CGCRC381 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6961936100 3287050681 47% 38749 9357
CGCRC382 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6959048700 2552325859 37% 30040 5148
CGCRC384 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7012798900 3293884583 47% 39158 3653
CGCRC385 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7542017900 3356570505 45% 39884 3686
CGCRC386 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6876059600 3064412286 45% 36431 2787
CGCRC387 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7399564700 3047254560 41% 36141 6675
CGCRC386 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6592692900 3137284885 48% 37285 5114
CGCRC389 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6651206300 3102100941 47% 36764 6123
CGCRC390 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7260616800 3376667585 47% 40048 4368
CGCRC391 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6883624500 3202877881 47% 37978 5029
CGLU316 Lung Cancer Pre-treatment Day −53 Y N 100 80930 7864415100 1991331171 25% 23601 3565
CGLU316 Lung Cancer Pre-treatment, Day −53 Y N 100 80930 7502591600 3730963390 50% 44262 3966
CGLU316 Lung Cancer Pro-treatment, Day −53 Y N 100 80930 6582515900 3187059470 48% 37813 3539
CGLU316 Lung Cancer Pre-treatment, Day −53 Y N 100 60930 6587281800 1947630979 30% 23094 4439
CGLU344 Lung Cancer Pretreatment, Day −21 Y N 100 80930 6151628500 2748983603 45% 32462 8063
CGLU344 Lung Cancer Pre-treatment, Day −21 Y N 100 80930 7842910900 1147703178 15% 13565 4303
CGLU344 Lung Cancer Pretreatment, Day −21 Y N 100 80930 5838083100 2291108925 39% 27067 4287
CGLU344 Lung Cancer Pre-treatment, Day −21 Y N 100 80930 7685989200 3722274529 48% 43945 3471
CGLU369 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 7080245300 1271457982 18% 15109 2354
CGLU369 Lung Cancer Pre-treatment, Day −2 Y N 100 00930 7078131900 1482448715 21% 17583 4275
CGLU369 Lung Cancer Pre-treatment, Day −2 Y N 100 60930 6904701700 2124660124 31% 25230 5278
CGLU369 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 7003452200 3162195578 45% 37509 6062
CGLU373 Lung Cancer Pro-treatment, Day −2 Y N 100 00930 6346267200 3053520676 48% 36137 6251
CGLU373 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 6517189900 3192984468 49% 38066 8040
CGLU373 Lung Cancer Pre-treatment, Day −2 Y N 100 60930 7767146300 3572598842 46% 42378 5306
CGLU373 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 7190999100 3273648804 46% 38784 4454
CGPLBR100 Breast Cancer Preoperative, Treatment naïve N Y 100 00930 7299964400 3750278051 51% 44794 3249
CGPLBR101 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 7420822800 3810365416 51% 45565 9784
CGPLBR102 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 6679304900 3269688319 49% 38679 7613
CGPLBR103 Breast Cancer Preoperative, Treatment naïve N Y 100 60930 7040304400 3495542468 50% 41786 6748
CGPLBR104 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 7188389200 3716096781 52% 44316 9448
CGPLBR38 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7810293900 4057576306 52% 48098 9868
CGPLBR39 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 7745701500 3805623239 49% 45084 11065
CGPLBR40 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7558990500 3652442341 48% 43333 12948
CGPLBR41 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 7900994600 3836800101 49% 45535 10847
CGPLBR44 Breast Cancer Preoperative, Treatment naïve Y N 100 80930 7017744200 3269110569 47% 38672 8344
CGPLBR48 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 5629044200 2611554623 46% 30860 8652
CGPLBR49 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 5784711600 2673457893 46% 31274 10429
CGPLBR55 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 8309154900 4306956261 52% 51143 8328
CGPLBR57 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 8636181000 4391502618 51% 52108 5857
CGPLBR59 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 8799457700 4152328555 47% 49281 5855
CGPLBR61 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 8163706700 3952010628 48% 46755 8522
CGPLBR63 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7020533100 3542447304 50% 41956 4773
CGPLBR67 Breast Cancer Preoperative, Treatment naïve Y N 100 80930 8264353900 3686093696 45% 43516 7752
CGPLBR68 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 7629312300 4078969547 53% 48389 7402
CGPLBR69 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7571501500 3857354512 51% 45322 7047
CGPLBR70 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7251760700 3641333708 50% 43203 8884
CGPLBR71 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 8515402600 4496696391 53% 53340 6805
CGPLBR72 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 8556946900 4389761697 51% 52081 5632
CGPLBR73 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7959392300 4006933338 50% 47555 8791
CGPLBR74 Breast Cancer Preoperative, Treatment naïve Y N 100 80930 8524536400 4063900599 48% 48252 7013
CGPLBR75 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 8260379100 3960599885 48% 46955 6319
CGPLBR76 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7774235200 3893622420 50% 46192 9628
CGPLBR77 Breast Cancer Preoperative, Treatment naïve Y N 100 80930 7572797600 3255963429 43% 38568 8263
CGPLBR80 Breast Cancer Preoperative, Treatment naïve Y N 100 80930 6845325800 3147476693 46% 37201 5595
CGPLBR82 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 8236705200 4170465005 51% 49361 12319
CGPLBR83 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7434568100 3676855019 49% 43628 5458
CGPLBR86 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7616282500 3644791327 48% 43490 7048
CGPLBR87 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 6194021300 3004882010 49% 35765 5306
CGPLBR88 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 6071567200 2847926237 47% 33945 10319
CGPLBR91 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 7192457700 3480203404 48% 41570 9912
CGPLBR92 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7678981800 3600279233 47% 42975 13580
CGPLBR93 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 7605717800 3998713397 53% 47866 10329
CGPLBR96 Breast Cancer Preoperative, Treatment naïve Y N 100 80930 6297446700 2463064737 39% 29341 7937
CGPLBR97 Breast Cancer Preoperative, Treatment naïve Y N 100 80930 7114921600 3557069027 50% 42488 10712
CGPLH35 Healthy Preoperative, Treatment naïve N Y 100 80930 6919126300 2312758764 33% 25570 1989
CGPLH36 Healthy Preoperative, Treatment naïve N Y 100 80930 6089923400 2038548115 33% 22719 1478
CGPLH37 Healthy Preoperative, Treatment naïve N Y 100 80930 5557270200 1935301929 35% 21673 2312
CGPLH42 Healthy Preoperative, Treatment naïve N Y 100 80930 5792045400 2388036949 41% 27197 2523
CGPLH43 Healthy Preoperative, Treatment naïve N Y 100 80930 5568321700 2017813329 36% 23228 1650
CGPLH45 Healthy Preoperative, Treatment naïve N Y 100 80930 8485593200 2770176078 33% 32829 3114
CGPLH46 Healthy Preoperative, Treatment naïve N Y 100 80930 5083171100 1899395790 37% 21821 1678
CGPLH47 Healthy Preoperative, Treatment naïve N Y 100 80930 6016388500 2062392156 34% 23459 1431
CGPLH48 Healthy Preoperative, Treatment naïve N Y 100 80930 4958945900 1809825992 36% 20702 1698
CGPLH49 Healthy Preoperative, Treatment naïve N Y 100 80930 7953812200 2511365904 32% 27006 1440
CGPLH50 Healthy Preoperative, Treatment naïve N Y 100 80930 6989407600 2561288100 37% 29177 2591
CGPLH51 Healthy Preoperative, Treatment naïve N Y 100 80930 7862073200 2525091396 32% 29999 1293
CGPLH52 Healthy Preoperative, Treatment naïve N Y 100 80930 6939636800 2397922699 35% 27029 2501
CGPLH54 Healthy Preoperative, Treatment naïve N Y 100 80930 10611934700 2290823134 22% 27175 3306
CGPLH55 Healthy Preoperative, Treatment naïve N Y 100 80930 9912569200 2521962244 25% 27082 3161
CGRLH56 Healthy Preoperative, Treatment naïve N Y 100 80930 5777591900 2023874863 35% 22916 1301
CGPLH57 Healthy Preoperative, Treatment naïve N Y 100 80930 9234904800 1493926244 16% 15843 1655
CGPLH59 Healthy Preoperative, Treatment naïve N Y 100 80930 9726052100 2987875484 31% 35427 2143
CGPLH63 Healthy Preoperative, Treatment naïve N Y 100 80930 8696405000 2521574759 29% 26689 1851
CGPLH64 Healthy Preoperative, Treatment naïve N Y 100 80930 5438852600 996198502 18% 11477 1443
CGPLH75 Healthy Preoperative, Treatment naïve Y N 100 80930 3446444000 1505718480 44% 17805 3016
CGPLH76 Healthy Preoperative, Treatment naïve N Y 100 80930 7499116400 3685762725 49% 43682 4643
CGPLH77 Healthy Preoperative, Treatment naïve Y N 100 80930 6512408400 2537359345 39% 30280 3131
CGPLH78 Healthy Preoperative, Treatment naïve N Y 100 80930 7642949300 3946069680 52% 46316 5358
CGPLH79 Healthy Preoperative, Treatment naïve N Y 100 80930 7785475700 3910639227 50% 45280 6714
CGPLH80 Healthy Preoperative, Treatment naïve N Y 100 80930 7918361500 3558236955 45% 42171 5062
CGPLH81 Healthy Preoperative, Treatment naïve Y N 100 80930 6646268900 3112369850 47% 37119 3678
CGPLH82 Healthy Preoperative, Treatment naïve N Y 100 80930 7744065000 3941700596 51% 46820 5723
CGPLH83 Healthy Preoperative, Treatment naïve Y N 100 80930 6957686000 1447603106 21% 17280 2875
CGPLH84 Healthy Preoperative, Treatment naïve Y N 100 80930 8326493200 3969908122 48% 47464 3647
CGPLH86 Healthy Preoperative, Treatment naïve N Y 100 80930 8664194700 4470145091 52% 53398 5094
CGPLH90 Healthy Preoperative, Treatment naïve N Y 100 80930 7516078800 3841504088 51% 45907 4414
CGPLLU13 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 5659546100 1721618955 30% 20587 6025
CGPLLU13 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 6199049700 2563659840 41% 30728 6514
CGPLLU13 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 5864396500 1194237002 20% 14331 3952
CGPLLU13 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 5080197700 1373550586 27% 16480 5389
CGPLLU14 Lung Cancer Pre-treatment, Day −38 N Y 100 80930 8668655700 398731089 46% 48628 3148
CGPLLU14 Lung Cancer Pre-treatment, Day −16 N Y 100 80930 8271043600 4105092738 50% 50152 4497
CGPLLU14 Lung Cancer Pre-treatment, Day −3 N Y 100 80930 7149809200 3405754720 48% 40382 6170
CGPLLU14 Lung Cancer Pre-treatment, Day 0 N Y 100 80930 6556332200 3289504484 50% 39004 4081
CGPLLU14 Lung Cancer Post-treatment, Day 0.33 N Y 100 80930 7410378300 3464236558 47% 41108 4259
CGPLLU14 Lung Cancer Post-treatment, Day 7 N Y 100 80930 7530190700 3752054349 50% 45839 2469
CGPLLU144 Lung Cancer Preoperative, Treatment naïve Y Y 100 80930 8716827400 4216576624 48% 49370 10771
CGPLLU146 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 8506844200 4195033049 49% 49084 6968
CGPLLU147 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 7416300600 3530746046 48% 41302 4691
CGPLLU161 Lung Cancer Preoperative, Treatment naïve N Y 100 80930 7789148700 3280139772 42% 38568 12229
CGPLLU162 Lung Cancer Preoperative, Treatment naïve Y Y 100 80930 7625462000 3470147667 46% 40918 10099
CGPLLU163 Lung Cancer Preoperative, Treatment naïve Y Y 100 80930 8019293200 3946533983 49% 46471 12108
CGPLLU164 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 8110030900 3592748235 44% 42161 6947
CGPLLU165 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 8389514600 4147501817 49% 48770 8996
CGPLLU168 Lung Cancer Preoperative, Treatment naïve Y Y 100 80930 7600630000 3868237773 50% 45625 9711
CGPLLU169 Lung Cancer Preoperative, Treatment naïve N Y 100 80930 9378353000 4800407624 51% 56547 10261
CGPLLU174 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 7481844600 3067532518 41% 36321 6137
CGPLLU175 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 8532324200 4002541569 47% 47084 7862
CGPLLU176 Lung Cancer Preoperative, Treatment naïve Y Y 100 80930 8143905000 4054098929 50% 47708 5588
CGPLLU177 Lung Cancer Preoperative, Treatment naïve Y Y 100 80930 8421611300 4197108809 50% 49476 8780
CGPLLU178 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 8483124700 4169577489 49% 48580 6445
CGPLLU179 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 7774358700 3304915738 43% 38768 6862
CGPLLU180 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 8192813800 3937552475 48% 46498 6568
CGPLLU197 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 7906779200 3082397881 39% 36381 5388
CGPLLU198 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 7175247200 3545719100 49% 42008 6817
CGPLLU202 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 6840112800 3427820669 50% 40670 7951
CGPLLU203 Lung Cancer Preoperative, Treatment naïve N Y 100 80930 7458749900 3762726574 50% 44500 9917
CGPLLU204 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 7445026400 3703545153 50% 44317 6856
CGPLLU205 Lung Cancer Preoperative, Treatment naïve Y Y 100 80930 9205429100 4350573991 47% 51627 9810
CGPLLU206 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 7397914600 3635210205 49% 43016 7124
CGPLLU207 Lung Cancer Preoperative, Treatment naïve Y Y 100 80930 7133043900 3736258011 52% 44291 8499
CGPLLU208 Lung Cancer Preoperative, Treatment naïve Y Y 100 80930 7346976400 3855814032 52% 45782 8940
CGPLLU209 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 6723337800 3362944595 50% 39531 11946
CGPLLU244 Lung Cancer Pre-treatment Day −7 N Y 100 80930 8305560600 4182616104 50% 50851 7569
CGPLLU244 Lung Cancer Pre-treatment, Day −1 N Y 100 80930 7739951100 3788487116 49% 45925 8552
CGPLLU244 Lung Cancer Post-treatment, Day 6 N Y 100 80930 8061928000 4225322272 52% 51279 8646
CGPLLU244 Lung Cancer Post-treatment, Day 62 N Y 100 80930 8894936700 4437962639 50% 53862 7361
CGPLLU245 Lung Cancer Pre-treatment, Day −32 N Y 100 80930 7679235200 3935822054 51% 47768 7266
CGPLLU245 Lung Cancer Pre-treatment Day 0 N Y 100 80930 8985252500 4824268339 54% 58338 10394
CGPLLU245 Lung Cancer Post-treatment, Day 7 N Y 100 80930 8518229300 4480236927 53% 54083 10125
CGPLLU245 Lung Cancer Post-treatment, Day 21 N Y 100 80930 9031131000 4824738475 53% 58313 10598
CGPLLU246 Lung Cancer Pre-treatment. Day −21 N Y 100 80930 8520360800 3509660305 41% 42349 8086
CGPLLU246 Lung Cancer Pre-treatment, Day 0 N Y 100 80930 5451467800 2828351657 52% 34243 8256
CGPLLU246 Lung Cancer Post-treatment, Day 9 N Y 100 80930 8137616600 4135036174 51% 50121 6466
CGPLLU246 Lung Cancer Post-treatment, Day 42 N Y 100 80930 8385724600 4413323333 53% 53495 7303
CGPLLU264 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 6254777700 3016326208 48% 36164 12138
CGPLLU264 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 6185331000 3087883231 50% 37003 8388
CGPLLU264 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 6274540300 2861143666 46% 34308 6817
CGPLLU264 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 5701274000 1241270938 22% 14886 4273
CGPLLU265 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6091276800 2922585558 48% 35004 7742
CGPLLU265 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6430107900 2945953499 46% 35219 8574
CGPLLU265 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 5869510300 2792208995 48% 33423 8423
CGPLLU265 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 5884330900 2588386038 44% 30977 9803
CGPLLU266 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 5807524900 2347651479 40% 28146 5793
CGPLLU266 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6064269800 2086938782 34% 24994 6221
CGPLLU266 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6785913900 3458588505 51% 41432 7785
CGPLLU266 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6513702000 2096370387 32% 25142 6598
CGPLLU267 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 6610761200 2576886619 39% 31095 4485
CGPLLU267 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 6156402000 2586081726 42% 30714 5309
CGPLLU267 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 6180799700 2013434756 33% 23902 3885
CGPLLU269 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6221168600 1499602843 24% 17799 6098
CGPLLU269 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 5353961600 1698331125 32% 20094 5252
CGPLLU269 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 5831612800 1521114956 26% 18067 6210
CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930 6229704000 1481468974 24% 17608 4633
CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930 6134366400 1351029627 22% 16170 7024
CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930 6491884900 1622578435 25% 19433 5792
CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930 5742881200 2349421128 41% 28171 5723
CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930 5503999300 1695782705 31% 20320 5907
CGPLLU43 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 6575907000 3002048491 46% 35997 5445
CGPLLU43 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 6204350900 3016077187 49% 36162 5704
CGPLLU43 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 5997724300 2989608757 50% 35873 6228
CGPLLU43 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 6026261500 2881177658 48% 34568 7221
CGPLLU86 Lung Cancer Pre-treatment, Day 0 N Y 100 80930 8222093400 3523035056 43% 41165 3614
CGPLLU86 Lung Cancer Post-treatment, Day 0.5 N Y 100 80930 8305719500 4271264008 51% 49508 6681
CGPLLU86 Lung Cancer Post-treatment, Day 7 N Y 100 80930 6787785300 3443658418 51% 40192 3643
CGPLLU86 Lung Cancer Post-treatment, Day 17 N Y 100 80930 6213229400 3120325926 50% 36413 3560
CGPLLU88 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 7252433900 3621678746 50% 42719 8599
CGPLLU88 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 7679995800 4004738253 52% 46951 6387
CGPLLU88 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6509178000 3316053733 51% 39274 2651
CGPLLU89 Lung Cancer Pre-treatment, Day 0 N Y 100 80930 7662496600 3781536306 49% 44097 7909
CGPLLU89 Lung Cancer Post-treatment, Day 7 N Y 100 80930 7005599600 3339612564 48% 38977 5034
CGPLLU89 Lung Cancer Post-treatment, Day 22 N Y 100 80930 8325998600 3094796789 37% 36061 2822
CGPLOV10 Ovarian Cancer Preoperative, Treatment naïve Y Y 100 80930 7073534200 3402306123 48% 39820 4059
CGPLOV11 Ovarian Cancer Preoperative, Treatment naïve Y Y 100 80930 6924062200 3324593050 48% 38796 7185
CGPLOV12 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 6552080100 3181854993 49% 37340 6114
CGPLOV13 Ovarian Cancer Preoperative, Treatment naïve Y Y 100 80930 6796755500 3264897084 48% 38340 7931
CGPLOV14 Ovarian Cancer Preoperative, Treatment naïve Y Y 100 80930 7856573900 3408425065 43% 39997 7712
CGPLOV15 Ovarian Cancer Preoperative, Treatment naïve Y Y 100 80930 7239201500 3322285607 46% 38953 6644
CGPLOV16 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 8570755900 4344288233 51% 51009 11947
CGPLOV17 Ovarian Cancer Preoperative, Treatment naïve Y N 100 80930 6910310400 2805243492 41% 32828 4307
CGPLOV18 Ovarian Cancer Preoperative, Treatment naïve N N 100 80930 8173037600 4064432407 50% 47714 5182
CGPLOV19 Ovarian Cancer Preoperative, Treatment naïve Y Y 100 80930 7732198900 3672564399 47% 43020 11127
CGPLOV20 Ovarian Cancer Preoperative, Treatment naïve Y Y 100 80930 7559602000 3678700179 49% 43230 4872
CGPLOV21 Ovarian Cancer Preoperative, Treatment naïve Y Y 100 80930 8949032900 4616255499 52% 54012 12777
CGPLOV22 Ovarian Cancer Preoperative, Treatment naïve Y Y 100 80930 8680136500 4049934586 47% 46912 9715
CGPLOV23 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 6660696600 3422631774 51% 40810 9460
CGPLOV24 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 8634287200 4272258165 49% 50736 8689
CGPLOV25 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 6978295000 3390206388 49% 40188 5856
CGPLOV26 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 7041038300 3728879661 53% 44341 8950
CGPLOV28 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 7429236900 3753051715 51% 45430 4155
CGPLOV31 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 8961384000 4621838729 51% 55429 5458
CGPLOV32 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 9344536800 4737698323 51% 57234 6165
CGPLOV37 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 8158083200 4184432898 51% 50648 6934
CGPLOV38 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 8654435400 4492987085 52% 53789 6124
CGPLOV40 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 9868640700 4934400809 50% 59049 7721
CGPLOV41 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 7689013600 3861448829 50% 46292 4469
CGPLOV42 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 9836516300 4864154366 49% 58302 7632
CGPLOV43 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 8756507100 4515479918 52% 54661 4310
CGPLOV44 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 7576310800 4120933922 54% 49903 4969
CGPLOV46 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 9346036300 5037820346 54% 61204 3927
CGPLOV47 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 10880620200 5491357828 50% 66363 6895
CGPLOV48 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 7658787800 3335991337 44% 40332 4066
CGPLOV49 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 10076208000 5519656698 55% 67117 5097
CGPLOV50 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 8239290400 4472380276 54% 54150 3836
CGPLPA118 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 9094827600 4828332902 53% 57021 4002
CGPLPA122 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 7303323100 3990160379 55% 47240 7875
CGPLPA124 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 7573482800 3965807442 52% 46388 8658
CGPLPA126 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 7904953600 4061463168 51% 47812 10498
CGPLPA128 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 7249238300 2244188735 31% 26436 3413
CGPLPA129 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 7559858900 4003725804 53% 47182 5733
CGPLPA130 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 6973946500 1247144905 18% 14691 1723
CGPLPA131 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 7226237900 3370664342 47% 39661 5054
CGPLPA134 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 7268866100 3754945844 52% 44306 7023
CGPLPA136 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 7476690700 4073978408 54% 48134 5244
CGPLPA140 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 7364654600 3771765342 51% 44479 7080
CGST102 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 5715504500 2644902854 46% 31309 4503
CGST110 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 9179291500 4298269268 47% 51666 3873
CGST114 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 7151572200 3254967293 46% 38496 4839
CGST13 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6449701500 3198545984 50% 38515 6731
CGST141 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6781001300 3440927391 51% 40762 5404
CGST16 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6396470600 2931380289 46% 35354 8148
CGST18 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6647324000 3138967777 47% 37401 4992
