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 1. 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 cfDNA 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 rescaled 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: 51525-7304(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 (MM), 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 DNA 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 100 μl of Atlantis Digestion Buffer or with 100 μl of micrococcal nuclease (MN) Digestion Buffer. Finally, cellular nucleic DNA was fragmented with 0.5U of Atlantis dsDNase at 42° C. for 20 minutes or 1.5U 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 3/4 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 3/4. 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% CI detected Sensitivity 95% CI
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% CI) Patients Accuracy (95% CI) 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.
TABLE 1
APPENDIX A: Summary of patients and samples analyzed
Age at
Sample Diag- Gen- TNM Site of
Patient Patient Type Type Timepoint nosis der Stage Staging Primary Tumor
CGCRC291 Colorectal Cancer cfDNA Preoperative treatment naïve 69 F IV T3N2M1 Coecum
CGCRC292 Colorectal Cancer cfDNA Preoperative treatment naïve 51 M IV T3N2M1 Sigmoid Colon
CGCRC293 Colorectal Cancer cfDNA Preoperative treatment naïve 55 M IV T3N2M1 Rectum
CGCRC294 Colorectal Cancer cfDNA Preoperative treatment naïve 67 F II T3N0M0 Sigmoid Colon
CGCRC296 Colorectal Cancer cfDNA Preoperative treatment naïve 76 F II T4N0M0 Coecum
CGCRC299 Colorectal Cancer cfDNA Preoperative treatment naïve 71 M I T1N0M0 Rectum
CGCRC300 Colorectal Cancer cfDNA Preoperative treatment naïve 65 M I T2N0M0 Rectum
CGCRC301 Colorectal Cancer cfDNA Preoperative treatment naïve 76 F I T2N0M0 Rectum
CGCRC302 Colorectal Cancer cfDNA Preoperative treatment naïve 73 M II T3N0M0 Transverse Colon
CGCRC304 Colorectal Cancer cfDNA Preoperative treatment naïve 86 F II T3N0M0 Rectum
CGCRC305 Colorectal Cancer cfDNA Preoperative treatment naïve 83 F II T3N0M0 Transverse Colon
CGCRC306 Colorectal Cancer cfDNA Preoperative treatment naïve 80 F II T4N0M0 Ascending Colon
CGCRC307 Colorectal Cancer cfDNA Preoperative treatment naïve 78 F II T3N0M0 Ascending Colon
CGCRC308 Colorectal Cancer cfDNA Preoperative treatment naïve 72 F III T4N2M0 Ascending Colon
CGCRC311 Colorectal Cancer cfDNA Preoperative treatment naïve 59 M I T2N0M0 Sigmoid Colon
CGCRC315 Colorectal Cancer cfDNA Preoperative treatment naïve 74 M III T3N1M0 Sigmoid Colon
CGCRC316 Colorectal Cancer cfDNA Preoperative treatment naïve 80 M III T3N2M0 Transverse Colon
CGCRC317 Colorectal Cancer cfDNA Preoperative treatment naïve 74 M III T3N2M0 Descending Colon
CGCRC318 Colorectal Cancer cfDNA Preoperative treatment naïve 81 M I T2N0M0 Coecum
CGCRC319 Colorectal Cancer cfDNA Preoperative treatment naïve 80 F III T2N1M0 Descending Colon
CGCRC320 Colorectal Cancer cfDNA Preoperative treatment naïve 73 F I T2N0M0 Ascending Colon
CGCRC321 Colorectal Cancer cfDNA Preoperative treatment naïve 68 M I T2N0M0 Rectum
CGCRC333 Colorectal Cancer cfDNA Preoperative treatment naïve NA F IV NA Colon/Rectum
CGCRC336 Colorectal Cancer cfDNA Preoperative treatment naïve NA M IV NA Colon/Rectum
CGCRC338 Colorectal Cancer cfDNA Preoperative treatment naïve NA F IV NA Colon/Rectum
CGCRC341 Colorectal Cancer cfDNA Preoperative treatment naïve NA F IV NA Colon/Rectum
CGCRC342 Colorectal Cancer cfDNA Preoperative treatment naïve NA M IV NA Colon/Rectum
CGLU316 Lung Cancer cfDNA Pre-treatment, Day −53 50 F IV T3N2M0 Left Upper Lobe of Lung
CGLU316 Lung Cancer cfDNA Pre-treatment, Day −4 50 F IV T3N2M0 Left Upper Lobe of Lung
CGLU316 Lung Cancer cfDNA Post-treatment, Day 18 50 F IV T3N2M0 Left Upper Lobe of Lung
CGLU316 Lung Cancer cfDNA Post-treatment, Day 87 50 F IV T3N2M0 Left Upper Lobe of Lung
CGLU344 Lung Cancer cfDNA Pre-treatment, Day −21 65 F IV T2N2M1 Right Upper Lobe of Lung
CGLU344 Lung Cancer cfDNA Pre-treatment, Day 0 65 F IV T2N2M1 Right Upper Lobe of Lung
CGLU344 Lung Cancer cfDNA Post-treatment, Day 0.1875 65 F IV T2N2M1 Right Upper Lobe of Lung
CGLU344 Lung Cancer cfDNA Post-treatment, Day 59 65 F IV T2N2M1 Right Upper Lobe of Lung
CGLU369 Lung Cancer cfDNA Pre-treatment, Day −2 48 F IV T2NxM1 Right Upper Lobe of Lung
CGLU369 Lung Cancer cfDNA Post-treatment, Day 12 48 F IV T2NxM1 Right Upper Lobe of Lung
CGLU369 Lung Cancer cfDNA Post-treatment, Day 68 48 F IV T2NxM1 Right Upper Lobe of Lung
CGLU369 Lung Cancer cfDNA Post-treatment, Day 110 48 F IV T2NxM1 Right Upper Lobe of Lung
CGLU373 Lung Cancer cfDNA Pre-treatment, Day −2 56 F IV T3N1M0 Right Upper Lobe of Lung
CGLU373 Lung Cancer cfDNA Post-treatment, Day 0.125 56 F IV T3N1M0 Right Upper Lobe of Lung
CGLU373 Lung Cancer cfDNA Post-treatment, Day 7 56 F IV T3N1M0 Right Upper Lobe of Lung
CGLU373 Lung Cancer cfDNA Post-treatment, Day 47 56 F IV T3N1M0 Right Upper Lobe of Lung
CGPLBR100 Breast Cancer cfDNA Preoperative treatment naïve 44 F III T2N2M0 Left Breast
CGPLBR101 Breast Cancer cfDNA Preoperative treatment naïve 46 F II T2N1M0 Left Breast
CGPLBR102 Breast Cancer cfDNA Preoperative treatment naïve 47 F II T2N1M0 Right Breast
CGPLBR103 Breast Cancer cfDNA Preoperative treatment naïve 48 F II T2N1M0 Left Breast
CGPLBR104 Breast Cancer cfDNA Preoperative treatment naïve 68 F II T2N0M0 Right Breast
CGPLBR12 Breast Cancer cfDNA Preoperative treatment naïve NA F III NA Breast
CGPLBR18 Breast Cancer cfDNA Preoperative treatment naïve NA F III NA Breast
CGPLBR23 Breast Cancer cfDNA Preoperative treatment naïve 53 F II NA Breast
CGPLBR24 Breast Cancer cfDNA Preoperative treatment naïve 52 F II NA Breast
CGPLBR28 Breast Cancer cfDNA Preoperative treatment naïve 59 F III NA Breast
CGPLBR30 Breast Cancer cfDNA Preoperative treatment naïve 61 F II NA Breast
CGPLBR31 Breast Cancer cfDNA Preoperative treatment naïve 54 F II NA Breast
CGPLBR32 Breast Cancer cfDNA Preoperative treatment naïve NA F II NA Breast
CGPLBR33 Breast Cancer cfDNA Preoperative treatment naïve 47 F II NA Breast
CGPLBR34 Breast Cancer cfDNA Preoperative treatment naïve 60 F II NA Breast
CGPLBR35 Breast Cancer cfDNA Preoperative treatment naïve 43 F II NA Breast
CGPLBR36 Breast Cancer cfDNA Preoperative treatment naïve 36 F II NA Breast
CGPLBR37 Breast Cancer cfDNA Preoperative treatment naïve 58 F II NA Breast
CGPLBR38 Breast Cancer cfDNA Preoperative treatment naïve 54 F I T1N0M0 Left Breast
CGPLBR40 Breast Cancer cfDNA Preoperative treatment naïve 66 F III T2N2M0 Left Breast
CGPLBR41 Breast Cancer cfDNA Preoperative treatment naïve 51 F III T3N1M0 Left Breast
CGPLBR45 Breast Cancer cfDNA Preoperative treatment naïve 57 F II NA Breast
CGPLBR46 Breast Cancer cfDNA Preoperative treatment naïve 54 F III NA Breast
CGPLBR47 Breast Cancer cfDNA Preoperative treatment naïve 54 F I NA Breast
CGPLBR48 Breast Cancer cfDNA Preoperative treatment naïve 47 F II T2N1M0 Left Breast
CGPLBR49 Breast Cancer cfDNA Preoperative treatment naïve 37 F II T2N1M0 Left Breast
CGPLBR50 Breast Cancer cfDNA Preoperative treatment naïve 51 F I NA Breast
CGPLBR51 Breast Cancer cfDNA Preoperative treatment naïve 53 F II NA Breast
CGPLBR52 Breast Cancer cfDNA Preoperative treatment naïve 68 F III NA Breast
CGPLBR55 Breast Cancer cfDNA Preoperative treatment naïve 53 F III T3N1M0 Right Breast
CGPLBR56 Breast Cancer cfDNA Preoperative treatment naïve 56 F II NA Breast
CGPLBR57 Breast Cancer cfDNA Preoperative treatment naïve 54 F III T2N2M0 Left Breast
CGPLBR59 Breast Cancer cfDNA Preoperative treatment naïve 42 F I T1N0M0 Left Breast
CGPLBR60 Breast Cancer cfDNA Preoperative treatment naïve 61 F II NA Left Breast
CGPLBR61 Breast Cancer cfDNA Preoperative treatment naïve 67 F II T2N1M0 Left Breast
CGPLBR63 Breast Cancer cfDNA Preoperative treatment naïve 48 F II T2N1M0 Left Breast
CGPLBR65 Breast Cancer cfDNA Preoperative treatment naïve 50 F II NA Left Breast
CGPLBR68 Breast Cancer cfDNA Preoperative treatment naïve 64 F III T4N1M0 Breast
CGPLBR69 Breast Cancer cfDNA Preoperative treatment naïve 43 F II T2N0M0 Breast
CGPLBR70 Breast Cancer cfDNA Preoperative treatment naïve 60 F II T2N1M0 Breast
CGPLBR71 Breast Cancer cfDNA Preoperative treatment naïve 65 F II T2N0M0 Breast
CGPLBR72 Breast Cancer cfDNA Preoperative treatment naïve 67 F II T2N0M0 Breast
CGPLBR73 Breast Cancer cfDNA Preoperative treatment naïve 60 F II T2N1M0 Breast
CGPLBR76 Breast Cancer cfDNA Preoperative treatment naïve 53 F II T2N0M0 Right Breast
CGPLBR81 Breast Cancer cfDNA Preoperative treatment naïve 54 F II NA Breast
CGPLBR82 Breast Cancer cfDNA Preoperative treatment naïve 70 F I T1N0M0 Right Breast
CGPLBR83 Breast Cancer cfDNA Preoperative treatment naïve 53 F II T2N1M0 Right Breast
CGPLBR84 Breast Cancer cfDNA Preoperative treatment naïve NA F III NA Breast
CGPLBR87 Breast Cancer cfDNA Preoperative treatment naïve 80 F II T2N1M0 Right Breast
CGPLBR88 Breast Cancer cfDNA Preoperative treatment naïve 48 F II T1N1M0 Left Breast
CGPLBR90 Breast Cancer cfDNA Preoperative treatment naïve 51 F II NA Right Breast
CGPLBR91 Breast Cancer cfDNA Preoperative treatment naïve 62 F III T2N2M0 Breast
CGPLBR92 Breast Cancer cfDNA Preoperative treatment naïve 58 F II T2N1M0 Breast
CGPLBR93 Breast Cancer cfDNA Preoperative treatment naïve 59 F II T1N0M0 Breast
CGPLH189 Healthy cfDNA Preoperative treatment naïve 74 M NA NA NA
CGPLH190 Healthy cfDNA Preoperative treatment naïve 67 M NA NA NA
CGPLH192 Healthy cfDNA Preoperative treatment naïve 74 M NA NA NA
CGPLH193 Healthy cfDNA Preoperative treatment naïve 72 F NA NA NA
CGPLH194 Healthy cfDNA Preoperative treatment naïve 75 F NA NA NA
CGPLH196 Healthy cfDNA Preoperative treatment naïve 64 M NA NA NA
CGPLH197 Healthy cfDNA Preoperative treatment naïve 74 M NA NA NA
CGPLH198 Healthy cfDNA Preoperative treatment naïve 66 M NA NA NA
CGPLH199 Healthy cfDNA Preoperative treatment naïve 75 F NA NA NA
CGPLH200 Healthy cfDNA Preoperative treatment naïve 51 M NA NA NA
CGPLH201 Healthy cfDNA Preoperative treatment naïve 68 F NA NA NA
CGPLH202 Healthy cfDNA Preoperative treatment naïve 73 M NA NA NA
CGPLH203 Healthy cfDNA Preoperative treatment naïve 59 M NA NA NA
CGPLH205 Healthy cfDNA Preoperative treatment naïve 68 F NA NA NA
CGPLH208 Healthy cfDNA Preoperative treatment naïve 75 F NA NA NA
CGPLH209 Healthy cfDNA Preoperative treatment naïve 74 M NA NA NA
CGPLH210 Healthy cfDNA Preoperative treatment naïve 75 M NA NA NA
CGPLH211 Healthy cfDNA Preoperative treatment naïve 75 F NA NA NA
CGPLH300 Healthy cfDNA Preoperative treatment naïve 72 F NA NA NA
CGPLH307 Healthy cfDNA Preoperative treatment naïve 53 M NA NA NA
CGPLH308 Healthy cfDNA Preoperative treatment naïve 60 M NA NA NA
CGPLH309 Healthy cfDNA Preoperative treatment naïve 61 F NA NA NA
CGPLH310 Healthy cfDNA Preoperative treatment naïve 55 F NA NA NA
CGPLH311 Healthy cfDNA Preoperative treatment naïve 50 M NA NA NA
CGPLH314 Healthy cfDNA Preoperative treatment naïve 59 M NA NA NA
CGPLH314 Healthy cfDNA, Preoperative treatment naïve 59 M NA NA NA
technical
replicate
CGPLH315 Healthy cfDNA Preoperative treatment naïve 59 F NA NA NA
CGPLH316 Healthy cfDNA Preoperative treatment naïve 64 M NA NA NA
CGPLH317 Healthy cfDNA Preoperative treatment naïve 53 F NA NA NA
CGPLH319 Healthy cfDNA Preoperative treatment naïve 60 F NA NA NA
CGPLH320 Healthy cfDNA Preoperative treatment naïve 75 F NA NA NA
CGPLH322 Healthy cfDNA Preoperative treatment naïve 53 F NA NA NA
CGPLH324 Healthy cfDNA Preoperative treatment naïve 59 F NA NA NA
CGPLH325 Healthy cfDNA Preoperative treatment naïve 54 M NA NA NA
CGPLH326 Healthy cfDNA Preoperative treatment naïve 67 F NA NA NA
CGPLH327 Healthy cfDNA Preoperative treatment naïve 50 M NA NA NA