CGST28 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6288486100 2884997993 46% 34538 2586
CGST30 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6141213100 3109994564 51% 37194 2555
CGST32 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6969139300 3099120469 44% 36726 3935
CGST33 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6560309400 3168371917 48% 37916 4597
CGST39 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 7043791400 2992501875 42% 35620 6737
CGST41 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6975053100 3224065662 46% 38300 4016
CGST45 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6130812200 2944524278 48% 35264 4745
CGST47 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 5961400000 3083523351 52% 37008 3112
CGST48 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6418652700 1497230327 23% 17782 2410
CGST58 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 5818344500 1274708429 22% 15281 2924
CGST80 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6388064600 3298497188 52% 39692 5280
CGST81 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 8655691400 1519121452 18% 17988 6419
APPENDIX C
Table 3. Targeted cfDNA fragment analyses in cancer patients
Stage at Amino Acid
Patient Patient Type Diagnosis Alteration Type Gene (Protein)
CGCRC291 Colorectal Cancer IV Tumor-derived STK11 39R > C
CGCRC291 Colorectal Cancer IV Tumor-derived TP53 272V > M
CGCRC291 Colorectal Cancer IV Tumor-derived TP53 167Q > X
CGCRC291 Colorectal Cancer IV Tumor-derived KRAS 12G > A
CGCRC291 Colorectal Cancer IV Tumor-derived APC 1260Q > X
CGCRC291 Colorectal Cancer IV Tumor-derived APC 1450R > X
CGCRC291 Colorectal Cancer IV Tumor-derived PIK3CA 542E > K
CGCRC292 Colorectal Cancer IV Tumor-derived KRAS 146A > V
CGCRC292 Colorectal Cancer IV Tumor-derived CTNNB1 41T > A
CGCRC292 Colorectal Cancer IV Germline EGFR 2284 − 4C > 3
CGCRC293 Colorectal Cancer IV Tumor-derived TP53 176C > S
CGCRC294 Colorectal Cancer II Tumor-derived APC 213R > X
CGCRC294 Colorectal Cancer II Tumor-derived APC 1367Q > X
CGCRC295 Colorectal Cancer IV Tumor-derived PDBFRA 49 + 4C > T
CGCRC295 Colorectal Cancer IV Hematopoietic IDH1 104G > V
CGCRC296 Colorectal Cancer II Germline EGFR 922E > K
CGCRC297 Colorectal Cancer III Germline KIT 18L > F
CGCRC298 Colorectal Cancer II Hematopoietic DNMT3A 882R > H
CGCRC298 Colorectal Cancer II Hematopoietic DNMT3A 714S > C
CGCRC298 Colorectal Cancer II Tumor-derived PIK3CA 414G > V
CGCRC299 Colorectal Cancer I Hematopoietic DNMT3A 735Y > C
CGCRC299 Colorectal Cancer I Hematopoietic DNMT3A 710C > S
CGCRC300 Colorectal Cancer I Hematopoietic DNMT3A 720R > G
CGCRC301 Colorectal Cancer I Tumor-derived ATM 2397Q > X
CGCRC302 Colorectal Cancer II Tumor-derived TP53 141C > Y
CGCRC302 Colorectal Cancer II Tumor-derived BRAF 600V > E
CGCRC303 Colorectal Cancer III Tumor-derived TP53 173V > L
CGCRC303 Colorectal Cancer III Hematopoietic DNMT3A 755F > S
CGCRC303 Colorectal Cancer III Hematopoietic DNMT3A 2173 + 1G > A
CGCRC304 Colorectal Cancer II Tumor-derived EGFR 1131T > S
CGCRC304 Colorectal Cancer II Tumor-derived ATM 3077 + 1G > A
CGCRC304 Colorectal Cancer II Hematopoietic ATM 3008R > C
CGCRC305 Colorectal Cancer II Tumor-derived GNA11 213R > Q
CGCRC305 Colorectal Cancer II Tumor-derived TP53 273R > H
CGCRC306 Colorectal Cancer II Tumor-derived TP53 196R > X
CGCRC306 Colorectal Cancer II Tumor-derived CDKN2A 107R > C
CGCRC306 Colorectal Cancer II Tumor-derived KRAS 61Q > K
CGCRC306 Colorectal Cancer II Germline PDGFRA 200T > S
CGCRC306 Colorectal Cancer II Tumor-derived EGFR 618H > R
CGCRC306 Colorectal Cancer II Tumor-derived PIK3CA 545E > A
CGCRC306 Colorectal Cancer II Germline ERBB4 1155R > X
CGCRC307 Colorectal Cancer II Tumor-derived JAK2 805L > V
CGCRC307 Colorectal Cancer II Tumor-derived SMARCB1 501 − 2A > G
CGCRC307 Colorectal Cancer II Tumor-derived GNAS 201R > C
CGCRC307 Colorectal Cancer II Tumor-derived BRAF 600V > E
CGCRC307 Colorectal Cancer II Tumor-derived FBXW7 465R > C
CGCRC307 Colorectal Cancer II Tumor-derived ERBB4 17A > V
CGCRC308 Colorectal Cancer III Hematopoietic DNMT3A 882R > H
CGCRC308 Colorectal Cancer III Germline EGFR 848P > L
CGCRC308 Colorectal Cancer III Tumor-derived APC 1480Q > X
CGCRC309 Colorectal Cancer III Tumor-derived AKT1 17E > K
CGCRC309 Colorectal Cancer III Tumor-derived BRAF 600V > E
CGCRC310 Colorectal Cancer II Tumor-derived KRAS 12G > V
CGCRC310 Colorectal Cancer II Tumor-derived APC 1513E > X
CGCRC310 Colorectal Cancer II Tumor-derived APC 1521E > X
CGCRC311 Colorectal Cancer I Hematopoietic DNMT3A 882R > H
CGCRC312 Colorectal Cancer III Tumor-derived APC 960S > X
CGCRC312 Colorectal Cancer III Tumor-derived NRAS 61Q > K
CGCRC313 Colorectal Cancer III Tumor-derived KRAS 12G > S
CGCRC313 Colorectal Cancer III Tumor-derived APC 876R > X
CGCRC314 Colorectal Cancer I Tumor-derived KRAS 12G > D
CGCRC314 Colorectal Cancer I Hematopoietic DNMT3A 738L > Q
CGCRC314 Colorectal Cancer I Tumor-derived APC 1379E > X
CGCRC315 Colorectal Cancer III Tumor-derived NRAS 12G > D
CGCRC315 Colorectal Cancer III Tumor-derived FBXW7 505R > C
Alteration Mutant
Mutation Hotspot Detected Allele
Patient Nucleotide Type Alteration in Tissue Fraction
CGCRC291 chr19_1207027-127027_C_T Substitution No No 0.14%
CGCRC291 chr17_7577124-7577124_C_T Substitution Yes No 0.10%
CGCRC291 chr17_7578431-7578431_G_A Substitution Yes Yes 22.85%
CGCRC291 chr12_25398284-25398284_C_G Substitution Yes Yes 14.65%
CGCRC291 chr5_112175069-112175069_C_T Substitution No Yes 11.23%
CGCRC291 chr5_11215639-11215639_C_T Substitution Yes Yes 11.05%
CGCRC291 chr3_178936082-178936082_G_A Substitution Yes Yes 18.11%
CGCRC292 chr12_25378561-25378561_G_A Substitution Yes No 1.41%
CGCRC292 chr3_41266124-41266124_A_G Substitution Yes Yes 0.13%
CGCRC292 chr7_55248982-55248982_C_G Substitution NA Yes 31.99%
CGCRC293 chr17_7578404-7578404_A_T Substitution No No 0.35%
CGCRC294 chr5_12116592-12116592_C_T Substitution Yes Yes 0.14%
CGCRC294 chr5_12175390-12175390_C_T Substitution Yes Yes 0.13%
CGCRC295 chr4_55124988-55124988_C_T Substitution No No 0.45%
CGCRC295 chr2_209113196-209113196_C_A Substitution No Yes 0.34%
CGCRC296 chr7_55266472-55266472_G_A Substitution NA Yes 30.48%
CGCRC297 chr4_55524233-55524233_C_T Substitution NA Yes 41.39%
CGCRC298 chr2_25457242-25457242_C_T Substitution Yes Yes 0.08%
CGCRC298 chr2_25463541-25463541_G_C Substitution No No 0.11%
CGCRC298 chr3_178927478-178927478_G_T Substitution No No 0.55%
CGCRC299 chr2_25463289-25463289_T_C Substitution No Yes 0.30%
CGCRC299 chr2_25463553-2546355_C_G Substitution No Yes 0.12%
CGCRC300 chr2_25463524-25463524_G_C Substitution No No 0.15%
CGCRC301 chr11_108199847-108199847_C_T Substitution No No 0.21%
CGCRC302 chr17_7578508-7578508_C_T Substitution Yes Yes 0.05%
CGCRC302 chr7_140453136-140453136_A_T Substitution Yes Yes 0.12%
CGCRC303 chr17_7578413-7578413_C_A Substitution Yes Yes 0.08%
CGCRC303 chr2_25463229-25463229_A_G Substitution No No 0.21%
CGCRC303 chr2_25463508-25463508_C_T Substitution No No 0.17%
CGCRC304 chr7_55273068-55273068_A_T Substitution No No 0.22%
CGCRC304 chr11_108142134-108142134_G_A Substitution No No 0.27%
CGCRC304 chr11_108236086-108236086_C_T Substitution No Yes 0.43%
CGCRC305 chr19_3118954-3118954_G_A Substitution No Yes 0.11%
CGCRC305 chr17_7577120-7577120_C_T Substitution Yes No 0.19%
CGCRC306 chr17_7578263-7578263_G_A Substitution Yes No 0.12%
CGCRC306 chr9_21971039-21971039_G_A Substitution No Yes 8.02%
CGCRC306 chr12_25380277-25380277_G_T Substitution Yes Yes 7.30%
CGCRC306 chr4_55130065-55130065_C_G Substitution NA Yes 34.78%
CGCRC306 chr7_55233103-55233103_A_G Substitution No Yes 8.32%
CGCRC306 chr3_178936092-178936092_A_C Substitution Yes No 0.96%
CGCRC306 chr2_2122596-2122596_G_A Substitution NA Yes 38.70%
CGCRC307 chr9_5080662-5080662_C_G Substitution No No 0.56%
CGCRC307 chr22_24145480-24145480_A_G Substitution No Yes 0.34%
CGCRC307 chr20_57484420-57484420_C_T Substitution Yes Yes# 0.24%
CGCRC307 chr7_140453136-140453136_A_T Substitution Yes Yes 0.38%
CGCRC307 chr4_153249385-153249385_G_A Substitution Yes Yes 0.31%
CGCRC307 chr2_213403205-213403205_G_A Substitution No No 0.15%
CGCRC308 chr2_25457242-25457242_C_T Substitution Yes No 0.06%
CGCRC308 chr7_55259485-55259485_C_T Substitution NA Yes 27.69%
CGCRC308 chr5_112175242-112175242_C_T Substitution No Yes 0.11%
CGCRC309 chr14_105246551-105246551_C_T Substitution Yes Yes 2.70%
CGCRC309 chr7_140453136-140453136_A_T Substitution Yes Yes 3.00%
CGCRC310 chr12_25398284-25398284_C_A Substitution Yes Yes 0.13%
CGCRC310 chr5_11215828-11215828_G_T Substitution No Yes 0.11%
CGCRC310 chr5_11215852-11215852_G_T Substitution No Yes 0.15%
CGCRC311 chr2_25457242-25457242_C_T Substitution Yes No 0.86%
CGCRC312 chr5_112174170-112174170_C_G Substitution No Yes 0.59%
CGCRC312 chr1_115256530-115256530_G_T Substitution Yes Yes 0.47%
CGCRC313 chr12_25398285-25398285_C_T Substitution Yes Yes 0.17%
CGCRC313 chr5_112173917-112173917_C_T Substitution Yes Yes 0.07%
CGCRC314 chr12_25398284-25398284_C_T Substitution Yes Yes 0.30%
CGCRC314 chr2_25463280-25463280_A_T Substitution No Yes 2.50%
CGCRC314 chr5_112175426-112175426_G_T Substitution Yes Yes 0.38%
CGCRC315 chr1_115258747-115258747_C_T Substitution Yes Yes 0.27%
CGCRC315 chr4_153247289-53247289_G_A Substitution Yes Yes 0.25%
Wild-type Fragments
25th
Minimum Percentile Mode Median
cfDNA cfDNA cfDNA cfDNA
Fragment Fragment Fragment Fragment
Distinct Size Size Size Size
Patient Coverage (bp) (bp) (bp) (bp)
CGCRC291 11688 100 151 167 159
CGCRC291 11779 100 155 171 159
CGCRC291 11026 100 156 166 159
CGCRC291 7632 97 152 169 157
CGCRC291 7218 101 155 167 159
CGCRC291 10757 86 154 166 167
CGCRC291 5429 100 151 171 167
CGCRC292 8120 101 157 167 169
CGCRC292 10693 100 155 169 168
CGCRC292 7587 97 158 166 171
CGCRC293 7672 95 159 168 170
CGCRC294 7339 84 155 166 167
CGCRC294 12054 89 159 167 170
CGCRC295 5602 101 157 164 170
CGCRC295 8330 100 157 166 169
CGCRC296 8375 89 161 166 172
CGCRC297 3580 102 159 164 170
CGCRC298 13032 100 159 168 171
CGCRC298 13475 93 158 169 170
CGCRC298 5815 100 156 168 169
CGCRC299 11995 100 154 164 165
CGCRC299 15363 96 151 166 164
CGCRC300 7487 100 162 170 173
CGCRC301 5881 100 156 169 169
CGCRC302 24784 84 154 165 164
CGCRC302 11763 95 159 165 165
CGCRC303 13967 95 160 169 171
CGCRC303 10161 81 160 169 172
CGCRC303 10845 100 160 169 172
CGCRC304 16168 90 153 167 164
CGCRC304 10502 100 152 165 163
CGCRC304 12987 101 154 165 165
CGCRC305 12507 100 159 169 171
CGCRC305 10301 100 156 168 168
CGCRC306 8594 101 157 165 169
CGCRC306 9437 90 159 167 171
CGCRC306 8090 100 152 163 168
CGCRC306 4585 103 158 167 170
CGCRC306 7395 81 160 166 171
CGCRC306 4885 100 152 167 167
CGCRC306 3700 100 159 166 171
CGCRC307 6860 100 158 170 170
CGCRC307 10065 95 157 168 169
CGCRC307 7520 102 156 167 168
CGCRC307 6623 76 157 169 168
CGCRC307 10606 100 155 167 168
CGCRC307 13189 90 158 168 171
CGCRC308 16287 90 159 168 169
CGCRC308 7729 100 160 164 170
CGCRC308 14067 92 157 170 169
CGCRC309 13036 85 157 170 169
CGCRC309 9084 101 157 166 168
CGCRC310 7393 100 153 165 164
CGCRC310 11689 100 152 166 164
CGCRC310 10273 100 153 166 164
CGCRC311 8456 94 160 171 172
CGCRC312 4719 100 160 165 173
CGCRC312 3391 101 157 172 170
CGCRC313 5013 100 163 166 174
CGCRC313 8150 72 161 171 174
CGCRC314 4684 100 158 165 169
CGCRC314 6902 85 159 165 170
CGCRC314 7229 102 158 167 170
CGCRC315 8739 94 155 167 169
CGCRC315 9623 101 158 166 170
Stage at Amino Acid
Patient Patient Type Diagnosis Alteration Type Gene (Protein)
CGCRC316 Colorectal Cancer III Tumor-derived TP53 245G > S
CGCRC316 Colorectal Cancer III Tumor-derived CDKN2A 1M > R
CGCRC316 Colorectal Cancer III Tumor-derived CTNNB1 37S > C
CGCRC316 Colorectal Cancer III Tumor-derived EGFR 2732 − 3C > T
CGCRC316 Colorectal Cancer III Hematopoietic ATM 3008R > P
CGCRC317 Colorectal Cancer III Tumor-derived TP53 220Y > C
CGCRC317 Colorectal Cancer III Tumor-derived ATM 1026W > R
CGCRC317 Colorectal Cancer III Tumor-derived APC 216R > X
CGCRC318 Colorectal Cancer I Hematopoietic DNMT3A 698W > X
CGCRC320 Colorectal Cancer I Germline KIT 18L > F
CGCRC320 Colorectal Cancer I Tumor-derived ERBB4 78R > W
CGCRC321 Colorectal Cancer I Tumor-derived CDKN2A 12S > L
CGCRC321 Colorectal Cancer I Hernatopcietic DNMT3A 882R > H
CGCRC321 Colorectal Cancer I Germline EGFR 511S > Y
CGCRC332 Colorectal Cancer IV Tumor-derived TP53 125T > R
CGCRC333 Colorectal Cancer IV Tumor-derived TP53 673 − 2A > G
CGCRC333 Colorectal Cancer IV Tumor-derived BRAF 600V > E
CGCRC333 Colorectal Cancer IV Tumor-derived ERBB4 891E > A
CGCRC334 Colorectal Cancer IV Tumor-derived TP53 245G > S
CGCRC334 Colorectal Cancer IV Germline EGFR 638T > M
CGCRC334 Colorectal Cancer IV Tumor-derived PIK3CA 104P > R
CGCRC335 Colorectal Cancer IV Tumor-derived BRAF 600V > E
CGCRC336 Colorectal Cancer IV Tumor-derived TP53 175R > H
CGCRC336 Colorectal Cancer IV Tumor-derived KRAS 12G > V
CGCRC336 Colorectal Cancer IV Tumor-derived APC 1286E > X
CGCRC337 Colorectal Cancer IV Tumor-derived STK11 734 + ST > A
CGCRC337 Colorectal Cancer IV Germline APC 485M > I
OGORC338 Colorectal Cancer IV Tumor-derived KRAS 12G > D
CGCRC339 Colorectal Cancer IV Tumor-derived KRAS 13G > D
CGCRC339 Colorectal Cancer IV Tumor-derived APC 876R > X
CGCRC339 Colorectal Cancer IV Tumor-derived PIK3CA 407C > F
CGCRC339 Colorectal Cancer IV Tumor-derived PIK3CA 1047H > L
CGCRC340 Colorectal Cancer IV Tumor-derived TP53 196R > X
CGCRC340 Colorectal Cancer IV Tumor-derived APC 1306E > X
CGPLBR38 Breast Cancer I Tumor-derived TP53 241S > P
CGPLBR40 Breast Cancer III Germline AR 392P > R
CGPLBR44 Breast Cancer III Hematopoietic DNMT3A 882R > H
CGPLBR44 Breast Cancer III Hematopoietic DNMT3A 705I > T
CGPLBR44 Breast Cancer III Tumor-derived PDGFRA 859V > M
CGPLBR48 Breast Cancer II Germline ALK 1231R > Q
CGPLBR48 Breast Cancer II Tumor-derived EGFR 669R > Q
CGPLBR55 Breast Cancer III Hematopoietic DNMT3A 743P > S
CGPLBR55 Breast Cancer III Tumor-derived GNAS 201R > H
CGPLBR55 Breast Cancer III Tumor-derived PIK3CA 345N > K
CGPLBR63 Breast Cancer II Germline FGFR3 403K > E
CGPLBR67 Breast Cancer II Hematopoietic DNMT3A 882R > H
CGPLBR67 Breast Cancer II Tumor-derived PIK3CA 545E > K
CGPLBR67 Breast Cancer II Tumor-derived ERBB4 1000D > A
CGPLBR69 Breast Cancer II Hematopoietic DNMT3A 774E > V
CGPLBR69 Breast Cancer II Germline CTNNB1 30Y > S
CGPLBR69 Breast Cancer II Germline IDH1 231Y > N
CGPLBR70 Breast Cancer II Tumor-derived ATM 2832R > H
CGPLBR70 Breast Cancer II Germline APC 1577E > D
CGPLBR71 Breast Cancer II Tumor-derived TP53 273R > H
CGPLBR72 Breast Cancer II Germline APC 1532D > G
CGPLBR73 Breast Cancer II Tumor-derived ALK 708S > P
CGPLBR73 Breast Cancer II Germline ERBB4 158A > E
CGPLBR74 Breast Cancer II Germline AR 20 + G1G > T
CGPLBR75 Breast Cancer II Tumor-derived PIK3CA 1047H > R
CGPLBR76 Breast Cancer II Germline KDR 1290S > N
CGPLBR76 Breast Cancer II Tumor-derived PIK3CA 1047H > R
CGPLBR77 Breast Cancer III Tumor-derived PTEN 170S > I
CGPLBR80 Breast Cancer II Tumor-derived CDKN2A 12S > L
CGPLBR83 Breast Cancer II Germline AR 728N > D
CGPLBR83 Breast Cancer II Tumor-derived ATM 322E > K
CGPLBR83 Breast Cancer II Germline ERBB4 539Y > S
CGPLBR86 Breast Cancer II Germline STK11 354F > L
Alteration Mutant
Mutation Hotspot Detected Allele
Patient Nucleotide Type Alteration in Tissue Fraction
CGCRC316 chr17_7577548-7577548_C_T Substitution Yes Yes 6.52%
CGCRC316 chr9_21974625-21974825_A_C Substitution No Yes 5.74%
CGCRC316 chr3_41266113-41266113_C_G Substitution Yes Yes 5.47%
CGCRC316 chr7_55266407-55266407_C_T Substitution No No 0.11%
CGCRC316 chr11_108236087-108236087_G_C Substitution No Yes 0.13%
CGCRC317 chr17_7578190-7578190_T_C Substitution Yes Yes 0.36%
CGCRC317 chr11_108142132-108142132_T_C Substitution No Yes 0.23%
CGCRC317 chr5_112128143-112128143_C_T Substitution Yes No 0.29%
CGCRC318 chr2_25463589-25463589_C_T Substitution No Yes 0.25%
CGCRC320 chr4_55524233-55524233_C_T Substitution NA Yes 34.76%
CGCRC320 chr2_212989479-212989479_G_A Substitution No No 0.12%
CGCRC321 chr9_21974792-21974792_C_T Substitution No No 0.20%
CGCRC321 chr2_25457242-25457242_C_A Substitution You No 0.08%
CGCRC321 chr7_55229225-55229225_G_C Substitution NA Yes 41.86%
CGCRC332 chr17_7579313-7579313_T_C Substitution No Yes 19.98%
CGCRC333 chr17_7577610-7577610_A_T Substitution No Yes 43.03%
CGCRC333 chr7_140453136-140453136_T_G Substitution Yes Yes 22.26%
CGCRC333 chr2_212495194-212495194_C_T Substitution No No 1.00%
CGCRC334 chr17_7577548-7577548_C_T Substitution Yes Yes 13.44%
CGCRC334 chr7_55238900-55238900_C_T Substitution NA Yes 35.28%
CGCRC334 chr3_178916924-178916924_C_G Substitution No No 3.85%
CGCRC335 chr7_140453136-140453136_A_T Substitution Yes Yes 0.32%
CGCRC336 chr17_7578406-7578406_C_T Substitution Yes Yes 75.76%
CGCRC336 chr12_25398284-25398284_C_A Substitution Yes Yes 42.87%
CGCRC336 chr5_112175147-112175147_G_T Substitution No Yes 81.61%
CGCRC337 chr19_1220718-1220718_T_A Substitution No No 0.12%
CGCRC337 chr5_112162851-112162851_G_A Substitution NA Yes 46.26%
OGORC338 chr12_25398284-25398284_C_T Substitution Yes Yes 27.03%
CGCRC339 chr12_25398281-25398281_C_T Substitution Yes Yes 1.94%
CGCRC339 chr5_112173917-112173917_C_T Substitution Yes Yes 2.35%
CGCRC339 chr3_178927457-178927457_G_T Substitution No Yes 3.14%
CGCRC339 chr3_178952085-178952085_A_T Substitution Yes Yes 1.71%
CGCRC340 chr17_7578263-7578263_G_A Substitution Yes Yes 18.26%
CGCRC340 chr5_112175207-112175207_G_T Substitution Yes Yes 22.57%
CGPLBR38 chr17_7577560-7577560_A_G Substitution No Yes 0.53%
CGPLBR40 chrX_66766163-66766163_C_G Substitution NA Yes 28.99%
CGPLBR44 chr2_25457242-25457242_C_T Substitution Yes Yes 1.82%
CGPLBR44 chr2_25463568-25463568_A_G Substitution No Yes 0.41%
CGPLBR44 chr4_55153609-55153609_G_A Substitution No Yes 0.13%
CGPLBR48 chr2_2936301-2936301_C_T Substitution NA Yes 34.61%
CGPLBR48 chr7_55240762-55240762_G_A Substitution No No 0.18%
CGPLBR55 chr2_25463266-25463266_G_A Substitution No No 0.18%
CGPLBR55 chr20_57484421-57484421_G_A Substitution Yes Yes 0.68%
CGPLBR55 chr_178921553-178921553_T_A Substitution Yes Yes 0.42%
CGPLBR63 chr3_1806188-1806188_A_G Substitution NA Yes 34.82%
CGPLBR67 chr4_25457242-25457242_C_T Substitution Yes Yes 0.11%
CGPLBR67 chr3_178936091-178936091_G_A Substitution Yes Yes 0.68%
CGPLBR67 chr2_212285302-212285302_T_G Substitution No No 0.28%
CGPLBR69 chr2_25463172-25463172_T_A Substitution No No 0.29%
CGPLBR69 chr3_41266092-41266092_A_C Substitution NA Yes 41.74%
CGPLBR69 chr2_209108158-209108158_A_T Substitution NA Yes 41.86%
CGPLBR70 chr11_108216546-108216546_G_A Substitution No No 0.36%
CGPLBR70 chr5_112176022-112176022_A_C Substitution NA Yes 40.28%
CGPLBR71 chr17_7577120-7577120_C_T Substitution Yes Yes 0.10%
CGPLBR72 chr5_112175886-112175886_A_G Substitution NA Yes 44.03%
CGPLBR73 chr2_29474053-29474053_A_G Substitution No No 0.27%
CGPLBR73 chr2_212652833-212652833_G_T Substitution NA Yes 35.58%
CGPLBR74 chrX_66788865-66788865_G_T Substitution NA Yes 36.23%
CGPLBR75 chr3_178952085-178952085_A_G Substitution Yes Yes 0.14%
CGPLBR76 chr4_55946310-55946310_C_T Substitution NA Yes 36.57%
CGPLBR76 chr3_178952085-178952085_A_G Substitution Yes Yes 0.12%
CGPLBR77 chr10_89711891-89711891_G_T Substitution No Yes 2.29%
CGPLBR80 chr9_21974792-21974792_G_A Substitution No No 0.54%
CGPLBR83 chrX_66937328-66937328_A_G Substitution NA Yes 42.66%
CGPLBR83 chr11_108117753-108117753_G_A Substitution No No 0.28%
CGPLBR83 chr2_212543783-212543783_T_G Substitution NA Yes 44.91%
CGPLBR86 chr19_1223125-1223125_C_G Substitution NA Yes 42.32%
Wild-type Fragments
25th
Minimum Percentile Mode Median
cfDNA cfDNA cfDNA cfDNA
Fragment Fragment Fragment Fragment
Distinct Size Size Size Size
Patient Coverage (bp) (bp) (bp) (bp)
CGCRC316 12880 100 150 166 163
CGCRC316 7479 93 157 164 168
CGCRC316 13682 100 149 165 162
CGCRC316 16716 85 153 166 156
CGCRC316 17060 100 150 166 153
CGCRC317 14587 84 152 166 154
CGCRC317 10483 100 152 164 155
CGCRC317 3497 101 149 166 163
CGCRC318 16436 98 158 170 170
CGCRC320 6521 100 163 170 175
CGCRC320 11633 100 162 174 174
CGCRC321 6918 88 161 167 174
CGCRC321 9559 94 159 171 170
CGCRC321 5545 100 159 172 172
CGCRC332 605 104 164 170 176
CGCRC333 1265 89 159 165 171
CGCRC333 3338 102 153 165 169
CGCRC333 3008 102 153 169 109
CGCRC334 1725 105 160 170 175
CGCRC334 1168 100 159 164 174
CGCRC334 1798 103 159 166 173
CGCRC335 2411 99 155 167 167
CGCRC336 757 104 156 171 170
CGCRC336 1080 102 150 166 167
CGCRC336 391 102 161 165 171
CGCRC337 6497 72 153 169 177
CGCRC337 1686 100 147 170 153
OGORC338 1408 105 153 164 156
CGCRC339 1256 105 158 168 159
CGCRC339 1639 101 158 165 172
CGCRC339 1143 100 154 170 167
CGCRC339 1584 108 161 171 173
CGCRC340 876 101 162 170 175
CGCRC340 796 105 159 164 174
CGPLBR38 9684 95 156 166 168
CGPLBR40 10277 78 162 168 173
CGPLBR44 10715 99 162 171 173
CGPLBR44 10837 100 159 169 171
CGPLBR44 12640 100 159 168 171
CGPLBR48 5631 100 164 170 179
CGPLBR48 12467 101 167 174 180
CGPLBR55 10527 101 158 169 169
CGPLBR55 6011 101 153 166 167
CGPLBR55 3973 101 153 166 166
CGPLBR63 3405 97 165 170 176
CGPLBR67 10259 87 157 166 168
CGPLBR67 5163 100 151 167 165
CGPLBR67 6250 100 155 166 187
CGPLBR69 7558 100 159 166 170
CGPLBR69 3938 101 154 169 166
CGPLBR69 2387 101 157 166 168
CGPLBR70 6916 100 158 171 169
CGPLBR70 3580 107 160 169 173
CGPLBR71 7930 85 156 166 158
CGPLBR72 2389 100 157 160 170
CGPLBR73 11348 95 161 173 174
CGPLBR73 3422 102 157 168 169
CGPLBR74 9784 101 163 175 174
CGPLBR75 7290 103 162 173 172
CGPLBR76 4342 104 166 171 179
CGPLBR76 11785 100 165 168 177
CGPLBR77 6161 100 158 166 169
CGPLBR80 3643 96 166 166 185
CGPLBR83 3479 105 162 164 174
CGPLBR83 3496 103 165 170 177
CGPLBR83 1748 100 164 173 175
CGPLBR86 4241 98 160 168 175
Stage at Amino Acid
Patient Patient Type Diagnosis Alteration Type Gene (Protein)
CGPLBR86 Breast Cancer II Germline SMARCB1 795 + 3A > G
CGPLBR87 Breast Cancer II Tumor-derived JAK2 215R > X
CGPLBR87 Breast Cancer II Hematopoietic DNMT3A 882R > H
CGPLBR87 Breast Cancer II Tumor-derived SMAD4 496R > C
CGPLBR87 Breast Cancer II Germline AR 651S > N
CGPLBR88 Breast Cancer II Tumor-derived CDK6 51E > K
CGPLBR88 Breast Cancer II Germline APC 1125V > A
CGPLBR92 Breast Cancer II Tumor-derived TP53 257L > P
CGPLBR96 Breast Cancer II Tumor-derived TP53 213R > X
CGPLBR96 Breast Cancer II Hematopoietic DNMT3A 531D > G
CGPLBR96 Breast Cancer II Tumor-derived AR 13R > Q
CGPLBR97 Breast Cancer II Hematopoietic DNMT3A 882R > H
CGPLBR97 Breast Cancer II Germline PDGFRA 401A > D
CGPLBR97 Breast Cancer II Tumor-derived GNAS 201R > H
CGPLLU144 Lung Cancer II Tumor-derived TP53 241S > F
CGPLLU144 Lung Cancer II Tumor-derived KRAS 12G > C
CGPLLU144 Lung Cancer II Tumor-derived EGFR 373P > S
CGPLLU144 Lung Cancer II Tumor-derived ATM 292P > L
CGPLLU144 Lung Cancer II Tumor-derived PIK3CA 545E > K
CGPLLU144 Lung Cancer II Tumor-derived ERBB4 426R > K
CGPLLU146 Lung Cancer II Tumor-derived JAK2 617V > F
CGPLLU146 Lung Cancer II Tumor-derived TP53 282R > P
CGPLLU146 Lung Cancer II Tumor-derived DNMT3A 737L > H
CGPLLU146 Lung Cancer II Tumor-derived RB1 861 + 2T > C
CGPLLU146 Lung Cancer II Tumor-derived ATM 581L > F
CGPLLU147 Lung Cancer III Tumor-derived TP53 248R > Q
CGPLLU147 Lung Cancer III Tumor-derived TP53 201L > X
CGPLLU147 Lung Cancer III Tumor-derived ALK 1537G > E
CGPLLU147 Lung Cancer III Germline PDGFRA 200T > S
CGPLLU162 Lung Cancer II Tumor-derived CDKN2A 12S > L
CGPLLU162 Lung Cancer II Tumor-derived EGFR 858L > R
CGPLLU162 Lung Cancer II Tumor-derived BRAF 354R > Q
CGPLLU163 Lung Cancer II Tumor-derived CDKN2A 12S > L
CGPLLU163 Lung Cancer II Hematopoietic DNMT3A 528Y > D
CGPLLU164 Lung Cancer II Tumor-derived STK11 216S > Y
CGPLLU164 Lung Cancer II Germline STK11 354F > L
CGPLLU164 Lung Cancer II Tumor-derived GNA11 606 − 3C > T
CGPLLU164 Lung Cancer II Tumor-derived TP53 278P > S
CGPLLU164 Lung Cancer II Tumor-derived TP53 161A > S
CGPLLU164 Lung Cancer II Tumor-derived TP53 160M > I
CGPLLU164 Lung Cancer II Tumor-derived ERBB4 1299P > L
CGPLLU164 Lung Cancer II Tumor-derived ERBB4 253N > S
CGPLLU165 Lung Cancer II Tumor-derived STK11 354F > L
CGPLLU165 Lung Cancer I Tumor-derived GNAS 201R > H
CGPLLU168 Lung Cancer I Tumor-derived TP53 136Q > X
CGPLLU168 Lung Cancer I Hematopoietic DNMT3A 736R > S
CGPLLU168 Lung Cancer I Tumor-derived EGFR 858L > R
CGPLLU174 Lung Cancer I Tumor-derived STK11 597 + 1G > T
CGPLLU174 Lung Cancer I Tumor-derived JAK2 160D > Y
CGPLLU174 Lung Cancer I Tumor-derived KRAS 12G > C
CGPLLU174 Lung Cancer I Hematopoietic DNMT3A 891R > W
CGPLLU174 Lung Cancer I Hematopoietic DNMT3A 715I > M
CGPLLU175 Lung Cancer I Tumor-derived TP53 179H > R
CGPLLU175 Lung Cancer I Hematopoietic DNMT3A 2598 − 1I > A
CGPLLU175 Lung Cancer I Hematopoietic DNMT3A 755F > L
CGPLLU175 Lung Cancer I Germline ATM 337R > C
CGPLLU175 Lung Cancer I Tumor-derived ERBB4 941Q > X
CGPLLU176 Lung Cancer I Hematopoietic DNMT3A 750P > S
CGPLLU176 Lung Cancer I Hematopoietic DNMT3A 735Y > C
CGPLLU177 Lung Cancer II Tumor-derived KRAS 12G > V
CGPLLU177 Lung Cancer II Hematopoietic DNMT3A 897V > G
CGPLLU177 Lung Cancer II Hematopoietic DNMT3A 862R > C
CGPLLU177 Lung Cancer II Hematopoietic DNMT3A 2173 + 1 > A
CGPLLU178 Lung Cancer I Tumor-derived CDH1 251 > M
CGPLLU178 Lung Cancer I Tumor-derived PIK3CA 861Q > X
CGPLLU179 Lung Cancer I Hematopoietic DNMT3A 879N > D
CGPLLU179 Lung Cancer I Germline APC 2611T > I
Alteration Mutant
Mutation Hotspot Detected Allele
Patient Nucleotide Type Alteration in Tissue Fraction
CGPLBR86 chr22_24159126-24159124_A_G Substitution NA Yes 42.38%
CGPLBR87 chr9_5054591-5054591_C_T Substitution No No 0.35%
CGPLBR87 chr2_25457242-25457242_C_T Substitution You No 0.31%
CGPLBR87 chr18_48604664-48604664_C_T Substitution No No 0.40%
CGPLBR87 chrX_66931310-66931310_G_A Substitution NA Yes 42.94%
CGPLBR88 chr7_92462487-92462487_C_T Substitution No No 0.13%
CGPLBR88 chr5_112174665-112174665_T_C Substitution NA Yes 31.19%
CGPLBR92 chr17_7577511-7577511_A_G Substitution No Yes 0.20%
CGPLBR96 chr17.fa:7578212-7578212_G_A Substitution Yes No 0.10%
CGPLBR96 chr2_25467484-25467484_C_T Substitution No Yes 5.81%
CGPLBR96 chrX_66765026-66765026_G_A Substitution No No 0.60%
CGPLBR97 chr2_25457242-25457242_C_T Substitution Yes Yes 0.11%
CGPLBR97 chr4_55136880-55136880_C_A Substitution NA Yes 34.12%
CGPLBR97 chr20_57484421-57484421_G_A Substitution Yes Yes 0.13%
CGPLLU144 chr17_7577559-7577559_G_A Substitution Yes Yes 1.95%
CGPLLU144 chr12_25398285-25398285_C_A Substitution Yes Yes 5.10%
CGPLLU144 chr7_55224336-55224336_C_T Substitution No Yes 0.16%
CGPLLU144 chr11_108115727-108115727_C_T Substitution No No 0.22%
CGPLLU144 chr3_178936091-178936091_G_A Substitution Yes Yes 2.94%
CGPLLU144 chr2_212568841-212568841_C_T Substitution No No 0.18%
CGPLLU146 chr9_5073770-5073770_G_T Substitution Yes No 0.25%
CGPLLU146 chr17_7577093-7577093_C_G Substitution No Yes 1.30%
CGPLLU146 chr2_25463283-25463283_A_T Substitution No Yes 0.84%
CGPLLU146 chr13_48937095-48937095_T_C Substitution No Yes 0.87%
CGPLLU146 chr11_108122699-108122699_A_T Substitution No No 0.20%
CGPLLU147 chr17_7577538-7577538_C_T Substitution Yes No 0.15%
CGPLLU147 chr17_7578247-7578247_A_T Substitution No Yes 0.55%
CGPLLU147 chr2_29416343-29416343_C_T Substitution No Yes 0.94%
CGPLLU147 chr4_55130065-55130065_C_G Substitution NA Yes 43.47%
CGPLLU162 chr9_21974792-21974792_G_A Substitution No No 0.22%
CGPLLU162 chr7_55259515-55259515_T_G Substitution Yes Yes 0.22%
CGPLLU162 chr7_140494187-140494187_C_T Substitution No No 0.14%
CGPLLU163 chr9_21974792-21974792_G_A Substitution No No 0.21%
CGPLLU163 chr2_25467494-25467494_A_C Substitution No Yes 0.15%
CGPLLU164 chr19_1220629-1220629_C_A Substitution No Yes 1.23%
CGPLLU164 chr19_1223125-1223125_C_G Substitution NA Yes 45.52%
CGPLLU164 chr19_3118919-3118919_C_T Substitution No No 0.20%
CGPLLU164 chr17_7577106-7577106_G_A Substitution Yes No 0.10%
CGPLLU164 chr17_7578449-7578449_C_A Substitution No Yes 1.78%
CGPLLU164 chr17_7578450-7578450_C_A Substitution No Yes 1.86%
CGPLLU164 chr2_212248371-212248371_G_A Substitution No Yes 0.96%
CGPLLU164 chr2_212587243-212587243_T_C Substitution No No 0.22%
CGPLLU165 chr19_1223125-1223125_C_G Substitution NA Yes 36.62%
CGPLLU165 chr20_57484421-57484421_G_A Substitution Yes Yes 0.16%
CGPLLU168 chr17.fa:7578524-7578524_G_A Substitution Yes Yes 0.06%
CGPLLU168 chr2_25463287-25463287_G_T Substitution No No 0.39%
CGPLLU168 chr7.fa:55259515-55259515_T_G Substitution Yes Yes 0.07%
CGPLLU174 chr19_1220505-1220505_G_T Substitution No Yes 0.33%
CGPLLU174 chr9_5050695-5050695_G_T Substitution No Yes 0.40%
CGPLLU174 chr12_25398285-25398285_C_A Substitution Yes Yes 0.16%
CGPLLU174 chr2_25457216-25457216_G_A Substitution No Yes 0.29%
CGPLLU174 chr2_25463537-25463537_G_C Substitution No Yes 0.26%
CGPLLU175 chr17_7578394-7578394_T_C Substitution Yes Yes 8.03%
CGPLLU175 chr2_25457216-25457216_C_T Substitution No No 0.21%
CGPLLU175 chr2_25463230-25463230_A_G Substitution No No 0.15%
CGPLLU175 chr11_108117798-108117798_C_T Substitution NA Yes 43.84%
CGPLLU175 chr2_212288925-212288925_G_A Substitution No Yes 3.64%
CGPLLU176 chr2_25463245-25463245_G_A Substitution No Yes 0.92%
CGPLLU176 chr2_25463289-25463289_T_C Substitution No Yes 0.12%
CGPLLU177 chr12_25398284-25398284_C_A Substitution Yes Yes 2.49%
CGPLLU177 chr2_25457197-25457197_A_C Substitution No Yes 1.53%
CGPLLU177 chr2_25457243-25457243_G_A Substitution Yes No 0.29%
CGPLLU177 chr2_25463508-25463508_C_T Substitution No No 0.13%
CGPLLU178 chr16_68844164-68844164_C_T Substitution No No 0.29%
CGPLLU178 chr3_178947145-178947145_C_T Substitution No No 0.17%
CGPLLU179 chr2_25457252-25457252_T_C Substitution No Yes 0.38%
CGPLLU179 chr5_112179123-112179123_C_T Substitution NA Yes 39.91%
Wild-type Fragments
25th
Minimum Percentile Mode Median
cfDNA cfDNA cfDNA cfDNA
Fragment Fragment Fragment Fragment
Distinct Size Size Size Size
Patient Coverage (bp) (bp) (bp) (bp)
CGPLBR86 3096 88 160 167 174
CGPLBR87 3680 101 162 168 175
CGPLBR87 6180 101 163 164 175
CGPLBR87 7746 86 160 167 175
CGPLBR87 2286 106 160 166 172
CGPLBR88 17537 89 185 200 223
CGPLBR88 5919 101 162 172 173
CGPLBR92 15530 77 150 164 152
CGPLBR96 9893 100 159 164 171
CGPLBR96 8620 95 162 167 173
CGPLBR96 8036 85 162 169 175
CGPLBR97 14856 93 160 168 170
CGPLBR97 5329 100 161 165 171
CGPLBR97 7010 97 158 169 170
CGPLLU144 11371 100 156 165 167
CGPLLU144 7641 100 155 167 166
CGPLLU144 9996 100 158 168 169
CGPLLU144 4956 101 159 166 169
CGPLLU144 6540 100 153 170 168
CGPLLU144 7648 101 156 164 166
CGPLLU146 5920 100 155 164 168
CGPLLU146 9356 100 155 166 168
CGPLLU146 7284 101 158 165 170
CGPLLU146 4183 103 160 166 170
CGPLLU146 6778 100 157 166 158
CGPLLU147 4807 100 155 166 170
CGPLLU147 5282 100 156 167 171
CGPLLU147 7122 100 158 174 173
CGPLLU147 2825 101 160 165 173
CGPLLU162 9940 95 161 164 174
CGPLLU162 13855 87 160 174 173
CGPLLU162 11251 100 153 167 165
CGPLLU163 10805 85 159 165 173
CGPLLU163 20185 83 158 166 170
CGPLLU164 8795 91 156 161 169
CGPLLU164 4561 92 157 164 169
CGPLLU164 8097 100 158 170 170
CGPLLU164 9241 100 155 165 157
CGPLLU164 10806 100 157 168 159
CGPLLU164 10919 100 157 168 159
CGPLLU164 5412 103 159 175 170
CGPLLU164 5151 101 160 166 169
CGPLLU165 7448 95 155 167 167
CGPLLU165 5822 102 154 166 166
CGPLLU168 15985 97 152 165 166
CGPLLU168 11070 100 156 165 168
CGPLLU168 11063 83 157 166 169
CGPLLU174 5881 88 162 165 174
CGPLLU174 3696 100 162 167 172
CGPLLU174 4941 101 162 167 172
CGPLLU174 7527 100 163 168 173
CGPLLU174 8353 101 162 168 173
CGPLLU175 10214 100 160 166 170
CGPLLU175 9739 100 157 168 158
CGPLLU175 9509 100 157 165 158
CGPLLU175 2710 101 157 165 157
CGPLLU175 6565 100 158 166 158
CGPLLU176 6513 101 164 168 175
CGPLLU176 5962 100 164 174 175
CGPLLU177 7044 102 160 165 170
CGPLLU177 9950 88 160 169 171
CGPLLU177 11233 100 160 168 171
CGPLLU177 10966 75 160 169 172
CGPLLU178 5378 100 162 176 172
CGPLLU178 7235 101 159 167 170
CGPLLU179 6350 103 161 169 171
CGPLLU179 2609 108 162 171 173
Stage at Amino Acid
Patient Patient Type Diagnosis Alteration Type Gene (Protein)
CGPLLU180 Lung Cancer I Tumor-derived STK11 237D > Y
CGPLLU180 Lung Cancer I Tumor-derived TP53 293G > V
CGPLLU180 Lung Cancer I Tumor-derived TP53 282R > P
CGPLLU180 Lung Cancer I Tumor-derived TP53 177P > L
CGPLLU180 Lung Cancer I Tumor-derived RB1 565S > X
CGPLLU197 Lung Cancer I Hematopoietic DNMT3A 882R > C
CGPLLU197 Lung Cancer I Hematopoietic DNMT3A 879N > D
CGPLLU198 Lung Cancer I Tumor-derived TP53 162I > N
CGPLLU198 Lung Cancer I Tumor-derived EGFR 858L > R
CGPLLU202 Lung Cancer I Tumor-derived EGFR 790T > M
CGPLLU202 Lung Cancer I Tumor-derived EGFR 868E > X
CGPLLU204 Lung Cancer I Tumor-derived KIT 956R > Q
CGPLLU205 Lung Cancer II Hematopoietic DNMT3A 736R > C
CGPLLU205 Lung Cancer II Hematopoietic DNMT3A 696Q > X
CGPLLU206 Lung Cancer III Tumor-derived TP53 672 + 1G > A
CGPLLU206 Lung Cancer III Tumor-derived TP53 131N > S
CGPLLU207 Lung Cancer II Tumor-derived TP53 376 − 1G > A
CGPLLU207 Lung Cancer II Germline ALK 419P > L
CGPLLU207 Lung Cancer II Tumor-derived EGFR 790T > M
CGPLLU208 Lung Cancer II Tumor-derived TP53 250P > L
CGPLLU208 Lung Cancer II Germline EGFR 224R > H
CGPLLU208 Lung Cancer II Tumor-derived EGFR 858L > R
CGPLLU208 Lung Cancer II Tumor-derived MYC 98R > W
CGPLLU209 Lung Cancer II Germline STK11 354F > L
CGPLLU209 Lung Cancer II Tumor-derived TP53 100Q > X
CGPLLU209 Lung Cancer II Tumor-derived CDKN2A 88E > X
CGPLLU209 Lung Cancer II Tumor-derived PDGFRA 921A > T
CGPLLU209 Lung Cancer II Germline EGFR 567M > V
CGPLOV10 Ovarian Cancer I Tumor-derived TP53 342R > X
CGPLOV11 Ovarian Cancer IV Tumor-derived TP53 248R > Q
CGPLOV11 Ovarian Cancer IV Germline TP53 63A > V
CGPLOV13 Ovarian Cancer IV Tumor-derived ALK 444W > C
CGPLOV13 Ovarian Cancer IV Germline PDGFRA 401A > D
CGPLOV13 Ovarian Cancer IV Tumor-derived KIT 135R > H
CGPLOV14 Ovarian Cancer I Tumor-derived HNF1A 230E > K
CGPLOV15 Ovarian Cancer III Tumor-derived TP53 278P > S
CGPLOV15 Ovarian Cancer III Tumor-derived EGFR 433H > D
CGPLOV17 Ovarian Cancer I Tumor-derived TP53 248R > Q
CGPLOV17 Ovarian Cancer I Germline PDGFRA 1071D > N
CGPLOV18 Ovarian Cancer I Germline APC 1125V > A
CGPLOV19 Ovarian Cancer II Germline FGFR3 403K > E
CGPLOV19 Ovarian Cancer II Tumor-derived TP53 273R > H
CGPLOV19 Ovarian Cancer II Germline AR 176S > R
CGPLOV19 Ovarian Cancer II Tumor-derived APC 1378Q > X
CGPLOV20 Ovarian Cancer II Tumor-derived TP53 195I > T
CGPLOV20 Ovarian Cancer II Germline EGFR 253K > R
CGPLOV21 Ovarian Cancer IV Germline STK11 354F > L
CGPLOV21 Ovarian Cancer IV Tumor-derived TP53 275C > Y
CGPLOV21 Ovarian Cancer IV Tumor-derived ERBB4 602S > T
CGPLOV22 Ovarian Cancer III Tumor-derived TP53 193H > P
CGPLOV22 Ovarian Cancer III Tumor-derived CTNNB1 41T > A
Alteration Mutant
Mutation Hotspot Detected Allele
Patient Nucleotide Type Alteration in Tissue Fraction
CGPLLU180 chr19_1220691-1220691_G_T Substitution No You 2.43%
CGPLLU180 chr17_7577060-7577060_C_A Substitution No Yes 2.07%
CGPLLU180 chr17_7577093-7577093_C_G Substitution No Yes 1.94%
CGPLLU180 chr17:fa_7578400-7578400_G_A Substitution Yes No 0.08%
CGPLLU180 chr13_48955578-48955578_C_G Substitution No Yes 1.01%
CGPLLU197 chr2_25457243-25457243_G_A Substitution Yes No 0.16%
CGPLLU197 chr2_25457252-25457252_T_C Substitution No No 0.38%
CGPLLU198 chr17_7578445-7578445_A_T Substitution No Yes 0.87%
CGPLLU198 chr7_55259515-55259515_T_G Substitution Yes Yes 0.52%
CGPLLU202 chr7:fa_55249071-55249071_C_T Substitution Yes Yes 0.05%
CGPLLU202 chr7_55259544-55259544_G_T Substitution No No 0.13%
CGPLLU204 chr4_55604659-55604659_G_A Substitution No No 0.26%
CGPLLU205 chr2_25463287-25463287_G_A Substitution No Yes 0.70%
CGPLLU205 chr2_25463598-25463598_G_A Substitution No Yes 3.47%
CGPLLU206 chr17_7578176-7578176_C_T Substitution Yes Yes 26.13%
CGPLLU206 chr17_7578538-7578538_T_C Substitution No No 0.21%
CGPLLU207 chr17_7578555-7578555_C_T Substitution Yes Yes 0.32%
CGPLLU207 chr2_29606625-29606625_A_G Substitution NA Yes 34.38%
CGPLLU207 chr7:fa_55249071-55249071_C_T Substitution Yes No 0.09%
CGPLLU208 chr17_7577532-7577532_G_A Substitution Yes Yes 1.33%
CGPLLU208 chr7_55220281-55220281_G_A Substitution NA Yes 39.34%
CGPLLU208 chr7_55259515-55259515_T_G Substitution Yes Yes 0.86%
CGPLLU208 chr8_128750755-128750755_C_T Substitution No No 0.17%
CGPLLU209 chr19_1223125-1223125_C_G Substitution NA Yes 26.84%
CGPLLU209 chr17_7579389-7579389_G_A Substitution No Yes 9.97%
CGPLLU209 chr9_21971096-21971096_C_A Substitution Yes Yes 9.13%
CGPLLU209 chr4_55155052-55155052_G_A Substitution No Yes 9.82%
CGPLLU209 chr7_55231493-55231493_A_G Substitution NA Yes 30.41%
CGPLOV10 chr17_7574003-7574003_G_A Substitution Yes Yes 3.14%
CGPLOV11 chr17_7577538-7577538_C_T Substitution Yes Yes 0.87%
CGPLOV11 chr17_7579499-7579499_G_A Substitution NA Yes 37.77%
CGPLOV13 chr2_29551296-29551296_C_A Substitution No Yes 0.12%
CGPLOV13 chr4_55136880-55136880_C_A Substitution NA Yes 37.98%
CGPLOV13 chr4_55564516-55564516_G_A Substitution No Yes 0.35%
CGPLOV14 chr12_121431484-121431484_G_A Substitution No No 0.14%
CGPLOV15 chr17_7577106-7577106_G_A Substitution Yes Yes 3.54%
CGPLOV15 chr7_55225445-55225445_C_G Substitution No No 0.19%
CGPLOV17 chr17_7577538-7577538_C_T Substitution Yes Yes 0.32%
CGPLOV17 chr4_55161382-55161382_G_A Substitution NA Yes 44.10%
CGPLOV18 chr5_112174665-112174665_T_C Substitution NA Yes 40.81%
CGPLOV19 chr4_1806186-1806186_A_G Substitution NA Yes 23.80%
CGPLOV19 chr17_7577120-7577120_C_T Substitution Yes Yes 36.83%
CGPLOV19 chrX_66765516-66765516_C_A Substitution NA Yes 65.29%
CGPLOV19 chr5_112175423-112175423_C_T Substitution Yes Yes 46.35%
CGPLOV20 chr17_7578265-7578265_A_G Substitution Yes Yes 0.21%
CGPLOV20 chr7_55221714-55221714_A_G Substitution NA Yes 44.05%
CGPLOV21 chr19_1223125-1223125_C_G Substitution NA Yes 7.68%
CGPLOV21 chr17_7577114-7577114_C_T Substitution No Yes 2.04%
CGPLOV21 chr2_212530114-212530114_C_G Substitution No No 14.36%
CGPLOV22 chr17_7578271-7578271_T_G Substitution No Yes 0.49%
CGPLOV22 chr3_41266124-41266124_A_G Substitution Yes Yes 0.34%
Wild-type Fragments
25th
Minimum Percentile Mode Median
cfDNA cfDNA cfDNA cfDNA
Fragment Fragment Fragment Fragment
Distinct Size Size Size Size
Patient Coverage (bp) (bp) (bp) (bp)
CGPLLU180 6065 91 158 165 170
CGPLLU180 6680 92 158 164 169
CGPLLU180 7790 92 158 167 168
CGPLLU180 9036 101 160 169 171
CGPLLU180 4679 100 157 169 158
CGPLLU197 7196 102 162 166 172
CGPLLU197 7147 100 161 166 172
CGPLLU198 9322 97 157 165 158
CGPLLU198 8303 100 160 173 172
CGPLLU202 14197 90 151 165 166
CGPLLU202 9279 51 150 168 167
CGPLLU204 7185 100 157 165 168
CGPLLU205 10739 96 156 165 166
CGPLLU205 12065 100 154 165 165
CGPLLU206 6746 94 148 165 164
CGPLLU206 11225 100 147 167 164
CGPLLU207 11224 100 159 165 170
CGPLLU207 4960 101 160 166 170
CGPLLU207 13216 85 161 165 172
CGPLLU208 9211 101 156 166 168
CGPLLU208 5253 100 159 164 170
CGPLLU208 10733 100 160 170 171
CGPLLU208 11421 100 158 165 171
CGPLLU209 11695 96 153 166 159
CGPLLU209 12771 94 155 163 168
CGPLLU209 16557 92 157 169 170
CGPLLU209 13057 97 158 167 171
CGPLLU209 8521 100 155 167 169
CGPLOV10 4421 101 161 165 172
CGPLOV11 7987 100 157 164 169
CGPLOV11 3782 97 160 166 171
CGPLOV13 12072 88 157 165 169
CGPLOV13 4107 103 159 166 169
CGPLOV13 6427 100 161 165 171
CGPLOV14 11418 92 154 166 171
CGPLOV15 7689 102 157 164 169
CGPLOV15 7617 101 159 167 171
CGPLOV17 4463 96 156 168 169
CGPLOV17 2884 110 157 170 170
CGPLOV18 2945 101 159 164 169
CGPLOV19 9727 95 158 167 172
CGPLOV19 4387 100 158 165 169
CGPLOV19 2775 93 161 171 171
CGPLOV19 3616 102 156 170 170
CGPLOV20 5404 94 159 165 170
CGPLOV20 3744 102 158 166 169
CGPLOV21 21823 81 158 166 169
CGPLOV21 18806 101 159 165 169
CGPLOV21 10801 89 160 166 169
CGPLOV22 11952 100 155 165 167
CGPLOV22 12399 92 150 165 164
Mutant Fragments
75th 25th
Mean Percentile Maximum Minimum Percentile
cfDNA cfDNA cfDNA cfDNA cfDNA
Fragment Fragment Fragment Fragment Fragment
Size Size Size Distinct Size Size
(bp) (bp) (bp) Coverage (bp) (bp)
179 186 400 19 100 142
182 185 400 21 132 166
180 183 400 5411 92 152
177 182 400 1903 100 148
184 185 400 1344 108 155
181 182 400 2108 100 153
176 180 400 1951 101 149
176 183 399 75 123 162
177 182 400 28 101 130
183 188 399 6863 100 160
188 186 400 34 77 154
175 179 396 9 138 147
184 185 400 21 115 145
179 185 397 30 137 149
179 182 397 44 125 155
185 186 400 8167 101 180
187 186 400 3552 102 158
184 187 399 15 93 137
183 185 400 26 137 163
181 182 397 35 118 147
172 175 400 71 133 152
169 174 400 55 130 153
189 187 390 17 149 155
176 183 400 18 156 170
169 175 397 51 108 143
166 173 397 26 118 147
184 186 400 45 116 151
185 186 400 25 157 165
185 187 400 25 124 168
167 175 394 86 121 155
167 173 397 45 124 143
170 175 396 108 126 147
190 189 400 23 131 148
182 182 399 42 138 155
189 187 399 25 126 153
192 193 400 977 101 149
173 179 391 525 102 140
181 185 399 4010 100 158
178 184 399 625 100 140
175 179 398 37 111 143
181 186 398 3184 102 159
180 183 399 47 111 148
183 184 397 39 111 146
185 184 400 24 110 146
176 180 400 32 117 146
180 184 399 43 111 143
185 187 400 29 109 140
179 182 399 20 128 152
176 184 396 7515 101 160
182 182 399 31 85 145
181 182 395 428 100 135
176 180 397 352 97 136
165 172 397 15 131 137
170 173 398 25 107 138
171 173 400 27 122 147
189 169 400 91 112 165
189 169 400 27 124 144
178 184 399 24 105 143
188 189 399 8 122 143
194 192 400 17 144 163
180 183 394 15 132 159
183 185 399 233 131 162
186 186 398 27 136 155
192 195 399 23 137 144
182 184 399 29 131 157
Difference Difference Adjusted P
between between Value of
Median Mean Difference
Mutant Fragments Mutant Mutant between
75th and and Mutant
Mode Median Mean Percentile Maximum Wild type Wild-type and
cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA Wild-type
Fragment Fragment Fragment Fragment Fragment Fragment Fragment cfDNA
Size Size Size Size Size Size Size Fragment
(bp) (bp) (bp) (bp) (bp) (bp) (bp) Size
233 165 180 230 305 −4.0 1.54 0.475
182 176 191 198 309 7.0 8.33 0.250
167 169 186 191 399 0.0 5.89 0.000
166 166 177 183 383 −1.0 −0.25 0.874
167 170 189 191 398 1.0 5.37 0.009
166 168 185 187 386 1.0 3.80 0.025
175 167 179 182 397 0.0 2.65 0.148
167 172 182 190 370 3.0 5.31 0.368
130 139 164 155 345 −29.5 −12.79 0.000
165 173 185 159 400 2.0 3.13 0.002
171 170 177 192 335 −0.5 −11.46 0.571
176 171 177 176 290 4.0 1.22 0.475
155 159 176 175 368 −11.0 −7.99 0.052
181 162 182 161 369 −8.0 3.49 0.061
155 169 185 194 338 0.0 5.78 0.623
166 171 184 187 400 −1.0 −1.27 0.212
168 170 185 185 399 0.0 −2.62 0.114
127 174 173 193 261 3.0 −11.00 0.507
166 167 179 180 364 −3.0 −4.34 0.430
176 163 172 176 336 −6.0 −9.35 0.166
170 165 169 173 301 0.0 3.57 0.668
165 164 166 166 325 0.0 −2.15 0.630
326 170 221 301 387 −3.0 32.43 0.453
174 174 210 219 372 5.0 33.84 0.368
268 152 164 176 268 −12.0 −5.12 0.000
153 156 174 158 327 −9.5 8.37 0.036
168 163 175 177 346 −8.0 −8.84 0.057
191 175 207 199 350 3.0 22.93 0.456
180 180 189 191 338 8.0 4.06 0.154
169 166 168 175 309 2.0 0.46 0.445
197 162 166 168 377 −1.0 −0.91 0.482
162 162 164 174 302 −3.0 −6.74 0.064
145 166 189 205 333 −5.0 −0.80 0.297
155 174 177 187 343 5.5 −4.51 0.171
176 176 188 229 305 7.0 −0.19 0.234
189 170 182 192 380 −1.0 −9.76 0.000
168 159 168 176 382 −7.0 −5.57 0.052
166 170 181 185 398 0.0 0.37 0.770
167 162 172 181 380 −9.0 −6.68 0.009
142 166 172 186 321 −1.0 −2.36 0.572
168 172 182 187 400 0.5 0.95 0.564
144 169 176 153 353 −1.0 −4.83 0.598
182 162 182 155 337 −7.0 −0.44 0.064
309 182 208 284 355 14.0 22.31 0.031
154 157 167 166 298 −11.0 −8.94 0.013
144 177 187 212 319 9.0 7.22 0.062
204 159 186 204 387 −12.0 3.32 0.031
180 163 166 180 219 −6.5 −13.04 0.155
170 171 177 185 400 1.0 1.08 0.166
137 166 167 176 316 −3.0 −14.62 0.469
138 149 158 166 340 −20.0 −23.47 0.000
132 147 149 159 326 21.0 26.04 0.000
132 144 163 171 323 −20.0 −1.73 0.000
159 161 175 190 299 −3.0 4.83 0.384
161 161 173 171 342 −3.0 2.54 0.354
168 173 196 192 397 1.0 6.83 0.571
154 154 167 172 320 −19.0 −22.39 0.000
132 159 183 190 367 −11.0 4.67 0.054
122 161 168 195 241 −13.0 −19.21 0.100
173 173 213 261 372 1.0 19.22 0.587
186 166 174 185 265 −3.0 −5.62 0.461
167 172 190 187 394 2.0 7.27 0.137
183 163 170 178 262 −7.0 −16.03 0.131
175 152 190 212 327 −17.0 −1.78 0.018
177 171 183 179 319 −1.0 −0.74 0.564
Mutant Fragments
75th 25th
Mean Percentile Maximum Minimum Percentile
cfDNA cfDNA cfDNA cfDNA cfDNA
Fragment Fragment Fragment Fragment Fragment
Size Size Size Distinct Size Size
(bp) (bp) (bp) Coverage (bp) (bp)
166 172 396 1616 100 146
175 180 400 806 96 158
165 172 399 1410 102 140
170 177 397 49 99 153
166 173 398 33 140 155
180 178 400 73 95 140
172 177 400 38 115 160
171 174 386 6 124 137
180 183 400 70 124 151
191 199 399 6586 96 162
184 188 400 41 112 172
181 198 399 35 149 168
182 184 399 20 166 180
183 186 397 5338 102 159
202 203 393 178 101 150
195 195 397 1350 104 153
185 189 400 1257 100 153
185 189 396 30 117 163
203 210 391 336 105 153
188 194 399 741 101 161
193 193 396 89 100 145
172 179 396 12 129 143
186 188 387 3559 91 155
177 183 392 873 102 149
194 200 377 1909 100 158
202 259 400 27 122 157
171 178 395 1818 103 147
178 182 374 546 102 151
179 184 397 26 132 142
195 194 400 53 117 157
176 179 397 40 124 150
188 191 390 38 107 153
205 207 399 217 102 146
196 195 397 266 111 147
186 184 400 76 123 157
179 186 400 9832 93 161
191 190 400 277 104 162
191 189 400 65 123 165
187 189 400 31 136 163
202 202 400 5286 102 166
196 201 400 102 138 166
181 182 397 30 138 158
181 181 400 64 113 158
176 179 398 27 121 163
191 192 398 2943 100 165
179 181 399 25 138 153
171 177 399 60 110 136
172 179 399 26 139 147
186 184 398 35 121 149
176 178 397 4000 103 155
176 178 385 2390 99 157
182 184 400 28 131 160
194 193 400 3545 100 161
179 180 398 15 121 146
188 187 400 2587 103 158
189 192 400 86 121 165
178 184 399 3339 101 157
179 187 391 3193 101 163
183 186 398 13 111 153
197 201 400 4140 102 166
191 194 400 16 130 143
183 183 400 209 125 154
211 230 400 41 158 176
193 193 400 3445 94 162
197 199 400 23 123 182
193 195 399 1787 100 163