CGPLH328 Healthy cfDNA Preoperative treatment naïve 68 F NA NA NA
CGPLH328 Healthy cfDNA, Preoperative treatment naïve 68 F NA NA NA
technical
replicate
CGPLH329 Healthy cfDNA Preoperative treatment naïve 59 M NA NA NA
CGPLH330 Healthy cfDNA Preoperative treatment naïve 75 M NA NA NA
CGPLH331 Healthy cfDNA Preoperative treatment naïve 55 M NA NA NA
CGPLH331 Healthy cfDNA, Preoperative treatment naïve 55 M NA NA NA
technical
replicate
CGPLH333 Healthy cfDNA Preoperative treatment naïve 60 M NA NA NA
CGPLH335 Healthy cfDNA Preoperative treatment naïve 74 M NA NA NA
CGPLH336 Healthy cfDNA Preoperative treatment naïve 53 F NA NA NA
CGPLH337 Healthy cfDNA Preoperative treatment naïve 53 F NA NA NA
CGPLH338 Healthy cfDNA Preoperative treatment naïve 75 M NA NA NA
CGPLH339 Healthy cfDNA Preoperative treatment naïve 70 M NA NA NA
CGPLH340 Healthy cfDNA Preoperative treatment naïve 62 M NA NA NA
CGPLH341 Healthy cfDNA Preoperative treatment naïve 61 F NA NA NA
CGPLH342 Healthy cfDNA Preoperative treatment naïve 49 F NA NA NA
CGPLH343 Healthy cfDNA Preoperative treatment naïve 58 M NA NA NA
CGPLH344 Healthy cfDNA Preoperative treatment naïve 50 M NA NA NA
CGPLH345 Healthy cfDNA Preoperative treatment naïve 55 F NA NA NA
CGPLH346 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH35 Healthy cfDNA Preoperative treatment naïve 48 F NA NA NA
CGPLH350 Healthy cfDNA Preoperative treatment naïve 65 M NA NA NA
CGPLH351 Healthy cfDNA Preoperative treatment naïve 71 M NA NA NA
CGPLH352 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH353 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH354 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH355 Healthy cfDNA Preoperative treatment naïve 70 M NA NA NA
CGPLH356 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH357 Healthy cfDNA Preoperative treatment naïve 52 F NA NA NA
CGPLH358 Healthy cfDNA Preoperative treatment naïve 55 M NA NA NA
CGPLH36 Healthy cfDNA Preoperative treatment naïve 36 F NA NA NA
CGPLH360 Healthy cfDNA Preoperative treatment naïve 60 M NA NA NA
CGPLH361 Healthy cfDNA Preoperative treatment naïve 50 M NA NA NA
CGPLH362 Healthy cfDNA Preoperative treatment naïve 72 F NA NA NA
CGPLH363 Healthy cfDNA Preoperative treatment naïve 68 F NA NA NA
CGPLH364 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH365 Healthy cfDNA Preoperative treatment naïve 68 F NA NA NA
CGPLH366 Healthy cfDNA Preoperative treatment naïve 61 M NA NA NA
CGPLH367 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH368 Healthy cfDNA Preoperative treatment naïve 50 M NA NA NA
CGPLH369 Healthy cfDNA Preoperative treatment naïve 55 F NA NA NA
CGPLH369 Healthy cfDNA, Preoperative treatment naïve 55 F NA NA NA
technical
replicate
CGPLH37 Healthy cfDNA Preoperative treatment naïve 39 F NA NA NA
CGPLH370 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH371 Healthy cfDNA Preoperative treatment naïve 57 F NA NA NA
CGPLH380 Healthy cfDNA Preoperative treatment naïve 53 F NA NA NA
CGPLH381 Healthy cfDNA Preoperative treatment naïve 56 F NA NA NA
CGPLH382 Healthy cfDNA Preoperative treatment naïve 53 F NA NA NA
CGPLH383 Healthy cfDNA Preoperative treatment naïve 62 F NA NA NA
CGPLH384 Healthy cfDNA Preoperative treatment naïve 50 M NA NA NA
CGPLH385 Healthy cfDNA Preoperative treatment naïve 69 M NA NA NA
CGPLH386 Healthy cfDNA Preoperative treatment naïve 62 M NA NA NA
CGPLH386 Healthy cfDNA Preoperative treatment naïve 62 M NA NA NA
technical
replicate
CGPLH387 Healthy cfDNA Preoperative treatment naïve 71 F NA NA NA
CGPLH388 Healthy cfDNA Preoperative treatment naïve 57 F NA NA NA
CGPLH389 Healthy cfDNA Preoperative treatment naïve 73 F NA NA NA
CGPLH390 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH391 Healthy cfDNA Preoperative treatment naïve 58 M NA NA NA
CGPLH391 Healthy cfDNA Preoperative treatment naïve 58 M NA NA NA
technical
replicate
CGPLH392 Healthy cfDNA Preoperative treatment naïve 57 F NA NA NA
CGPLH393 Healthy cfDNA Preoperative treatment naïve 54 M NA NA NA
CGPLH394 Healthy cfDNA Preoperative treatment naïve 55 F NA NA NA
CGPLH395 Healthy cfDNA Preoperative treatment naïve 56 F NA NA NA
CGPLH395 Healthy cfDNA Preoperative treatment naïve 56 F NA NA NA
technical
replicate
CGPLH396 Healthy cfDNA Preoperative treatment naïve 50 M NA NA NA
CGPLH398 Healthy cfDNA Preoperative treatment naïve 68 M NA NA NA
CGPLH399 Healthy cfDNA Preoperative treatment naïve 62 F NA NA NA
CGPLH400 Healthy cfDNA Preoperative treatment naïve 64 M NA NA NA
CGPLH400 Healthy cfDNA Preoperative treatment naïve 64 M NA NA NA
technical
replicate
CGPLH401 Healthy cfDNA Preoperative treatment naïve 50 M NA NA NA
CGPLH401 Healthy cfDNA Preoperative treatment naïve 50 M NA NA NA
technical
replicate
CGPLH402 Healthy cfDNA Preoperative treatment naïve 57 F NA NA NA
CGPLH403 Healthy cfDNA Preoperative treatment naïve 64 M NA NA NA
CGPLH404 Healthy cfDNA Preoperative treatment naïve 50 M NA NA NA
CGPLH405 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH406 Healthy cfDNA Preoperative treatment naïve 57 M NA NA NA
CGPLH407 Healthy cfDNA Preoperative treatment naïve 75 F NA NA NA
CGPLH408 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH409 Healthy cfDNA Preoperative treatment naïve 53 M NA NA NA
CGPLH410 Healthy cfDNA Preoperative treatment naïve 52 M NA NA NA
CGPLH411 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH412 Healthy cfDNA Preoperative treatment naïve 53 F NA NA NA
CGPLH413 Healthy cfDNA Preoperative treatment naïve 54 F NA NA NA
CGPLH414 Healthy cfDNA Preoperative treatment naïve 56 M NA NA NA
CGPLH415 Healthy cfDNA Preoperative treatment naïve 59 M NA NA NA
CGPLH416 Healthy cfDNA Preoperative treatment naïve 58 F NA NA NA
CGPLH417 Healthy cfDNA Preoperative treatment naïve 70 M NA NA NA
CGPLH418 Healthy cfDNA Preoperative treatment naïve 70 F NA NA NA
CGPLH419 Healthy cfDNA Preoperative treatment naïve 65 F NA NA NA
CGPLH42 Healthy cfDNA Preoperative treatment naïve 54 F NA NA NA
CGPLH420 Healthy cfDNA Preoperative treatment naïve 51 M NA NA NA
CGPLH422 Healthy cfDNA Preoperative treatment naïve 68 F NA NA NA
CGPLH423 Healthy cfDNA Preoperative treatment naïve 54 M NA NA NA
CGPLH424 Healthy cfDNA Preoperative treatment naïve 53 F NA NA NA
CGPLH425 Healthy cfDNA Preoperative treatment naïve 53 F NA NA NA
CGPLH426 Healthy cfDNA Preoperative treatment naïve 68 M NA NA NA
CGPLH427 Healthy cfDNA Preoperative treatment naïve 68 M NA NA NA
CGPLH428 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH429 Healthy cfDNA Preoperative treatment naïve 63 F NA NA NA
CGPLH43 Healthy cfDNA Preoperative treatment naïve 49 F NA NA NA
CGPLH430 Healthy cfDNA Preoperative treatment naïve 69 F NA NA NA
CGPLH431 Healthy cfDNA Preoperative treatment naïve 59 F NA NA NA
CGPLH432 Healthy cfDNA Preoperative treatment naïve 59 F NA NA NA
CGPLH434 Healthy cfDNA Preoperative treatment naïve 59 M NA NA NA
CGPLH435 Healthy cfDNA Preoperative treatment naïve 50 M NA NA NA
CGPLH436 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH437 Healthy cfDNA Preoperative treatment naïve 56 M NA NA NA
CGPLH438 Healthy cfDNA Preoperative treatment naïve 69 M NA NA NA
CGPLH439 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH440 Healthy cfDNA Preoperative treatment naïve 72 M NA NA NA
CGPLH441 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH442 Healthy cfDNA Preoperative treatment naïve 59 F NA NA NA
CGPLH443 Healthy cfDNA Preoperative treatment naïve 52 F NA NA NA
CGPLH444 Healthy cfDNA Preoperative treatment naïve 60 F NA NA NA
CGPLH445 Healthy cfDNA Preoperative treatment naïve 53 F NA NA NA
CGPLH446 Healthy cfDNA Preoperative treatment naïve 51 F NA NA NA
CGPLH447 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH448 Healthy cfDNA Preoperative treatment naïve 51 F NA NA NA
CGPLH449 Healthy cfDNA Preoperative treatment naïve 51 F NA NA NA
CGPLH45 Healthy cfDNA Preoperative treatment naïve 58 F NA NA NA
CGPLH450 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH451 Healthy cfDNA Preoperative treatment naïve 54 F NA NA NA
CGPLH452 Healthy cfDNA Preoperative treatment naïve 69 M NA NA NA
CGPLH453 Healthy cfDNA Preoperative treatment naïve 53 F NA NA NA
CGPLH455 Healthy cfDNA Preoperative treatment naïve 55 F NA NA NA
CGPLH455 Healthy cfDNA Preoperative treatment naïve 55 F NA NA NA
technical
replicate
CGPLH456 Healthy cfDNA Preoperative treatment naïve 54 F NA NA NA
CGPLH457 Healthy cfDNA Preoperative treatment naïve 54 F NA NA NA
CGPLH458 Healthy cfDNA Preoperative treatment naïve 52 F NA NA NA
CGPLH459 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH46 Healthy cfDNA Preoperative treatment naïve 35 F NA NA NA
CGPLH460 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH463 Healthy cfDNA Preoperative treatment naïve 53 F NA NA NA
CGPLH464 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH465 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH466 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH466 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
technical
replicate
CGPLH467 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH468 Healthy cfDNA Preoperative treatment naïve 53 M NA NA NA
CGPLH469 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH47 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH470 Healthy cfDNA Preoperative treatment naïve 68 F NA NA NA
CGPLH471 Healthy cfDNA Preoperative treatment naïve 70 F NA NA NA
CGPLH472 Healthy cfDNA Preoperative treatment naïve 69 F NA NA NA
CGPLH473 Healthy cfDNA Preoperative treatment naïve 62 M NA NA NA
CGPLH474 Healthy cfDNA Preoperative treatment naïve 63 M NA NA NA
CGPLH475 Healthy cfDNA Preoperative treatment naïve 67 F NA NA NA
CGPLH476 Healthy cfDNA Preoperative treatment naïve 65 F NA NA NA
CGPLH477 Healthy cfDNA Preoperative treatment naïve 61 F NA NA NA
CGPLH478 Healthy cfDNA Preoperative treatment naïve 51 F NA NA NA
CGPLH479 Healthy cfDNA Preoperative treatment naïve 52 M NA NA NA
CGPLH48 Healthy cfDNA Preoperative treatment naïve 38 F NA NA NA
CGPLH480 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH481 Healthy cfDNA Preoperative treatment naïve 53 F NA NA NA
CGPLH482 Healthy cfDNA Preoperative treatment naïve 50 M NA NA NA
CGPLH483 Healthy cfDNA Preoperative treatment naïve 66 M NA NA NA
CGPLH484 Healthy cfDNA Preoperative treatment naïve 72 M NA NA NA
CGPLH485 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH486 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH487 Healthy cfDNA Preoperative treatment naïve 50 M NA NA NA
CGPLH488 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH49 Healthy cfDNA Preoperative treatment naïve 39 F NA NA NA
CGPLH490 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH491 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH492 Healthy cfDNA Preoperative treatment naïve 51 F NA NA NA
CGPLH493 Healthy cfDNA Preoperative treatment naïve 64 M NA NA NA
CGPLH494 Healthy cfDNA Preoperative treatment naïve 53 F NA NA NA
CGPLH495 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH496 Healthy cfDNA Preoperative treatment naïve 74 M NA NA NA
CGPLH497 Healthy cfDNA Preoperative treatment naïve 68 F NA NA NA
CGPLH497 Healthy cfDNA Preoperative treatment naïve 68 F NA NA NA
technical
replicate
CGPLH498 Healthy cfDNA Preoperative treatment naïve 54 F NA NA NA
CGPLH499 Healthy cfDNA Preoperative treatment naïve 52 F NA NA NA
CGPLH50 Healthy cfDNA Preoperative treatment naïve 55 F NA NA NA
CGPLH500 Healthy cfDNA Preoperative treatment naïve 51 F NA NA NA
CGPLH501 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH502 Healthy cfDNA Preoperative treatment naïve 53 F NA NA NA
CGPLH503 Healthy cfDNA Preoperative treatment naïve 67 M NA NA NA
CGPLH504 Healthy cfDNA Preoperative treatment naïve 57 F NA NA NA