204 207 400 4100 100 159
Difference Difference Adjusted P
between between Value of
Median Mean Difference
Mutant Fragments Mutant Mutant between
75th and and Mutant
Mode Median Mean Percentile Maximum Wild type Wild-type and
cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA Wild-type
Fragment Fragment Fragment Fragment Fragment Fragment Fragment cfDNA
Size Size Size Size Size Size Size Fragment
(bp) (bp) (bp) (bp) (bp) (bp) (bp) Size
164 159 163 170 354 −3.5 −3.57 0.000
169 169 173 184 366 1.0 3.80 0.054
149 154 164 170 398 −8.0 −0.35 0.816
143 182 206 284 333 16.0 36.25 0.000
154 170 108 180 296 7.0 14.38 0.104
140 155 173 178 324 −9.0 −6.66 0.000
164 167 182 179 329 1.5 10.09 0.479
170 156 153 168 178 −7.5 −18.98 0.411
151 164 182 183 385 −6.0 1.71 0.064
168 175 193 196 399 0.0 −1.79 0.166
176 177 195 195 373 3.0 11.02 0.397
175 175 181 186 312 1.0 −13.40 0.587
185 191 205 219 357 21.0 23.48 0.013
175 171 183 185 394 −1.0 0.03 0.984
168 171 198 240 357 −5.0 −4.34 0.571
163 171 201 258 400 0.0 5.94 0.066
168 170 189 202 392 1.0 4.37 0.064
164 172 175 179 372 3.0 −10.29 0.463
141 171 200 240 399 4.0 3.10 0.571
169 176 190 194 400 2.0 1.96 0.571
171 171 197 229 393 −2.0 3.42 0.479
143 153 163 166 275 −14.0 −8.99 0.084
164 173 195 211 398 3.0 5.92 0.001
163 164 177 181 400 −3.0 −0.39 0.880
167 176 202 242 398 5.0 7.98 0.061
164 179 199 231 350 2.0 −3.82 0.685
169 162 173 180 396 1.0 1.92 0.372
166 166 180 182 381 0.0 2.87 0.416
138 171 183 188 351 1.5 3.29 0.572
165 169 192 198 336 −3.0 −2.86 0.451
169 166 181 176 309 −1.0 4.53 0.539
180 174 185 210 326 0.5 −2.59 0.576
144 163 188 212 360 −12.0 −17.11 0.004
150 166 188 204 379 −8.0 −7.53 0.208
171 169 182 182 346 1.0 −3.64 0.479
166 172 180 186 399 −1.0 1.04 0.155
160 176 201 200 384 3.0 9.95 0.061
166 172 198 192 371 1.0 7.08 0.560
171 167 201 199 387 −4.0 14.14 0.341
168 181 201 203 400 2.0 −0.86 0.587
161 179 199 209 372 −1.5 2.90 0.679
189 185 191 191 311 16.0 9.25 0.000
163 167 179 176 318 0.0 −2.85 0.679
200 171 187 190 392 5.0 10.89 0.314
176 176 187 192 398 0.0 −3.83 0.015
138 167 181 184 340 −1.0 2.00 0.571
147 147 161 159 327 −19.0 −9.77 0.000
180 176 176 184 344 9.0 3.52 0.015
360 161 197 195 360 −9.0 10.77 0.314
166 167 176 178 397 0.5 0.65 0.610
164 168 178 180 400 0.0 1.78 0.314
168 167 177 179 338 −2.0 −5.83 0.463
169 173 194 192 399 0.0 0.40 0.825
166 166 172 204 221 −2.0 −7.32 0.564
162 169 189 186 399 −1.0 1.12 0.598
183 177 189 193 373 3.0 −0.01 0.293
165 169 177 184 400 0.0 −1.73 0.598
178 173 180 186 389 1.0 0.22 0.839
153 161 171 179 323 −11.0 −12.36 0.061
169 179 197 200 400 0.0 −0.32 0.839
143 157 173 173 325 −20.0 −18.40 0.000
175 170 196 233 357 1.0 12.55 0.025
197 186 215 220 374 1.0 3.72 0.603
175 174 194 194 399 0.0 0.65 0.714
248 224 232 260 359 47.0 34.97 0.000
163 176 192 194 400 1.0 −0.85 0.718
164 173 200 202 400 −2.0 −3.65 0.062
Mutant Fragments
75th 25th
Mean Percentile Maximum Minimum Percentile
cfDNA cfDNA cfDNA cfDNA cfDNA
Fragment Fragment Fragment Fragment Fragment
Size Size Size Distinct Size Size
(bp) (bp) (bp) Coverage (bp) (bp)
196 195 400 3096 79 159
202 203 400 73 142 178
205 203 400 23 161 168
195 196 400 170 125 158
195 192 400 2089 101 162
238 280 400 125 84 192
197 194 400 5715 108 163
172 173 398 109 78 148
196 191 399 35 119 161
189 190 400 826 102 162
194 195 400 95 135 160
184 184 400 27 128 150
179 184 399 4771 103 161
187 185 399 7 417 154
179 179 395 330 106 152
172 177 399 536 106 151
179 183 400 45 136 163
182 182 397 16 138 146
172 177 397 293 101 152
171 177 399 23 130 152
180 183 399 54 104 161
184 184 400 154 96 149
186 187 399 79 102 163
183 185 400 44 118 149
182 184 400 35 136 164
192 191 400 13 138 164
199 205 400 50 128 155
191 193 400 81 108 150
190 191 389 2597 101 159
192 197 400 58 92 173
183 189 400 74 90 147
175 178 400 37 144 163
194 202 400 61 93 164
184 186 400 66 104 158
191 190 396 101 126 155
188 185 394 4718 100 156
186 186 399 30 134 161
180 180 397 34 139 163
182 182 400 262 101 150
182 182 400 277 101 150
180 182 395 65 121 158
177 182 400 16 144 172
185 184 399 7186 100 154
181 179 394 21 108 164
177 180 400 18 111 127
179 181 400 72 121 156
177 182 400 30 106 160
200 199 399 36 131 147
184 185 392 20 144 173
182 184 395 16 147 156
186 187 399 34 159 168
186 186 396 5 116 182
185 183 399 1073 100 142
179 180 400 46 109 151
181 181 400 30 146 154
176 179 392 2742 102 154
174 180 399 298 103 140
197 194 399 67 115 164
195 194 399 19 156 165
178 182 395 189 105 138
183 185 398 227 123 160
185 184 397 53 78 161
190 188 395 50 130 161
186 187 398 28 139 150
179 184 400 24 130 153
185 185 394 48 111 154
189 187 398 2337 100 163
Difference Difference Adjusted P
between between Value of
Median Mean Difference
Mutant Fragments Mutant Mutant between
75th and and Mutant
Mode Median Mean Percentile Maximum Wild type Wild-type and
cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA Wild-type
Fragment Fragment Fragment Fragment Fragment Fragment Fragment cfDNA
Size Size Size Size Size Size Size Fragment
(bp) (bp) (bp) (bp) (bp) (bp) (bp) Size
161 173 194 191 397 −1.0 −2.45 0.251
178 184 237 338 377 9.0 35.30 0.114
168 171 189 186 380 −4.0 −16.38 0.435
173 173 188 190 400 −2.0 −6.17 0.293
169 176 203 203 400 4.5 8.80 0.000
194 207 243 324 400 −16.0 5.51 0.574
164 174 200 196 400 1.0 2.87 0.065
149 158 166 173 302 −4.0 −5.94 0.190
172 171 191 180 390 0.0 −4.34 0.627
166 171 187 187 395 −2.0 −1.94 0.475
161 170 182 184 400 −5.0 −11.54 0.155
150 169 174 185 319 −1.0 −9.68 0.571
168 171 179 183 400 0.0 0.15 0.880
154 167 164 174 117 −3.0 −22.90 0.155
165 166 178 178 361 −1.0 −1.35 0.685
167 163 172 175 363 −3.0 −0.34 0.880
175 172 185 191 380 3.0 6.52 0.368
146 155 162 170 224 −14.0 −19.82 0.007
169 164 170 174 392 2.0 1.37 0.646
162 162 163 177 232 −4.0 −7.62 0.252
154 176 195 206 383 7.5 14.58 0.064
157 163 176 185 347 −5.5 −7.87 0.154
177 174 200 203 372 4.0 14.61 0.270
163 163 185 186 338 −7.0 1.98 0.039
204 181 194 203 369 13.0 11.80 0.039
169 169 198 173 333 −1.0 6.05 0.610
161 171 216 301 360 0.0 17.02 0.623
108 173 198 224 385 0.0 6.48 0.624
165 172 185 187 397 −1.0 −5.17 0.005
192 192 202 200 397 18.0 9.79 0.007
142 167 176 182 391 −6.5 −6.78 0.061
185 172 192 186 375 6.0 17.15 0.005
181 181 197 211 370 8.0 3.34 0.169
194 174 189 194 379 3.5 4.60 0.270
176 176 194 213 331 7.0 2.50 0.718
164 168 190 187 393 −1.0 2.54 0.113
175 175 190 208 339 5.0 4.07 0.302
165 170 178 175 349 3.0 −1.65 0.407
152 165 181 186 393 −4.0 −0.65 0.876
147 166 182 185 393 −3.0 0.36 0.926
161 167 186 188 338 −4.0 6.15 0.234
179 179 187 180 376 10.0 9.98 0.130
167 166 183 181 396 −1.0 −1.73 0.154
164 173 196 200 357 7.0 14.95 0.213
127 158 189 186 352 −8.0 12.47 0.179
173 166 183 179 396 −2.0 4.31 0.427
174 174 180 156 282 5.0 3.09 0.252
143 177 196 227 298 2.5 −4.24 0.479
266 178 199 215 269 6.0 15.13 0.252
156 164 177 169 302 8.0 4.82 0.119
168 176 206 196 365 3.0 20.55 0.415
182 185 201 192 329 12.0 14.62 0.263
164 152 157 164 346 −18.0 −27.67 0.000
143 175 174 183 325 7.0 −5.22 0.054
146 168 186 181 367 −0.5 5.19 0.568
164 166 176 176 387 −1.0 −0.24 0.874
148 150 152 162 288 −18.0 −22.25 0.000
250 173 187 201 366 2.0 9.89 0.425
165 185 197 199 361 10.0 2.20 0.154
141 150 164 175 348 −20.0 −14.58 0.000
168 169 185 184 396 −2.0 1.68 0.706
175 175 189 158 392 4.0 3.80 0.241
168 168 184 175 377 −4.5 −5.86 0.234
173 170 170 173 354 −2.5 −15.88 0.416
176 170 193 199 359 0.0 13.13 0.598
170 168 173 183 295 −3.0 −11.80 0.270
166 172 187 185 394 −1.0 −1.27 0.564
Mutant Fragments
75th 25th
Mean Percentile Maximum Minimum Percentile
cfDNA cfDNA cfDNA cfDNA cfDNA
Fragment Fragment Fragment Fragment Fragment
Size Size Size Distinct Size Size
(bp) (bp) (bp) Coverage (bp) (bp)
198 200 396 172 83 152
190 188 400 215 123 151
184 184 400 207 121 151
191 189 397 17 143 170
181 182 398 52 122 152
191 189 399 17 109 161
191 189 399 40 136 164
180 181 399 127 88 149
181 186 400 68 141 166
169 179 398 10 81 167
170 181 398 33 107 162
175 181 391 23 112 156
175 177 400 109 130 153
172 176 400 684 105 153
179 178 398 2946 100 138
175 178 399 30 121 165
187 186 400 63 140 155
181 184 400 4754 101 160
182 187 400 31 131 162
181 183 400 150 110 144
179 184 400 5290 95 159
181 186 400 140 101 155
187 190 397 20 92 141
190 192 400 8065 85 156
174 182 400 2586 101 147
185 188 400 2808 100 150
182 187 400 2227 100 154
176 183 396 8425 100 155
186 188 399 142 112 146
186 185 399 104 132 158
183 185 392 3462 101 160
182 183 399 25 94 140
177 181 399 3789 101 159
181 184 400 57 131 152
183 191 400 36 118 154
187 185 399 362 110 152
182 188 400 20 158 163
185 187 397 23 126 151
188 189 400 2980 100 158
183 163 391 2793 91 158
185 189 395 7357 100 158
184 184 398 5186 101 157
182 187 400 15595 64 159
186 185 400 6749 101 158
193 190 400 23 127 148
182 185 394 3901 101 160
179 180 400 4633 100 158
175 179 400 734 101 151
175 180 394 4022 101 159
184 182 400 117 116 156
172 176 395 65 109 145
Difference Difference Adjusted P
between between Value of
Median Mean Difference
Mutant Fragments Mutant Mutant between
75th and and Mutant
Mode Median Mean Percentile Maximum Wild type Wild-type and
cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA Wild-type
Fragment Fragment Fragment Fragment Fragment Fragment Fragment cfDNA
Size Size Size Size Size Size Size Fragment
(bp) (bp) (bp) (bp) (bp) (bp) (bp) Size
160 166 193 226 396 −4.0 −4.93 0.490
159 163 188 196 365 −6.0 −1.72 0.735
157 161 181 179 365 −7.0 −3.01 0.571
217 214 198 217 294 43.0 7.08 0.000
167 164 179 173 372 −4.5 −2.07 0.137
173 171 181 174 392 −1.0 −9.24 0.576
166 171 185 185 335 −1.0 −5.86 0.571
131 162 168 178 311 −6.0 −11.80 0.005
175 176 198 207 387 4.0 17.11 0.184
167 167 159 176 182 1.0 −10.20 0.589
167 167 174 185 322 0.0 4.57 0.636
190 164 175 190 349 −4.0 −0.92 0.308
169 166 175 178 382 0.0 −0.09 0.987
167 166 172 175 385 1.0 0.00 0.999
157 155 172 174 398 −9.0 −7.28 0.000
165 176 198 219 325 12.0 22.37 0.007
154 167 201 215 372 −3.0 13.70 0.286
170 170 179 161 393 0.0 −1.72 0.154
162 174 180 185 352 2.0 2.26 0.494
166 162 176 173 385 −6.0 −5.86 0.314
167 169 179 164 400 −1.0 0.11 0.909
175 167 179 180 352 −4.5 −2.77 0.589
241 168 178 209 283 −3.0 −9.82 0.479
164 169 190 190 399 0.0 −0.08 0.942
165 165 169 179 386 −3.5 −4.59 0.000
158 167 189 200 399 −3.0 4.17 0.007
162 171 183 190 398 0.0 1.00 0.564
165 169 176 184 400 0.0 0.54 0.568
140 159 180 193 352 −13.0 −5.41 0.463
159 167 189 180 331 −2.0 3.05 0.657
173 172 184 167 396 1.0 0.82 0.576
140 158 159 163 341 −11.0 −23.47 0.027
168 169 176 161 395 0.0 −0.66 0.576
170 170 179 184 327 −1.0 −2.41 0.568
201 182 187 201 328 11.0 3.60 0.114
143 180 207 268 389 11.0 20.70 0.000
311 174 198 209 311 3.0 15.25 0.475
184 168 185 185 328 −1.0 −1.49 0.571
169 170 187 189 398 0.0 −0.84 0.637
167 170 161 182 389 1.0 −2.30 0.171
175 171 162 187 399 −1.0 −2.37 0.008
165 170 185 186 400 1.0 1.72 0.240
167 170 181 185 397 −1.0 −1.39 0.245
167 170 185 187 400 0.0 −0.52 0.702
148 194 222 292 378 24.0 29.58 0.027
167 171 182 155 398 2.0 0.32 0.821
169 170 185 157 400 1.0 6.16 0.000
155 165 176 178 366 −4.0 0.48 0.823
167 168 172 178 399 −1.0 −2.84 0.000
156 172 199 184 399 5.0 15.08 0.084
177 167 181 181 306 3.0 9.11 0.293
APPENDIX-D
Table 4 Summary of whole genome cfDNA analyses
High
Total Quality
Analysis Patient Read Bases Bases
Patient Timepoint type Type Length Sequenced Analyzed Coverage
CGCRC291 Preoperative treatment naïve WGS Colorectal Cancer 100 7232125000 4695396600 1.86
CGCRC292 Preoperative treatment naïve WGS Colorectal Cancer 100 6794092800 4471065400 1.77
CGCRC293 Preoperative treatment naïve WGS Colorectal Cancer 100 8373899600 5686176000 2.26
CGCRC294 Preoperative treatment naïve WGS Colorectal Cancer 100 3081312000 5347045800 2.12
CGCRC296 Preoperative treatment naïve WGS Colorectal Cancer 100 10072029200 6770998200 2.69
CGCRC299 Preoperative treatment naïve WGS Colorectal Cancer 100 10971591600 7632723200 3.03
CGCRC300 Preoperative treatment naïve WGS Colorectal Cancer 100 9894332600 6699951000 2.66
CGCRC301 Preoperative treatment naïve WGS Colorectal Cancer 100 7357346200 5021002000 1.99
CGCRC302 Preoperative treatment naïve WGS Colorectal Cancer 100 11671913000 8335275800 3.31
CGCRC304 Preoperative treatment naïve WGS Colorectal Cancer 100 19011739200 12957614200 5.14
CGCRC305 Preoperative treatment naïve WGS Colorectal Cancer 100 7177341400 4809957200 1.91
CGCRC306 Preoperative treatment naïve WGS Colorectal Cancer 100 8302233200 5608043600 2.23
CGCRC307 Preoperative treatment naïve WGS Colorectal Cancer 100 8034720400 5342620000 2.12
CGCRC308 Preoperative treatment naïve WGS Colorectal Cancer 100 8670084800 5934037200 2.35
CGCRC311 Preoperative treatment naïve WGS Colorectal Cancer 100 6947634400 4704601800 1.87
CGCRC315 Preoperative treatment naïve WGS Colorectal Cancer 100 5205544000 3419565400 1.36
CGCRC316 Preoperative treatment naïve WGS Colorectal Cancer 100 6405388600 4447534800 1.76
CGCRC317 Preoperative treatment naïve WGS Colorectal Cancer 100 6060390400 4104616600 1.63
CGCRC318 Preoperative treatment naïve WGS Colorectal Cancer 100 6848768600 4439404800 1.76
CGCRC319 Preoperative treatment naïve WGS Colorectal Cancer 100 10545294400 7355181600 2.92
CGCRC320 Preoperative treatment naïve WGS Colorectal Cancer 100 5961999200 3945054000 1.57
CGCRC321 Preoperative treatment naïve WGS Colorectal Cancer 100 8248095400 5614355000 2.23
CGCRC333 Preoperative treatment naïve WGS Colorectal Cancer 100 10540267600 6915490600 2.74
CGCRC336 Preoperative treatment naïve WGS Colorectal Cancer 100 10675581800 7087691800 2.81
CGCRC338 Preoperative treatment naïve WGS Colorectal Cancer 100 13788172600 8970308600 3.56
CGCRC341 Preoperative treatment naïve WGS Colorectal Cancer 100 10753467600 7311539200 2.90
CGCRC342 Preoperative treatment naïve WGS Colorectal Cancer 100 11836966000 7552793200 3.00
CGH14 Human adult elutriated lymphocytes WGS Healthy 100 36525427600 24950300200 9.90
CGH15 Human adult elutriated lymphocytes WGS Healthy 100 29930855000 23754049400 9.43
CGLU316 Pre-treatment, Day-53 WGS Lung Cancer 100 10354123200 6896471400 2.74
CGLU316 Pre-treatment, Day-4 WGS Lung Cancer 100 7870039200 5254938800 2.09
CGLU316 Post-treatment, Day 18 WGS Lung Cancer 100 8155322000 5416262400 2.15
CGLU316 Post-treatment, Day 81 WGS Lung Cancer 100 9442310400 6087893400 2.42
CGLU344 Pre-treatment, Day-21 WGS Lung Cancer 100 8728318600 5769097200 2.29
CGLU344 Pre-treatment, Day 0 WGS Lung Cancer 100 11710246400 7826902600 3.11
CGLU344 Post-treatment, Day 0.1875 WGS Lung Cancer 100 11569683000 7654701600 3.04
CGLU344 Post-treatment, Day 59 WGS Lung Cancer 100 11042459200 6320133800 2.51
CGLU369 Pre-treatment, Day-2 WGS Lung Cancer 100 8630932800 5779595800 2.29
CGLU369 Post-treatment, Day 12 WGS Lung Cancer 100 9227709600 6136755200 2.44
CGLU369 Post-treatment, Day 68 WGS Lung Cancer 100 7995282600 5239077200 2.08
CGLU369 Post-treatment, Day 110 WGS Lung Cancer 100 8750541000 5626139000 2.23
CGLU373 Pre-treatment, Day-2 WGS Lung Cancer 100 11746059600 7547485800 3.00
CGLU373 Post-treatment, Day 0.125 WGS Lung Cancer 100 13801136800 9255579400 3.67
CGLU373 Post-treatment, Day 7 WGS Lung Cancer 100 11537896800 7654111200 3.04
CGLU373 Post-treatment, Day 47 WGS Lung Cancer 100 8046326400 5397702400 2.14
CGPLBR100 Preoperative treatment naïve WGS Breast Cancer 100 8440532400 5729474800 2.27
CGPLBR101 Preoperative treatment naïve WGS Breast Cancer 100 9786253600 6673495200 2.65
CGPLBR102 Preoperative treatment naïve WGS Breast Cancer 100 8664980400 5669781600 2.25
CGPLBR103 Preoperative treatment naïve WGS Breast Cancer 100 9346936200 6662883400 2.64
CGPLBR104 Preoperative treatment naïve WGS Breast Cancer 100 9443375400 6497061000 2.58
CGPLBR12 Preoperative treatment naïve WGS Breast Cancer 100 7017577800 4823327400 1.91
CGPLBR18 Preoperative treatment naïve WGS Breast Cancer 100 10309652800 7130386000 2.83
CGPLBR23 Preoperative treatment naïve WGS Breast Cancer 100 9034484800 6219625800 2.47
CGPLBR24 Preoperative treatment naïve WGS Breast Cancer 100 9891454200 6601857400 2.62
CGPLBR28 Preoperative treatment naïve WGS Breast Cancer 100 7997607200 5400803200 2.14
CGPLBR30 Preoperative treatment naïve WGS Breast Cancer 100 5502597200 5885822400 2.34
CGPLBR31 Preoperative treatment naïve WGS Breast Cancer 100 12660085600 8551995600 3.39
CGPLBR32 Preoperative treatment naïve WGS Breast Cancer 100 8773498600 5839034600 2.32
CGPLBR33 Preoperative treatment naïve WGS Breast Cancer 100 10931742800 6967030600 2.76
CGPLBR34 Preoperative treatment naïve WGS Breast Cancer 100 10861398600 7453225800 2.96
CGPLBR35 Preoperative treatment naïve WGS Breast Cancer 100 9180193600 6158440200 2.44
CGPLBR36 Preoperative treatment naïve WGS Breast Cancer 100 9159948400 6091817800 2.42
CGPLBR37 Preoperative treatment naïve WGS Breast Cancer 100 10307505800 6929530600 2.75
CGPLBR38 Preoperative treatment naïve WGS Breast Cancer 100 9983824000 6841725400 2.71
CGPLBR40 Preoperative treatment naïve WGS Breast Cancer 100 10148823800 7024345400 2.79
CGPLBR41 Preoperative treatment naïve WGS Breast Cancer 100 11168192000 7562945800 3.00
CGPLBR45 Preoperative treatment naïve WGS Breast Cancer 100 8793780600 6011109400 2.39
CGPLBR46 Preoperative treatment naïve WGS Breast Cancer 100 7228607600 4706130000 1.87
CGPLBR47 Preoperative treatment naïve WGS Breast Cancer 100 7906911400 5341655000 2.12
CGPLBR48 Preoperative treatment naïve WGS Breast Cancer 100 6992032000 4428636200 1.76
CGPLBR49 Preoperative treatment naïve WGS Breast Cancer 100 7311195000 4559460200 1.81
CGPLBR50 Preoperative treatment naïve WGS Breast Cancer 100 11107960600 7582776600 3.01
CGPLBR51 Preoperative treatment naïve WGS Breast Cancer 100 8393547400 5102069000 2.02
CGPLBR52 Preoperative treatment naïve WGS Breast Cancer 100 9491894800 6141729000 2.44
CGPLBR55 Preoperative treatment naïve WGS Breast Cancer 100 9380109800 6518855200 2.59
CGPLBR56 Preoperative treatment naïve WGS Breast Cancer 100 12191816800 8293011200 3.29
CGPLBR57 Preoperative treatment naïve WGS Breast Cancer 100 9847584400 6713638000 2.66
CGPLBR59 Preoperative treatment naïve WGS Breast Cancer 100 7476477000 5059873200 2.01
CGPLBR60 Preoperative treatment naïve WGS Breast Cancer 100 6531354600 4331253800 1.72
CGPLBR61 Preoperative treatment naïve WGS Breast Cancer 100 9311029200 6430920800 2.55
CGPLBR63 Preoperative treatment naïve WGS Breast Cancer 100 8971949000 6044009600 2.40
CGPLBR65 Preoperative treatment naïve WGS Breast Cancer 100 7197301400 4835015200 1.92
CGPLBR63 Preoperative treatment naïve WGS Breast Cancer 100 10003774000 6974918800 2.77
CGPLBR69 Preoperative treatment naïve WGS Breast Cancer 100 10080881800 6903459200 2.74
CGPLBR70 Preoperative treatment naïve WGS Breast Cancer 100 8824002800 6002533800 2.38
CGPLBR71 Preoperative treatment naïve WGS Breast Cancer 100 10164136800 6994668600 2.78
CGPLBR72 Preoperative treatment naïve WGS Breast Cancer 100 18418841400 12328783000 4.89
CGPLBR73 Preoperative treatment naïve WGS Breast Cancer 100 10281460200 7078613200 2.81
CGPLBR76 Preoperative treatment naïve WGS Breast Cancer 100 10105270400 6800705000 2.70
CGPLBR81 Preoperative treatment naïve WGS Breast Cancer 100 5087126000 3273367200 1.30
CGPLBR82 Preoperative treatment naïve WGS Breast Cancer 100 10576496600 7186662600 2.85
CGPLBR83 Preoperative treatment naïve WGS Breast Cancer 100 8977124400 5947525000 2.36
CGPLBR84 Preoperative treatment naïve WGS Breast Cancer 100 6272538600 4066870600 1.61
CGPLBR87 Preoperative treatment naïve WGS Breast Cancer 100 8460954800 5375710200 2.13
CGPLBR83 Preoperative treatment naïve WGS Breast Cancer 100 8665810400 5499893200 2.18
CGPLBR90 Preoperative treatment naïve WGS Breast Cancer 100 6663469200 4392442400 1.74
CGPLBR91 Preoperative treatment naïve WGS Breast Cancer 100 10933002400 7647842000 3.03
CGPLBR92 Preoperative treatment naïve WGS Breast Cancer 100 10392674000 6493593000 2.58
CGPLBR93 Preoperative treatment naïve WGS Breast Cancer 100 5659836000 3931106800 1.56
CGPLH189 Preoperative treatment naïve WGS Healthy 100 11400610400 7655568800 3.04
CGPLH190 Preoperative treatment naïve WGS Healthy 100 11444671600 7581175200 3.01
CGPLH192 Preoperative treatment naïve WGS Healthy 100 12199010800 8126804800 3.22
CGPLH193 Preoperative treatment naïve WGS Healthy 100 10201897600 6635285400 2.63
CGPLH194 Preoperative treatment naïve WGS Healthy 100 11005087400 7081652600 2.81
CGPLH196 Preoperative treatment naïve WGS Healthy 100 12891462800 8646881800 3.43
CGP6H197 Preoperative treatment naïve WGS Healthy 100 11961841600 3052855200 3.20
CGPLH193 Preoperative treatment naïve WGS Healthy 100 13605489000 8885716000 3.53
CGPLH199 Preoperative treatment naïve WGS Healthy 100 1818090200 5615316000 2.23
CGPLH200 Preoperative treatment naïve WGS Healthy 100 14400027600 9310342000 3.69
CGPLH201 Preoperative treatment naïve WGS Healthy 100 6208766806 4171843400 1.66
CGPLH202 Preoperative treatment naïve WGS Healthy 100 11282922800 7363530600 2.92
CGPLH203 Preoperative treatment naïve WGS Healthy 100 13540689600 9068747600 3.60
CGPLH205 Preoperative treatment naïve WGS Healthy 100 10343537800 6696983600 2.66
CGPLH208 Preoperative treatment naïve WGS Healthy 100 12796300000 3272073400 3.28
CGPLH209 Preoperative treatment naïve WGS Healthy 100 13123035400 3531813600 3.39
CGPLH210 Preoperative treatment naïve WGS Healthy 100 10184218800 6832204600 2.71
CGPLH211 Preoperative treatment naïve WGS Healthy 100 14655260200 3887067600 3.53
CGPLH300 Preoperative treatment naïve WGS Healthy 100 7062083400 4553351200 1.81
CGPLH307 Preoperative treatment naïve WGS Healthy 100 7239128200 4547697200 1.80
CGPLH308 Preoperative treatment naïve WGS Healthy 100 8512551400 5526653600 2.19
CGPLH309 Preoperative treatment naïve WGS Healthy 100 11664474200 7431836600 2.95
CGPLH310 Preoperative treatment naïve WGS Healthy 100 11045691000 7451506200 2.96
CGPLH311 Preoperative treatment naïve WGS Healthy 100 10406803200 6786479600 2.69
CGPLH314 Preoperative treatment naïve WGS Healthy 100 10371343800 6925866600 2.75
CGPLH315 Preoperative treatment naïve WGS Healthy 100 9508538400 6208744600 2.46
CGPLH316 Preoperative treatment naïve WGS Healthy 100 10131063600 6891181000 2.73
CGPLH317 Preoperative treatment naïve WGS Healthy 100 8364314400 5302232600 2.10
CGPLH319 Preoperative treatment naïve WGS Healthy 100 8780528200 5585897000 2.22
CGPLH320 Preoperative treatment naïve WGS Healthy 100 8956232600 5784619200 2.30
CGPLH322 Preoperative treatment naïve WGS Healthy 100 9563837800 6445517800 2.56
CGPLH324 Preoperative treatment naïve WGS Healthy 100 6765038600 4469201600 1.77
CGPLH325 Preoperative treatment naïve WGS Healthy 100 8008213400 5099262800 2.02
CGPLH326 Preoperative treatment naïve WGS Healthy 100 9554226200 6112544000 2.43
CGPLH327 Preoperative treatment naïve WGS Healthy 100 8239168800 5351280200 2.12
CGPLH328 Preoperative treatment naïve WGS Healthy 100 7197086300 4516894800 1.79
CGPLH329 Preoperative treatment naïve WGS Healthy 100 8921554800 5493709800 2.18
CGPLH330 Preoperative treatment naïve WGS Healthy 100 10693603400 7077793600 2.81
CGPLH331 Preoperative treatment naïve WGS Healthy 100 8982792000 5538096200 2.20
CGPLH333 Preoperative treatment naïve WGS Healthy 100 7856985400 5178829600 2.06
CGPLH335 Preoperative treatment naïve WGS Healthy 100 9370663400 6035739400 2.40
CGPLH336 Preoperative treatment naïve WGS Healthy 100 8002498200 5340331400 2.12
CGPLH337 Preoperative treatment naïve WGS Healthy 100 7399022000 4954467600 1.97
CGPLH338 Preoperative treatment naïve WGS Healthy 100 8917121600 6170927200 2.45
CGPLH339 Preoperative treatment naïve WGS Healthy 100 8591130800 5866411400 2.33
CGPLH340 Preoperative treatment naïve WGS Healthy 100 8046351000 5368062000 2.13
CGPLH341 Preoperative treatment naïve WGS Healthy 100 7914788600 5200304800 2.06
CGPLH342 Preoperative treatment naïve WGS Healthy 100 8633413000 5701972400 2.26
CGPLH343 Preoperative treatment naïve WGS Healthy 100 6694769800 4410670860 1.75
CGPLH344 Preoperative treatment naïve WGS Healthy 100 7628192400 4961476600 1.97
CGPLH345 Preoperative treatment naïve WGS Healthy 100 7121569406 4747223000 1.88
CGPLH346 Preoperative treatment naïve WGS Healthy 100 7707924600 4873321600 1.93
CGPLH35 Preoperative treatment naïve WGS Healthy 100 47305985200 4774186200 12.63
CGPLH350 Preoperative treatment naïve WGS Healthy 100 9745839800 6054055200 2.40
CGPLH351 Preoperative treatment naïve WGS Healthy 100 13317435800 8714465000 3.46
CGPLH352 Preoperative treatment naïve WGS Healthy 100 7059351600 4752309400 1.89
CGPLH353 Preoperative treatment naïve WGS Healthy 100 8435782400 5215098200 2.09
CGPLH354 Preoperative treatment naïve WGS Healthy 100 8018644000 4857577660 1.93
CGPLH355 Preoperative treatment naïve WGS Healthy 100 8624675800 5709726400 2.27
CGPLH356 Preoperative treatment naïve WGS Healthy 100 8817952800 5729595200 2.27
CGPLH357 Preoperative treatment naïve WGS Healthy 100 11931696200 7690004400 3.05
CGPLH358 Preoperative treatment naïve WGS Healthy 100 12802561200 8451274800 3.35
CGPLH36 Preoperative treatment naïve WGS Healthy 100 40173545600 3914810400 10.52
CGPLH360 Preoperative treatment naïve WGS Healthy 100 7280078400 4918566200 1.95
CGPLH361 Preoperative treatment naïve WGS Healthy 100 7493498400 4966813800 1.97
CGPLH362 Preoperative treatment naïve WGS Healthy 100 11345644200 7532133600 2 99
CGPLH363 Preoperative treatment naïve WGS Healthy 100 6111382800 3965952400 1.57
CGPLH364 Preoperative treatment naïve WGS Healthy 100 10823490400 7195657000 2.86
CGPLH365 Preoperative treatment naïve WGS Healthy 100 5938367400 3954556200 1.57
CGPLH366 Preoperative treatment naïve WGS Healthy 100 7063168600 4731853060 1.88
CGPLH367 Preoperative treatment naïve WGS Healthy 100 7119631800 4627888200 1.84
CGPLH368 Preoperative treatment naïve WGS Healthy 100 7726718400 4975233400 1.97
CGPLH369 Preoperative treatment naïve WGS Healthy 100 10967584200 7130956800 2.83
CGPLH37 Preoperative treatment naïve WGS Healthy 100 45970545400 4591328800 12.15
CGPLH370 Preoperative treatment naïve WGS Healthy 100 9237170006 6106373800 2.42
CGPLH371 Preoperative treatment naïve WGS Healthy 100 8077798800 5237070600 2.08
CGPLH380 Preoperative treatment naïve WGS Healthy 100 14049589200 8614241200 3.42
CGPLH381 Preoperative treatment naïve WGS Healthy 100 16743792000 10767862800 4.27
CGPLH382 Preoperative treatment naïve WGS Healthy 100 18474025200 12276437200 4.87
CGPLH383 Preoperative treatment naïve WGS Healthy 100 13215954000 8430420600 3.36
CGPLH384 Preoperative treatment naïve WGS Healthy 100 8481814000 5463636260 2.17
CGPLH385 Preoperative treatment naïve WGS Healthy 100 9596118800 6445445600 2.56
CGPLH386 Preoperative treatment naïve WGS Healthy 100 7399540400 4915484800 1.95
CGPLH387 Preoperative treatment naïve WGS Healthy 100 6860332600 4339724400 1.72
CGPLH388 Preoperative treatment naïve WGS Healthy 100 8679705600 5463945400 2.17
CGPLH389 Preoperative treatment naïve WGS Healthy 100 7266863600 4702386000 1.87
CGPLH390 Preoperative treatment naïve WGS Healthy 100 7509035600 4913901800 1.95
CGPLH391 Preoperative treatment naïve WGS Healthy 100 7252286000 4702404800 1.87
CGPLH392 Preoperative treatment naïve WGS Healthy 100 7302618200 4722407000 1.87
CGPLH393 Preoperative treatment naïve WGS Healthy 100 8879138000 5947871800 2.36
CGPLH394 Preoperative treatment naïve WGS Healthy 100 8737031000 5599777400 2.22
CGPLH395 Preoperative treatment naïve WGS Healthy 100 7783904800 4907146000 1.95
CGPLH396 Preoperative treatment naïve WGS Healthy 100 7585567200 5076638200 2.01
CGPLH393 Preoperative treatment naïve WGS Healthy 100 13001418200 8607025000 3.42
CGPLH399 Preoperative treatment naïve WGS Healthy 100 9867699200 5526646000 2.19
CGPLH400 Preoperative treatment naïve WGS Healthy 100 10573939000 6290438200 2.50
CGPLH401 Preoperative treatment naïve WGS Healthy 100 9415150000 6139638000 2.44
CGPLH402 Preoperative treatment naïve WGS Healthy 100 5541458000 2912027800 1.18
CGPLH403 Preoperative treatment naïve WGS Healthy 100 6470913200 3549172600 1.41
CGPLH404 Preoperative treatment naïve WGS Healthy 100 7369651800 4120205000 1.64
CGPLH405 Preoperative treatment naïve WGS Healthy 100 7360239000 4293522600 1.70
CGPLH406 Preoperative treatment naïve WGS Healthy 100 6026125400 3426007400 1.36
CGPLH407 Preoperative treatment naïve WGS Healthy 100 7073375200 4079286800 1.62
CGPLH408 Preoperative treatment naïve WGS Healthy 100 8006103200 5121285600 2.03
CGPLH409 Preoperative treatment naïve WGS Healthy 100 7343124600 4432335600 1.76
CGPLH410 Preoperative treatment naïve WGS Healthy 100 7551842000 4818779600 1.91
CGPLH411 Preoperative treatment naïve WGS Healthy 100 6119676400 3636478400 1.44
CGPLH412 Preoperative treatment naïve WGS Healthy 100 7960821200 4935752200 1.96
CGPLH413 Preoperative treatment naïve WGS Healthy 100 7623405400 4827888400 1.92
CGPLH414 Preoperative treatment naïve WGS Healthy 100 7381312400 4743337200 1.88
CGPLH415 Preoperative treatment naïve WGS Healthy 100 7240754200 4162208800 1.65
CGPLH416 Preoperative treatment naïve WGS Healthy 100 7745658600 4670226000 1.85
CGPLH417 Preoperative treatment naïve WGS Healthy 100 7627498600 4403085600 1.75
CGPLH418 Preoperative treatment naïve WGS Healthy 100 9090285000 5094814000 2.02
CGPLH419 Preoperative treatment naïve WGS Healthy 100 7914120200 5078389800 2.02
CGPLH42 Preoperative treatment naïve WGS Healthy 100 39492040600 3901039400 10.32
CGPLH420 Preoperative treatment naïve WGS Healthy 100 70143072800 4711393600 1.87
CGPLH422 Preoperative treatment naïve WGS Healthy 100 9103972800 6053559800 2.40
CGPLH423 Preoperative treatment naïve WGS Healthy 100 10154714200 6128800200 2.43
CGPLH424 Preoperative treatment naïve WGS Healthy 100 11002394000 6573756000 2.61
CGPLH425 Preoperative treatment naïve WGS Healthy 100 14681352600 9272557000 3.68
CGPLH426 Preoperative treatment naïve WGS Healthy 100 8336731000 5177430800 2.05
CGPLH427 Preoperative treatment naïve WGS Healthy 100 8242924400 5632991800 2.24
CGPLH428 Preoperative treatment naïve WGS Healthy 100 8512550400 5604756600 2.22
CGPLH429 Preoperative treatment naïve WGS Healthy 100 8369802800 5477121400 2.17
CGPLH43 Preoperative treatment naïve WGS Healthy 100 38513193400 3815698400 10.10
CGPLH430 Preoperative treatment naïve WGS Healthy 100 10357365400 6841611000 2.71
CGPLH431 Preoperative treatment naïve WGS Healthy 100 7599875800 5006909000 1.99
CGPLH432 Preoperative treatment naïve WGS Healthy 100 7932532400 4932304200 1.96
CGPLH434 Preoperative treatment naïve WGS Healthy 100 10417028600 6965093800 2.76
CGPLH435 Preoperative treatment naïve WGS Healthy 100 6747793800 5677115290 2.29
CGPLH436 Preoperative treatment naïve WGS Healthy 100 7990589400 5228737800 2.07
GGPLH437 Preoperative treatment naïve WGS Healthy 100 10156991200 6935537200 2.75
CGPLH438 Preoperative treatment naïve WGS Healthy 100 9473604000 6445455600 2.56
CGPLH439 Preoperative treatment naïve WGS Healthy 100 8303723400 5439877200 2.16
CGPLH440 Preoperative treatment naïve WGS Healthy 100 9055233800 6018631400 2.39
CGPLH441 Preoperative treatment naïve WGS Healthy 100 10290682000 6896415200 2.74
CGPLH442 Preoperative treatment naïve WGS Healthy 100 9876551600 6591249800 2.62
CGPLH443 Preoperative treatment naïve WGS Healthy 100 9837225800 6360740800 2.52
CGPLH444 Preoperative treatment naïve WGS Healthy 100 9199271400 5795941660 2.26
CGPLH445 Preoperative treatment naïve WGS Healthy 100 8089236400 5218259800 2.07
CGPLH446 Preoperative treatment naïve WGS Healthy 100 7890664200 5181606000 2.06
CGPLH447 Preoperative treatment naïve WGS Healthy 100 7775775000 5120239800 2.03
CGPLH448 Preoperative treatment naïve WGS Healthy 100 8686964800 5605079200 2.22
CGPLH449 Preoperative treatment naïve WGS Healthy 100 8604545400 5527726600 2.19
CGPLH45 Preoperative treatment naïve WGS Healthy 100 39029653000 3771601200 9.98
CGPLH450 Preoperative treatment naïve WGS Healthy 100 8428254800 5439950000 2.16
CGPLH451 Preoperative treatment naïve WGS Healthy 100 8128977600 5186265600 2.06
CGPLH452 Preoperative treatment naïve WGS Healthy 100 6474313400 4216316400 1.67
CGPLH453 Preoperative treatment naïve WGS Healthy 100 9831832800 6224917600 2.47
CGPLH455 Preoperative treatment naïve WGS Healthy 100 7373753000 4593473600 1.82
CGPLH456 Preoperative treatment naïve WGS Healthy 100 8455416200 5457148200 2.17
CGPLH457 Preoperative treatment naïve WGS Healthy 100 8647618000 5534503800 2.20
CGPLH458 Preoperative treatment naïve WGS Healthy 100 6633156400 4415186060 1.79
CGPLH459 Preoperative treatment naïve WGS Healthy 100 8361048200 5497193800 2.18
CGPLH46 Preoperative treatment naïve WGS Healthy 100 35361484600 3516232800 9.30
CGPLH460 Preoperative treatment naïve WGS Healthy 100 6788835400 4472282800 1.77
CGPLH463 Preoperative treatment naïve WGS Healthy 100 8534880800 5481759200 2.18
CGPLH464 Preoperative treatment naïve WGS Healthy 100 6692520006 4184463400 1.66
CGPLH465 Preoperative treatment naïve WGS Healthy 100 7772884600 4878430800 1.94
CGPLH466 Preoperative treatment naïve WGS Healthy 100 9056275000 5830877400 2.31
CGPLH467 Preoperative treatment naïve WGS Healthy 100 6931419200 4585861000 1.82
CGPLH468 Preoperative treatment naïve WGS Healthy 100 9334067400 6314830460 2.51
CGPLH469 Preoperative treatment naïve WGS Healthy 100 7376691000 4545246600 1.80
CGPLH47 Preoperative treatment naïve WGS Healthy 100 38485647600 3534883600 9.35
CGPLH470 Preoperative treatment naïve WGS Healthy 100 7899727600 5221650600 2.07
CGPLH471 Preoperative treatment naïve WGS Healthy 100 9200430600 6102371000 2.42
CGPLH472 Preoperative treatment naïve WGS Healthy 100 8143742400 5399946600 2.14
CGPLH473 Preoperative treatment naïve WGS Healthy 100 8123924600 5419825400 2.15
CGPLH474 Preoperative treatment naïve WGS Healthy 100 3853071400 6084059400 2.41
CGPLH475 Preoperative treatment naïve WGS Healthy 100 8115374000 5291718000 2.10
CGPLH476 Preoperative treatment naïve WGS Healthy 100 8163162000 5096869660 2.02
CGPLH477 Preoperative treatment naïve WGS Healthy 100 8350093206 5465468600 2.17
CGPLH478 Preoperative treatment naïve WGS Healthy 100 8259642200 5406516200 2.15
CGPLH479 Preoperative treatment naïve WGS Healthy 100 8027598600 5417376800 2.15
CGPLH48 Preoperative treatment naïve WGS Healthy 100 42232410000 4165893400 11.02
CGPLH480 Preoperative treatment naïve WGS Healthy 100 7832983200 5020127000 1.99
CGPLH481 Preoperative treatment naïve WGS Healthy 100 7578518800 4883280800 1.94
CGPLH482 Preoperative treatment naïve WGS Healthy 100 8279364800 5652263600 2.24
CGPLH483 Preoperative treatment naïve WGS Healthy 100 8660338800 5823859200 2.31
CGPLH484 Preoperative treatment naïve WGS Healthy 100 8445420000 5794328000 2.30
CGPLH485 Preoperative treatment naïve WGS Healthy 100 8371255406 5490207800 2.18
CGPLH486 Preoperative treatment naïve WGS Healthy 100 8216712200 5506871000 2.19
CGPLH487 Preoperative treatment naïve WGS Healthy 100 7936294200 5309250200 2.11
CGPLH488 Preoperative treatment naïve WGS Healthy 100 8355603600 545316000 2.16
CGPLH49 Preoperative treatment naïve WGS Healthy 100 33912191800 3310056000 8.76
CGPLH490 Preoperative treatment naïve WGS Healthy 100 7768712400 5175567800 2.05
CGPLH491 Preoperative treatment naïve WGS Healthy 100 9070904000 6011275000 2.39
CGPLH492 Preoperative treatment naïve WGS Healthy 100 7208727200 4753213800 1.89
CGPLH493 Preoperative treatment naïve WGS Healthy 100 10542882600 7225870800 2.87
CGPLH494 Preoperative treatment naïve WGS Healthy 100 10908197600 7046645000 2.80
CGPLH495 Preoperative treatment naïve WGS Healthy 100 8945040400 5891697800 2.34
CGPLH496 Preoperative treatment naïve WGS Healthy 100 10859729400 7549608000 3.00
CGPLH497 Preoperative treatment naïve WGS Healthy 100 9630507400 6473162800 2.57
CGPLH498 Preoperative treatment naïve WGS Healthy 100 10060232600 6744622800 2.68
CGPLH499 Preoperative treatment naïve WGS Healthy 100 10221293600 6951282800 2.76
CGPLH50 Preoperative treatment naïve WGS Healthy 100 41248860600 4073272890 10.78
CGPLH500 Preoperative treatment naïve WGS Healthy 100 9703168209 6239893800 2.48
CGPLH501 Preoperative treatment naïve WGS Healthy 100 9104779800 6161602800 2.45
CGPLH502 Preoperative treatment naïve WGS Healthy 100 8514467400 5290881400 2.10
CGPLH503 Preoperative treatment naïve WGS Healthy 100 9019992209 6100383400 2.42
CGPLH504 Preoperative treatment naïve WGS Healthy 100 9320330200 6109750200 2.46
CGPLH505 Preoperative treatment naïve WGS Healthy 100 7499497400 4914559000 1.95
CGPLH506 Preoperative treatment naïve WGS Healthy 100 10526142000 6963312600 2.76
CGPLH507 Preoperative treatment naïve WGS Healthy 100 9091018400 6146678600 2.44
CGPLH508 Preoperative treatment naïve WGS Healthy 100 10989315600 7360201400 2.92
CGPLH509 Preoperative treatment naïve WGS Healthy 100 9729084600 6702691600 2.66
CGPLH51 Preoperative treatment naïve WGS Healthy 100 35967451400 3492833200 9.24
CGPLH510 Preoperative treatment naïve WGS Healthy 100 11162691600 7626795400 3.03
CGPLH511 Preoperative treatment naïve WGS Healthy 100 11888619600 8110427600 3.22
CGPLH512 Preoperative treatment naïve WGS Healthy 100 10726438400 7110078000 2.82
CGPLH513 Preoperative treatment naïve WGS Healthy 100 10701564200 7105271400 2.84
CGPLH514 Preoperative treatment naïve WGS Healthy 100 8822067000 5958773800 2.36
CGPLH515 Preoperative treatment naïve WGS Healthy 100 7792074800 5317464600 2.11
CGPLH516 Preoperative treatment naïve WGS Healthy 100 8642620000 5846439400 2.32
CGPLH517 Preoperative treatment naïve WGS Healthy 100 11915929600 0013937000 3.18
CGPLH518 Preoperative treatment naïve WGS Healthy 100 12804517400 3606661600 3.42
CGPLH519 Preoperative treatment naïve WGS Healthy 100 11513222200 7922798400 3.14
CGPLH52 Preoperative treatment naïve WGS Healthy 100 49247304200 4849531400 12.83
CGPLH520 Preoperative treatment naïve WGS Healthy 100 8942102400 6030683400 2.39
CGPLH54 Preoperative treatment naïve WGS Healthy 100 45399346400 4466164600 11.82
CGPLH55 Preoperative treatment naïve WGS Healthy 100 42547725000 4283337600 11.33
CGPLH56 Preoperative treatment naïve WGS Healthy 100 33460308000 3226338000 8.53
CGPLH51 Preoperative treatment naïve WGS Healthy 100 36504735200 3509125000 9.28
CGPLH59 Preoperative treatment naïve WGS Healthy 100 39642810600 3820011000 10.11
CGPLH625 Preoperative treatment naïve WGS Healthy 100 6408225000 4115487600 1.63
CGPLH626 Preoperative treatment naïve WGS Healthy 100 9915193600 6391657000 2.54
CGPLH63 Preoperative treatment naïve WGS Healthy 100 37447047600 3506737000 9.26
CGPLH639 Preoperative treatment naïve WGS Healthy 100 8158965890 5216049600 2.07
CGPLH64 Preoperative treatment naïve WGS Healthy 100 34275506800 3264503000 8.63
CGPLH640 Preoperative treatment naïve WGS Healthy 100 8058876800 5333551800 2.12
CGPLH642 Preoperative treatment naïve WGS Healthy 100 7545555600 4909732800 1.95
CGPLH643 Preoperative treatment naïve WGS Healthy 100 7865776800 5254772000 2.09
CGPLH644 Preoperative treatment naïve WGS Healthy 100 6890139000 4599387400 1.83
CGPLH646 Preoperative treatment naïve WGS Healthy 100 7757219400 5077408200 2.01
CGPLH75 Preoperative treatment naïve WGS Healthy 100 23882926000 2250344400 5.95
CGPLH76 Preoperative treatment naïve WGS Healthy 100 30631483600 3086042200 8.16
CGPLH77 Preoperative treatment naïve WGS Healthy 100 31651741400 3041290200 8.04
CGPLH78 Preoperative treatment naïve WGS Healthy 100 31165831200 3130079800 8.28
CGPLH79 Preoperative treatment naïve WGS Healthy 100 31935043000 3128488200 8.27
CGPLH80 Preoperative treatment naïve WGS Healthy 100 32965093000 3311371800 8.76
CGPLH81 Preoperative treatment naïve WGS Healthy 100 27035311200 2455084400 6.49
CGPLH82 Preoperative treatment naïve WGS Healthy 100 28447051200 2893358200 7.65
CGPLH83 Preoperative treatment naïve WGS Healthy 100 26702240200 2459494000 6.50
CGPLH84 Preoperative treatment naïve WGS Healthy 100 251713861400 2524467400 6.68
CGPLLU13 Pre-treatment, Day-2 WGS Lung Cancer 100 9126585600 5915061800 2.35
CGPLLU13 Post-treatment, Day 5 WGS Lung Cancer 100 7739120200 5071745800 2.01
CGPLLU13 Post-treatment, Day 28 WGS Lung Cancer 100 9081585400 5764371600 2.29
CGPLLU13 Post-treatment, Day 91 WGS Lung Cancer 100 9576557000 6160760200 2.44
CGPLLU14 Pre-treatment, Day-38 WGS Lung Cancer 100 13659198400 9033455800 3.58
CGPLLU14 Pre-treatment, Day-16 WGS Lung Cancer 100 7178855800 4856643600 1.93
CGPLLU14 Pre-treatment, Day-3 WGS Lung Cancer 100 7653473000 4816193600 1.91
CGPLLU14 Pre-treatment, Day 0 WGS Lung Cancer 100 7351997400 5193256600 2.06
CGPLLU14 Post-treatment, Day 0.33 WGS Lung Cancer 100 7193040800 4869701600 1.93
CGPLLU14 Post-treatment, Day 7 WGS Lung Cancer 100 7102000000 4741432600 1.88
CGPLLU144 Preoperative treatment naïve WGS Lung Cancer 100 4934813600 3415936400 1.36
CGPLLU147 Preoperative treatment naïve WGS Lung Cancer 100 24409561000 2118672800 5.61
CGPLLU161 Preoperative treatment naïve WGS Lung Cancer 100 8998813400 6016145000 2.39
CGPLLU162 Preoperative treatment naïve WGS Lung Cancer 100 9709792400 6407866400 2.54
CGPLLU163 Preoperative treatment naïve WGS Lung Cancer 100 9150620200 6063569800 2.41
CGPLLU165 Preoperative treatment naïve WGS Lung Cancer 100 28374436400 2651138600 7.01
CGPLLU168 Preoperative treatment naïve WGS Lung Cancer 100 5692739400 3695191000 1.47
CGPLLU169 Preoperative treatment naïve WGS Lung Cancer 100 9093975600 5805320800 2.30
CGPLLU175 Preoperative treatment naïve WGS Lung Cancer 100 33794816800 3418750400 9.04
CGPLLU176 Preoperative treatment naïve WGS Lung Cancer 100 8778553800 5794950200 2.30
CGPLLU177 Preoperative treatment naïve WGS Lung Cancer 100 3734614800 2578696200 1.02
CGPLLU180 Preoperative treatment naïve WGS Lung Cancer 100 28305936600 2756034200 7.29
CGPLLU198 Preoperative treatment naïve WGS Lung Cancer 100 32344959200 2218577200 5.86
CGPLLU202 Preoperative treatment naïve WGS Lung Cancer 100 21110128200 1831279400 4.84
CGPLLU203 Preoperative treatment naïve WGS Lung Cancer 100 4304235600 2806429000 1.15
CGPLLU205 Preoperative treatment naïve WGS Lung Cancer 100 10502467000 7386984800 2.93
CGPLLU206 Preoperative treatment naïve WGS Lung Cancer 100 21888248200 2026666000 5.36
CGPLLU207 Preoperative treatment naïve WGS Lung Cancer 100 10806230600 7363049000 2.92
CGPLLU208 Preoperative treatment naïve WGS Lung Cancer 100 7795426800 5199545800 2.06
CGPLLU209 Preoperative treatment naïve WGS Lung Cancer 100 26174542000 2621961800 6.93
CGPLLU244 Pre-treatment, Day-7 WGS Lung Cancer 100 9967531400 6704365800 2.66
CGPLLU244 Pre-treatment, Day-1 WGS Lung Cancer 100 9547119200 5785172600 2.30
CGPLLU944 Post-treatment, Day 6 WGS Lung Cancer 100 9535898600 6452174000 2.56
CGPLLU244 Post-treatment, Day 62 WGS Lung Cancer 100 6783628000 5914149000 2.35
CGPLLU245 Pre-treatment, Day-32 WGS Lung Cancer 100 10025823200 6313303800 2.51
CGPLLU245 Pre-treatment, Day 0 WGS Lung Cancer 100 9462480400 6612867800 2.62
CGPLLU245 Post-treatment, Day 7 WGS Lung Cancer 100 9143025000 6431013200 2.55
CGPLLU245 Post-treatment, Day 21 WGS Lung Cancer 100 9072713800 6368533000 2.53
CGPLLU946 Pre-treatment, Day-21 WGS Lung Cancer 100 9579787000 6458003400 2.56
CGPLLU246 Pre-treatment, Day 0 WGS Lung Cancer 100 9512703600 6440535600 2.56
CGPLLU246 Post-treatment, Day 9 WGS Lung Cancer 100 9012645000 6300939200 2.50
CGPLLU246 Post-treatment, Day 42 WGS Lung Cancer 100 11136103000 7358747400 2.92
CGPLLU264 Pre-treatment, Day-1 WGS Lung Cancer 100 9196305000 6239803600 2.49
CGPLLU264 Post-treatment, Day 6 WGS Lung Cancer 100 8247416600 5600454200 2.22
CGPLLU264 Post-treatment, Day 27 WGS Lung Cancer 100 8681022200 5856109000 2.32
CGPLLU264 Post-treatment, Day 69 WGS Lung Cancer 100 3931976400 5974246000 2.37
CGPLLU265 Pre-treatment, Day 0 WGS Lung Cancer 100 9460534000 6111185200 2.43
CGPLLU265 Post-treatment, Day 3 WGS Lung Cancer 100 8051601200 4984166600 1.98
CGPLLU265 Post-treatment, Day 7 WGS Lung Cancer 100 8082224600 5110092600 2.03
CGPLLU265 Post-treatment, Day 84 WGS Lung Cancer 100 8368637400 5369526400 2.13
CGPLLU266 Pre-treatment, Day 0 WGS Lung Cancer 100 8583766400 5846473600 2.32
CGPLLU266 Post-treatment, Day 16 WGS Lung Cancer 100 8795793600 5984531400 2.37
CGPLLU266 Post-treatment, Day 83 WGS Lung Cancer 100 9157947600 6227735060 2.47
CGPLLU266 Post-treatment, Day 328 WGS Lung Cancer 100 7299455400 5049379000 2.00
CGPLLU267 Pre-treatment, Day-1 WGS Lung Cancer 100 10658657800 6892067000 2.73
CGPLLU267 Post-treatment, Day 34 WGS Lung Cancer 100 8492833400 5101097800 2.02
CGPLLU267 Post-treatment, Day 90 WGS Lung Cancer 100 12030314800 7757930400 3.09
CGPLLU269 Pre-treatment, Day 0 WGS Lung Cancer 100 9170168000 5830454400 2.31
CGPLLU269 Post-treatment, Day 9 WGS Lung Cancer 100 8905640400 5290461400 2.10
CGPLLU269 Post-treatment, Day 28 WGS Lung Cancer 100 8455306600 5387927400 2.14
CGPLLU271 Post-treatment, Day 259 WGS Lung Cancer 100 8112060400 5404979000 2.14
CGPLLU271 Pre-treatment, Day 0 WGS Lung Cancer 100 13150818200 8570453400 3.40
CGPLLU271 Post-treatment, Day 6 WGS Lung Cancer 100 9008880600 5854051400 2.32
CGPLLU271 Post-treatment, Day 20 WGS Lung Cancer 100 8670913000 5461577000 2.17
CGPLLU271 Post-treatment, Day 104 WGS Lung Cancer 100 8887441400 5609039000 2.23
CGPLLU43 Pre-treatment, Day-1 WGS Lung Cancer 100 6407811200 5203486400 2.06
CGPLLU43 Post-treatment, Day 6 WGS Lung Cancer 100 9964335200 5626714400 2.23
CGPLLU43 Post-treatment, Day 27 WGS Lung Cancer 100 8902283000 5485656200 2.18
CGPLLU43 Post-treatment, Day 83 WGS Lung Cancer 100 9201509200 5875064200 2.33
CGPLLU86 Pre-treatment, Day 0 WGS Lung Cancer 100 9152729200 6248173200 2.48
CGPLLU86 Post-treatment, Day 0.5 WGS Lung Cancer 100 6703253000 4663026800 1.85
CGPLLU86 Post-treatment, Day 7 WGS Lung Cancer 100 6590121400 4559562400 1.81
CGPLLU86 Post-treatment, Day 17 WGS Lung Cancer 100 8653551800 5900136000 2.34
CGPLLU88 Pre-treatment, Day 0 WGS Lung Cancer 100 8096528000 8505475400 2.18
CGPLLU88 Post-treatment, Day 7 WGS Lung Cancer 100 0283192200 5784217600 2.30
CGPLLU88 Post-treatment, Day 297 WGS Lung Cancer 100 9297110800 6407258000 2.54
CGPLLU89 Pre-treatment, Day 0 WGS Lung Cancer 100 7042145200 5356095400 2.13
CGPLLU89 Post-treatment, Day 7 WGS Lung Cancer 100 7234220200 4930375200 1.96