CGPLH504 Healthy cfDNA Preoperative treatment naïve 57 F NA NA NA
technical
replicate
CGPLH505 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH506 Healthy cfDNA Preoperative treatment naïve 51 F NA NA NA
CGPLH507 Healthy cfDNA Preoperative treatment naïve 56 M NA NA NA
CGPLH508 Healthy cfDNA Preoperative treatment naïve 54 F NA NA NA
CGPLH508 Healthy cfDNA Preoperative treatment naïve 54 F NA NA NA
technical
replicate
CGPLH509 Healthy cfDNA Preoperative treatment naïve 60 M NA NA NA
CGPLH51 Healthy cfDNA Preoperative treatment naïve 48 F NA NA NA
CGPLH510 Healthy cfDNA Preoperative treatment naïve 67 M NA NA NA
CGPLH511 Healthy cfDNA Preoperative treatment naïve 75 M NA NA NA
CGPLH512 Healthy cfDNA Preoperative treatment naïve 52 M NA NA NA
CGPLH513 Healthy cfDNA Preoperative treatment naïve 57 M NA NA NA
CGPLH514 Healthy cfDNA Preoperative treatment naïve 55 F NA NA NA
CGPLH515 Healthy cfDNA Preoperative treatment naïve 68 F NA NA NA
CGPLH516 Healthy cfDNA Preoperative treatment naïve 65 F NA NA NA
CGPLH517 Healthy cfDNA Preoperative treatment naïve 54 F NA NA NA
CGPLH517 Healthy cfDNA Preoperative treatment naïve 54 F NA NA NA
technical
replicate
CGPLH518 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH519 Healthy cfDNA Preoperative treatment naïve 54 M NA NA NA
CGPLH522 Healthy cfDNA Preoperative treatment naïve 40 F NA NA NA
CGPLH520 Healthy cfDNA Preoperative treatment naïve 51 F NA NA NA
CGPLH54 Healthy cfDNA Preoperative treatment naïve 47 F NA NA NA
CGPLH55 Healthy cfDNA Preoperative treatment naïve 46 F NA NA NA
CGPLH56 Healthy cfDNA Preoperative treatment naïve 42 F NA NA NA
CGPLH57 Healthy cfDNA Preoperative treatment naïve 39 F NA NA NA
CGPLH59 Healthy cfDNA Preoperative treatment naïve 34 F NA NA NA
CGPLH625 Healthy cfDNA Preoperative treatment naïve 53 F NA NA NA
CGPLH625 Healthy cfDNA Preoperative treatment naïve 53 F NA NA NA
CGPLH626 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
technical
replicate
CGPLH63 Healthy cfDNA Preoperative treatment naïve 47 F NA NA NA
CGPLH639 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH64 Healthy cfDNA Preoperative treatment naïve 55 F NA NA NA
CGPLH640 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH642 Healthy cfDNA Preoperative treatment naïve 54 F NA NA NA
CGPLH643 Healthy cfDNA Preoperative treatment naïve 55 F NA NA NA
CGPLH644 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH646 Healthy cfDNA Preoperative treatment naïve 50 F NA NA NA
CGPLH75 Healthy cfDNA Preoperative treatment naïve 46 F NA NA NA
CGPLH76 Healthy cfDNA Preoperative treatment naïve 53 F NA NA NA
CGPLH77 Healthy cfDNA Preoperative treatment naïve 46 F NA NA NA
CGPLH78 Healthy cfDNA Preoperative treatment naïve 34 F NA NA NA
CGPLH79 Healthy cfDNA Preoperative treatment naïve 37 F NA NA NA
CGPLH80 Healthy cfDNA Preoperative treatment naïve 37 F NA NA NA
CGPLH81 Healthy cfDNA Preoperative treatment naïve 54 F NA NA NA
CGPLH82 Healthy cfDNA Preoperative treatment naïve 38 F NA NA NA
CGPLH83 Healthy cfDNA Preoperative treatment naïve 60 F NA NA NA
CGPLH84 Healthy cfDNA Preoperative treatment naïve 45 F NA NA NA
CGPLLU13 Lung Cancer cfDNA Pre treatment, Day 2 72 F IV T1BN2bM1a Right Lung
CGPLLU13 Lung Cancer cfDNA Post-treatment, Day 5 72 F IV T1BN2bM1a Right Lung
CGPLLU13 Lung Cancer cfDNA Post-treatment, Day 28 72 F IV T1BN2bM1a Right Lung
CGPLLU13 Lung Cancer cfDNA Post-treatment, Day 91 72 F IV T1BN2bM1a Right Lung
CGPLLU14 Lung Cancer cfDNA Pre-treatment, Day −38 55 F IV T1N1M0 Right Lower Lobe of Lung
CGPLLU14 Lung Cancer cfDNA Pre-treatment, Day −16 55 F IV T1N1M0 Right Lower Lobe of Lung
CGPLLU14 Lung Cancer cfDNA Pre-treatment, Day −3 55 F IV T1N1M0 Right Lower Lobe of Lung
CGPLLU14 Lung Cancer cfDNA Pre-treatment, Day 0 55 F IV T1N1M0 Right Lower Lobe of Lung
CGPLLU14 Lung Cancer cfDNA Post-treatment, Day 0.33 55 F IV T1N1M0 Right Lower Lobe of Lung
CGPLLU14 Lung Cancer cfDNA Post-treatment, Day 7 55 F IV T1N1M0 Right Lower Lobe of Lung
CGPLLU144 Lung Cancer cfDNA Preoperative treatment naïve 52 M II T2aN1M0 Lung
CGPLLU147 Lung Cancer cfDNA Preoperative treatment naïve 60 M III T3N2M0 Lung
CGPLLU161 Lung Cancer cfDNA Preoperative treatment naïve 41 F II T3N0M0 Lung
CGPLLU162 Lung Cancer cfDNA Preoperative treatment naïve 38 M II T1N1M0 Right Lung
CGPLLU163 Lung Cancer cfDNA Preoperative treatment naïve 66 M II T1N1M0 Left Lung
CGPLLU165 Lung Cancer cfDNA Preoperative treatment naïve 68 F II T1N1M0 Right Lung
CGPLLU168 Lung Cancer cfDNA Preoperative treatment naïve 70 F I T2aN0M0 Lung
CGPLLU169 Lung Cancer cfDNA Preoperative treatment naïve 64 M I T1bN0M0 Lung
CGPLLU175 Lung Cancer cfDNA Preoperative treatment naïve 47 M I T2N0M0 Lung
CGPLLU176 Lung Cancer cfDNA Preoperative treatment naïve 58 M I T2N0M0 Lung
CGPLLU177 Lung Cancer cfDNA Preoperative treatment naïve 45 M II T3N0M0 Right Lung
CGPLLU180 Lung Cancer cfDNA Preoperative treatment naïve 57 M I T2N0M0 Right Lung
CGPLLU198 Lung Cancer cfDNA Preoperative treatment naïve 49 F I T2N0M0 Left Lung
CGPLLU202 Lung Cancer cfDNA Preoperative treatment naïve 68 M I T2N0M0 Right Lung
CGPLLU203 Lung Cancer cfDNA Preoperative treatment naïve 66 M II T3N0M0 Right Lung
CGPLLU205 Lung Cancer cfDNA Preoperative treatment naïve 65 M II T3N0M0 Left Lung
CGPLLU206 Lung Cancer cfDNA Preoperative treatment naïve 55 M III T3N1M0 Right Lung
CGPLLU207 Lung Cancer cfDNA Preoperative treatment naïve 60 F II T2N1M0 Lung
CGPLLU208 Lung Cancer cfDNA Preoperative treatment naïve 56 F II T2N1M0 Lung
CGPLLU209 Lung Cancer cfDNA Preoperative treatment naïve 65 M II T2aN0M0 Lung
CGPLLU244 Lung Cancer cfDNA Pre-treatment, Day −7 66 F IV NA Right Upper Lobe of Lung
CGPLLU244 Lung Cancer cfDNA Pre-treatment, Day −1 66 F IV NA Right Upper Lobe of Lung
CGPLLU244 Lung Cancer cfDNA Post-treatment, Day 6 66 F IV NA Right Upper Lobe of Lung
CGPLLU244 Lung Cancer cfDNA Post-treatment, Day 62 66 F IV NA Right Upper Lobe of Lung
CGPLLU245 Lung Cancer cfDNA Pre-treatment, Day −32 49 M IV T2aN2M1B Left Upper Lobe of Lung
CGPLLU245 Lung Cancer cfDNA Pre-treatment, Day 0 49 M IV T2aN2M1B Left Upper Lobe of Lung
CGPLLU245 Lung Cancer cfDNA Post-treatment, Day 7 49 M IV T2aN2M1B Left Upper Lobe of Lung
CGPLLU245 Lung Cancer cfDNA Post-treatment, Day 21 49 M IV T2aN2M1B Left Upper Lobe of Lung
CGPLLU246 Lung Cancer cfDNA Pre-treatment, Day −21 65 F IV NA Right Lower Lobe of Lung
CGPLLU246 Lung Cancer cfDNA Pre-treatment, Day 0 65 F IV NA Right Lower Lobe of Lung
CGPLLU246 Lung Cancer cfDNA Post-treatment, Day 9 65 F IV NA Right Lower Lobe of Lung
CGPLLU246 Lung Cancer cfDNA Post-treatment, Day 42 65 F IV NA Right Lower Lobe of Lung
CGPLLU264 Lung Cancer cfDNA Pre-treatment, Day −1 84 M IV T1N2BM1 Left Middle Lung
CGPLLU264 Lung Cancer cfDNA Post-treatment, Day 6 84 M IV T1N2BM1 Left Middle Lung
CGPLLU264 Lung Cancer cfDNA Post-treatment, Day 27 84 M IV T1N2BM1 Left Middle Lung
CGPLLU264 Lung Cancer cfDNA Post-treatment, Day 69 84 M IV T1N2BM1 Left Middle Lung
CGPLLU265 Lung Cancer cfDNA Pre-treatment, Day 0 71 F IV T1N0Mx Left Lower Lobe of Lung
CGPLLU265 Lung Cancer cfDNA Post-treatment, Day 3 71 F IV T1N0Mx Left Lower Lobe of Lung
CGPLLU265 Lung Cancer cfDNA Post-treatment, Day 7 71 F IV T1N0Mx Left Lower Lobe of Lung
CGPLLU265 Lung Cancer cfDNA Post-treatment, Day 84 71 F IV T1N0Mx Left Lower Lobe of Lung
CGPLLU266 Lung Cancer cfDNA Pre-treatment, Day 0 78 M IV T2aN1 Left Lower Lobe of Lung
CGPLLU266 Lung Cancer cfDNA Post-treatment, Day 16 78 M IV T2aN1 Left Lower Lobe of Lung
CGPLLU266 Lung Cancer cfDNA Post-treatment, Day 83 78 M IV T2aN1 Left Lower Lobe of Lung
CGPLLU266 Lung Cancer cfDNA Post-treatment, Day 328 78 M IV T2aN1 Left Lower Lobe of Lung
CGPLLU267 Lung Cancer cfDNA Pre-treatment, Day −1 55 F IV T3NxM1a Right Upper Lobe of Lung
CGPLLU267 Lung Cancer cfDNA Post-treatment, Day 34 55 F IV T3NxM1a Right Upper Lobe of Lung
CGPLLU267 Lung Cancer cfDNA Post-treatment, Day 90 55 F IV T3NxM1a Right Upper Lobe of Lung
CGPLLU269 Lung Cancer cfDNA Pre-treatment, Day 0 52 F IV T1CNxM1C Right Paratracheal Lesion
CGPLLU269 Lung Cancer cfDNA Post-treatment, Day 9 52 F IV T1CNxM1C Right Paratracheal Lesion
CGPLLU269 Lung Cancer cfDNA Post-treatment, Day 28 52 F IV T1CNxM1C Right Paratracheal Lesion
CGPLLU271 Lung Cancer cfDNA Post-treatment, Day 259 73 M IV T1aNxM1 Left Upper Lobe of Lung
CGPLLU271 Lung Cancer cfDNA Pre-treatment, Day 0 73 M IV T1aNxM1 Left Upper Lobe of Lung
CGPLLU271 Lung Cancer cfDNA Post-treatment, Day 6 73 M IV T1aNxM1 Left Upper Lobe of Lung
CGPLLU271 Lung Cancer cfDNA Post-treatment, Day 20 73 M IV T1aNxM1 Left Upper Lobe of Lung
CGPLLU271 Lung Cancer cfDNA Post-treatment, Day 104 73 M IV T1aNxM1 Left Upper Lobe of Lung
CGPLLU43 Lung Cancer cfDNA Pre-treatment, Day −1 57 F IV T1BN0M0 Right Lower Lobe of Lung
CGPLLU43 Lung Cancer cfDNA Post-treatment, Day 6 57 F IV T1BN0M0 Right Lower Lobe of Lung
CGPLLU43 Lung Cancer cfDNA Post-treatment, Day 27 57 F IV T1BN0M0 Right Lower Lobe of Lung
CGPLLU43 Lung Cancer cfDNA Post-treatment, Day 83 57 F IV T1BN0M0 Right Lower Lobe of Lung
CGPLLU86 Lung Cancer cfDNA Pre-treatment, Day 0 55 M IV NA Left Upper Lobe of Lung
CGPLLU86 Lung Cancer cfDNA Post-treatment, Day 0.5 55 M IV NA Left Upper Lobe of Lung
CGPLLU86 Lung Cancer cfDNA Post-treatment, Day 7 55 M IV NA Left Upper Lobe of Lung
CGPLLU86 Lung Cancer cfDNA Post-treatment, Day 17 55 M IV NA Left Upper Lobe of Lung
CGPLLU88 Lung Cancer cfDNA Pre-treatment, Day 0 59 M IV NA Right Middle Lobe of Lung
CGPLLU88 Lung Cancer cfDNA Post-treatment, Day 7 59 M IV NA Right Middle Lobe of Lung
CGPLLU88 Lung Cancer cfDNA Post-treatment, Day 297 59 M IV NA Right Middle Lobe of Lung
CGPLLU89 Lung Cancer cfDNA Pre-treatment, Day 0 54 F IV NA Right Upper Lobe of Lung
CGPLLU89 Lung Cancer cfDNA Post-treatment, Day 7 54 F IV NA Right Upper Lobe of Lung
CGPLLU89 Lung Cancer cfDNA Post-treatment, Day 22 54 F IV NA Right Upper Lobe of Lung
CGPLOV11 Ovarian Cancer cfDNA Preoperative treatment naïve 51 F IV T3cN0M1 Right Ovary
CGPLOV12 Ovarian Cancer cfDNA Preoperative treatment naïve 45 F I T1aN0MX Ovary
CGPLOV13 Ovarian Cancer cfDNA Preoperative treatment naïve 62 F IV T1bN0M1 Right Ovary
CGPLOV15 Ovarian Cancer cfDNA Preoperative treatment naïve 54 F III T3N1M0 Ovary
CGPLOV16 Ovarian Cancer cfDNA Preoperative treatment naïve 40 F III T3aN0M0 Ovary
CGPLOV19 Ovarian Cancer cfDNA Preoperative treatment naïve 52 F II T2aN0M0 Ovary
CGPLOV20 Ovarian Cancer cfDNA Preoperative treatment naïve 52 F II T2aN0M0 Left Ovary
CGPLOV21 Ovarian Cancer cfDNA Preoperative treatment naïve 51 M IV TanyN1M1 Ovary
CGPLOV22 Ovarian Cancer cfDNA Preoperative treatment naïve 54 F III T1cNXMX Left Ovary
CGPLOV23 Ovarian Cancer cfDNA Preoperative treatment naïve 47 F I T1aN0M0 Ovary
CGPLOV24 Ovarian Cancer cfDNA Preoperative treatment naïve 14 F I T1aN0M0 Ovary
CGPLOV25 Ovarian Cancer cfDNA Preoperative treatment naïve 18 F I T1aN0M0 Ovary
CGPLOV26 Ovarian Cancer cfDNA Preoperative treatment naïve 35 F I T1aN0M0 Ovary
CGPLOV28 Ovarian Cancer cfDNA Preoperative treatment naïve 63 F I T1aNxM0 Right Ovary
CGPLOV31 Ovarian Cancer cfDNA Preoperative treatment naïve 45 F III T3aNxM0 Right Ovary
CGPLOV32 Ovarian Cancer cfDNA Preoperative treatment naïve 53 F I T1aNxM0 Left Ovary
CGPLOV37 Ovarian Cancer cfDNA Preoperative treatment naïve 40 F I T1cN0M0 Ovary
CGPLOV38 Ovarian Cancer cfDNA Preoperative treatment naïve 46 F I T1cN0M0 Ovary
CGPLOV40 Ovarian Cancer cfDNA Preoperative treatment naïve 53 F IV T3aN0M0 Ovary
CGPLOV41 Ovarian Cancer cfDNA Preoperative treatment naïve 57 F IV T3N0M1 Ovary
CGPLOV42 Ovarian Cancer cfDNA Preoperative treatment naïve 52 F I T3N0M1 Ovary
CGPLOV43 Ovarian Cancer cfDNA Preoperative treatment naïve 30 F I T3aN0M0 Ovary
CGPLOV44 Ovarian Cancer cfDNA Preoperative treatment naïve 69 F I T1aN0M0 Ovary
CGPLOV46 Ovarian Cancer cfDNA Preoperative treatment naïve 58 F I T1bN0M0 Ovary
CGPLOV47 Ovarian Cancer cfDNA Preoperative treatment naïve 41 F I T1aN0M0 Ovary
CGPLOV48 Ovarian Cancer cfDNA Preoperative treatment naïve 52 F I T1bN0M0 Ovary
CGPLOV49 Ovarian Cancer cfDNA Preoperative treatment naïve 68 F III T3bN0M0 Ovary
CGPLOV50 Ovarian Cancer cfDNA Preoperative treatment naïve 30 F III T3cN0M0 Ovary
CGPLPA112 Pancreatic Cancer cfDNA Preoperative treatment naïve 58 M II NA Intra Pancreatic Bile Duct
CGPLPA113 Duodenal Cancer cfDNA Preoperative treatment naïve 71 M I NA Intra Pancreatic Bile Duct
CGPLPA114 Bile Duct Cancer cfDNA Preoperative treatment naïve NA F II NA Intra Pancreatic Bile Duct
CGPLPA115 Bile Duct Cancer cfDNA Preoperative treatment naïve NA M IV NA Intra Hepatic Bile Duct
CGPLPA117 Bile Duct Cancer cfDNA Preoperative treatment naïve NA M II NA Intra Pancreatic Bile Duct
CGPLPA118 Bile Duct Cancer cfDNA Preoperative treatment naïve 68 F I NA Bile Duct
CGPLPA122 Bile Duct Cancer cfDNA Preoperative treatment naïve 62 F II NA Bile Duct
CGPLPA124 Bile Duct Cancer cfDNA Preoperative treatment naïve 83 F II NA Bile Duct
CGPLPA125 Bile Duct Cancer cfDNA Preoperative treatment naïve 58 M II NA Bile Duct
CGPLPA126 Bile Duct Cancer cfDNA Preoperative treatment naïve 68 M II NA Bile Duct
CGPLPA127 Bile Duct Cancer cfDNA Preoperative treatment naïve 71 F IV NA Bile Duct
CGPLPA128 Bile Duct Cancer cfDNA Preoperative treatment naïve 67 M II NA Bile Duct
CGPLPA129 Bile Duct Cancer cfDNA Preoperative treatment naïve 56 F II NA Bile Duct
CGPLPA130 Bile Duct Cancer cfDNA Preoperative treatment naïve 82 F II NA Bile Duct
CGPLPA131 Bile Duct Cancer cfDNA Preoperative treatment naïve 71 M II NA Bile Duct
CGPLPA134 Bile Duct Cancer cfDNA Preoperative treatment naïve 68 M II NA Bile Duct
CGPLPA135 Bile Duct Cancer cfDNA Preoperative treatment naïve 67 F I NA Bile Duct
CGPLPA136 Bile Duct Cancer cfDNA Preoperative treatment naïve 69 F II NA Bile Duct
CGPLPA137 Bile Duct Cancer cfDNA Preoperative treatment naïve NA M II NA Bile Duct
CGPLPA139 Bile Duct Cancer cfDNA Preoperative treatment naïve NA M IV NA Bile Duct
CGPLPA14 Pancreatic Cancer cfDNA Preoperative treatment naïve 68 M II NA Pancreas
CGPLPA140 Bile Duct Cancer cfDNA Preoperative treatment naïve 52 M II NA Extra Hepatic Bile Duct
CGPLPA141 Bile Duct Cancer cfDNA Preoperative treatment naïve 68 F II NA Extra Hepatic Bile Duct
CGPLPA15 Pancreatic Cancer cfDNA Preoperative treatment naïve 70 F II NA Pancreas
CGPLPA155 Bile Duct Cancer cfDNA Preoperative treatment naïve NA F II NA NA
CGPLPA156 Pancreatic Cancer cfDNA Preoperative treatment naïve 73 F II NA Pancreas
CGPLPA165 Bile Duct Cancer cfDNA Preoperative treatment naïve 42 M I NA Bile Duct
CGPLPA168 Bile Duct Cancer cfDNA Preoperative treatment naïve 58 M II NA Bile Duct
CGPLPA17 Pancreatic Cancer cfDNA Preoperative treatment naïve 65 M II NA Pancreas
CGPLPA184 Bile Duct Cancer cfDNA Preoperative treatment naïve 75 F II NA Bile Duct
CGPLPA187 Bile Duct Cancer cfDNA Preoperative treatment naïve 67 F II NA Bile Duct
CGPLPA23 Pancreatic Cancer cfDNA Preoperative treatment naïve 58 F II NA Pancreas
CGPLPA25 Pancreatic Cancer cfDNA Preoperative treatment naïve 65 F II NA Pancreas
CGPLPA26 Pancreatic Cancer cfDNA Preoperative treatment naïve 64 M II NA Pancreas
CGPLPA28 Pancreatic Cancer cfDNA Preoperative treatment naïve 79 F II NA Pancreas
CGPLPA33 Pancreatic Cancer cfDNA Preoperative treatment naïve 67 F II NA Pancreas
CGPLPA34 Pancreatic Cancer cfDNA Preoperative treatment naïve 73 M II NA Pancreas
CGPLPA37 Pancreatic Cancer cfDNA Preoperative treatment naïve 67 F II NA Pancreas
CGPLPA38 Pancreatic Cancer cfDNA Preoperative treatment naïve 65 M II NA Pancreas
CGPLPA39 Pancreatic Cancer cfDNA Preoperative treatment naïve 67 F II NA Pancreas
CGPLPA40 Pancreatic Cancer cfDNA Preoperative treatment naïve 64 M II NA Pancreas
CGPLPA42 Pancreatic Cancer cfDNA Preoperative treatment naïve 73 M II NA Pancreas
CGPLPA46 Pancreatic Cancer cfDNA Preoperative treatment naïve 59 F II NA Pancreas
CGPLPA47 Pancreatic Cancer cfDNA Preoperative treatment naïve 67 M II NA Pancreas
CGPLPA48 Pancreatic Cancer cfDNA Preoperative treatment naïve 72 F I NA Pancreas
CGPLPA52 Pancreatic Cancer cfDNA Preoperative treatment naïve 63 M II NA Pancreas
CGPLPA53 Pancreatic Cancer cfDNA Preoperative treatment naïve 46 M I NA Pancreas
CGPLPA58 Pancreatic Cancer cfDNA Preoperative treatment naïve 74 F II NA Pancreas
CGPLPA59 Pancreatic Cancer cfDNA Preoperative treatment naïve 59 F II NA Pancreas
CGPLPA67 Pancreatic Cancer cfDNA Preoperative treatment naïve 55 M III NA Pancreas
CGPLPA69 Pancreatic Cancer cfDNA Preoperative treatment naïve 70 M I NA Pancreas
CGPLPA71 Pancreatic Cancer cfDNA Preoperative treatment naïve 64 M II NA Pancreas
CGPLPA74 Pancreatic Cancer cfDNA Preoperative treatment naïve 71 F II NA Pancreas
CGPLPA76 Pancreatic Cancer cfDNA Preoperative treatment naïve 69 M II NA Pancreas
CGPLPA85 Pancreatic Cancer cfDNA Preoperative treatment naïve 77 F II NA Pancreas
CGPLPA86 Pancreatic Cancer cfDNA Preoperative treatment naïve 66 M II NA Pancreas
CGPLPA92 Pancreatic Cancer cfDNA Preoperative treatment naïve 72 M II NA Pancreas
CGPLPA93 Pancreatic Cancer cfDNA Preoperative treatment naïve 48 M II NA Pancreas
CGPLPA94 Pancreatic Cancer cfDNA Preoperative treatment naïve 72 F II NA Pancreas
CGPLPA95 Pancreatic Cancer cfDNA Preoperative treatment naïve 64 F II NA Pancreas
CGST102 Gastric cancer cfDNA Preoperative treatment naïve 76 F II T3N0M0 Stomach
CGST11 Gastric cancer cfDNA Preoperative treatment naïve 49 M IV TXNXM1 Stomach
CGST110 Gastric cancer cfDNA Preoperative treatment naïve 77 M III T4AN3aM0 Stomach
CGST114 Gastric cancer cfDNA Preoperative treatment naïve 65 M III T4AN1M0 Stomach
CGST13 Gastric cancer cfDNA Preoperative treatment naïve 72 F II T1AN2M0 Stomach
CGST131 Gastric cancer cfDNA Preoperative treatment naïve 63 M III T2N3aM0 Stomach
CGST141 Gastric cancer cfDNA Preoperative treatment naïve 38 F III T3N2M0 Stomach
CGST16 Gastric cancer cfDNA Preoperative treatment naïve 78 M III T4AN3aM0 Stomach
CGST18 Gastric cancer cfDNA Preoperative treatment naïve 56 M II T3N0M0 Stomach
CGST21 Gastric cancer cfDNA Preoperative treatment naïve 39 M II T2N1 (mi)M0 Stomach
CGST26 Gastric cancer cfDNA Preoperative treatment naïve 51 M IV TXNXM1 Stomach
CGST28 Gastric cancer cfDNA Preoperative treatment naïve 55 M X TXNXMx Stomach
CGST30 Gastric cancer cfDNA Preoperative treatment naïve 64 F III T3N2M0 Stomach
CGST32 Gastric cancer cfDNA Preoperative treatment naïve 67 M II T3N1M0 Stomach
CGST33 Gastric cancer cfDNA Preoperative treatment naïve 61 M I T2N0M0 Stomach
CGST38 Gastric cancer cfDNA Preoperative treatment naïve 71 F 0 T0N0M0 Stomach
CGST39 Gastric cancer cfDNA Preoperative treatment naïve 51 M IV TXNXM1 Stomach
CGST41 Gastric cancer cfDNA Preoperative treatment naïve 66 F IV TXNXM1 Stomach
CGST45 Gastric cancer cfDNA Preoperative treatment naïve 41 F II T3N0M0 Stomach
CGST47 Gastric cancer cfDNA Preoperative treatment naïve 74 F I T1AN0M0 Stomach
CGST48 Gastric cancer cfDNA Preoperative treatment naïve 62 M IV TXNXM1 Stomach
CGST53 Gastric cancer cfDNA Preoperative treatment naïve 70 M 0 T0N0M0 Stomach
CGST58 Gastric cancer cfDNA Preoperative treatment naïve 58 M III T4AN3bM0 Stomach
CGST67 Gastric cancer cfDNA Preoperative treatment naïve 69 M I T1BN0M0 Stomach
CGST77 Gastric cancer cfDNA Preoperative treatment naïve 70 M IV TXNXM1 Stomach
CGST80 Gastric cancer cfDNA Preoperative treatment naïve 58 M III T3N3aM0 Stomach
CGST81 Gastric cancer cfDNA Preoperative treatment naïve 64 F I T2N0Mx Stomach
CGH14 Healthy Human adult NA NA M NA NA NA
elutriated
lymophocytes
CGH15 Healthy Human adult NA NA F NA NA NA
elutriated
lymophocytes
Whole Tar- Tar-
Genome geted geted
Volume cfDNA Fragment Fragment Muta-
Degree of Location of of Ex- cfDNA Profile Profile tion
Histopathological Differen- Metastases at Plasma tracted Input Anal- Anal- Anal-
Patient Diagnosis tiation Diagnosis (ml) (ng/ml) (ng/ml) ysis ysis ysis
CGCRC291 Adenocarcinoma Moderate Synchronous Liver 7.9 7.80 7.80 Y Y Y
CGCRC292 Adenocarcinoma Moderate Synchronous 7.9 6.73 6.73 Y Y Y
liver, Lung
CGCRC293 Adenocarcinoma Moderate Synchronous Liver 7.2 3.83 3.83 Y Y Y
CGCRC294 Adenocarcinoma Moderate None 8.4 18.87 18.87 Y Y Y
CGCRC296 Adenocarcinoma Poor None 4.3 31.24 31.24 Y Y Y
CGCRC299 Adenocarcinoma Moderate None 8.8 10.18 10.18 Y Y Y
CGCRC300 Adenocarcinoma Moderate None 4.3 10.48 10.48 Y Y Y
CGCRC301 Adenocarcinoma Moderate None 4.1 6.51 6.51 Y Y Y
CGCRC302 Adenocarcinoma Moderate None 4.3 52.13 52.13 Y Y Y
CGCRC304 Adenocarcinoma Moderate None 4.1 30.19 30.19 Y Y Y
CGCRC305 Adenocarcinoma Moderate None 8.6 9.10 9.10 Y Y Y
CGCRC306 Adenocarcinoma Moderate None 4.5 24.31 24.31 Y Y Y
CGCRC307 Adenocarcinoma Moderate None 8.5 14.26 14.26 Y Y Y
CGCRC308 Adenocarcinoma Moderate None 4.3 46.87 46.87 Y Y Y
CGCRC311 Adenocarcinoma Moderate None 8.5 3.91 3.91 Y Y Y
CGCRC315 Adenocarcinoma Moderate None 8.6 9.67 9.67 Y Y Y
CGCRC316 Adenocarcinoma Moderate None 4.9 52.16 52.16 Y Y Y
CGCRC317 Adenocarcinoma Moderate None 8.8 16.08 16.08 Y Y Y
CGCRC318 Adenocarcinoma Moderate None 9.8 18.24 18.24 Y Y Y
CGCRC319 Adenocarcinoma Moderate None 4.2 53.54 53.54 Y N Y
CGCRC320 Adenocarcinoma Moderate None 4.5 30.37 30.37 Y Y Y
CGCRC321 Adenocarcinoma Moderate None 9.3 4.25 4.25 Y Y Y
CGCRC333 Adenocarcinoma NA Liver 4.0 113.88 113.88 Y Y Y
CGCRC336 Adenocarcinoma NA Liver 4.4 211.74 211.74 Y Y Y
CGCRC338 Adenocarcinoma NA Liver 2.3 109.76 109.76 Y Y Y
CGCRC341 Adenocarcinoma NA Liver 4.6 156.62 156.62 Y N Y
CGCRC342 Adenocarcinoma NA Liver 3.9 56.09 56.09 Y N Y
CGLU316 Adeno, Squamous, Poor Lung 5.0 2.38 2.38 Y N Y
Small Cell Carcinoma
CGLU316 Adeno, Squamous, Poor Lung 5.0 2.11 2.11 Y N Y
Small Cell Carcinoma
CGLU316 Adeno, Squamous, Poor Lung 5.0 0.87 1.07 Y N Y
Small Cell Carcinoma
CGLU316 Adeno, Squamous, Poor Lung 2.0 8.74 8.75 Y N Y
Small Cell Carcinoma
CGLU344 Adenocarcinoma NA Pleura, Liver, 5.0 34.77 25.00 Y N Y
Peritoneum
CGLU344 Adenocarcinoma NA Pleura, Liver, 5.0 15.63 15.64 Y N Y
Peritoneum
CGLU344 Adenocarcinoma NA Pleura, Liver, 5.0 9.22 9.22 Y N Y
Peritoneum
CGLU344 Adenocarcinoma NA Pleura, Liver, 5.0 5.31 5.32 Y N Y
Peritoneum
CGLU369 Adenocarcinoma NA Brain 2.0 11.28 11.28 Y N Y
CGLU369 Adenocarcinoma NA Brain 5.0 10.09 10.09 Y N Y
CGLU369 Adenocarcinoma NA Brain 5.0 6.69 6.70 Y N Y
CGLU369 Adenocarcinoma NA Brain 5.0 8.41 8.42 Y N Y
CGLU373 Adenocarcinoma Moderate None 5.0 6.35 6.35 Y N Y
CGLU373 Adenocarcinoma Moderate None 5.0 6.28 6.28 Y N Y
CGLU373 Adenocarcinoma Moderate None 5.0 3.82 3.82 Y N Y
CGLU373 Adenocarcinoma Moderate None 3.5 5.55 5.55 Y N Y
CGPLBR100 Infiltration Ductal Carcinoma NA None 4.0 4.25 4.25 Y N Y
CGPLBR101 Infiltration Lobular Carcinoma Moderate None 4.0 37.88 37.88 Y N Y
CGPLBR102 Infiltration Ductal Carcinoma Moderate None 3.6 13.87 13.67 Y N Y
CGPLBR103 Infiltration Ductal Carcinoma Moderate None 3.6 7.11 7.11 Y N Y
CGPLBR104 Infiltration Lobular Carcinoma Moderate None 4.7 19.89 19.89 Y N Y
CGPLBR12 Ductal Carcinoma insitu NA NA 4.3 4.21 4.21 Y N N
with Microinvasion
CGPLBR18 Infiltration Lobular Carcinoma NA NA 4.1 40.39 30.49 Y N N
CGPLBR23 Infiltration Ductal Carcinoma NA None 4.7 20.09 20.09 Y N N
CGPLBR24 Infiltration Ductal Carcinoma NA None 3.6 58.33 34.72 Y N N
CGPLBR28 Infiltration Ductal Carcinoma NA None 4.2 12.86 12.86 Y N N
CGPLBR30 Infiltration Ductal Carcinoma NA None 4.1 59.73 30.49 Y N N
CGPLBR31 Infiltration Ductal Carcinoma NA None 3.4 23.94 23.94 Y N N