CGPLLU89 Post-treatment, Day 22 WGS Lung Cancer 100 6242889800 4057361000 1.61
CGPLOV11 Preoperative treatment naïve WGS Ovarian Cancer 100 8985130400 5871959600 2.33
CGPLOV12 Preoperative treatment naïve WGS Ovarian Cancer 100 9705820000 6430505400 2.55
CGPLOV13 Preoperative treatment naïve WGS Ovarian Cancer 100 10307949400 7029712000 2.79
CCPLOV15 Preoperative treatment naïve WGS Ovarian Cancer 100 8472829400 8562142400 2.21
CGPLOV16 Preoperative treatment naïve WGS Ovarian Cancer 100 10977781000 7538581600 2.99
CGPLOV19 Preoperative treatment naïve WGS Ovarian Cancer 100 8800876200 5855304000 2.32
CGPLOV20 Preoperative treatment naïve WGS Ovarian Cancer 100 8714443600 5605165800 2.26
CGPLOV21 Preoperative treatment naïve WGS Ovarian Cancer 100 10180394800 7120260400 2.83
CGPLOV22 Preoperative treatment naïve WGS Ovarian Cancer 100 10107760000 6821916800 2.71
CGPLOV23 Preoperative treatment naïve WGS Ovarian Cancer 100 10643399800 7206330800 2.86
CGPLOV24 Preoperative treatment naïve WGS Ovarian Cancer 100 6780929000 4623300400 1.83
CGPLOV25 Preoperative treatment naïve WGS Ovarian Cancer 100 7817548600 5359975200 2.13
CGPLOV26 Preoperative treatment naïve WGS Ovarian Cancer 100 11763101400 8178024400 3.25
CGPLOV28 Preoperative treatment naïve WGS Ovarian Cancer 100 9522546400 6259423400 2.48
CGPLOV31 Preoperative treatment naïve WGS Ovarian Cancer 100 9104831200 6109358400 2.42
CGPLOV32 Preoperative treatment naïve WGS Ovarian Cancer 100 9222073600 6035150000 2.39
CGPLOV37 Preoperative treatment naïve WGS Ovarian Cancer 100 8898328600 5971018200 2.37
CGPLOV38 Preoperative treatment naïve WGS Ovarian Cancer 100 8756825200 5861536600 2.33
CGPLOV40 Preoperative treatment naïve WGS Ovarian Cancer 100 9709391600 6654707200 2.64
CGPLOV41 Preoperative treatment naïve WGS Ovarian Cancer 100 8923625000 5973070400 2.37
CGPLOV42 Preoperative treatment naïve WGS Ovarian Cancer 100 10719380400 7353214200 2.92
CGPLOV43 Preoperative treatment naïve WGS Ovarian Cancer 100 10272189000 6423288600 2.55
CGPLOV44 Preoperative treatment naïve WGS Ovarian Cancer 100 9861862600 6769185800 2.69
CGPLOV46 Preoperative treatment naïve WGS Ovarian Cancer 100 8788956400 5789863400 2.30
CGPLOV47 Preoperative treatment naïve WGS Ovarian Cancer 100 9380561800 6480763600 2.57
CCPLOV48 Preoperative treatment naïve WGS Ovarian Cancer 100 9258552600 6380106400 2.53
CCPLOV49 Preoperative treatment naïve WGS Ovarian Cancer 100 8787025400 6134503600 2.43
CGFLOV50 Preoperative treatment naïve WGS Ovarian Cancer 100 10144154400 6984721400 2.77
CGPLPA2 Preoperative treatment naïve WGS Pancreatic Cancer 100 12740651400 9045622000 3.59
CGPLPA113 Preoperative treatment naïve WGS Duodenal Canner 100 8802479000 5909030800 2.34
CGPLPA114 Preoperative treatment naïve WGS Bile Duct Cancer 100 8792313600 6019061000 2.39
CGPLPA115 Preoperative treatment naïve WGS Bile Duct Cancer 100 8636551400 5958809000 2.36
CGPLPA117 Preoperative treatment naïve WGS Bile Duct Cancer 100 9128885200 6288833200 2.50
CGPLPA118 Preoperative treatment naïve WGS Bile Duct Cancer 100 7931485800 5407532800 2.15
CGPLPA122 Preoperative treatment naïve WGS Bile Duct Cancer 100 10888985000 7530118800 2.99
CGPLPA124 Preoperative treatment naïve WGS Bile Duct Cancer 100 8062012400 5860171000 2.33
CGPLPA125 Preoperative treatment naïve WGS Bile Duct Cancer 100 9715576600 6390321000 2.54
CGPLPA126 Preoperative treatment naïve WGS Bile Duct Cancer 100 8056768800 5651600800 2.24
CGPLPA127 Preoperative treatment naïve WGS Bile Duct Cancer 100 8000301000 5382987600 2.14
CGPLPAI28 Preoperative treatment naïve WGS Bile Duct Cancer 100 6165751600 4256521400 1.69
CGPLPA129 Preoperative treatment naïve WGS Bile Duct Cancer 100 7143147400 4917370400 1.95
CGPLPA130 Preoperative treatment naïve WGS Bile Duct Cancer 100 5664335000 3603919400 1.43
CGPLPA131 Preoperative treatment naïve WGS Bile Duct Cancer 100 8292982000 5844942000 2.32
CGPLPA134 Preoperative treatment naïve WGS Bile Duct Cancer 100 7088917000 5048887600 2.00
CGPLPA135 Preoperative treatment naïve WGS Bile Duct Cancer 100 8750665600 5800613200 2.30
CGPLPA136 Preoperative treatment naïve WGS Bile Duct Cancer 100 7539715800 5248227600 2.08
CGPLPA137 Preoperative treatment naïve WGS Bile Duct Cancer 100 8391815400 5901273800 2.34
CGPLPA139 Preoperative treatment naïve WGS Bile Duct Cancer 100 8992280200 6328314400 2.51
CGPLPA14 Preoperative treatment naïve WGS Pancreatic Cancer 100 8787706200 5731317600 2.27
CGPLPA140 Preoperative treatment naïve WGS Bile Duct Cancer 100 16365641800 11216732000 4.45
CGPLPA141 Preoperative treatment naïve WGS Bile Duct Cancer 100 15086298000 10114790200 4.01
CGPLPA15 Preoperative treatment naïve WGS Pancreatic Cancer 100 8255566800 5531677600 2 20
CGPLPA155 Preoperative treatment naïve WGS Bile Duct Cancer 100 9457155800 6621881800 2.63
CGPLPA156 Preoperative treatment naïve WGS Pancreatic Cancer 100 9345385800 6728653000 2.67
CGPLPA165 Preoperative treatment naïve WGS Bile Duct Cancer 100 8356604600 0829895800 2.31
CGPLPA168 Preoperative treatment naïve WGS Bile Duct Cancer 100 10365661600 7048115600 2.80
CGPLPA17 Preoperative treatment naïve WGS Pancreatic Cancer 100 8073547400 4687803000 1.86
CGPLPA184 Preoperative treatment naïve WGS Bile Duct Cancer 100 9014218400 6230922200 2.47
CGPLPA187 Preoperative treatment naïve WGS Bile Duct Cancer 100 8883536200 6140874400 2.44
CGPLPA23 Preoperative treatment naïve WGS Pancreatic Cancer 100 9835452000 6246525400 2.48
CGPLPA25 Preoperative treatment naïve WGS Pancreatic Cancer 100 10077515400 6103322200 2.42
CGPLPA26 Preoperative treatment naïve WGS Pancreatic Cancer 100 8354272400 5725781000 2.21
CGPLPA28 Preoperative treatment naïve WGS Pancreatic Cancer 100 8477461600 5688846800 2.26
CGPLPA33 Preoperative treatment naïve WGS Pancreatic Cancer 100 7287615600 4506723800 1.82
CGPLPA34 Preoperative treatment naïve WGS Pancreatic Cancer 100 6122902400 4094828000 1.62
CGPLPA37 Preoperative treatment naïve WGS Pancreatic Cancer 100 12714888200 8527779200 3.38
CGPLPA38 Preoperative treatment naïve WGS Pancreatic Cancer 100 8525500600 5501341400 2.18
CGPLPA39 Preoperative treatment naïve WGS Pancreatic Cancer 100 10502663600 6812333000 2.70
CGPLPA40 Preoperative treatment naïve WGS Pancreatic Cancer 100 9083670000 0394717800 2.14
CGPLPA42 Preoperative treatment naïve WGS Pancreatic Cancer 100 5072126600 3800395200 1.54
CGPLPA46 Preoperative treatment naïve WGS Pancreatic Cancer 100 4720090200 2626298800 1.04
CGPLPA47 Preoperative treatment naïve WGS Pancreatic Cancer 100 7317385800 4543833000 1.80
CGPLPA48 Preoperative treatment naïve WGS Pancreatic Cancer 100 7553856200 5022695600 1.90
CGPLPA52 Preoperative treatment naïve WGS Pancreatic Cancer 100 5655875000 3551861600 1.41
COPLPA53 Preoperative treatment naïve WGS Pancreatic Cancer 100 9504749000 6323344800 2.51
CGPLPA58 Preoperative treatment naïve WGS Pancreatic Cancer 100 8088090200 5118138200 2.03
CGPLPA59 Preoperative treatment naïve WGS Pancreatic Cancer 100 14547364600 9617773600 3.82
CGPLPA67 Preoperative treatment naïve WGS Pancreatic Cancer 100 8222177400 5351172000 2.12
CGPLPA69 Preoperative treatment naïve WGS Pancreatic Cancer 100 7899181400 5006114800 1.90
CGPLPA71 Preoperative treatment naïve WGS Pancreatic Cancer 100 7340620400 4955417400 1.97
CGPLPA74 Preoperative treatment naïve WGS Pancreatic Cancer 100 6666371400 4571394200 1.81
CGPLPA76 Preoperative treatment naïve WGS Pancreatic Cancer 100 9755658600 6412606800 2.54
CGPLPA85 Preoperative treatment naïve WGS Pancreatic Cancer 100 10853223000 7309498600 2.90
CGPLPA86 Preoperative treatment naïve WGS Pancreatic Cancer 100 8744365400 5514523200 2.19
CGPLPA92 Preoperative treatment naïve WGS Pancreatic Cancer 100 8073791200 5390492800 2.14
CGPLPA93 Preoperative treatment naïve WGS Pancreatic Cancer 100 10390273000 7186589400 2.85
CGPLPA94 Preoperative treatment naïve WGS Pancreatic Cancer 100 11060347600 7641336400 3.03
CGPLPA95 Preoperative treatment naïve WGS Pancreatic Cancer 100 12416627200 7206503800 2.86
CGST102 Preoperative treatment naïve WGS Pancreatic Cancer 100 6637004600 4545072600 1.80
CGST11 Preoperative treatment naïve WGS Pancreatic Cancer 100 9718427800 6259679600 2.48
CGST110 Preoperative treatment naïve WGS Pancreatic Cancer 100 9319661600 6359317400 2.52
CGST114 Preoperative treatment naïve WGS Pancreatic Cancer 100 6865213000 4841171600 1.92
CGST13 Preoperative treatment naïve WGS Pancreatic Cancer 100 9284554800 6360843800 2.52
CGST131 Preoperative treatment naïve WGS Gastric cancer 100 5924382000 3860677200 1.53
CGST141 Preoperative treatment naïve WGS Gastric cancer 100 8486380800 5860491000 2.33
CGST16 Preoperative treatment naïve WGS Gastric cancer 100 13820725800 9377828000 3.72
CGST18 Preoperative treatment naïve WGS Gastric cancer 100 7781288000 5278862400 2.09
CGST21 Preoperative treatment naïve WGS Gastric cancer 100 7171165400 4103970800 1.63
CGST26 Preoperative treatment naïve WGS Gastric cancer 100 8983961800 6053405600 2.40
CGST28 Preoperative treatment naïve WGS Gastric cancer 100 9683035400 6745116400 2.68
CGST30 Preoperative treatment naïve WGS Gastric cancer 100 8684086600 5741416000 2.28
CGST32 Preoperative treatment naïve WGS Gastric cancer 100 8568194600 5783369200 2.29
CGST33 Preoperative treatment naïve WGS Gastric cancer 100 9351699600 6448718400 2.56
CGST38 Preoperative treatment naïve WGS Gastric cancer 100 8409876400 5770989200 2.29
CGST39 Preoperative treatment naïve WGS Gastric cancer 100 10573763000 7597016000 3.01
CGST41 Preoperative treatment naïve WGS Gastric cancer 100 9434854200 6609415400 2.62
CGST45 Preoperative treatment naïve WGS Gastric cancer 100 8203868600 5625223000 2.23
CGST47 Preoperative treatment naïve WGS Gastric cancer 100 8938597600 6178990600 2.45
CGST48 Preoperative treatment naïve WGS Gastric cancer 100 9106628800 6517085200 2.59
CGST53 Preoperative treatment naïve WGS Gastric cancer 100 9005374200 5854996200 2.32
CGST58 Preoperative treatment naïve WGS Gastric cancer 100 10020368600 6133458400 2.43
CGST67 Preoperative treatment naïve WGS Gastric cancer 100 9198135600 5911071000 2.35
CGST77 Preoperative treatment naïve WGS Gastric cancer 100 8228789400 5119116800 2.03
CGST80 Preoperative treatment naïve WGS Gastric cancer 100 10596963400 7283152800 2.89
CGST81 Preoperative treatment naïve WGS Gastric cancer 100 5494881200 5038064000 2.32
APPENDIX E
Table 5. High coverage whole genome cfDNA analyses of healthy individuals and lung cancer patients
Correlation
Correlation of GC
of Corrected Correlation
Fragment Fragment of
Ratio Ratio Fragment Correlation
Profile Profile Ratio of
to Median to Median Profile Fragment
Median Fragment Fragment to Median Ratio
cfDNA Ratio Ratio Fragment Profile to
Fragment Profile of Profile of Ratio Lymphocyte
Analysis Stage at Size Healthy Healthy Profile of Nucleosome
Patient Patient Type Type Timepoint Diagnosis (bp) Individuals Individuals Lymphocytes Distances
CGPLH75 Healthy WGS Preoperative treatment naïve NA 168 0.977 0.952 0.920 −0.886
CGPLH77 Healthy WGS Preoperative treatment naïve NA 166 0.970 0.960 0.904 −0.912
CGPLH80 Healthy WGS Preoperative treatment naïve NA 168 0.955 0.949 0.960 −0.917
CGPLH81 Healthy WGS Preoperative treatment naïve NA 167 0.949 0.953 0.869 −0.883
CGPLH82 Healthy WGS Preoperative treatment naïve NA 166 0 969 0.949 0.954 −0.917
CGPLH83 Healthy WGS Preoperative treatment naïve NA 167 0.949 0.939 0.919 −0.904
CGPLH84 Healthy WGS Preoperative treatment naïve NA 168 0 967 0.948 0.951 −0.913
CGPLH52 Healthy WGS Preoperative treatment naïve NA 167 0.946 0.968 0.952 −0.924
CGPLH35 Healthy WGS Preoperative treatment naïve NA 166 0.981 0.973 0.945 −0.921
CGPLH37 Healthy WGS Preoperative treatment naïve NA 168 0.968 0.970 0.951 −0.922
CGPLH51 Healthy WGS Preoperative treatment naïve NA 167 0.968 0.976 0.948 −0.925
CGPLH55 Healthy WGS Preoperative treatment naïve NA 166 0.947 0.964 0.948 −0.917
CGPLH48 Healthy WGS Preoperative treatment naïve NA 168 0.959 0.965 0.960 −9.923
CGPLH50 Healthy WGS Preoperative treatment naïve NA 167 0.960 0.968 0.952 −0.921
CGPLH36 Healthy WGS Preoperative treatment naïve NA 168 0.955 0.954 0.955 −0.919
CGPLH42 Healthy WGS Preoperative treatment naïve NA 167 0.973 0.963 0.948 −0.918
CGPLH43 Healthy WGS Preoperative treatment naïve NA 166 0.952 0.958 0.953 −0.928
CGPLH59 Healthy WGS Preoperative treatment naïve NA 168 0.970 0.965 0.951 −0.925
CGPLH45 Healthy WGS Preoperative treatment naïve NA 168 0.965 0.950 0.949 −0.911
CGPLH47 Healthy WGS Preoperative treatment naïve NA 167 0.952 0.944 0.954 −0.921
CGPLH46 Healthy WGS Preoperative treatment naïve NA 168 0.966 0.985 0.953 −0.923
CGPLH63 Healthy WGS Preoperative treatment naïve NA 168 0.977 0.968 0.939 −0.920
CAPLH51 Healthy WGS Preoperative treatment naïve NA 168 0.935 0.955 0.957 −0.914
CAPLH57 Healthy WGS Preoperative treatment naïve NA 169 0.965 0.954 0.955 −0.917
CGPLH49 Healthy WGS Preoperative treatment naïve NA 168 0.958 0.951 0.950 −0.924
CGPLH56 Healthy WGS Preoperative treatment naïve NA 166 0.940 0.957 0.959 −0.911
CGPLH64 Healthy WGS Preoperative treatment naïve NA 169 0.960 0.940 0.949 −0.918
CGPLH78 Healthy WGS Preoperative treatment naïve NA 166 0.956 0.936 0.958 −0.911
CGPLH79 Healthy WGS Preoperative treatment naïve NA 168 0.960 0.957 0.953 −0.917
CGPLH76 Healthy WGS Preoperative treatment naïve NA 167 0.969 0.965 0.953 −0.917
CGPLLU175 Lung Cancer WGS Preoperative treatment naïve I 165 0.316 0.284 0.244 −0.262
CGPLLU180 Lung Cancer WGS Preoperative treatment naïve I 166 0.907 0.846 0.826 −0.819
CGPLLU198 Lung Cancer WGS Preoperative treatment naïve I 166 0.972 0.946 0.928 −0.911
CGPLLU202 Lung Cancer WGS Preoperative treatment naïve I 160 0.821 0.605 0.905 −0.843
CGPLLU165 Lung Cancer WGS Preoperative treatment naïve II 163 0.924 0.961 0.815 −0.851
CGPLLU209 Lung Cancer WGS Preoperative treatment naïve II 163 0.578 0.526 0.513 −0.534
CGPLLU147 Lung Cancer WGS Preoperative treatment naïve III 166 0.953 0.919 0.939 −0.912
CGPLLU206 Lung Cancer WGS Preoperative treatment naïve III 158 0.488 0.343 0.460 −0.481
APPENDIX F
Table 6. Monitoring response to therapy using whole genome analyses of cfDNA fragmentation profiles and targeted mutations analyses
Progression-
free
Survival
Patient Patient Type Analysis Type Timepoint Stage (months)
CGPLLU14 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-38 IV 15.4
CGPLLU14 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-16 IV 15.4
CGPLLU14 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-3 IV 15.4
CGPLLU14 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV 15.4
CGPLLU14 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 0.33 IV 15.4
CGPLLU14 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 7 IV 15.4
CGPLLU88 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV 18.0
CGPLLU88 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 7 IV 18.0
CGPLLU88 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 297 IV 18.0
CGPLLU244 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-7 IV 1.2
CGPLLU244 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-1 IV 1.2
CGPLLU244 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 6 IV 1.2
CGPLLU244 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 62 IV 1.2
CGPLLU245 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-32 IV 1.7
CGPLLU245 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV 1.7
CGPLLU245 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 7 IV 1.7
CGPLLU245 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 21 IV 1.7
CGPLLU246 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-21 IV 1.3
CGPLLU246 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV 1.3
CGPLLU246 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 9 IV 1.3
CGPLLU246 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 42 IV 1.1
CGPLLU86 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV 12.4
CGPLLU86 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 0.5 IV 12.4
CGPLLU86 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 7 IV 12.4
CGPLLU86 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 17 IV 12.4
CGPLLU89 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV 6.7
CGPLLU89 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 7 IV 6.7
CGPLLU89 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 22 IV 6.7
CGLU316 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-53 IV 1.4
CGLU316 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-4 IV 1.4
CGLU316 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 18 IV 1.4
CGLU316 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 87 IV 1.4
CGLU344 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-21 IV Ongoing
CGLU344 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV Ongoing
CGLU344 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 0.1675 IV Ongoing
CGLU344 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 59 IV Ongoing
CGLU369 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-2 IV 7.5
CGLU369 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 12 IV 7.5
CGLU369 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 68 IV 7.5
CGLU369 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 110 IV 7.5
CGLU373 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-2 IV Ongoing
CGLU373 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 0.125 IV Ongoing
CGLU373 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 7 IV Ongoing
CGLU373 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 47 IV Ongoing
CGPLLU13 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-2 IV 1.5
CGPLLU13 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 5 IV 1.5
CGPLLU13 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 28 IV 1.5
CGPLLU13 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 91 IV 1.5
CGPLLU264 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-1 IV Ongoing
CGPLLU264 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 6 IV Ongoing
CGPLLU264 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 27 IV Ongoing
CGPLLU264 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 69 IV Ongoing
CGPLLU265 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV Ongoing
CGPLLU265 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 3 IV Ongoing
CGPLLU265 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 7 IV Ongoing
CGPLLU265 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 84 IV Ongoing
CGPLLU266 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV 9.6
CGPLLU266 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 16 IV 9.6
CGPLLU266 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 83 IV 9.6
CGPLLU266 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 328 IV 9.6
CGPLLU267 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-1 IV 3.9
CGPLLU267 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 34 IV 3.9
CGPLLU267 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 90 IV 3.9
CGPLLU269 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV Ongoing
CGPLLU269 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 9 IV Ongoing
CGPLLU269 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 28 IV Ongoing
CGPLLU271 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV 8.2
CGPLLU271 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 6 IV 8.2
CGPLLU271 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 20 IV 8.2
CGPLLU271 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 104 IV 8.2
CGPLLU271 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 259 IV 8.2
CGPLLU43 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-1 IV Ongoing
CGPLLU43 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 6 IV Ongoing
CGPLLU43 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 27 IV Ongoing
CGPLLU43 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 83 IV Ongoing
Correlation
of
Fragment
Ratio Correlation
Profile to of
Median Fragment
Fragment Ratio
Ratio Profile to Maximum
Profile of Lymphocyte Mutant
Healthy Nucleosome Allele
Patient Individuals Distances Targeted Mutation Fraction
CGPLLU14 0.941 −0.841 EGFR 861L > Q 0.89%
CGPLLU14 0.933 −0.833 EGFR 861L > Q 0.18%
CGPLLU14 0.908 −0.814 EGFR 719G > S 0.49%
CGPLLU14 0.883 −0.752 EGFR 861L > Q 1.39%
CGPLLU14 0.820 −0.692 EGFR 719G > S 1.05%
CGPLLU14 0.927 −0.887 EGFR 861L > Q 0.00%
CGPLLU88 0.657 −0.584 EGFR 7459ELREA > T 9.06%
CGPLLU88 0.939 −0.799 EGFR 790T > M 0.15%
CGPLLU88 0.946 −0.869 EGFR 7459ELREA > T 0.93%
CGPLLU244 0.850 −0.706 EGFR 858L > R 4.98%
CGPLLU244 0.867 −0.764 EGFR 62L > R 3.41%
CGPLLU244 0.703 −0.639 EGFR 858L > R 5.57%
CGPLLU244 0.659 −0.660 EGFR 858L > R 11.80%
CGPLLU245 0.871 −0.724 EGFR 745KELREA > K 10.60%
CGPLLU245 0.736 −0.608 EGFR 745KELREA > K 14.10%
CGPLLU245 0.731 −0.559 EGFR 745KELREA > K 8.56%
CGPLLU245 0.613 −0.426 EGFR 745KELREA > K 10.69%
CGPLLU246 0.897 −0.757 EGFR 790T > M 0.49%
CGPLLU246 0.469 −0.376 EGFR 858L > R 6.17%
CGPLLU246 0.874 −0.746 EGFR 858L > R 1.72%
CGPLLU246 0.775 −0.665 EGFR 858L > R 5.29%
CGPLLU86 0.817 −0.630 EGFR 746ELREATS > D 0.00%
CGPLLU86 0.916 −0.811 EGFR 746ELREATS > D 0.19%
CGPLLU86 0.859 −0.694 EGFR 746ELREATS > D 0.00%
CGPLLU86 0.932 −0.848 EGFR 746ELREATS > D 0.00%
CGPLLU89 0.864 −0.729 EGFR 747LREATS > − 0.42%
CGPLLU89 0.908 −0.803 EGFR 747LREATS > − 0.20%
CGPLLU89 0.853 −0.881 EGFR 747LREATS > − 0.00%
CGLU316 0.331 −0.351 EGFR L861Q 15.72%
CGLU316 0.225 −0.253 EGFR L861Q 45.67%
CGLU316 0.336 −0.364 EGFR G719A 33.38%
CGLU316 0.340 −0.364 EGFR L861Q 66.01%
CGLU344 0.935 −0.818 EGFR E746_A75Cdel 0.00%
CGLU344 0.919 −0.774 EGFR E746_A75Cdel 0.22%
CGLU344 0.953 −0.860 EGFR E746_A75Cdel 0.40%
CGLU344 0.944 −0.832 EGFR E746_A75Cdel 0.00%
CGLU369 0.825 −0.826 EGFR L858R 20.61%
CGLU369 0.950 −0.903 EGFR L858R 0.22%
CGLU369 0.945 −0.889 EGFR L858R 0.16%
CGLU369 0.886 −0.883 EGFR L858R 0.10%
CGLU373 0.922 −0.804 EGFR E746_A75Cdel 0.82%
CGLU373 0.959 −0.853 EGFR E746_A75Cdel 0.00%
CGLU373 0.967 −0.886 EGFR E746_A75Cdel 0.15%
CGLU373 0.951 −0.890 EGFR E746_A75Cdel 0.00%
CGPLLU13 0.425 −0.400 EGFR E746_A75Cdel 7.66%
CGPLLU13 0.272 −0.257 EGFR E746_A75Cdel 13.10%
CGPLLU13 0.584 −0.536 EGFR E746_A75Cdel 6.09%
CGPLLU13 0.530 −0.513 EGFR E746_A75Cdel 9.28%
CGPLLU264 0.946 −0.824 EGFR D761N 0.00%
CGPLLU264 0.927 −0.788 EGFR D761N 0.16%
CGPLLU264 0.962 −0.856 EGFR D761N 0.00%
CGPLLU264 0.960 −0.894 EGFR D761N 0.00%
CGPLLU265 0.953 −0.859 EGFR L858R 0.21%
CGPLLU265 0.949 −0.842 EGFR L858R 0.21%
CGPLLU265 0.955 −0.844 EGFR T790M 0.21%
CGPLLU265 0.946 −0.825 EGFR L858R 0.00%
CGPLLU266 0.951 −0.904 NA 0.00%
CGPLLU266 0.959 −0.886 NA 0.00%
CGPLLU266 0.961 −0.880 NA 0.00%
CGPLLU266 0.958 −0.855 NA 0.00%
CGPLLU267 0.919 −0.863 EGFR L858R 1.93%
CGPLLU267 0.863 −0.889 EGFR L858R 0.14%
CGPLLU267 0.962 −0.876 EGFR L858R 0.38%
CGPLLU269 0.951 −0.864 EGFR L858R 0.10%
CGPLLU269 0.941 −0.694 EGFR L858R 0.00%
CGPLLU269 0.957 −0.676 EGFR L858R 0.00%
CGPLLU271 0.871 −0.284 EGFR E746_A75Cdel 3.36%
CGPLLU271 0.947 −0.826 EGFR E746_A75Cdel 0.17%
CGPLLU271 0.952 −0.839 EGFR E746_A75Cdel 0.00%
CGPLLU271 0.944 −0.810 EGFR E746_A75Cdel 0.00%
CGPLLU271 0.950 −0.831 EGFR E746_A75Cdel 0.44%
CGPLLU43 0.944 −0.903 NA 0.00%
CGPLLU43 0.956 −0.899 NA 0.00%
CGPLLU43 0.959 −0.901 NA 0.00%
CGPLLU43 0.965 −0.896 NA 0.00%
APPENDIX-G
Table 7 Whole genome cfDNA analyses in healthy individuals and cancer patients
Correlation
of
Fragment
Ratio
Profile to
Median
Median Fragment
cfDNA Ratio
Size Profile
Stage at Fragment of Healthy
Patient Patient Type Analysis Type Timepoint Diagnosis (bp) Individuals
CGCRC291 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve V 163 0.1972
CGCRC292 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve V 166 0.7604
CGCRC293 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve V 166 0.9335
CGCRC294 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 166 0.6531
CGCRC296 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 166 0.8161
CGCRC299 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 162 0.7325
CGCRC300 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 167 0.9382
CGCRC301 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 165 0.8252
CGCRC302 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 163 0.7499
CGCRC304 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 162 0.4642
CGCRC305 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 165 0.8909
CGCRC306 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 165 0.8523
CGCRC307 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 165 0.9140
CGCRC308 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve III 165 0.8734
CGCRC311 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 166 0.8535
CGCRC315 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve III 167 0.6083
CGCRC316 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve III 161 0.1546
CGCRC317 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve III 163 0.6242
CGCRC318 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 166 0.8824
CGCRC319 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve III 160 0.5979
CGCRC320 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 167 0.7949
CGCRC321 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 164 0.7804
CGCRC333 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve V 163 0.4263
CGCRC335 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve V 162 0.6466
CGCRC338 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve V 162 0.7740
CGCRC341 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve V 164 0.8995
CGCRC342 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve V 158 0.2524
CGPLBR100 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve III 166 0.9440
CGPLBR101 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 169 0.8864
CGPLBR102 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 168 0.9617
CGPLBR103 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 167 0.9498
CGPLBR104 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve III 164 0.8490
CGPLBR12 Breast Cancer WGS Preoperative treatment naïve III 163 0.8350
CGPLBR18 Breast Cancer WGS Preoperative treatment naïve II 166 0.8411
CGPLBR23 Breast Cancer WGS Preoperative treatment naïve II 156 0.9714
CGPLBR24 Breast Cancer WGS Preoperative treatment naïve III 166 0.8402
CGPLBR28 Breast Cancer WGS Preoperative treatment naïve II 161 0.9584
CGPLBR30 Breast Cancer WGS Preoperative treatment naïve II 167 0.6951
CGPLBR31 Breast Cancer WGS Preoperative treatment naïve II 166 0.9719
CGPLBR32 Breast Cancer WGS Preoperative treatment naïve II 166 0.9590
CGPLBR33 Breast Cancer WGS Preoperative treatment naïve II 163 0.9706
CGPLBR34 Breast Cancer WGS Preoperative treatment naïve II 168 0.8735
CGPLBR35 Breast Cancer WGS Preoperative treatment naïve II 169 0.9655
CGPLBR36 Breast Cancer WGS Preoperative treatment naïve II 167 0.9394
CGPLBR37 Breast Cancer WGS Preoperative treatment naïve I 165 0.9691
CGPLBR38 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve III 167 0.9105
CGPLBR40 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve III 168 0.9273
CGPLBR41 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 164 0.9626
CGPLBR45 Breast Cancer WGS Preoperative treatment naïve III 168 0.9615
CGPLBR46 Breast Cancer WGS Preoperative treatment naïve I 166 0.9322
CGPLBR47 Breast Cancer WGS Preoperative treatment naïve II 169 0.9461
CGPLBR48 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 171 0.7686
CGPLBR49 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 160 0.8867
CGPLBR50 Breast Cancer WGS Preoperative treatment naïve II 165 0.8593
CGPLBR51 Breast Cancer WGS Preoperative treatment naïve III 164 0.9359
CGPLBR52 Breast Cancer WGS Preoperative treatment naïve III 165 0.8688
CGPLBR55 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 163 0.9634
CGPLBR56 Breast Cancer WGS Preoperative treatment naïve III 166 0.9459
CGPLBR57 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 168 0.9672
CGPLBR59 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 167 0.9438
CGPLBR60 Breast Cancer WGS Preoperative treatment naïve II 163 0.9479
CGPLBR61 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 165 0.9611
CGPLBR63 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 168 0.9555
CGPLBR65 Breast Cancer WGS Preoperative treatment naïve II 167 0.9506
CGPLBR68 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve III 163 0.9154
CGPLBR69 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 165 0.9460
CGPLBR70 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 168 0.9651
CGPLBR71 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 165 0.9577
CGPLBR72 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 167 0.9786
CGPLBR73 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 167 0.9576
CGPLBR76 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 170 0.9410
CGPLBR81 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 170 0.9643
CGPLBR82 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 166 0.9254
CGPLBR83 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 169 0.9451
CGPLBR84 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve III 169 0.9315
CGPLBR87 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 166 0.9154
CGPLBR88 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 169 0.9370
CGPLBR90 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 169 0.9002
CGPLBR91 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve III 164 0.7955
CGPLBR92 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 162 0.6774
CGPLBR93 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 164 0.8773
CGPLH189 Healthy WGS Preoperative treatment naïve NA 168 0.9325
CGPLH190 Healthy WGS Preoperative treatment naïve NA 167 0.9403
CGPLH192 Healthy WGS Preoperative treatment naïve NA 167 0.9646
CGPLH193 Healthy WGS Preoperative treatment naïve NA 167 0.9423
CGPLH194 Healthy WGS Preoperative treatment naïve NA 168 0.9567
CGPLH196 Healthy WGS Preoperative treatment naïve NA 167 0.9709
CGPLH197 Healthy WGS Preoperative treatment naïve NA 166 0.9605
CGPLH198 Healthy WGS Preoperative treatment naïve NA 167 0.9238
CGPLH199 Healthy WGS Preoperative treatment naïve NA 165 0.9618
CGPLH200 Healthy WGS Preoperative treatment naïve NA 167 0.9183
CGPLH201 Healthy WGS Preoperative treatment naïve NA 168 0.9548
CGPLH202 Healthy WGS Preoperative treatment naïve NA 168 0.9471
CGPLH203 Healthy WGS Preoperative treatment naïve NA 167 0.9534
CGPLH205 Healthy WGS Preoperative treatment naïve NA 168 0.9075
CGPLH208 Healthy WGS Preoperative treatment naïve NA 168 0.9422
CGPLH209 Healthy WGS Preoperative treatment naïve NA 169 0.9556
CGPLH210 Healthy WGS Preoperative treatment naïve NA 169 0.9447
CGPLH211 Healthy WGS Preoperative treatment naïve NA 169 0.9538
CGPLH300 Healthy WGS Preoperative treatment naïve NA 168 0.9019
CGPLH307 Healthy WGS Preoperative treatment naïve NA 168 0.9576
CGPLH308 Healthy WGS Preoperative treatment naïve NA 168 0.9481
CGPLH309 Healthy WGS Preoperative treatment naïve NA 168 0.9672
CGPLH310 Healthy WGS Preoperative treatment naïve NA 165 0.9547
CGPLH311 Healthy WGS Preoperative treatment naïve NA 167 0.9302
CGPLH314 Healthy WGS Preoperative treatment naïve NA 167 0.9482
CGPLH315 Healthy WGS Preoperative treatment naïve NA 167 0.8659
CGPLH316 Healthy WGS Preoperative treatment naïve NA 165 0.9374
CGPLH317 Healthy WGS Preoperative treatment naïve NA 169 0.9542
CGPLH319 Healthy WGS Preoperative treatment naïve NA 167 0.9578
CGPLH320 Healthy WGS Preoperative treatment naïve NA 164 0.8913
CGPLH322 Healthy WGS Preoperative treatment naïve NA 167 0.8751
CGPLH324 Healthy WGS Preoperative treatment naïve NA 169 0.9519
CGPLH325 Healthy WGS Preoperative treatment naïve NA 167 0.9124
CGPLH326 Healthy WGS Preoperative treatment naïve NA 166 0.9574
CGPLH327 Healthy WGS Preoperative treatment naïve NA 168 0.9533
CGPLH328 Healthy WGS Preoperative treatment naïve NA 166 0.9643
CGPLH329 Healthy WGS Preoperative treatment naïve NA 167 0.9609
CGPLH330 Healthy WGS Preoperative treatment naïve NA 167 0.9118
CGPLH331 Healthy WGS Preoperative treatment naïve NA 166 0.9679
CGPLH333 Healthy WGS Preoperative treatment naïve NA 169 0.9474
CGPLH335 Healthy WGS Preoperative treatment naïve NA 167 0.8909
CGPLH336 Healthy WGS Preoperative treatment naïve NA 169 0.9248
CGPLH337 Healthy WGS Preoperative treatment naïve NA 167 0.9533
CGPLH338 Healthy WGS Preoperative treatment naïve NA 165 0.9388
CGPLH339 Healthy WGS Preoperative treatment naïve NA 167 0.9396
CGPLH340 Healthy WGS Preoperative treatment naïve NA 167 0.9488
CGPLH341 Healthy WGS Preoperative treatment naïve NA 166 0.9533
CGPLH342 Healthy WGS Preoperative treatment naïve NA 166 0.7858
CGPLH343 Healthy WGS Preoperative treatment naïve NA 167 0.9421
CGPLH344 Healthy WGS Preoperative treatment naïve NA 169 0.9192
CGPLH345 Healthy WGS Preoperative treatment naïve NA 169 0.9345
CGPLH346 Healthy WGS Preoperative treatment naïve NA 169 0.9475
CGPLH350 Healthy WGS Preoperative treatment naïve NA 171 0.9570
CGPLH351 Healthy WGS Preoperative treatment naïve NA 168 0.8176
CGPLH352 Healthy WGS Preoperative treatment naïve NA 168 0.9521
CGPLH353 Healthy WGS Preoperative treatment naïve NA 167 0.9435
CGPLH354 Healthy WGS Preoperative treatment naïve NA 168 0.9481
CGPLH355 Healthy WGS Preoperative treatment naïve NA 167 0.9613
CGPLH356 Healthy WGS Preoperative treatment naïve NA 168 0.9474
CGPLH357 Healthy WGS Preoperative treatment naïve NA 167 0.9255
CGPLH358 Healthy WGS Preoperative treatment naïve NA 167 0.7777
CGPLH360 Healthy WGS Preoperative treatment naïve NA 168 0.8500
CGPLH361 Healthy WGS Preoperative treatment naïve NA 167 0.9261
CGPLH362 Healthy WGS Preoperative treatment naïve NA 167 0.9236
CGPLH363 Healthy WGS Preoperative treatment naïve NA 167 0.9488
CGPLH364 Healthy WGS Preoperative treatment naïve NA 168 0.9311
CGPLH365 Healthy WGS Preoperative treatment naïve NA 165 0.9371
CGPLH366 Healthy WGS Preoperative treatment naïve NA 167 0.9536
CGPLH367 Healthy WGS Preoperative treatment naïve NA 166 0.8748
CGPLH368 Healthy WGS Preoperative treatment naïve NA 169 0.9490
CGPLH369 Healthy WGS Preoperative treatment naïve NA 167 0.9428
CGPLH370 Healthy WGS Preoperative treatment naïve NA 167 0.9642
CGPLH371 Healthy WGS Preoperative treatment naïve NA 168 0.9621
CGPLH380 Healthy WGS Preoperative treatment naïve NA 170 0.9652
CGPLH381 Healthy WGS Preoperative treatment naïve NA 169 0.9541
CGPLH382 Healthy WGS Preoperative treatment naïve NA 167 0.9380
CGPLH383 Healthy WGS Preoperative treatment naïve NA 168 0.9700
CGPLH384 Healthy WGS Preoperative treatment naïve NA 169 0.8061
CGPLH385 Healthy WGS Preoperative treatment naïve NA 167 0.8856
CGPLH386 Healthy WGS Preoperative treatment naïve NA 167 0.6920
CGPLH387 Healthy WGS Preoperative treatment naïve NA 169 0.9583
CGPLH388 Healthy WGS Preoperative treatment naïve NA 167 0.9348
CGPLH389 Healthy WGS Preoperative treatment naïve NA 168 0.9409
CGPLH390 Healthy WGS Preoperative treatment naïve NA 167 0.9216
CGPLH391 Healthy WGS Preoperative treatment naïve NA 166 0.9334
CGPLH392 Healthy WGS Preoperative treatment naïve NA 167 0.9165
CGPLH393 Healthy WGS Preoperative treatment naïve NA 169 0.9256
CGPLH394 Healthy WGS Preoperative treatment naïve NA 167 0.9257
CGPLH395 Healthy WGS Preoperative treatment naïve NA 166 0.8611
CGPLH396 Healthy WGS Preoperative treatment naïve NA 167 0.7884
CGPLH398 Healthy WGS Preoperative treatment naïve NA 167 0.9463
CGPLH399 Healthy WGS Preoperative treatment naïve NA 169 0.8780
CGPLH400 Healthy WGS Preoperative treatment naïve NA 168 0.6662
CGPLH401 Healthy WGS Preoperative treatment naïve NA 167 0.9428
CGPLH402 Healthy WGS Preoperative treatment naïve NA 167 0.9353
CGPLH403 Healthy WGS Preoperative treatment naïve NA 168 0.9329
CGPLH404 Healthy WGS Preoperative treatment naïve NA 169 0.9402
CGPLH405 Healthy WGS Preoperative treatment naïve NA 166 0.9579
CGPLH406 Healthy WGS Preoperative treatment naïve NA 167 0.8188
CGPLH407 Healthy WGS Preoperative treatment naïve NA 169 0.9527
CGPLH408 Healthy WGS Preoperative treatment naïve NA 167 0.9584
CGPLH049 Healthy WGS Preoperative treatment naïve NA 168 0.9220
CGPLH410 Healthy WGS Preoperative treatment naïve NA 168 0.9102
CGPLH411 Healthy WGS Preoperative treatment naïve NA 167 0.9392
CGPLH412 Healthy WGS Preoperative treatment naïve NA 167 0.9561
CGPLH413 Healthy WGS Preoperative treatment naïve NA 167 0.9451
CGPLH414 Healthy WGS Preoperative treatment naïve NA 168 0.9258
CGPLH415 Healthy WGS Preoperative treatment naïve NA 169 0.9217
CGPLH416 Healthy WGS Preoperative treatment naïve NA 167 0.9672
CGPLH417 Healthy WGS Preoperative treatment naïve NA 168 0.9578
CGPLH418 Healthy WGS Preoperative treatment naïve NA 169 0.9376
CGPLH419 Healthy WGS Preoperative treatment naïve NA 167 0.9228
CGPLH420 Healthy WGS Preoperative treatment naïve NA 169 0.9164
CGPLH422 Healthy WGS Preoperative treatment naïve NA 166 0.9069
CGPLH423 Healthy WGS Preoperative treatment naïve NA 169 0.9606
CGPLH424 Healthy WGS Preoperative treatment naïve NA 167 0.9553
CGPLH425 Healthy WGS Preoperative treatment naïve NA 168 0.9722
CGPLH426 Healthy WGS Preoperative treatment naïve NA 168 0.9560
CGPLH427 Healthy WGS Preoperative treatment naïve NA 167 0.9594
CGPLH428 Healthy WGS Preoperative treatment naïve NA 167 0.9591
CGPLH429 Healthy WGS Preoperative treatment naïve NA 168 0.9358
CGPLH430 Healthy WGS Preoperative treatment naïve NA 167 0.9639
CGPLH431 Healthy WGS Preoperative treatment naïve NA 167 0.9570
CGPLH432 Healthy WGS Preoperative treatment naïve NA 168 0.9485
CGPLH434 Healthy WGS Preoperative treatment naïve NA 168 0.9671
CGPLH435 Healthy WGS Preoperative treatment naïve NA 170 0.9133
CGPLH436 Healthy WGS Preoperative treatment naïve NA 168 0.9360
CGPLH437 Healthy WGS Preoperative treatment naïve NA 170 0.9445
CGPLH438 Healthy WGS Preoperative treatment naïve NA 170 0.9537
CGPLH439 Healthy WGS Preoperative treatment naïve NA 171 0.9547
CGPLH440 Healthy WGS Preoperative treatment naïve NA 169 0.9562
CGPLH441 Healthy WGS Preoperative treatment naïve NA 167 0.9660
CGPLH442 Healthy WGS Preoperative treatment naïve NA 167 0.9569
CGPLH443 Healthy WGS Preoperative treatment naïve NA 170 0.9431
CGPLH444 Healthy WGS Preoperative treatment naïve NA 171 0.9429
CGPLH445 Healthy WGS Preoperative treatment naïve NA 171 0.9446
CGPLH446 Healthy WGS Preoperative treatment naïve NA 167 0.9502
CGPLH447 Healthy WGS Preoperative treatment naïve NA 169 0.9421
CGPLH448 Healthy WGS Preoperative treatment naïve NA 167 0.9553
CGPLH449 Healthy WGS Preoperative treatment naïve NA 167 0.9550
CGPLH450 Healthy WGS Preoperative treatment naïve NA 167 0.9572
CGPLH451 Healthy WGS Preoperative treatment naïve NA 169 0.9548
CGPLH452 Healthy WGS Preoperative treatment naïve NA 167 0.9498
CGPLH453 Healthy WGS Preoperative treatment naïve NA 166 0.9572
CGPLH455 Healthy WGS Preoperative treatment naïve NA 166 0.9626
CGPLH456 Healthy WGS Preoperative treatment naïve NA 168 0.9537
CGPLH457 Healthy WGS Preoperative treatment naïve NA 167 0.9429
CGPLH458 Healthy WGS Preoperative treatment naïve NA 167 0.9511
CGPLH459 Healthy WGS Preoperative treatment naïve NA 168 0.9609
CGPLH460 Healthy WGS Preoperative treatment naïve NA 168 0.9331
CGPLH463 Healthy WGS Preoperative treatment naïve NA 167 0.9506
CGPLH464 Healthy WGS Preoperative treatment naïve NA 170 0.9133
CGPLH465 Healthy WGS Preoperative treatment naïve NA 167 0.9251
CGPLH466 Healthy WGS Preoperative treatment naïve NA 167 0.9679
CGPLH467 Healthy WGS Preoperative treatment naïve NA 168 0.9273
CGPLH468 Healthy WGS Preoperative treatment naïve NA 167 0.8353
CGPLH469 Healthy WGS Preoperative treatment naïve NA 169 0.8225
CGPLH470 Healthy WGS Preoperative treatment naïve NA 168 0.9073
CGPLH471 Healthy WGS Preoperative treatment naïve NA 167 0.9354
CGPLH472 Healthy WGS Preoperative treatment naïve NA 166 0.8509
CGPLH473 Healthy WGS Preoperative treatment naïve NA 167 0.9206
CGPLH474 Healthy WGS Preoperative treatment naïve NA 168 0.8474
CGPLH475 Healthy WGS Preoperative treatment naïve NA 167 0.9155
CGPLH476 Healthy WGS Preoperative treatment naïve NA 169 0.8807
CGPLH477 Healthy WGS Preoperative treatment naïve NA 169 0.9129
CGPLH478 Healthy WGS Preoperative treatment naïve NA 167 0.9588
CGPLH479 Healthy WGS Preoperative treatment naïve NA 167 0.9303
CGPLH480 Healthy WGS Preoperative treatment naïve NA 169 0.9522
CGPLH481 Healthy WGS Preoperative treatment naïve NA 168 0.9558
CGPLH482 Healthy WGS Preoperative treatment naïve NA 168 0.9379
CGPLH483 Healthy WGS Preoperative treatment naïve NA 168 0.9518
CGPLH484 Healthy WGS Preoperative treatment naïve NA 166 0.9630
CGPLH485 Healthy WGS Preoperative treatment naïve NA 168 0.9547
CGPLH486 Healthy WGS Preoperative treatment naïve NA 169 0.9199
CGPLH487 Healthy WGS Preoperative treatment naïve NA 169 0.9575
CGPLH488 Healthy WGS Preoperative treatment naïve NA 167 0.9618
CGPLH490 Healthy WGS Preoperative treatment naïve NA 167 0.8950
CGPLH491 Healthy WGS Preoperative treatment naïve NA 168 0.9631
CGPLH492 Healthy WGS Preoperative treatment naïve NA 170 0.9335
CGPLH493 Healthy WGS Preoperative treatment naïve NA 168 0.8718
CGPLH494 Healthy WGS Preoperative treatment naïve NA 169 0.9623
CGPLH495 Healthy WGS Preoperative treatment naïve NA 166 0.8777
CGPLH496 Healthy WGS Preoperative treatment naïve NA 166 0.8788
CGPLH497 Healthy WGS Preoperative treatment naïve NA 167 0.9576
CGPLH498 Healthy WGS Preoperative treatment naïve NA 167 0.9526
CGPLH499 Healthy WGS Preoperative treatment naïve NA 167 0.9733
CGPLH500 Healthy WGS Preoperative treatment naïve NA 168 0.9542
CGPLH501 Healthy WGS Preoperative treatment naïve NA 169 0.9526
CGPLH052 Healthy WGS Preoperative treatment naïve NA 167 0.9512
CGPLH503 Healthy WGS Preoperative treatment naïve NA 169 0.8947
CGPLH504 Healthy WGS Preoperative treatment naïve NA 167 0.9561
CGPLH505 Healthy WGS Preoperative treatment naïve NA 166 0.9554
CGPLH506 Healthy WGS Preoperative treatment naïve NA 167 0.9733
CGPLH507 Healthy WGS Preoperative treatment naïve NA 168 0.9222
CGPLH508 Healthy WGS Preoperative treatment naïve NA 167 0.9674
CGPLH509 Healthy WGS Preoperative treatment naïve NA 167 0.9475
CGPLH510 Healthy WGS Preoperative treatment naïve NA 167 0.9459
CGPLH511 Healthy WGS Preoperative treatment naïve NA 168 0.9714
CGPLH512 Healthy WGS Preoperative treatment naïve NA 168 0.9442
CGPLH513 Healthy WGS Preoperative treatment naïve NA 166 0.9705
CGPLH514 Healthy WGS Preoperative treatment naïve NA 167 0.9690
CGPLH515 Healthy WGS Preoperative treatment naïve NA 167 0.9568
CGPLH516 Healthy WGS Preoperative treatment naïve NA 168 0.9508
CGPLH517 Healthy WGS Preoperative treatment naïve NA 168 0.9635
CGPLH518 Healthy WGS Preoperative treatment naïve NA 168 0.9647
CGPLH519 Healthy WGS Preoperative treatment naïve NA 166 0.9366
CGPLH520 Healthy WGS Preoperative treatment naïve NA 166 0.9649
CGPLH625 Healthy WGS Preoperative treatment naïve NA 166 0.8766
CGPLH626 Healthy WGS Preoperative treatment naïve NA 170 0.9011
CGPLH639 Healthy WGS Preoperative treatment naïve NA 165 0.9482
CGPLH640 Healthy WGS Preoperative treatment naïve NA 166 0.9131
CGPLH642 Healthy WGS Preoperative treatment naïve NA 167 0.9641
CGPLH643 Healthy WGS Preoperative treatment naïve NA 169 0.8450
CGPLH644 Healthy WGS Preoperative treatment naïve NA 170 0.9398
CGPLH646 Healthy WGS Preoperative treatment naïve NA 172 0.296
CGPLLU141 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 164 0.8702
CGPLLU161 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 165 0.9128
CGPLLU162 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 165 0.7753
CGPLLUl63 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 166 0.4770
CGPLLU168 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 163 0.9164
CGPLLU169 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 163 0.9326
CGPLLU176 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 168 0.9572
CGPLLU177 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 166 0.8472
CGPLLU203 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 164 0.9119
CGPLLU205 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 163 0.9518
CGPLLU207 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 166 0.9344
CGPLLU208 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 164 0.9091
CGPLOV11 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve V 166 0.8902
CGPLOV12 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 167 0.8779
CGPLOV13 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve V 166 0.7560
CGPLOV15 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve III 165 0.8585
CGPLOV16 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve III 165 0.9052
CGPLOV19 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 165 0.7854
CGPLOV20 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 165 0.8711
CGPLOV21 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve V 167 0.8942
CGPLOV22 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve III 164 0.8944
CGPLOV23 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 169 0.8510
CGPLOV24 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 166 0.9449
CGPLOV25 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 166 0.9590
CGPLOV26 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 161 0.8148
CGPLOV28 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 167 0.9635
CGPLOV31 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve III 167 0.9461
CGPLOV32 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 168 0.9582
CGPLOV37 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 170 0.9397
CGPLOV38 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 166 0.5779
CGPLOV40 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve V 170 0.6097
CGPLOV41 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve V 167 0.9403
CGPLOV42 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 166 0.9265
CGPLOV43 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 167 0.9626
CGPLOV44 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 164 0.9536
CGPLOV46 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 166 0.9622
CGPLOV47 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 165 0.9704
CGPLOV48 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 167 0.9675