CGPLBR32 Infiltration Ductal Carcinoma NA None 4.4 71.23 28.41 Y N N
CGPLBR33 Infiltration Lobular Carcinoma NA None 4.4 11.00 11.00 Y N N
CGPLBR34 Infiltration Lobular Carcinoma NA None 4.4 23.61 23.61 Y N N
CGPLBR35 Ductal Carcinoma insitu NA None 4.5 22.58 22.58 Y N N
with Microinvasion
CGPLBR36 Infiltration Ductal Carcinoma NA None 4.4 17.23 17.73 Y N N
CGPLBR37 Infiltration Ductal Carcinoma NA None 4.4 9.39 9.39 Y N N
CGPLBR38 Infiltration Ductal Carcinoma Moderate None 4.0 5.77 5.77 Y Y Y
CGPLBR40 Infiltration Ductal Carcinoma Poor None 4.6 15.69 15.69 Y Y Y
CGPLBR41 Infiltration Ductal Carcinoma Moderate None 4.5 11.56 11.56 Y N Y
CGPLBR45 Infiltration Ductal Carcinoma NA None 4.5 20.36 20.36 Y N N
CGPLBR46 Infiltration Ductal Carcinoma NA None 3.5 20.17 20.17 Y N N
CGPLBR47 Infiltration Ductal Carcinoma NA None 4.5 13.89 13.89 Y N N
CGPLBR48 Infiltration Ductal Carcinoma Poor None 3.9 7.07 7.07 Y Y Y
CGPLBR49 Infiltration Ductal Carcinoma Poor None 4.0 5.74 5.74 Y N Y
CGPLBR50 Infiltration Ductal Carcinoma NA None 4.5 45.58 27.78 Y N N
CGPLBR51 Infiltration Ductal Carcinoma NA None 4.0 8.83 8.83 Y N N
CGPLBR52 Infiltration Ductal Carcinoma NA None 4.5 80.71 27.78 Y N N
CGPLBR55 Infiltration Ductal Carcinoma Poor None 4.3 4.57 4.57 Y Y Y
CGPLBR56 Infiltration Ductal Carcinoma NA None 4.5 22.16 22.16 Y N N
CGPLBR57 Infiltration Ductal Carcinoma NA None 4.3 4.02 4.02 Y N Y
CGPLBR59 Infiltration Ductal Carcinoma Moderate None 4.1 8.24 8.24 Y N Y
CGPLBR60 Infiltration Ductal Carcinoma NA None 4.5 11.09 11.09 Y N N
CGPLBR61 Infiltration Ductal Carcinoma Moderate None 4.1 13.25 13.25 Y N Y
CGPLBR63 Infiltration Ductal Carcinoma Moderate None 4.0 6.19 6.19 Y Y Y
CGPLBR65 Infiltration Ductal Carcinoma NA None 3.5 41.75 35.71 Y N N
CGPLBR68 Infiltration Ductal Carcinoma Poor None 3.4 10.41 10.41 Y N Y
CGPLBR69 Infiltration Ductal Carcinoma Moderate None 4.4 4.07 4.07 Y Y Y
CGPLBR70 Infiltration Ductal Carcinoma Moderate None 3.4 11.94 11.94 Y Y Y
CGPLBR71 Infiltration Ductal Carcinoma Poor None 3.1 7.64 7.64 Y Y Y
CGPLBR72 Infiltration Ductal Carcinoma Well None 3.9 4.43 4.43 Y Y Y
CGPLBR73 Infiltration Ductal Carcinoma Moderate None 3.3 14.69 14.69 Y Y Y
CGPLBR76 Infiltration Ductal Carcinoma Well None 4.9 8.71 8.71 Y Y Y
CGPLBR81 Infiltration Ductal Carcinoma NA None 2.5 83.14 50.00 Y N N
CGPLBR82 Infiltration Lobular Carcinoma Moderate None 4.8 23.39 23.39 Y N Y
CGPLBR83 Infiltration Ductal Carcinoma Moderate None 3.7 100.17 100.17 Y Y Y
CGPLBR84 Infiltration Ductal Carcinoma NA NA 3.6 16.95 16.95 Y N N
CGPLBR87 Papillary Carcinoma Well None 3.6 277.39 69.44 Y Y Y
CGPLBR88 Infiltration Ductal Carcinoma Poor None 3.6 49.75 49.75 Y Y Y
CGPLBR90 Infiltration Ductal Carcinoma NA None 3.0 14.24 14.24 Y N N
CGPLBR91 Infiltration Lobular Carcinoma Poor None 3.2 22.41 22.41 Y N Y
CGPLBR92 Infiltration Medullary Carcinoma Poor None 3.1 81.00 81.00 Y Y Y
CGPLBR93 Infiltration Ductal Carcinoma Moderate None 3.3 27.94 27.94 Y N Y
CGPLH189 NA NA NA 5.0 5.84 5.84 Y N N
CGPLH190 NA NA NA 4.7 18.07 18.07 Y N N
CGPLH192 NA NA NA 4.7 12.19 12.19 Y N N
CGPLH193 NA NA NA 5.0 5.47 5.47 Y N N
CGPLH194 NA NA NA 5.0 9.98 9.98 Y N N
CGPLH196 NA NA NA 5.0 11.69 11.69 Y N N
CGPLH197 NA NA NA 5.0 5.69 5.69 Y N N
CGPLH198 NA NA NA 5.0 4.36 4.36 Y N N
CGPLH199 NA NA NA 5.0 9.77 9.77 Y N N
CGPLH200 NA NA NA 5.0 5.60 5.60 Y N N
CGPLH201 NA NA NA 5.0 8.82 8.82 v N N
CGPLH202 NA NA NA 5.0 5.54 5.54 Y N N
CGPLH203 NA NA NA 5.0 9.03 9.03 Y N N
CGPLH205 NA NA NA 5.0 4.74 4.74 Y N N
CGPLH208 NA NA NA 5.0 4.67 4.67 Y N N
CGPLH209 NA NA NA 5.0 5.15 5.15 Y N N
CGPLH210 NA NA NA 5.0 5.41 5.41 Y N N
CGPLH211 NA NA NA 5.0 6.24 6.24 Y N N
CGPLH300 NA NA NA 4.4 6.75 6.75 Y N N
CGPLH307 NA NA NA 4.5 3.50 3.50 Y N N
CGPLH308 NA NA NA 4.5 6.01 6.01 Y N N
CGPLH309 NA NA NA 4.5 5.21 5.21 Y N N
CGPLH310 NA NA NA 4.5 15.25 15.25 Y N N
CGPLH311 NA NA NA 4.5 4.47 4.47 Y N N
CGPLH314 NA NA NA 4.5 9.62 9.62 Y N N
CGPLH314 NA NA NA 4.4 16.24 16.24 Y N N
CGPLH315 NA NA NA 4.2 11.55 11.55 Y N N
CGPLH316 NA NA NA 4.5 28.92 27.78 Y N N
CGPLH317 NA NA NA 4.5 7.62 7.62 Y N N
CGPLH319 NA NA NA 4.2 4.41 4.41 Y N N
CGPLH320 NA NA NA 4.5 6.93 6.93 Y N N
CGPLH322 NA NA NA 4.2 8.17 8.17 Y N N
CGPLH324 NA NA NA 5.0 6.63 6.63 Y N N
CGPLH325 NA NA NA 4.6 4.15 4.15 Y N N
CGPLH326 NA NA NA 4.5 6.06 6.06 Y N N
CGPLH327 NA NA NA 1.8 1.24 1.24 Y N N
CGPLH328 NA NA NA 4.4 3.42 3.42 Y N N
CGPLH328 NA NA NA 4.9 5.47 5.47 Y N N
CGPLH329 NA NA NA 4.5 5.27 5.27 Y N N
CGPLH330 NA NA NA 4.3 10.21 10.21 Y N N
CGPLH331 NA NA NA 4.6 2.63 2.63 Y N N
CGPLH331 NA NA NA 4.3 4.15 4.15 Y N N
CGPLH333 NA NA NA 4.7 4.06 4.06 Y N N
CGPLH335 NA NA NA 4.4 9.39 9.39 Y N N
CGPLH336 NA NA NA 4.6 6.64 6.64 Y N N
CGPLH337 NA NA NA 4.2 4.48 4.48 Y N N
CGPLH338 NA NA NA 4.5 59.44 27.78 Y N N
CGPLH339 NA NA NA 4.5 12.27 12.27 Y N N
CGPLH340 NA NA NA 4.5 4.86 4.86 Y N N
CGPLH341 NA NA NA 4.1 7.62 7.62 Y N N
CGPLH342 NA NA NA 4.2 18.29 18.29 Y N N
CGPLH343 NA NA NA 4.5 3.49 3.49 Y N N
CGPLH344 NA NA NA 4.2 8.41 8.41 Y N N
CGPLH345 NA NA NA 4.5 9.73 9.73 Y N N
CGPLH346 NA NA NA 4.5 7.86 7.86 Y N N
CGPLH35 NA NA NA 4.0 13.15 13.15 Y N Y
CGPLH350 NA NA NA 3.5 6.09 6.09 Y N N
CGPLH351 NA NA NA 4.0 15.91 15.91 Y N N
CGPLH352 NA NA NA 4.2 6.47 6.47 Y N N
CGPLH353 NA NA NA 4.2 4.47 4.47 Y N N
CGPLH354 NA NA NA 4.2 17.49 17.49 Y N N
CGPLH355 NA NA NA 4.2 11.58 11.58 Y N N
CGPLH356 NA NA NA 4.5 3.94 3.94 Y N N
CGPLH357 NA NA NA 4.2 11.79 11.79 Y N N
CGPLH358 NA NA NA 4.2 21.06 21.06 Y N N
CGPLH36 NA NA NA 4.0 13.00 13.00 Y N Y
CGPLH360 NA NA NA 4.2 3.48 3.48 Y N N
CGPLH361 NA NA NA 4.3 6.98 6.98 Y N N
CGPLH362 NA NA NA 4.4 8.49 8.49 Y N N
CGPLH363 NA NA NA 4.5 4.44 4.44 Y N N
CGPLH364 NA NA NA 4.5 17.31 17.31 Y N N
CGPLH365 NA NA NA 4.5 0.55 0.55 Y N N
CGPLH366 NA NA NA 4.5 4.88 4.88 Y N N
CGPLH367 NA NA NA 4.4 6.48 6.48 Y N N
CGPLH368 NA NA NA 4.3 2.63 2.63 Y N N
CGPLH369 NA NA NA 4.3 10.18 10.18 Y N N
CGPLH369 NA NA NA 4.4 10.71 10.71 Y N N
CGPLH37 NA NA NA 4.0 9.73 9.73 Y N Y
CGPLH370 NA NA NA 4.5 7.22 7.22 Y N N
CGPLH371 NA NA NA 4.6 5.62 5.62 Y N N
CGPLH380 NA NA NA 4.2 6.61 6.61 Y N N
CGPLH381 NA NA NA 4.2 27.38 27.33 Y N N
CGPLH382 NA NA NA 4.5 11.58 11.58 Y N N
CGPLH383 NA NA NA 4.5 25.50 25.50 Y N N
CGPLH384 NA NA NA 4.5 15.66 15.66 Y N N
CGPLH385 NA NA NA 4.5 19.35 19.35 Y N N
CGPLH386 NA NA NA 4.5 6.46 6.46 Y N N
CGPLH386 NA NA NA 4.6 6.54 6.54 Y N N
CGPLH387 NA NA NA 4.5 6.19 6.19 Y N N
CGPLH388 NA NA NA 4.5 6.62 6.62 Y N N
CGPLH389 NA NA NA 4.6 14.78 14.78 Y N N
CGPLH390 NA NA NA 4.5 12.14 12.14 Y N N
CGPLH391 NA NA NA 4.5 8.88 8.88 Y N N
CGPLH391 NA NA NA 4.5 8.37 8.37 Y N N
CGPLH392 NA NA NA 4.5 8.39 8.39 Y N N
CGPLH393 NA NA NA 4.5 5.27 5.27 Y N N
CGPLH394 NA NA NA 4.4 3.79 3.79 Y N N
CGPLH395 NA NA NA 4.4 9.56 9.56 Y N N
CGPLH395 NA NA NA 4.4 5.40 5.40 Y N N
CGPLH396 NA NA NA 4.4 20.31 20.31 Y N N
CGPLH398 NA NA NA 4.3 13.01 13.01 Y N N
CGPLH399 NA NA NA 4.4 4.79 4.79 Y N N
CGPLH400 NA NA NA 4.4 7.70 7.70 Y N N
CGPLH400 NA NA NA 4.4 6.26 6.26 Y N N
CGPLH401 NA NA NA 4.3 13.01 13.01 Y N N
CGPLH401 NA NA NA 4.4 11.13 11.13 Y N N
CGPLH402 NA NA NA 4.5 2.89 2.89 Y N N
CGPLH403 NA NA NA 4.3 4.41 4.41 Y N N
CGPLH404 NA NA NA 4.2 6.38 6.38 Y N N
CGPLH405 NA NA NA 4.4 7.28 7.28 Y N N
CGPLH406 NA NA NA 4.2 5.40 5.40 Y N N
CGPLH407 NA NA NA 4.0 13.30 13.30 Y N N
CGPLH408 NA NA NA 4.2 5.18 5.18 Y N N
CGPLH409 NA NA NA 3.7 3.98 3.98 Y N N
CGPLH410 NA NA NA 4.1 6.91 6.91 Y N N
CGPLH411 NA NA NA 4.1 3.30 3.30 Y N N
CGPLH412 NA NA NA 4.1 5.55 5.55 Y N N
CGPLH413 NA NA NA 4.5 8.18 8.18 Y N N
CGPLH414 NA NA NA 3.8 5.85 5.85 Y N N
CGPLH415 NA NA NA 4.7 10.20 10.20 Y N N
CGPLH416 NA NA NA 4.5 11.73 11.73 Y N N
CGPLH417 NA NA NA 4.2 10.98 10.98 Y N N
CGPLH418 NA NA NA 4.5 10.96 10.96 Y N N
CGPLH419 NA NA NA 4.5 10.17 10.17 Y N N
CGPLH42 NA NA NA 4.0 14.30 14.30 Y N Y
CGPLH420 NA NA NA 4.2 12.32 12.32 Y N N
CGPLH422 NA NA NA 4.6 5.42 5.42 Y N N
CGPLH423 NA NA NA 4.2 2.85 2.85 Y N N
CGPLH424 NA NA NA 4.7 1.66 1.66 Y N N
CGPLH425 NA NA NA 4.4 5.98 5.98 Y N N
CGPLH426 NA NA NA 4.4 2.84 2.84 Y N N
CGPLH427 NA NA NA 4.4 10.86 10.86 Y N N
CGPLH428 NA NA NA 4.5 6.27 6.27 Y N N
CGPLH429 NA NA NA 4.5 3.89 3.89 Y N N
CGPLH43 NA NA NA 4.0 8.50 8.50 Y N Y
CGPLH430 NA NA NA 4.2 10.33 10.33 Y N N
CGPLH431 NA NA NA 4.8 12.81 12.81 Y N N
CGPLH432 NA NA NA 4.8 2.42 2.42 Y N N
CGPLH434 NA NA NA 4.6 8.83 8.83 Y N N
CGPLH435 NA NA NA 4.5 8.95 8.95 Y N N
CGPLH436 NA NA NA 4.5 4.29 4.29 Y N N
CGPLH437 NA NA NA 4.6 18.07 18.07 Y N N
CGPLH438 NA NA NA 4.8 16.62 16.62 Y N N
CGPLH439 NA NA NA 4.7 4.38 4.38 Y N N
CGPLH440 NA NA NA 4.7 4.32 4.32 Y N N
CGPLH441 NA NA NA 4.7 7.80 7.80 Y N N
CGPLH442 NA NA NA 4.5 6.15 6.15 Y N N
CGPLH443 NA NA NA 4.4 3.44 3.44 Y N N
CGPLH444 NA NA NA 4.4 4.12 4.12 Y N N
CGPLH445 NA NA NA 4.4 4.38 4.38 Y N N
CGPLH446 NA NA NA 4.4 2.92 2.92 Y N N
CGPLH447 NA NA NA 4.6 3.87 3.87 Y N N
CGPLH448 NA NA NA 4.4 5.29 5.29 Y Y N
CGPLH449 NA NA NA 4.5 3.77 3.77 Y N N
CGPLH45 NA NA NA 4.0 10.85 10.85 Y N Y
CGPLH450 NA NA NA 4.5 5.62 5.62 Y N N
CGPLH451 NA NA NA 4.6 7.24 7.24 Y N N
CGPLH452 NA NA NA 4.4 2.54 2.54 Y N N
CGPLH453 NA NA NA 4.6 9.11 9.11 Y N N
CGPLH455 NA NA NA 4.4 2.64 2.64 Y N N
CGPLH455 NA NA NA 4.5 2.42 2.42 Y N N
CGPLH456 NA NA NA 4.5 3.11 3.11 Y N N
CGPLH457 NA NA NA 4.4 5.92 5.92 Y N N
CGPLH458 NA NA NA 4.5 16.04 16.04 Y N N
CGPLH459 NA NA NA 4.4 6.52 6.52 Y N N
CGPLH46 NA NA NA 4.0 8.25 8.25 Y N Y
CGPLH460 NA NA NA 4.6 5.24 5.24 Y N N
CGPLH463 NA NA NA 4.5 22.77 22.77 Y N N
CGPLH464 NA NA NA 4.4 2.90 2.90 Y N N
CGPLH465 NA NA NA 4.5 4.76 4.76 Y N N
CGPLH466 NA NA NA 4.6 5.68 5.68 Y N N
CGPLH466 NA NA NA 4.5 6.75 6.75 Y N N
CGPLH467 NA NA NA 4.5 4.59 4.59 Y N N
CGPLH468 NA NA NA 4.5 11.19 11.19 Y N N
CGPLH469 NA NA NA 4.5 3.25 3.25 Y N N
CGPLH47 NA NA NA 4.0 7.43 7.43 Y N Y
CGPLH470 NA NA NA 4.5 13.64 13.64 Y N N
CGPLH471 NA NA NA 4.3 13.00 13.00 Y N N
CGPLH472 NA NA NA 4.2 10.17 10.17 Y N N
CGPLH473 NA NA NA 4.3 2.98 2.98 Y N N
CGPLH474 NA NA NA 4.3 29.15 29.15 Y N N
CGPLH475 NA NA NA 4.0 7.26 7.26 Y N N
CGPLH476 NA NA NA 4.3 6.16 6.16 Y N N
CGPLH477 NA NA NA 4.3 15.21 15.21 Y N N
CGPLH478 NA NA NA 4.4 7.29 7.29 Y N N
CGPLH479 NA NA NA 4.5 8.73 8.73 Y N N
CGPLH48 NA NA NA 4.0 6.38 6.38 Y N Y
CGPLH480 NA NA NA 4.4 10.62 10.62 Y N N
CGPLH481 NA NA NA 4.3 6.75 6.75 Y N N
CGPLH482 NA NA NA 4.3 23.58 23.58 Y N N
CGPLH483 NA NA NA 4.4 14.44 14.44 Y N N
CGPLH484 NA NA NA 4.2 14.32 14.32 Y N N
CGPLH485 NA NA NA 4.3 9.64 9.64 Y N N
CGPLH486 NA NA NA 4.3 10.16 10.16 Y N N
CGPLH487 NA NA NA 4.4 6.11 6.11 Y N N
CGPLH488 NA NA NA 4.5 7.88 7.88 Y N N
CGPLH49 NA NA NA 4.0 6.60 6.60 Y N Y
CGPLH490 NA NA NA 4.5 4.18 4.18 Y N N
CGPLH491 NA NA NA 4.5 13.16 13.16 Y N N
CGPLH492 NA NA NA 4.5 3.83 3.83 Y N N
CGPLH493 NA NA NA 4.5 25.06 25.06 Y N N
CGPLH494 NA NA NA 4.4 5.24 5.24 Y N N
CGPLH495 NA NA NA 4.4 5.03 5.03 Y N N
CGPLH496 NA NA NA 4.5 34.01 27.78 Y N N
CGPLH497 NA NA NA 4.5 8.24 8.24 Y N N
CGPLH497 NA NA NA 4.4 5.88 5.88 Y N N
CGPLH498 NA NA NA 4.4 5.33 5.33 Y N N
CGPLH499 NA NA NA 4.5 7.85 7.85 Y N N
CGPLH50 NA NA NA 4.0 7.05 7.05 Y N Y
CGPLH500 NA NA NA 4.5 3.49 3.49 Y N N
CGPLH501 NA NA NA 4.3 6.29 6.29 Y N N
CGPLH502 NA NA NA 4.5 2.74 2.24 Y N N
CGPLH503 NA NA NA 4.5 11.01 11.01 Y N N
CGPLH504 NA NA NA 4.3 6.60 6.60 Y N N
CGPLH504 NA NA NA 4.2 10.02 10.02 Y N N
CGPLH505 NA NA NA 4.1 5.23 5.23 Y N N
CGPLH506 NA NA NA 4.5 12.23 12.23 Y N N
CGPLH507 NA NA NA 4.1 9.89 9.89 Y N N
CGPLH508 NA NA NA 4.5 8.88 8.88 Y N N
CGPLH508 NA NA NA 4.4 9.55 9.55 Y N N
CGPLH509 NA NA NA 4.0 9.79 9.79 Y N N
CGPLH51 NA NA NA 4.0 7.85 7.85 Y N Y
CGPLH510 NA NA NA 4.2 14.20 14.20 Y N N
CGPLH511 NA NA NA 4.5 12.94 12.94 Y N N
CGPLH512 NA NA NA 4.3 8.60 8.60 Y N N
CGPLH513 NA NA NA 4.3 6.54 6.54 Y N N
CGPLH514 NA NA NA 4.4 10.94 10.94 Y N N
CGPLH515 NA NA NA 4.5 8.71 8.71 Y N N
CGPLH516 NA NA NA 4.5 7.32 7.32 Y N N
CGPLH517 NA NA NA 4.6 5.16 5.16 Y N N
CGPLH517 NA NA NA 4.5 9.74 9.74 Y N N
CGPLH518 NA NA NA 4.4 5.92 5.92 Y N N
CGPLH519 NA NA NA 4.4 6.96 6.96 Y N N
CGPLH522 NA NA NA 4.0 9.90 9.90 Y N Y
CGPLH520 NA NA NA 4.3 8.27 8.27 Y N N
CGPLH54 NA NA NA 4.0 14.18 14.18 Y N Y
CGPLH55 NA NA NA 4.0 7.35 7.35 Y N Y
CGPLH56 NA NA NA 4.0 5.20 5.20 Y N Y
CGPLH57 NA NA NA 4.0 7.15 7.15 Y N Y
CGPLH59 NA NA NA 4.0 6.03 6.03 Y N Y
CGPLH625 NA NA NA 4.5 2.64 2.64 Y N N
CGPLH625 NA NA NA 4.5 2.69 2.69 Y N N
CGPLH626 NA NA NA 4.0 11.12 11.12 Y N N
CGPLH63 NA NA NA 4.0 10.10 10.10 Y N Y
CGPLH639 NA NA NA 4.5 2.00 2.00 Y N N
CGPLH64 NA NA NA 4.0 8.03 8.03 Y N Y
CGPLH640 NA NA NA 4.5 9.36 9.36 Y N N
CGPLH642 NA NA NA 4.5 4.99 4.99 Y N N
CGPLH643 NA NA NA 4.4 7.12 7.12 Y N N
CGPLH644 NA NA NA 4.4 5.06 5.06 Y N N
CGPLH646 NA NA NA 4.4 6.75 6.75 Y N N
CGPLH75 NA NA NA 4.0 3.87 3.87 Y N Y
CGPLH76 NA NA NA 4.0 4.03 4.03 Y N Y
CGPLH77 NA NA NA 4.0 5.89 5.89 Y N Y
CGPLH78 NA NA NA 4.0 2.51 2.51 Y N Y
CGPLH79 NA NA NA 4.0 3.68 3.68 Y N Y
CGPLH80 NA NA NA 4.0 1.94 1.94 Y N Y
CGPLH81 NA NA NA 4.0 5.16 5.16 Y N Y
CGPLH82 NA NA NA 4.0 3.30 3.30 Y N Y
CGPLH83 NA NA NA 4.0 5.04 5.04 Y N Y
CGPLH84 NA NA NA 4.0 3.33 3.33 Y N Y
CGPLLU13 Adenocarcinoma NA Bone 5.0 7.67 7.67 Y N Y
CGPLLU13 Adenocarcinoma NA Bone 4.5 8.39 8.39 Y N Y
CGPLLU13 Adenocarcinoma NA Bone 3.2 8.66 8.66 Y N Y
CGPLLU13 Adenocarcinoma NA Bone 5.0 5.97 5.97 Y N Y
CGPLLU14 Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y
CGPLLU14 Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y
CGPLLU14 Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y
CGPLLU14 Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y
CGPLLU14 Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y
CGPLLU14 Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y
CGPLLU144 Adenocarcinoma Poor None 3.5 31.51 31.51 Y Y Y
CGPLLU147 Adenosquamous Carcinoma Poor None 3.8 6.72 6.72 Y Y Y
CGPLLU161 Adenocarcinoma Well None 4.0 83.04 83.04 Y N Y
CGPLLU162 Adenocarcinoma Moderate None 3.1 40.32 40.32 Y Y Y
CGPLLU163 Adenocarcinoma Poor None 5.0 54.03 54.03 Y Y Y
CGPLLU165 Adenocarcinoma Well None 4.5 20.13 20.13 Y Y Y
CGPLLU168 Adenocarcinoma Poor None 4.3 19.38 19.38 Y Y Y
CGPLLU169 Squamous Cell Carcinoma Moderate None 4.2 13.70 13.70 Y N Y
CGPLLU175 Squamous Cell Carcinoma Moderate None 4.4 16.84 16.84 Y Y Y
CGPLLU176 Adenosquamous Carcinoma Moderate None 3.2 7.86 7.86 Y Y Y
CGPLLU177 Adenocarcinoma NA None 3.9 19.07 19.07 Y Y Y
CGPLLU180 Large Cell Carcinoma Poor None 3.2 19.31 19.31 Y Y Y
CGPLLU198 Adenocarcinoma Moderate None 4.2 14.09 14.09 Y Y Y
CGPLLU202 Adenocarcinoma NA None 4.4 24.72 24.72 Y Y Y
CGPLLU203 Squamous Cell Carcinoma Well None 4.2 26.24 26.24 Y N Y
CGPLLU205 Adenocarcinoma Poor None 4.0 18.56 18.55 Y Y Y
CGPLLU206 Squamous Cell Carcinoma Poor None 3.5 18.24 18.24 Y Y Y
CGPLLU207 Adenocarcinoma Well None 4.0 17.29 17.29 Y Y Y
CGPLLU208 Adenocarcinoma Moderate None 3.0 24.34 24.34 Y Y Y
CGPLLU209 Large Cell Carcinoma Poor None 5.5 53.95 53.95 Y Y Y
CGPLLU244 Adenocarcinoma Moderate/ Liver, Rib, 4.5 17.84 17.48 Y N Y
Poor Brain, Pleura
CGPLLU244 Adenocarcinoma Moderate/ Liver, Rib, 4.5 17.84 17.84 Y N Y
Poor Brain, Pleura
CGPLLU244 Adenocarcinoma Moderate/ Liver, Rib, 4.5 17.84 17.84 Y N Y
Poor Brain, Pleura
CGPLLU244 Adenocarcinoma Moderate/ Liver, Rib, 4.5 17.84 17.84 Y N Y
Poor Brain, Pleura
CGPLLU245 Adenocarcinoma NA Brain 4.7 19.42 19.42 Y N Y
CGPLLU245 Adenocarcinoma NA Brain 4.7 19.42 19.42 Y N Y
CGPLLU245 Adenocarcinoma NA Brain 4.7 19.42 19.42 Y N Y
CGPLLU245 Adenocarcinoma NA Brain 4.7 19.42 19.42 Y N Y
CGPLLU246 Adenocarcinoma Poor Pleura 5.5 18.51 18.51 Y N Y
CGPLLU246 Adenocarcinoma Poor Pleura 5.5 18.51 18.51 Y N Y
CGPLLU246 Adenocarcinoma Poor Pleura 5.5 18.51 18.51 Y N Y
CGPLLU246 Adenocarcinoma Poor Pleura 5.5 18.51 18.51 Y N Y
CGPLLU264 Adenocarcinoma NA Lung 4.0 22.97 22.97 Y N Y
CGPLLU264 Adenocarcinoma NA Lung 4.5 10.53 10.53 Y N Y
CGPLLU264 Adenocarcinoma NA Lung 3.0 7.15 7.15 Y N Y
CGPLLU264 Adenocarcinoma NA Lung 4.0 9.60 9.60 Y N Y
CGPLLU265 Adenocarcinoma NA Lung 4.2 7.16 7.16 Y N Y
CGPLLU265 Adenocarcinoma NA None 4.0 8.11 8.11 Y N Y
CGPLLU265 Adenocarcinoma NA None 4.2 7.53 7.53 Y N Y
CGPLLU265 Adenocarcinoma NA None 5.0 16.17 16.17 Y N Y
CGPLLU266 Adenocarcinoma Moderate None 5.0 5.32 5.32 Y N Y
CGPLLU266 Adenocarcinoma Moderate None 3.5 6.31 6.31 Y N Y
CGPLLU266 Adenocarcinoma Moderate None 5.0 7.64 7.64 Y N Y
CGPLLU266 Adenocarcinoma Moderate None 5.0 14.39 14.39 Y N Y
CGPLLU267 Squamous Cell Carcinoma Poor Lung 4.5 2.87 2.87 Y N Y
CGPLLU267 Squamous Cell Carcinoma Poor Lung 4.5 3.34 3.34 Y N Y
CGPLLU267 Squamous Cell Carcinoma Poor Lung 3.5 3.00 3.00 Y N Y
CGPLLU269 Adenocarcinoma NA Brain, Liver, 5.0 11.40 11.40 Y N Y
Bone, Pleura
CGPLLU269 Adenocarcinoma NA Brain, Liver, 5.0 8.35 8.35 Y N Y
Bone, Pleura
CGPLLU269 Adenocarcinoma NA Brain, Liver, 3.5 17.79 17.79 Y N Y
Bone, Pleura
CGPLLU271 Adenocarcinoma NA Pleura 4.0 4.70 4.70 Y N Y
CGPLLU271 Adenocarcinoma NA Pleura 5.0 18.86 18.86 Y N Y
CGPLLU271 Adenocarcinoma NA Pleura 4.5 13.84 13.84 Y N Y
CGPLLU271 Adenocarcinoma NA Pleura 3.5 13.46 13.46 Y N Y
CGPLLU271 Adenocarcinoma NA Pleura 4.0 13.77 13.77 Y N Y
CGPLLU43 Adenocarcinoma Moderate None 4.9 2.17 2.17 Y N Y
CGPLLU43 Adenocarcinoma Moderate None 3.7 3.26 3.26 Y N Y
CGPLLU43 Adenocarcinoma Moderate None 4.0 4.12 4.12 Y N Y
CGPLLU43 Adenocarcinoma Moderate None 3.7 8.20 8.20 Y N Y
CGPLLU86 Adenocarcinoma NA Lung 4.0 7.90 7.90 Y N Y
CGPLLU86 Adenocarcinoma NA Lung 4.C 7.90 7.90 Y N Y
CGPLLU86 Adenocarcinoma NA Lung 4.0 7.90 7.90 Y N Y
CGPLLU86 Adenocarcinoma NA Lung 4.0 7.90 7.90 Y N Y
CGPLLU88 Adenocarcinoma NA None 5.0 27.66 27.66 Y N Y
CGPLLU88 Adenocarcinoma NA None 5.0 6.49 6.49 Y N Y
CGPLLU88 Adenocarcinoma NA None 4.0 3.04 3.04 Y N Y
CGPLLU89 Adenocarcinoma NA Brain, Bone, Lung 8.0 8.43 8.43 Y N Y
CGPLLU89 Adenocarcinoma NA Brain, Bone, Lung 8.0 8.43 8.43 Y N Y
CGPLLU89 Adenocarcinoma NA Brain, Bone, Lung 8.0 8.43 8.43 Y N Y
CGPLOV11 Endometrioid Adenocarcinoma Moderate Omentum 3.4 17.35 17.35 Y Y Y
CGPLOV12 Endometrioid Adenocarcinoma NA None 3.2 12.44 12.44 Y N Y
CGPLOV13 Endometrioid Adenocarcinoma Poor Omentum 3.8 27.00 27.00 Y Y Y
CGPLOV15 Adenocarcinoma Poor None 5.0 4.77 4.77 Y Y Y
CGPLOV16 Serous Adenocarcinoma Moderate None 4.5 27.28 27.28 Y Y
CGPLOV19 Endometrioid Adenocarcinoma Moderate None 5.0 23.46 23.46 Y Y Y
CGPLOV20 Endometrioid Adenocarcinoma Poor None 4.2 5.67 5.67 Y Y Y
CGPLOV21 Serous Adenocarcinoma Poor Omentum, 4.3 56.32 56.32 Y Y Y
Appendix
CGPLOV22 Serous Adenocarcinoma Well None 4.6 17.42 17.42 Y Y Y
CGPLOV23 Serous Adenocarcinoma Poor None 5.0 26.73 26.73 Y N Y
CGPLOV24 Germ Cell Tumor Poor None 4.2 10.71 10.71 Y N Y
CGPLOV25 Germ Cell Tumor Poor None 4.8 6.78 6.78 Y N Y
CGPLOV26 Germ Cell Tumor Poor None 4.5 27.90 27.90 Y N Y
CGPLOV28 Serous Carcinoma NA None 3.2 10.74 10.74 Y N Y
CGPLOV31 Clear Cell adenocarcinoma NA None 4.0 14.45 14.45 Y N Y
CGPLOV32 Mucinous Cystadenoma NA None 3.2 27.36 27.36 Y N Y
CGPLOV37 Serous Carcinoma NA None 3.2 46.88 46.88 Y N Y
CGPLOV38 Serous Carcinoma NA None 2.4 34.29 34.29 Y N Y
CGPLOV40 Serous Carcinoma NA Omentum, Uterus, 1.6 193.60 156.25 Y N Y
Appendix
CGPLOV41 Serous Carcinoma NA Omentum, Uterus, 4.4 10.03 10.03 Y N Y
Cervix
CGPLOV42 Serous Carcinoma NA None 4.2 49.51 49.51 Y N Y
CGPLOV43 Mucinous Cystadenocarcinoma NA None 4.4 9.09 9.09 Y N Y
CGPLOV44 Mucinous Adenocarcinoma NA None 4.5 8.79 8.79 Y N Y
CGPLOV46 Serous Carcinoma NA None 4.1 8.97 8.97 Y N Y
CGPLOV47 Serous Cystadenoma NA None 4.5 19.35 19.35 Y N Y
CGPLOV48 Serous Carcinoma NA None 3.5 22.80 22.80 Y N Y
CGPLOV49 Serous Carcinoma NA None 4.2 16.48 16.48 Y N Y
CGPLOV50 Serous Carcinoma NA None 4.5 8.89 8.89 Y N Y
CGPLPA112 NA NA None 35 18.52 18.52 Y N N
CGPLPA113 NA NA None 4.8 8.24 8.24 Y N N
CGPLPA114 NA NA None 4.8 26.43 26.43 Y N N
CGPLPA115 NA NA NA 5.0 31.41 31.41 Y N N
CGPLPA117 NA NA None 3.4 2.29 2.29 Y N N
CGPLPA118 Intra-Ampullary Bile Duct NA None 3.8 9.93 9.93 Y N Y
CGPLPA122 Intra-Pancreatic Bile Duct NA None 3.8 66.54 32.89 Y N Y
CGPLPA124 Intra-Ampullary Bile Duct moderate None 4.6 29.24 27.17 Y N Y
CGPLPA125 Intra-Pancreatic Bile Duct poor None 2.7 8.31 8.31 Y N N
CGPLPA126 Intra-Pancreatic Bile Duct NA None 4.3 80.56 29.07 Y N Y
CGPLPA127 Extra-Pancreatic Bile Duct NA NA 3.0 20.60 20.60 Y N N
CGPLPA128 Intra-Pancreatic Bile Duct NA None 3.9 5.91 5.91 Y N Y
CGPLPA129 Intra-Pancreatic Bile Duct NA None 4.6 27.07 27.07 Y N Y
CGPLPA130 Intra-Ampullary Bile Duct well None 4.0 4.34 4.34 Y N Y
CGPLPA131 Intra-Pancreatic Bile Duct NA None 3.9 68.95 32.05 Y N Y
CGPLPA134 Intra-Pancreatic Bile Duct NA None 4.1 58.08 30.49 Y N Y
CGPLPA135 Intra-Pancreatic Bile Duct NA NA 3.9 4.22 4.22 Y N N
CGPLPA136 Intra-Pancreatic Bile Duct NA None 4.1 20.23 20.23 Y N Y
CGPLPA137 NA NA NA 4.0 5.75 5.75 Y N N
CGPLPA139 NA NA NA 4.0 14.89 14.89 Y N N
CGPLPA14 Ductal Adenocarcinoma Poor None 4.0 1.30 1.30 Y N N
CGPLPA140 Intra Pancreatic Bile Duct Poor None 4.7 29.34 26.60 Y N Y
CGPLPA141 Intra Pancreatic Bile Duct Moderate None 2.8 53.67 44.64 Y N N
CGPLPA15 Ductal Acenocarcinoma Well Lymph Node 4.0 1.92 1.92 Y N N
CGPLPA155 NA NA NA 4.0 25.72 25.72 Y N N
CGPLPA156 Ductal Adenocarcinoma Poor Lymph Node 4.5 7.54 7.54 Y N N
CGPLPA165 Intra-Pancreatic Bile Duck well None 3.9 10.48 10.48 Y N N
with Medullary Features
CGPLPA168 Extra-Pancreatic Bile Duct NA NA 3.0 139.12 34.72 Y N N
CGPLPA17 Ductal Adenocarcinoma Well Lymph Node 4.0 13.08 13.08 Y N N
CGPLPA184 Intra-Pancreatic Bile Duct NA None NA NA NA Y N N
CGPLPA187 Intra-Pancreatic Bile Duct NA None NA NA NA Y N N
CGPLPA23 Ductal Adenocarcinoma Moderate Lymph Node 4.0 16.62 16.62 Y N N
CGPLPA25 Ductal Adenocarcinoma Poor Lymph Node 4.0 8.71 8.71 Y N N
CGPLPA26 Ductal Adenocarcinoma Well Lymph Node 4.0 6.97 6.97 Y N N
CGPLPA28 Ductal Adenocarcinoma Well Lymph Node 4.0 18.13 18.13 Y N N
CGPLPA33 Ductal Adenocarcinoma Well Lymph Node 4.0 1.80 1.80 Y N N
CGPLPA34 Ductal Adenocarcinoma Well Lymph Node 4.0 3.36 3.36 Y N N
CGPLPA37 Ductal Adenocarcinoma NA Lymph Node 4.0 21.83 21.83 Y N N
CGPLPA38 Ductal Adenocarcinoma Moderate None 4.0 5.29 5.29 Y N N
CGPLPA39 Ductal Adenocarcinoma Well Lymph Node 4.0 11.73 11.73 Y N N
CGPLPA40 Ductal Adenocarcinoma Well Lymph Node 4.0 4.78 4.78 Y N N
CGPLPA42 Ductal Adenocarcinoma Moderate Lymph Node 4.0 3.41 3.41 Y N N
CGPLPA46 Ductal Adenocarcinoma Poor Lymph Node 4.0 0.74 0.74 Y N N
CGPLPA47 Ductal Adenocarcinoma Well Lymph Node 4.0 6.01 6.01 Y N N
CGPLPA48 Ductal Adenocarcinoma Well None NA NA NA Y N N
CGPLPA52 Ductal Adenocarcinoma Moderate None 2.5 9.86 9.86 Y N N
CGPLPA53 Ductal Adenocarcinoma Poor Lymph Node 3.0 14.48 14.48 Y N N
CGPLPA58 Ductal Adenocarcinoma NA None 3.0 6.87 6.87 Y N N
CGPLPA59 Ductal Adenocarcinoma or Well Lymph Node NA NA NA Y N N
Adenome
CGPLPA67 Ductal Adenocarcinoma Well Lymph Node 3.2 9.72 9.72 Y N N
CGPLPA69 Ductal Adenocarcinoma Well None 2.0 1.72 1.72 Y N N
CGPLPA71 Ductal Adenocarcinoma Well Lymph Node 2.2 39.07 39.07 Y N N
CGPLPA74 Ductal Adenocarcinoma Moderate Lymph Node 2.5 4.99 4.99 Y N N
CGPLPA76 Ductal Adenocarcinoma Poor None 2.5 23.19 23.19 Y N N
CGPLPA85 Ductal Adenocarcinoma Poor Lymph Node 3.0 152.46 41.67 Y N N
CGPLPA86 Ductal Adenocarcinoma Moderate Lymph Node 2.5 11.02 11.02 Y N N
CGPLPA92 Ductal Adenocarcinoma NA Lymph Node 2.0 5.34 5.34 Y N N
CGPLPA93 Ductal Adenocarcinoma Poor None 3.0 96.28 41.67 Y N N
CGPLPA94 Ductal Adenocarcinoma NA Lymph Node 3.0 29.66 29.66 Y N N
CGPLPA95 Ductal Adenocarcinoma Well Lymph Node NA NA NA Y N N
CGST102 Tubular Adenocarcinoma Moderate None 4.1 8.03 8.03 Y N Y