CGPLOV49 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve III 164 0.8998
CGPLOV50 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve III 165 0.9682
CGPLPA112 Pancreatic Cancer WGS Preoperative treatment naïve II 164 0.8914
CGPLPA113 Doudenal Cancer WGS Preoperative treatment naïve I 170 0.8751
CGPLPA114 Bile Duct Cancer WGS Preoperative treatment naïve II 166 0.9098
CGPLPA115 Bile Duct Cancer WGS Preoperative treatment naïve V 165 0.8053
CGPLPA117 Bile Duct Cancer WGS Preoperative treatment naïve II 165 0.9395
CGPLPA118 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 167 0.9406
CGPLPA122 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 164 0.8231
CGPLPA124 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 166 0.9108
CGPLPA125 Bile Duct Cancer WGS Preoperative treatment naïve II 166 0.9675
CGPLPA126 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 166 0.9155
CGPLPA127 Bile Duct Cancer WGS Preoperative treatment naïve V 167 0.8916
CGPLPA128 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 167 0.9262
CGPLPA129 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 166 0.9220
CGPLPA130 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 169 0.8586
CGPLPA131 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 165 0.7707
CGPLPA134 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 160 0.7502
CGPLPA135 Bile Duct Cancer WGS Preoperative treatment naïve I 165 0.9495
CGPLPA136 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 164 0.9289
CGPLPA137 Bile Duct Cancer WGS Preoperative treatment naïve II 166 0.9588
CGPLPA139 Bile Duct Cancer WGS Preoperative treatment naïve V 166 0.9511
CGPLPA14 Pancreatic Cancer WGS Preoperative treatment naïve II 167 0.8718
CGPLPA140 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 166 0.9215
CGPLPA141 Bile Duct Cancer WGS Preoperative treatment naïve II 165 0.9172
CGPLPA15 Pancreatic Cancer WGS Preoperative treatment naïve II 167 0.9111
CGPLPA155 Bile Duct Cancer WGS Preoperative treatment naïve II 165 0.9496
CGPLPA156 Pancreatic Cancer WGS Preoperative treatment naïve II 167 0.9479
CCPLPA165 Bile Duct Cancer WGS Preoperative treatment naïve I 168 0.9596
CGPLPA168 Bile Duct Cancer WGS Preoperative treatment naïve II 162 0.7838
CGPLPA17 Pancreatic Cancer WGS Preoperative treatment naïve II 166 0.8624
CGPLPA184 Bile Duct Cancer WGS Preoperative treatment naïve II 165 0.9100
CGPLPA187 Bile Duct Cancer WGS Preoperative treatment naïve II 165 0.8577
CGPLPA23 Pancreatic Cancer WGS Preoperative treatment naïve II 165 0.7887
CGPLPA25 Pancreatic Cancer WGS Preoperative treatment naïve II 166 0.9549
CGPLPA26 Pancreatic Cancer WGS Preoperative treatment naïve II 166 0.9598
CGPLPA28 Pancreatic Cancer WGS Preoperative treatment naïve II 165 0.9069
CGPLPA33 Pancreatic Cancer WGS Preoperative treatment naïve II 166 0.8361
CGPLPA34 Pancreatic Cancer WGS Preoperative treatment naïve II 168 0.8946
CGPLPA37 Pancreatic Cancer WGS Preoperative treatment naïve II 165 0.8840
CGPLPA38 Pancreatic Cancer WGS Preoperative treatment naïve II 167 0.8746
CGPLPA39 Pancreatic Cancer WGS Preoperative treatment naïve II 167 0.8562
CGPLPA40 Pancreatic Cancer WGS Preoperative treatment naïve II 165 0.8563
CGPLPA42 Pancreatic Cancer WGS Preoperative treatment naïve II 167 0.9126
CGPLPA46 Pancreatic Cancer WGS Preoperative treatment naïve II 169 0.8274
CGPLPA47 Pancreatic Cancer WGS Preoperative treatment naïve II 166 0.8376
CGPLPA48 Pancreatic Cancer WGS Preoperative treatment naïve I 167 0.9391
CGPLPA52 Pancreatic Cancer WGS Preoperative treatment naïve II 167 0.9452
CGPLPA53 Pancreatic Cancer WGS Preoperative treatment naïve I 163 0.9175
CGPLPA58 Pancreatic Cancer WGS Preoperative treatment naïve II 165 0.9587
CGPLPA59 Pancreatic Cancer WGS Preoperative treatment naïve II 163 0.9230
CGPLPA67 Pancreatic Cancer WGS Preoperative treatment naïve II 166 0.9574
CGPLPA69 Pancreatic Cancer WGS Preoperative treatment naïve I 168 0.9172
CGPLPA71 Pancreatic Cancer WGS Preoperative treatment naïve II 167 0.9424
CGPLPA74 Pancreatic Cancer WGS Preoperative treatment naïve II 166 0.9688
CGPLPA76 Pancreatic Cancer WGS Preoperative treatment naïve II 163 0.9681
CGPLPA85 Pancreatic Cancer WGS Preoperative treatment naïve II 165 0.9137
CGPLPA86 Pancreatic Cancer WGS Preoperative treatment naïve II 165 0.8875
CGPLPA92 Pancreatic Cancer WGS Preoperative treatment naïve II 167 0.9389
CGPLPA93 Pancreatic Cancer WGS Preoperative treatment naïve II 166 0.8585
CGPLPA94 Pancreatic Cancer WGS Preoperative treatment naïve II 162 0.9365
CGPLPA95 Pancreatic Cancer WGS Preoperative treatment naïve II 163 0.8542
CGST102 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 167 0.9496
CGST11 Gastric cancer WGS Preoperative treatment naïve IV 169 0.9419
CGST110 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 167 0.9626
CGST114 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 164 0.9535
CGST13 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 166 0.9369
CGST131 Gastric cancer WGS Preoperative treatment naïve II 171 0.9428
CGST141 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 168 0.9621
CGST16 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 166 0.7804
CGST18 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 169 0.9523
CGST21 Gastric cancer WGS Preoperative treatment naïve II 165 −0.4778
CGST26 Gastric cancer WGS Preoperative treatment naïve IV 166 0.9554
CGST28 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve X 169 0.9076
CGST30 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 169 0.9246
CGST32 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 169 0.9431
CGST33 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 168 0.7999
CGST38 Gastric cancer WGS Preoperative treatment naïve 0 168 0.9368
CGST39 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve IV 164 0.8742
CGST41 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve IV 168 0.8194
CGST45 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 168 0.9576
CGST47 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 168 0.9641
CGST48 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve IV 167 0.7469
CGST53 Gastric cancer WGS Preoperative treatment naïve 0 173 0.0019
CGST58 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 169 0.9470
CGST67 Gastric cancer WGS Preoperative treatment naïve I 170 0.9352
CGST77 Gastric cancer WGS Preoperative treatment naïve IV 170 0.0043
CGST80 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve II 168 0.9313
CGST81 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naïve I 168 0.9480
Correlation
of GC
Corrected
Fragment
Ratio
Profile to Mutant
Median Alelle
Fragment Fraction Detected Detected Fraction
Ratio of Reads using using Detected
Profile Mapped to DELFI DELFI using
of Healthy Mitochondrial DELFI (95% (98% Targeted
Patient Individuals Genome Score specificity) specificity) sequencing*
CGCRC291 0.5268 0.0484% 0.9976 Y Y 22.85%
CGCRC292 0.8835 0 0270% 0.7299 Y N 1.41%
CGCRC293 0.9206 0.0748% 0.5234 N N 3.35%
CGCRC294 0.8904 0.0135% 0.8757 Y Y 0.17%
CGCRC296 0.8395 0.0369% 0.9951 Y Y ND
CGCRC299 0.9268 0.0392% 0.9648 Y Y ND
CGCRC300 0.9303 0.0235% 0.4447 N N ND
CGCRC301 0.9151 0.0310% 0.2190 N N 3.21%
CGCRC302 0.9243 0.0112% 0.9897 Y Y 3.12%
CGCRC304 0.9360 0.0093% 0.9358 Y Y 3.27%
CGCRC305 0.9250 0 0120% 0.8988 Y Y 3.19%
CGCRC306 0.8186 0.0781% 0.9466 Y Y 8.02%
CGCRC307 0.9342 0.0781% 0.7042 Y N 0.56%
CGCRC308 0.9324 0.0078% 0.9082 Y Y 0.11%
CGCRC311 0.9156 0.0173% 0.1867 N N ND
CGCRC315 0.8846 0.0241% 0.6422 Y N 0.27%
CGCRC316 0.5879 0.0315% 0.9971 Y Y 5.52%
CGCRC317 0.8944 0.0184% 0.9855 Y Y 0.36%
CGCRC318 0.9140 0.0156% 0.5615 N N ND
CGCRC319 0.8230 0.1259% 0.9925 Y Y 0.11%
CGCRC320 0.9101 0.0383% 0.8019 Y Y 0.64%
CGCRC321 0.9091 0.0829% 0.9759 Y Y 3.20%
CGCRC333 0.4355 0.4264% 0.9974 Y Y 43.03%
CGCRC335 0.6858 0.1154% 0.9887 Y Y 81.61%
CGCRC338 0.7573 0.1436% 0.9976 Y Y 36.00%
CGCRC341 0.9181 0.0197% 0.9670 Y Y ND
CGCRC342 0.1845 0.1732% 0.9987 Y Y 30.72%
CGPLBR100 0.8946 0.1234% 0.8664 Y Y ND
CGPLBR101 0.9304 0.0709% 0.9385 Y Y ND
CGPLBR102 0.9345 0.4742% 0.9052 Y Y 0.25%
CGPLBR103 0.9251 0.0775% 0.5994 N N ND
CGPLBR104 0.9192 0.0532% 0.9950 Y Y 0.13%
CGPLBR12 0.7760 0.1407% 0.7598 Y Y —
CGPLBR18 0.9534 0.0267% 0.3886 N N —
CGPLBR23 0.9312 0.0144% 0.1235 N N —
CGPLBR24 0.8766 0.0210% 0.7480 Y Y —
CGPLBR28 0.8120 0.1456% 0.9630 Y Y —
CGPLBR30 0.6611 0.0952% 0.9956 Y Y —
CGPLBR31 0.9556 0.0427% 0.2227 N N —
CGPLBR32 0.9229 0.0308% 0.9815 Y Y —
CGPLBR33 0.9432 0.0617% 0.2863 N N —
CGPLBR34 0.9425 0.0115% 0.1637 N N —
CGPLBR35 0.9348 0.1371% 0.5057 N N —
CGPLBR36 0.8884 0.0813% 0.4017 N N —
CGPLBR37 0.9496 0.0518% 0.0314 N N —
CGPLBR38 0.0349 0.1352% 0.8983 Y Y 0.53%
CGPLBR40 0.9244 0.0929% 0.9046 Y Y ND
CGPLBR41 0.9346 0.0544% 0.9416 Y Y 0.32%
CGPLBR45 0.9285 0.0296% 0.3860 N N —
CGPLBR46 0.9005 0.0345% 0.7270 Y N —
CGPLBR47 0.9028 0.0591% 0.6247 Y Y —
CGPLBR48 0.8246 0.0504% 0.9973 Y Y 0.18%
CGPLBR49 0.7887 0.0377% 0.9946 Y Y ND
CGPLBR50 0.9332 0.0137% 0.6820 Y N —
CGPLBR51 0.9160 0.0863% 0.6915 Y N —
CGPLBR52 0.9196 0.0165% 0.6390 Y N —
CGPLBR55 0.9341 0.0356% 0.9494 Y Y 0.68%
CGPLBR56 0.9428 0.2025% 0.4700 N N —
CGPLBR57 0.9416 0.0902% 0.9090 Y Y ND
CGPLBR59 0.9130 0.0761% 0.5828 N N ND
CGPLBR60 0.8916 0.0626% 0.8779 Y Y —
CGPLBR61 0.9422 0.0601% 0.4417 N N 0.44%
CGPLBR63 0.9132 0.0514% 0.8788 Y Y ND
CGPLBR65 0.8970 0.0264% 0.9048 Y Y —
CGPLBR68 0.9532 0.0164% 0.7883 Y Y ND
CGPLBR69 0.9474 0.0279% 0.0600 N N ND
CGPLBR70 0.9388 0.0171% 0.6447 Y N 0.36%
CGPLBR71 0.9368 0.0271% 0.6706 Y N 0.10%
CGPLBR72 0.9640 0.0263% 0.6129 N N ND
CGPLBR73 0.9421 0.0142% 0.0746 N N 0.27%
CGPLBR76 0.9254 0.0775% 0.9334 Y Y 0.12%
CGPLBR81 0.8193 0.0241% 0.9899 Y Y —
CGPLBR82 0.9288 0.1640% 0.9834 Y Y 0.12%
CGPLBR83 0.9138 0.0419% 0.9810 Y Y 0.28%
CGPLBR84 0.8659 0.0274% 0.9901 Y Y —
CGPLBR87 0.8797 0.0294% 0.9968 Y Y 0.45%
CGPLBR88 0.8547 0.0181% 0.9958 Y Y 0.38%
CGPLBR90 0.8330 0.0417% 0.9667 Y Y —
CGPLBR91 0.9408 0.0799% 0.8710 Y Y ND
CGPLBR92 0.8835 0.1042% 0.9856 Y Y 0.20%
CGPLBR93 0.9072 0.0352% 0.7253 Y N ND
CGPLH189 0.8947 0.0591% 0.1748 N N —
CGPLH190 0.9369 0.1193% 0.5168 N N —
CGPLH192 0.9487 0.0276% 0.0178 N N —
CGPLH193 0.9442 0.0420% 0.5794 N N —
CGPLH194 0.9289 0.0407% 0.1616 N N —
CGPLH196 0.9512 0.0266% 0.0999 N N —
CGPLH197 0.9416 0.0334% 0.4699 N N —
CGPLH198 0.9457 0.0302% 0.6571 Y N —
CGPLH199 0.9439 0.0170% 0.5584 N N —
CGPLH200 0.9391 0.0362% 0.3833 N N —
CGPLH201 0.9180 0.0470% 0.8395 Y Y —
CGPLH202 0.9436 0.0501% 0.1088 N N —
CGPLH203 0.9575 0.0455% 0.2485 N N —
CGPLH205 0.9283 0.0409% 0.4401 N N —
CGPLH208 0.9409 0.0371% 0.2706 N N —
CGPLH209 0.9367 0.0427% 0.2213 N N —
CGPLH210 0.9181 0.0279% 0.3500 N N —
CGPLH211 0.9410 0.0317% 0.1752 N N —
CGPLH300 0.9200 0.0397% 0.0226 N N —
CGPLH307 0.9167 0.0388% 0.1789 N N —
CGPLH308 0.8352 0.0311% 0.0155 N N —
CGPLH309 0.9451 0.0226% 0.0441 N N —
CGPLH310 0.9527 0.0145% 0.7135 Y N —
CGPLH311 0.9348 0.0202% 0.2589 N N —
CGPLH314 0.9491 0.0212% 0.1632 N N —
CGPLH315 0.9427 0.0071% 0.3450 N N —
CGPLH316 0.9552 0.0191% 0.4697 N N —
CGPLH317 0.9352 0 0232% 0.4330 N N —
CGPLH319 0.9189 0.0263% 0.2232 N N —
CGPLH320 0.9165 0.0222% 0.1095 N N —
CGPLH322 0.9411 0.0248% 0.0749 N N —
CGPLH324 0.9133 0.0402% 0.0128 N N —
CGPLH325 0.9202 0.0711% 0.0102 N N —
CGPLH326 0.9408 0.0213% 0.0475 N N —
CGPLH327 0.9071 0.1275% 0.4891 N N —
CGPLH328 0.9332 0.0256% 0.0234 N N —
CGPLH329 0.8396 0.0269% 0.0139 N N —
CGPLH330 0.9403 0.0203% 0.2642 N N —
CGPLH331 0.9377 0.0314% 0.0304 N N —
CGPLH333 0.9132 0.0350% 0.1633 N N —
CGPLH335 0.9333 0.0285% 0.0096 N N —
CGPLH336 0.9159 0.0158% 0.3872 N N —
CGPLH337 0.9262 0.0367% 0.2976 N N —
CGPLH338 0.9303 0.0103% 0.0431 N N —
CGPLH339 0.9338 0.0280% 0.0379 N N —
CGPLH340 0.9321 0.0210% 0.0379 N N —
CGPLH341 0.9187 0.0448% 0.1775 N N —
CGPLH342 0.8986 0.0283% 0.0904 N N —
CGPLH343 0.9067 0.0632% 0.0160 N N —
CGPLH344 0.8998 0.0257% 0.0120 N N —
CGPLH345 0.9107 0.0445% 0.0031 N N —
CGPLH346 0.9074 0.0208% 0.0686 N N —
CGPLH350 0.9388 0.0284% 0.0071 N N —
CGPLH351 0.9294 0.0223% 0.0207 N N —
CGPLH352 0.9190 0.0613% 0.0512 N N —
CGPLH353 0.9130 0.0408% 0.0132 N N —
CGPLH354 0.9121 0.0318% 0.0082 N N —
CGPLH355 0.9308 0.0400% 0.6407 Y N —
CGPLH356 0.8312 0.0427% 0.2437 N N —
CGPLH357 0.9540 0.0217% 0.0070 N N —
CGPLH358 0.9372 0.0174% 0.1451 N N —
CGPLH360 0.8775 0.0395% 0.0048 N N —
CGPLH361 0.9283 0.0268% 0.1524 N N —
CGPLH362 0.9503 0.0309% 0.4832 N N —
CGPLH363 0.9187 0.0620% 0.0199 N N —
CGPLH364 0.9480 0.0282% 0.8719 Y Y —
CGPLH365 0.9051 0.1740% 0.9638 Y Y —
CGPLH366 0.9170 0.0344% 0.0952 N N —
CGPLH367 0.9181 0.0353% 0.1235 N N —
CGPLH368 0.9076 0.1073% 0.1252 N N —
CGPLH369 0.9541 0.0246% 0.2821 N N —
CGPLH370 0.9423 0.0410% 0.0989 N N —
CGPLH371 0.9414 0.0734% 0.2173 N N —
CGPLH380 0.9424 0.0523% 0.0128 N N —
CGPLH381 0.9501 0.0435% 0.0152 N N —
CGPLH382 0.9584 0.0340% 0.0326 N N —
CGPLH383 0.9407 0.0389% 0.0035 N N —
CGPLH384 0.9043 0.0207% 0.0258 N N —
CGPLH385 0.9246 0.0165% 0.0566 N N —
CGPLH386 0.8859 0.0502% 0.2677 N N —
CGPLH387 0.9223 0.0375% 0.0081 N N —
CGPLH388 0.9266 0.0527% 0.0499 N N —
CGPLH389 0.9035 0.0667% 0.6585 Y N —
CGPLH390 0.9182 0.0229% 0.0837 N N —
CGPLH391 0.9162 0.0223% 0.0716 N N —
CGPLH392 0.9014 0.0424% 0.1305 N N —
CGPLH393 0.9045 0.0407% 0.0037 N N —
CGPLH394 0.9292 0.0522% 0.1073 N N —
CGPLH395 0.9254 0.0424% 0.0171 N N —
CGPLH396 0.8928 0.0393% 0.0303 N N —
CGPLH398 0.9578 0.0242% 0.3195 N N —
CGPLH399 0.9195 0.0579% 0.0685 N N —
CGPLH400 0.9047 0.0300% 0.2103 N N —
CGPLH401 0.9339 0.0146% 0.0620 N N —
CGPLH402 0.8800 0.1516% 0.0395 N N —
CGPLH403 0.8829 0.0515% 0.0223 N N —
CGPLH404 0.8948 0.0528% 0.0027 N N —
CGPLH405 0.9204 0.0358% 0.0188 N N —
CGPLH406 0.8592 0.0667% 0.0206 N N —
CGPLH407 0.9099 0.0229% 0.0040 N N —
CGPLH408 0.9192 0.0415% 0.1257 N N —
CGPLH409 0.8950 0.0302% 0.0056 N N —
CGPLH410 0.9006 0.0453% 0.0019 N N —
CGPLH411 0.8857 0.0621% 0.0188 N N —
CGPLH412 0.9191 0.0140% 0.0417 N N —
CGPLH413 0.9145 0.0355% 0.0084 N N —
CGPLH414 0.9127 0.0290% 0.0294 N N —
CGPLH415 0.9025 0.0296% 0.0131 N N —
CGPLH416 0.9388 0.0198% 0.0645 N N —
CGPLH417 0.9192 0.0241% 0.0836 N N —
CGPLH418 0.9234 0.0306% 0.0052 N N —
CGPLH419 0.9295 0.0280% 0.0489 N N —
CGPLH420 0.9109 0.0187% 0.0420 N N —
CGPLH422 0.9006 0.0208% 0.0324 N N —
CGPLH423 0.8288 0.0532% 0.0139 N N —
CGPLH424 0.9265 0.1119% 0.0864 N N —
CGPLH425 0.9488 0.0722% 0.0156 N N —
CGPLH426 0.9080 0.0548% 0.1075 N N —
CGPLH427 0.9257 0.0182% 0.0470 N N —
CGPLH428 0.9272 0.0346% 0.0182 N N —
CGPLH429 0.8757 0.0593% 0.8143 Y Y —
CGPLH430 0.9307 0.0258% 0.0389 N N —
CGPLH431 0.9185 0.0234% 0.0174 N N —
CGPLH432 0.9082 0.0433% 0.0181 N N —
CGPLH434 0.9442 0.0297% 0.0050 N N —
CGPLH435 0.9097 0.0179% 0.0441 N N —
CGPLH436 0.9158 0.0290% 0.0958 N N —
CGPLH437 0.9245 0.0156% 0.0136 N N —
CGPLH438 0.9138 0.0169% 0.1041 N N —
CGPLH439 0.9028 0.0226% 0.0078 N N —
CGPLH440 0.8295 0.0330% 0.0687 N N —
CGPLH441 0.9430 0.0178% 0.0085 N N —
CGPLH442 0.9405 0.0169% 0.0582 N N —
CGPLH443 0.8801 0.0207% 0.0578 N N —
CGPLH444 0.8068 0.0464% 0.0097 N N —
CGPLH445 0.8750 0.0267% 0.1939 N N —
CGPLH446 0.9257 0.0281% 0.0340 N N —
CGPLH447 0.8968 0.0167% 0.0017 N N —
CGPLH448 0.9191 0.0401% 0.0389 N N —
CGPLH449 0.9254 0.0236% 0.0116 N N —
CGPLH450 0.9195 0.0331% 0.0597 N N —
CGPLH451 0.9167 0.0262% 0.0104 N N —
CGPLH452 0.8948 0.0480% 0.4722 N N —
CGPLH453 0.9339 0.0186% 0.3419 N N —
CGPLH455 0.9322 0.0455% 0.4536 N N —
CGPLH456 0.9098 0.0207% 0.0385 N N —
CGPLH457 0.9022 0.0298% 0.0384 N N —
CGPLH458 0.9275 0.0298% 0.1891 N N —
CGPLH459 0.9209 0.0281% 0.0371 N N —
CGPLH460 0.8863 0.0227% 0.1157 N N —
CGPLH463 0.9372 0.0130% 0.0865 N N —
CGPLH464 0.8511 0.0659% 0.2040 N N —
CGPLH465 0.9164 0.0325% 0.0124 N N —
CGPLH466 0.9408 0.0155% 0.1733 N N —
CGPLH467 0.9024 0.0229% 0.2303 N N —
CGPLH468 0.9345 0.0247% 0.5427 N N —
CGPLH469 0.8799 0.0201% 0.5351 N N —
CGPLH470 0.9228 0.0715% 0.0327 N N —
CGPLH471 0.9333 0.0150% 0.0406 N N —
CGPLH472 0.8915 0.0481% 0.6152 N N —
CGPLH473 0.9128 0.0443% 0.2995 N N —
CGPLH474 0.9245 0.0316% 0.8246 Y N —
CGPLH475 0.9233 0.0269% 0.0736 N N —
CGPLH476 0.9059 0.0236% 0.0143 N N —
CGPLH477 0.9376 0.0382% 0.1111 N N —
CGPLH478 0.9344 0.0256% 0.0628 N N —
CGPLH479 0.9207 0.0221% 0.0648 N N —
CGPLH480 0.9046 0.0672% 0.7473 Y N —
CGPLH481 0.9113 0.0311% 0.0282 N N —
CGPLH482 0.9336 0.0162% 0.0058 N N —
CGPLH483 0.9275 0.0251% 0.0495 N N —
CGPLH484 0.9366 0.0261% 0.0048 N N —
CGPLH485 0.9128 0.0291% 0.1084 N N —
CGPLH486 0.9042 0.0220% 0.0820 N N —
CGPLH487 0.9098 0.0594% 0.2154 N N —
CGPLH488 0.8299 0.0409% 0.0903 N N —
CGPLH490 0.8794 0.0432% 0.0424 N N —
CGPLH491 0.8332 0.0144% 0.0223 N N —
CGPLH492 0.8799 0.0322% 0.0311 N N —
CGPLH493 0.9330 0.0065% 0.0280 N N —
CGPLH494 0.9303 0.0232% 0.0824 N N —
CGPLH495 0.8908 0.0513% 0.0465 N N —
CGPLH496 0.8398 0.0208% 0.0572 N N —
CGPLH497 0.9330 0.0335% 0.0404 N N —
CGPLH498 0.9315 0.0403% 0.0752 N N —
CGPLH499 0.9442 0.0198% 0.0149 N N —
CGPLH500 0.9240 0.0433% 0.0754 N N —
CGPLH501 0.9308 0.0300% 0.0159 N N —
CGPLH052 0.9200 0.0351% 0.0841 N N —
CGPLH503 0.8939 0.0398% 0.0649 N N —
CGPLH504 0.9324 0.0440% 0.1231 N N —
CGPLH505 0.9243 0.0605% 0.1869 N N —
CGPLH506 0.9498 0.0284% 0.0180 N N —
CGPLH507 0.9192 0.0186% 0.0848 N N —
CGPLH508 0.9410 0.0150% 0.1077 N N —
CGPLH509 0.9323 0.0163% 0.0828 N N —
CGPLH510 0.9548 0.0128% 0.0376 N N —
CGPLH511 0.9493 0.0224% 0.1779 N N —
CGPLH512 0.9244 0.0094% 0.0076 N N —
CGPLH513 0.9595 0.0441% 0.5250 N N —
CGPLH514 0.9369 0.0114% 0.3131 N N —
CGPLH515 0.9283 0.0352% 0.4936 N N —
CGPLH516 0.8298 0.0175% 0.0916 N N —
CGPLH517 0.9494 0.0161% 0.0059 N N —
CGPLH518 0.9432 0.0274% 0.0130 N N —
CGPLH519 0.9351 0.0171% 0.0949 N N —
CGPLH520 0.9476 0.0241% 0.0844 N N —
CGPLH625 0.9231 0.0697% 0.4977 N N —
CGPLH626 0.9269 0.0231% 0.3100 N N —
CGPLH639 0.9410 0.0549% 0.0773 N N —
CGPLH640 0.9264 0.0232% 0.0327 N N —
CGPLH642 0.8376 0.0768% 0.0555 N N —
CGPLH643 0.9271 0.0579% 0.1325 N N —
CGPLH644 0.8948 0.0621% 0.3819 N N —
CGPLH646 0.8691 0.0462% 0.2423 N N —
CGPLLU144 0.6861 0.0423% 0.9892 Y Y 5.10%
CGPLLU161 0.9187 0.0273% 0.9955 Y Y 0.20%
CGPLLU162 0.0836 0.1410% 0.9966 Y Y 0.22%
CGPLLUl63 0.3033 0.0724% 0.9940 Y Y 0.21%
CGPLLU168 0.6842 0.0712% 0.9861 Y Y 0.07%
CGPLLU169 0.9189 0.0846% 0.9856 Y Y 0.13%
CGPLLU176 0.9081 0.0626% 0.8769 Y Y ND
CGPLLU177 0.6790 0.0564% 0.9924 Y Y 3.22%
CGPLLU203 0.8741 0.0568% 0.9178 Y Y 0.11%
CGPLLU205 0.9476 0.0495% 0.9877 Y Y ND
CGPLLU207 0.9379 0.0421% 0.9908 Y Y 0.32%
CGPLLU208 0.8942 0.0815% 0.9273 Y Y 1.33%
CGPLOV11 0.8872 0.0469% 0.9343 Y Y 0.87%
CGPLOV12 0.8973 0.2767% 0.9764 Y Y ND
CGPLOV13 0.9146 0.1017% 0.9690 Y Y 0.35%
CGPLOV15 0.8552 0.0876% 0.9945 Y Y 3.54%
CGPLOV16 0.9046 0.0400% 0.9683 Y Y 1.12%
CGPLOV19 0.7578 0.1089% 0.9989 Y Y 46.35%
CGPLOV20 0.9154 0.0581% 0.9749 Y Y 0.21%
CGPLOV21 0.8889 0.0677% 0.9961 Y Y 14.36%
CGPLOV22 0.9355 0.0251% 0.9775 Y Y 0.49%
CGPLOV23 0.8850 0.1520% 0.9910 Y Y 1.39%
CGPLOV24 0.8995 0.0303% 0.9856 Y Y ND
CGPLOV25 0.9228 0.0141% 0.8544 Y Y ND
CGPLOV26 0.9351 0.0646% 0.9946 Y Y ND
CGPLOV28 0.9378 0.0547% 0.8160 Y Y ND
CGPLOV31 0.9283 0.1605% 0.9795 Y Y ND
CGPLOV32 0.9338 0.1351% 0.8609 Y Y ND
CGPLOV37 0.8831 0.0985% 0.9849 Y Y 0.29%
CGPLOV38 0.6502 0.0490% 0.9990 Y Y 4.89%
CGPLOV40 0.8127 0.6145% 0.9963 Y Y 6.73%
CGPLOV41 0.8929 0.1110% 0.9484 Y Y 0.60%
CGPLOV42 0.9086 0.0489% 0.9979 Y Y 1.24%
CGPLOV43 0.9342 0.0432% 0.6042 N N ND
CGPLOV44 0.9173 0.1946% 0.9962 Y Y 0.37%
CGPLOV46 0.9291 0.0801% 0.9128 Y Y ND
CGPLOV47 0.9461 0.0270% 0.3410 N N 3.20%
CGPLOV48 0.9429 0.0422% 0.4874 N N 10.70%
CGPLOV49 0.8083 0.1527% 0.9897 Y Y 2.03%
CGPLOV50 0.9382 0.0907% 0.9955 Y Y ND
CGPLPA112 0.9429 0.0268% 0.0856 N N —
CGPLPA113 0.7674 1.0116% 0.9935 Y Y —
CGPLPA114 0.9246 0.0836% 0.7598 Y Y —
CGPLPA115 0.8810 0.0763% 0.9974 Y Y —
CGPLPA117 0.8767 0.1084% 0.9049 Y Y —
CGPLPA118 0.9001 0.1842% 0.9859 Y Y 0.14%
CGPLPA122 0.8058 0.2047% 0.9983 Y Y 37.22%
CGPLPA124 0.9238 0.1542% 0.8791 Y Y 0.62%
CGPLPA125 0.9373 0.0273% 0.0228 N N —
CGPLPA126 0.9139 0.4349% 0.9908 Y Y ND
CGPLPA127 0.8117 0.4371% 0.9789 Y Y —
CGPLPA128 0.9003 0.1317% 0.9812 Y Y ND
CGPLPA129 0.9155 0.0642% 0.9839 Y Y ND
CGPLPA130 0.8499 0.1055% 0.9895 Y Y ND
CGPLPA131 0.9195 0.0760% 0.9685 Y Y 0.21%
CGPLPA134 0.8847 0.0260% 0.9896 Y Y 0.93%
CGPLPA135 0.9184 0.0558% 0.6594 Y N —
CGPLPA136 0.9050 0.0769% 0.9596 Y Y 0.10%
CGPLPA137 0.9320 0.0499% 0.7282 Y N —
CGPLPA139 0.9374 0.0465% 0.0743 N N —
CGPLPA14 0.9069 0.0515% 0.9824 Y Y —
CGPLPA140 0.9548 0.0330% 0.9751 Y Y 3.21%
CGPLPA141 0.9381 0.0920% 0.9388 Y Y —
CGPLPA15 0.8927 0.0160% 0.8737 Y Y —
CGPLPA155 0.9313 0.0260% 0.8013 Y Y —
CGPLPA156 0.9432 0.0290% 0.0159 N N —
CCPLPA165 0.9309 0.0558% 0.2158 N N —
CGPLPA168 0.7757 0.3123% 0.9878 Y Y —
CGPLPA17 0.6771 1.2600% 0.9956 Y Y —
CGPLPA184 0.9203 0.0897% 0.9926 Y Y —
CGPLPA187 0.8968 0.0658% 0.9875 Y Y —
CGPLPA23 0.6938 0.5785% 0.9984 Y Y —
CGPLPA25 0.9239 0.0380% 0.8103 Y Y —
CGPLPA26 0.9356 0.0247% 0.8231 Y Y —
CGPLPA28 0.8930 0.0546% 0.9036 Y Y —
CGPLPA33 0.8553 0.0894% 0.9967 Y Y —
CGPLPA34 0.8885 0.0438% 0.7977 Y Y —
CGPLPA37 0.9294 0.0410% 0.9924 Y Y —
CGPLPA38 0.8941 0.0372% 0.9851 Y Y —
CGPLPA39 0.7972 0.5058% 0.9951 Y Y —
CGPLPA40 0.8865 0.2268% 0.9920 Y Y —
CGPLPA42 0.8863 0.0283% 0.3544 N N —
CGPLPA46 0.7525 1.0982% 0.9952 Y Y —
CGPLPA47 0.8439 0.1598% 0.9946 Y Y —
CGPLPA48 0.9207 1.0232% 0.2251 N N —
CGPLPA52 0.8863 0.0154% 0.0963 N N —
CGPLPA53 0.8776 0.1824% 0.8946 Y Y —
CGPLPA58 0.9224 0.0803% 0.9056 Y Y —
CGPLPA59 0.9193 0.1479% 0.9759 Y Y —
CGPLPA67 0.9248 0.0329% 0.6716 Y N —
CGPLPA69 0.8592 0.0458% 0.1245 Y Y —
CGPLPA71 0.8888 0.0479% 0.0524 Y Y —
CGPLPA74 0.9372 0.0292% 0.0108 Y Y —
CGPLPA76 0.9441 0.0345% 0.0897 Y Y —
CGPLPA85 0.9337 0.0363% 0.0508 Y Y —
CGPLPA86 0.8042 0.7564% 0.9864 Y Y —
CGPLPA92 0.9003 0.1458% 0.7061 N N —
CGPLPA93 0.8023 0.6250% 0.9978 Y Y —
CGPLPA94 0.9433 0.0180% 0.9025 Y Y —
CGPLPA95 0.8571 0.0815% 0.9941 Y Y —
CGST102 0.9057 0.0704% 0.8581 Y Y 0.43%
CGST11 0.9161 0.0651% 0.1435 N N —
CGST110 0.9232 0.0817% 0.8900 Y Y ND
CGST114 0.9038 0.0317% 0.5893 N N ND
CGST13 0.9156 0.0321% 0.9754 Y Y ND
CGST131 0.8886 0.2752% 0.9409 Y Y —
CGST141 0.9205 0.0388% 0.2008 N N ND
CGST16 0.8355 0.1744% 0.9974 Y Y 0.93%
CGST18 0.9111 0.0298% 0.3842 N N 0.14%
CGST21 0.2687 0.2295% 0.9910 Y Y —
CGST26 0.9140 0.0399% 0.5009 N N —
CGST28 0.7832 0.1295% 0.9955 Y Y 1.62%
CGST30 0.9121 0.0338% 0.9183 Y Y 0.42%
CGST32 0.8639 0.0247% 0.9512 Y Y 2.99%
CGST33 0.7770 0.0798% 0.9805 Y Y 2.32%
CGST38 0.8758 0.0540% 0.9416 Y Y —
CGST39 0.9401 0.0287% 0.8480 Y Y ND
CGST41 0.9284 0.0398% 0.9253 Y Y ND
CGST45 0.9036 0.0220% 0.9713 Y Y ND
CGST47 0.9096 0.0157% 0.9687 Y Y 0.45%
CGST48 0.5445 0.0220% 0.9975 Y Y 4.21%
CGST53 0.7888 0.1140% 0.9914 Y Y —
CGST58 0.9094 0.0696% 0.9705 Y Y ND
CGST67 0.8853 0.3245% 0.9002 Y Y —
CGST77 0.8295 0.1851% 0.9981 Y Y —
CGST80 0.8845 0.0490% 0.9513 Y Y 1.04%
CGST81 0.8851 0.0138% 0.9748 Y Y 0.21%
*NO indicates not detected. please see reference 10 for additional information on targeted sequencing analyes. DELFI cancer detection at 95% and 98% specificity is based on scores greater than 0.6200 and 0.7500 respectively.