CGST11 Mixed Carcinoma Moderate None 3.8 3.57 3.57 Y N N
CGST110 Tubular Adenocarcinoma Moderate None 3.8 5.00 5.00 Y N Y
CGST114 Tubular Adenocarcinoma Poor None 4.4 10.35 10.35 Y N Y
CGST13 Signet Ring Cell Carcinoma Poor None 4.4 24.33 24.33 Y N Y
CGST131 Signet ring cel carcinoma Poor None 4.0 4.28 4.28 Y N N
CGST141 Signet Cell Carcinoma Poor None 4.4 10.84 10.84 Y N Y
CGST16 Tubular Adenocarcinoma Poor None 4.0 40.69 40.69 Y N Y
CGST18 Mucinous Adenocarcinoma Well None 4.3 9.78 9.78 Y N Y
CGST21 Papillary Adenocarcinoma Moderate None 4.0 0.83 0.83 Y N N
CGST26 Signet ring cel carcinoma Poor None 3.5 5.56 5.56 Y N N
CGST28 Undifferentiated Carcinoma Poor None 4.0 5.86 5.86 Y N Y
CGST30 Signet Ring Cell Carcinoma Poor None 3.0 4.22 4.22 Y N Y
CGST32 Tubular Adenocarcinoma Moderate None 4.0 11.49 11.49 Y N Y
CGST33 Tubular Adenocarcinoma Moderate None 3.5 5.71 5.71 Y N Y
CGST38 Mucinous adenocarcinoma NA None 4.0 NA NA Y N N
CGST39 Signet Ring Cell Carcinoma Poor None 3.5 20.69 20.69 Y N Y
CGST41 Signet Ring Cell Carcinoma Poor None 3.5 7.83 7.83 Y N Y
CGST45 Signet Ring Cell Carcinoma Poor None 3.8 7.14 7.14 Y N Y
CGST47 Tubular Adenocarcinoma Moderate None 4.0 4.55 4.55 Y N Y
CGST48 Tubular Adenocarcinoma Poor None 4.5 8.79 8.79 Y N Y
CGST53 NA NA None 3.8 15.82 15.82 V N N
CGST58 Signet Ring Cell Carcinoma Poor None 3.8 19.81 19.81 Y N Y
CGST67 Tubular adenocarcinoma Moderate None 3.0 23.01 23.01 Y N N
CGST77 Tubular adenocarcinoma Moderate None 4.5 15.09 15.09 Y N N
CGST80 Mucinous Adenocarcinoma Poor None 4.5 8.56 8.56 Y N Y
CGST81 Signet Ring Cell Carcinoma Poor None 3.5 37.32 37.32 Y N Y
CGH14 NA NA NA NA NA NA Y N N
CGH15 NA NA NA NA NA NA Y N N
*NA denotes data not available or not applicable for healthy individuals.
TABLE 2
APPENDIX B: Summary of targeted cfDNA analyses
Bases Mapped Percent Mapped
Fragment Profile Mutation Bases in Bases Mapped to Target to Target Total Distinct
Patient Patient Type Timepoint Analysis Analysis Read Length Target Region to Genome Regions 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
CGCRC233 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 6300244000 2818734206 45% 33117 5953
CGCRC294 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7786872600 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
CGCRC297 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
CGCRC315 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 7056703200 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 3944578280 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
CGGRC338 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 8029493700 4179383804 52% 49380 5831
CGCRC339 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 7938963600 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
CGGRC346 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
CGGRC357 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6013464600 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 3261
CGCRC381 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6951936100 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
CGCRC388 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6532692900 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
CGCRC331 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 Pre-treatment, Day −53 Y N 100 80930 6582515900 3187059470 48% 37813 3539
CGLU316 Lung Cancer Pre-treatment, Day −53 Y N 100 80930 6587281800 1947630979 30% 23094 4439
CGLU344 Lung Cancer Pre-treatment, 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 Pre-treatment, 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 2364
CGLU369 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 7078131900 1482448715 21% 17583 4275
CGLU369 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 6904701700 2124660124 31% 25230 5278
CGLU369 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 7003462200 3162195578 45% 37509 6062
CGLU373 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 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 80930 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 80930 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 80930 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 3836600101 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 8254353900 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 8250379100 3960599885 48% 46955 6319
CGPLBP76 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7774235200 3893522420 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
CGPLBP92 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
CGPLH56 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 1447503106 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 3980731089 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 7690630000 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 7996779200 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 7468749900 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 2826351657 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 3254777700 3016326208 48% 36164 12138
CGPLLU264 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 6185331000 8087883231 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 8430107900 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 7765
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 6156102000 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% 40132 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 2661
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 7005599500 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 3402308123 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 Y 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 8981384000 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 4120933322 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 4802
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 2992801875 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 6368064600 3298497188 52% 39692 5280
CGST81 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 8655691400 1519121452 18% 17988 6419
TABLE 3
APPENDIX C: Targeted cfDNA fragment analyses in cancer patients
Patient Stage at Amino Acid Mutation
Patient Type Diagnosis Alteration Type Gene (Protein) Nucleotide Type
CGCRC291 Colorectal IV Tumor-derived STK11 39R>C chr19_1207027-1207027_C_T Substitution
Cancer
CGCRC291 Colorectal IV Tumor-derived TP53 272V>M chr17_7577124-7577124_C_T Substitution
Cancer
CGCRC291 Colorectal IV Tumor-derived TP53 167Q>X chr17_7578431-7578431_G_A Substitution
Cancer
CGCRC291 Colorectal IV Tumor-derived KRAS 12G>A chr12_25398284-25398284_C_G Substitution
Cancer
CGCRC291 Colorectal IV Tumor-derived APC 1260Q>X chr5_112175069-112175069_C_T Substitution
Cancer
CGCRC291 Colorectal IV Tumor-derived APC 1450R>X chr5_112175639-112175639_C_T Substitution
Cancer
CGCRC291 Colorectal IV Tumor-derived PIK3CA 542E>K chr3_178936082-178936082_G_A Substitution
Cancer
CGCRC292 Colorectal IV Tumor-derived KRAS 146A>V chr12_25378561-25378561_G_A Substitution
Cancer
CGCRC292 Colorectal IV Tumor-derived CTNNB1 41T>A chr3_41266124-41266124_A_G Substitution
Cancer
CGCRC292 Colorectal IV Germline EGFR 2284−4C>G chr7_55248982-55248982_C_G Substitution
Cancer
CGCRC293 Colorectal IV Tumor-derived TP53 176C>S chr17_7578404-7578404_A_T Substitution
Cancer
CGCRC294 Colorectal II Tumor-derived APC 213R>X chr5_112116592-112116592_C_T Substitution
Cancer
CGCRC294 Colorectal II Tumor-derived APC 1367Q>X chr5_112175390-112175390_C_T Substitution
Cancer
CGCRC295 Colorectal IV Tumor-derived PDGFRA 49+4C>T chr4_55124988-55124988_C_T Substitution
Cancer
CGCRC295 Colorectal IV Hematopoietic IDH1 104G>V chr2_209113196-209113196_C_A Substitution
Cancer
CGCRC296 Colorectal II Germline EGFR 922E>K chr7_55266472-55266472_G_A Substitution
Cancer
CGCRC297 Colorectal III Germline KIT 18L>F chr4_55524233-55524233_C_T Substitution
Cancer
CGCRC298 Colorectal II Hematopoietic DNMT3A 882R>H chr2_25457242-25457242_C_T Substitution
Cancer
CGCRC298 Colorectal II Hematopoietic DNMT3A 714S>C chr2_25463541-25463541_G_C Substitution
Cancer
CGCRC298 Colorectal II Tumor-derived PIK3CA 414G>V chr3_178927478-178927478_G_T Substitution
Cancer
CGCRC299 Colorectal I Hematopoietic DNMT3A 735Y>C chr2_25463289-25463289_T_C Substitution
Cancer
CGCRC299 Colorectal I Hematopoietic DNMT3A 710C>S chr2_25463553-25463553_C_G Substitution
Cancer
CGCRC300 Colorectal I Hematopoietic DNMT3A 720R>G chr2_25463524-25463524_G_C Substitution
Cancer
CGCRC301 Colorectal I Tumor-derived ATM 2397Q>X chr11_108199847-108199847_C_T Substitution
Cancer
CGCRC302 Colorectal II Tumor-derived TP53 141C>Y chr17_7578508-7578508_C_T Substitution
Cancer
CGCRC302 Colorectal II Tumor-derived BRAF 600V>E chr7_140453136-140453136_A_T Substitution
Cancer
CGCRC303 Colorectal III Tumor-derived TP53 173V>L chr17_7578413-7578413_C_A Substitution
Cancer
CGCRC303 Colorectal III Hematopoietic DNMT3A 755F>3 chr2_25463229-25463229_A_G Substitution
Cancer
CGCRC303 Colorectal III Hematopoietic DNMT3A 2173+1G>A chr2_25463508-25463508_C_T Substitution
Cancer
CGCRC304 Colorectal II Tumor-derived EGFR 1131T>S chr7_55273068-55273068_A_T Substitution
Cancer
CGCRC304 Colorectal II Tumor-derived ATM 3077+1G>A chr11_108142134-108142134_G_A Substitution
Cancer
CGCRC304 Colorectal II Hematopoietic ATM 3008R>C chr11_108236086-108236086_C_T Substitution
Cancer
CGCRC305 Colorectal II Tumor-derived GNA11 213R>Q chr19_3118954-3118954_G_A Substitution
Cancer
CGCRC305 Colorectal II Tumor-derived TP53 273R>H chr17_7577120-7577120_C_T Substitution
Cancer
CGCRC306 Colorectal II Tumor-derived TP53 196R>X chr17_7578263-7578263_G_A Substitution
Cancer
CGCRC306 Colorectal II Tumor-derived CDKN2A 107R>C chr9_21971039-21971039_G_A Substitution
Cancer
CGCRC306 Colorectal II Tumor-derived KRAS 61Q>K chr12_25380277-25380277_G_T Substitution
Cancer
CGCRC306 Colorectal II Germline PDGFRA 200T>S chr4_55130065-55130065_C_G Substitution
Cancer
CGCRC306 Colorectal II Tumor-derived EGFR 618H>R chr7_55233103-55233103_A_G Substitution
Cancer
CGCRC306 Colorectal II Tumor-derived PIK3CA 545E>A chr3_178936092-178936092_A_C Substitution
Cancer
CGCRC306 Colorectal II Germline ERBB4 1155R>X chr2_212251596-212251596_G_A Substitution
Cancer
CGCRC307 Colorectal II Tumor-derived JAK2 805L>V chr9_5080662-5080662_C_G Substitution
Cancer
CGCRC307 Colorectal II Tumor-derived SMARCB1 501−2A>G chr22_24145480-24145480_A_G Substitution
Cancer
CGCRC307 Colorectal II Tumor-derived GNAS 201R>C chr20_57484420-57484420_C_T Substitution
Cancer
CGCRC307 Colorectal II Tumor-derived BRAF 600V>E chr7_140453136-140453136_A_T Substitution
Cancer
CGCRC307 Colorectal II Tumor-derived FBXW7 465R>C chr4_153249365-153249385_G_A Substitution
Cancer
CGCRC307 Colorectal II Tumor-derived ER8B4 17A>V chr2_213403205-213403205_G_A Substitution
Cancer
CGCRC308 Colorectal III Hematopoietic DNMT3A 882R>H chr2_25457242-25457242_C_T Substitution
Cancer
CGCRC308 Colorectal III Germline EGFR 848P>L chr7_55259485-55259485_C_T Substitution
Cancer
CGCRC308 Colorectal III Tumor-derived APC 1480Q>X chr5_112175729-112175729_C_T Substitution
Cancer
CGCRC309 Colorectal III Tumor-derived AKT1 17E>K chr14_105246551-105246551_C_T Substitution
Cancer
CGCRC309 Colorectal III Tumor-derived BRAF 600V>E chr7_140453136-140453136_A_T Substitution
Cancer
CGCRC310 Colorectal II Tumor-derived KRAS 12G>V chr12_25398284-25398284_C_A Substitution
Cancer
CGCRC310 Colorectal II Tumor-derived APC 1513E>X chr5_112175828-112175828_G_T Substitution
Cancer
CGCRC310 Colorectal II Tumor-derived APC 1521E>X chr5_112175852-112175352_G_T Substitution
Cancer
CGCRC311 Colorectal I Hematopoietic DNMT3A 882R>H chr2_25457242-25457242_C_T Substitution
Cancer
CGCRC312 Colorectal III Tumor-derived APC 960S>X chr5_112174170-112174170_C_G Substitution
Cancer
CGCRC312 Colorectal III Tumor-derived NRAS 61Q>K chr1_115256530-115256530_G_T Substitution
Cancer
CGCRC313 Colorectal III Tumor-derived KRAS 12G>S chr12_25398285-25398285_C_T Substitution
Cancer
CGCRC313 Colorectal III Tumor-derived APC 876R>X chr5_112173917-112173917_C_T Substitution
Cancer
CGCRC314 Colorectal I Tumor-derived KRAS 12G>D chr12_25398284-25398284_C_T Substitution
Cancer
CGCRC314 Colorectal I Hematopoietic DNMT3A 738L>Q chr2_25463280-25463280_A_T Substitution
Cancer
CGCRC314 Colorectal I Tumor-derived APC 1379E>X chr5_112175426-112175426_G_T Substitution
Cancer
CGCRC315 Colorectal III Tumor-derived NRAS 12G>D chr1_115258747-115258747_C_T Substitution
Cancer
CGCRC315 Colorectal III Tumor-derived FBXW7 505R>C chr4_153247289-153247289_G_A Substitution
Cancer
CGCRC316 Colorectal III Tumor-derived TP53 245G>S chr17_7577548-7577548_C_T Substitution
Cancer
CGCRC316 Colorectal III Tumor-derived CDKN2A 1M>R chr9_21974825-21974825_A_C Substitution
Cancer
CGCRC316 Colorectal III Tumor-derived CTNNB1 37S>C chr3_41266113-41266113_C_G Substitution
Cancer
CGCRC316 Colorectal III Tumor-derived EGFR 2702−3C>T chr7_55266407-55266407_C_T Substitution
Cancer
CGCRC316 Colorectal III Hematopoietic ATM 3008R>P chr11_108236087-108236087_G_C Substitution
Cancer
CGCRC317 Colorectal III Tumor-derived TP53 220Y>C chr17_7578190-7578190_T_C Substitution
Cancer
CGCRC317 Colorectal III Tumor-derived ATM 1026W>R chr11_108142132-108142132_T_C Substitution
Cancer
CGCRC317 Colorectal III Tumor-derived APC 216R>X chr5_112128143-112128143_C_T Substitution
Cancer
CGCRC318 Colorectal I Hematopoietic DNMT3A 698W>X chr2_25463589-25463589_C_T Substitution
Cancer
CGCRC320 Colorectal I Germline KIT 18L>F chr4_55524233-55524233_C_T Substitution
Cancer
CGCRC320 Colorectal I Tumor-derived ERBB4 78R>W chr2_212989479-212989479_G_A Substitution
Cancer
CGCRC321 Colorectal I Tumor-derived CDKN2A 12S>L chr9_21974792-21974792_G_A Substitution
Cancer
CGCRC321 Colorectal I Hematopoietic DNMT3A 882R>H chr2_25457242-25457242_C_T Substitution
Cancer
CGCRC321 Colorectal I Germline EGFR 511S>Y chr7_55229225-55229225_C_A Substitution
Cancer
CGCRC332 Colorectal IV Tumor-derived TP53 125T>R chr17_7579313-7579313_G_C Substitution
Cancer
CGCRC333 Colorectal IV Tumor-derived TP53 673−2A>G chr17_7577610-7577610_T_C Substitution
Cancer
CGCRC333 Colorectal IV Tumor-derived BRAF 600V>E chr7_140453136-140453136_A_T Substitution
Cancer
CGCRC333 Colorectal IV Tumor-derived ERBB4 691E>A chr2_212495194-212495194_T_G Substitution
Cancer
CGCRC334 Colorectal IV Tumor-derived TP53 245G>S chr17_7577548-7577548_C_T Substitution
Cancer
CGCRC334 Colorectal IV Germline EGFR 638T>M chr7_55238900-55238900_C_T Substitution
Cancer
CGCRC334 Colorectal IV Tumor-derived PIK3CA 104P>R chr3_178916924-178916924_C_G Substitution
Cancer
CGCRC335 Colorectal IV Tumor-derived BRAF 600V>E chr7_140453136-140453136_A_T Substitution
Cancer
CGCRC336 Colorectal IV Tumor-derived TP53 175R>H chr17_7578406-7578406_C_T Substitution
Cancer
CGCRC336 Colorectal IV Tumor-derived KRAS 12G>V chr12_25398284-25398284_C_A Substitution
Cancer
CGCRC336 Colorectal IV Turner-derived APC 1286E>X chr5_112175147-112175147_G_T Substitution
Cancer
CGCRC337 Colorectal IV Tumor-derived STK11 734+2T>A chr19_1220718-1220718_T_A Substitution
Cancer
CGCRC337 Colorectal IV Germline APC 485M>I chr5_112162851-112162851_G_A Substitution
Cancer
CGCRC338 Colorectal IV Tumor-derived KRAS 12G>D chr12_25398284-25398284_C_T Substitution
Cancer
CGCRC339 Colorectal IV Tumor-derived KRAS 13G>D chr12_25393281-25398281_C_T Substitution
Cancer
CGCRC339 Colorectal IV Tumor-derived APC 876R>X chr5_112173917-112173917_C_T Substitution
Cancer
CGCRC339 Colorectal IV Tumor-derived PIK3CA 407C>F chr3_178927457-178927457_G_T Substitution
Cancer
CGCRC339 Colorectal IV Tumor-derived PIK3CA 1047H>L chr3_178952085-178952085_A_T Substitution
Cancer
CGCRC340 Colorectal IV Tumor-derived TP53 196R>X chr17_7578263-7578263_G_A Substitution
Cancer
CGCRC340 Colorectal IV Tumor-derived APC 1306E>X chr5_112175207-112175207_G_T Substitution
Cancer
CGPLBR38 Breast I Tumor-derived TP53 241S>P chr17_7577560-7577560_A_G Substitution
Cancer
CGPLBR40 Breast III Germline AR 392P>R chrX_66766163-66766163_C_G Substitution
Cancer
CGPLBR44 Breast III Hematopoietic DNMT3A 882R>H chr2_25457242-25457242_C_T Substitution
Cancer
CGPLBR44 Breast III Hematopoietic DNMT3A 705I>T chr2_25463568-25463568_A_G Substitution
Cancer
CGPLBR44 Breast III Tumor-derived PDGFRA 859V>M chr4_55153609-55153609_G_A Substitution
Cancer
CGPLBR48 Breast II Germline ALK 1231R>Q chr2_29436901-29436901_C_T Substitution
Cancer
CGPLBR48 Breast II Tumor-derived EGFR 669R>Q chr7_55240762-55240762_G_A Substitution
Cancer
CGPLBR55 Breast III Hematopoietic DNMT3A 743P>S chr2_25463266-25463266_G_A Substitution
Cancer
CGPLBR55 Breast III Tumor-derived GNAS 201R>H chr20_57484421-57484421_G_A Substitution
Cancer
CGPLBR55 Breast III Tumor-derived PIK3CA 345N>K chr3_178921553-178921553_T_A Substitution
Cancer
CGPLBR63 Breast II Germline FGFR3 403K>E chr4_1806188-1806188_A_G Substitution
Cancer
CGPLBR67 Breast III Hematopoietic DNMT3A 882R>H chr2_25457242-25457242_C_T Substitution
Cancer
CGPLBR67 Breast III Tumor-derived PIK3CA 545E>K chr3_178936091-178936091_G_A Substitution
Cancer
CGPLBR67 Breast III Tumor-derived ERBB4 1000D>A chr2_212285302-212285302_T_G Substitution
Cancer
CGPLBR69 Breast II Hematopoietic DNMT3A 774E>V chr2_25463172-25463172_T_A Substitution
Cancer
CGPLBR69 Breast II Germline CTNNB1 30Y>S chr3_41266092-41266092_A_C Substitution
Cancer
CGPLBR69 Breast II Germline IDH1 231Y>N chr2_209108158-209108158_A_T Substitution
Cancer
CGPLBR70 Breast II Tumor-derived ATM 2832R>H chr11_108216546-108216546_G_A Substitution
Cancer
CGRLBR70 Breast II Germline APC 1577E>D chr5_112176022-112176022_A_C Substitution
Cancer
CGPLBR71 Breast II Tumor-derived TP53 273R>H chr17_7577120-7577120_C_T Substitution
Cancer
CGPLBR72 Breast II Germline APC 1532D>G chr5_112175886-112175886_A_G Substitution
Cancer
CGPLBR73 Breast II Tumor-derived ALK 708S>P chr2_29474053-29474053_A_G Substitution
Cancer
CGPLBR73 Breast II Germline ERBB4 158A>E chr2_212652833-212652833_G_T Substitution
Cancer
CGPLBR74 Breast II Germline AR 20+1G>T chrX_66788865-66788865_G_T Substitution
Cancer
CGPLBR75 Breast II Tumor-derived PIK3CA 1047H>R chr3_178952085-178352085_A_G Substitution
Cancer
CGPLBR76 Breast II Germline KDR 1290S>N chr4_55946310-55946310_C_T Substitution
Cancer
CGPLBR76 Breast II Tumor-derived PIK3CA 1047H>R chr3_178952085-178952085_A_G Substitution
Cancer
CGPLBR77 Breast III Tumor-derived PTEN 170S>I chr10_89711891-89711891_G_T Substitution
Cancer
CGPLBR80 Breast II Tumor-derived CDKN2A 12S>L chr9_21974792-21974792_G_A Substitution
Cancer
CGPLBR83 Breast II Germline AR 728N>D chrX_66937328-66937328_A_G Substitution
Cancer
GGPLBR83 Breast II Tumor-derived ATM 322E>K chr11_108117753-108117753_G_A Substitution
Cancer
CGPLBR83 Breast II Germline ERBB4 539Y>S chr2_212543783 212543783_T_G Substitution
Cancer
CGPLBR86 Breast II Germline STK11 354F>L chr19_1223125-1223125_C_G Substitution
Cancer
CGPLBR86 Breast II Germline SMARCB1 795+3A>G chr22_24159126-24159126_A_G Substitution
Cancer
CGPLBR87 Breast II Tumor-derived JAK2 215R>X chr9_5054591-5054591_C_T Substitution
Cancer
CGPLBR87 Breast II Hematopoietic DNMT3A 882R>H chr2_25457242-25457242_C_T Substitution
Cancer
CGPLBR87 Breast II Tumor-derived SMAD4 496R>C chr18_48304664-48604664_C_T Substitution
Cancer
CGPLBR87 Breast II Germline AR 651S>N chrX_66931310-66931310_G_A Substitution
Cancer
CGPLBR88 Breast II Tumor-derived CDK6 51E>K chr7_92462487-92462487_G_T Substitution
Cancer
CGPLBR88 Breast II Germline APC 1125V>A chr5_112174665-112174665_T_C Substitution
Cancer
CGPLBR92 Breast II Tumor-derived TP53 257L>P chr17_7577511-7577511_A_G Substitution
Cancer
CGPLBR96 Breast II Tumor-derived TP53 213R>X chr17.fa:7578212-7576212_G_A Substitution
Cancer
CGPLBR96 Breast II Hematopoietic DNMT3A 531D>G chr2_25467484-25467484_T_G Substitution
Cancer
CGPLBR96 Breast II Tumor-derived AR 13R>Q chrX_66765026-66765026_G_A Substitution
Cancer
CGPLBR97 Breast II Hematopoietic DNMT3A 882R>H chr2_25457242-25457242_C_T Substitution
Cancer
CGPLBR97 Breast II Germline PDGFRA 401A>D chr4_55136880-55136880_C_A Substitution
Cancer
CGPLBR97 Breast II Tumor-derived GNAS 201R>H chr20_57484421-57484421_G_A Substitution
Cancer
CGPLLU144 Lung II Tumor-derived TP53 241S>F chr17_7577559-7577559_G_A Substitution
Cancer
CGPLLU144 Lung II Tumor-derived KRAS 12G>C chr12_25398285-25398285_C_A Substitution
Cancer
CGPLLU144 Lung II Tumor-derived EGFR 373P>S chr7_55224336-55224336_C_T Substitution
Cancer
CGPLLU144 Lung II Tumor-derived ATM 292P>L chr11_108115727-108115727_C_T Substitution
Cancer
CGPLLU144 Lung II Tumor-derived PIK3CA 545E>K chr3_178936091 178936091_G_A Substitution
Cancer
CGPLLU144 Lung II Tumor-derived ERBB4 426R>K chr2_212568841-212568841_C_T Substitution
Cancer
CGPLLU146 Lung II Hematopoietic JAK2 617V>F chr9_5073770-5073770_G_T Substitution
Cancer
CGPLLU146 Lung II Tumor-derived TP53 282R>P chr17_7577093-7577093_C_G Substitution
Cancer
CGPLLU146 Lung II Hematopoietic DNMT3A 737L>H chr2_25463283-25463283_A_T Substitution
Cancer
CGPLLU146 Lung II Tumor-derived RB1 861+2T>C chr13_48937095-48937095_T_C Substitution
Cancer
CGPLLU146 Lung II Tumor-derived ATM 581L>F chr11_108122699-108122699_A_T Substitution
Cancer
CGPLLU147 Lung III Tumor-derived TP53 248R>Q chr17_7577538-7577538_C_T Substitution
Cancer
CGPLLU147 Lung III Tumor-derived TP53 201L>X chr17_7573247-7578247_A_T Substitution
Cancer
CGPLLU147 Lung III Tumor-derived ALK 1537G>E chr2_29416343-29416343_C_T Substitution
Cancer
CGPLLU147 Lung III Germline PDGFRA 200T>S chr4_55130065-55130065_C_G Substitution
Cancer
CGPLLU162 Lung II Tumor-derived CDKN2A 12S>L chr9_21974792-21974792_G_A Substitution
Cancer
CGPLLU162 Lung II Tumor-derived EGFR 858L>R chr7_55259515-55259515_T_G Substitution
Cancer
CGPLLU162 Lung II Tumor-derived BRAF 354R>Q chr7_140494187-140494187_C_T Substitution
Cancer
CGPLLU163 Lung II Tumor-derived CDKN2A 12S>L chr9_21974792-21974792_G_A Substitution
Cancer
CGPLLU163 Lung II Hematopoietic DNMT3A 528Y>D chr2_25467494-25467494_A_C Substitution
Cancer
CGPLLU164 Lung II Tumor-derived STK11 216S>Y chr19_1220629-1220629_C_A Substitution
Cancer
CGPLLU164 Lung II Germline STK11 354F>L chr19_1223125-1223125_C_G Substitution
Cancer
CGPLLU164 Lung II Tumor-derived GNA11 606−3C>T chr19_3118919-3118919_C_T Substitution
Cancer
CGPLLU164 Lung II Tumor-derived TP53 278P>S chr17_7577106-7577106_G_A Substitution
Cancer
CGPLLU164 Lung II Tumor-derived TP53 161A>S chr17_7578449-7578449_C_A Substitution
Cancer
CGPLLU164 Lung II Tumor-derived TP53 160M>I chr17_7578450-7578450_C_A Substitution
Cancer
CGPLLU164 Lung II Tumor-derived ERBB4 1299P>L chr2_212248371-212248371_G_A Substitution
Cancer
CGPLLU164 Lung II Tumor-derived ERBB4 253N>S chr2_212587243-212587243_T_C Substitution
Cancer
CGPLLU165 Lung II Germline STK11 354F>L chr19_1223125-1223125_C_G Substitution
Cancer
CGPLLU165 Lung II Tumor-derived GNAS 201R>H chr20_57484421-57484421_G_A Substitution
Cancer
CGPLLU168 Lung I Tumor-derived TP53 136Q>X chr17.fa:7578524-7578524_G_A Substitution
Cancer
CGPLLU168 Lung I Hematopoietic DNMT3A 736R>S chr2_25463287-25463287_G_T Substitution
Cancer
CGPLLU168 Lung I Tumor-derived EGFR 858L>R chr7.fa:55259515-55259515_T_G Substitution
Cancer
CGPLLU174 Lung I Tumor-derived STK11 597+1G>T chr19_1220505-1220505_G_T Substitution
Cancer
CGPLLU174 Lung I Tumor-derived JAK2 160D>Y chr9_5050695-5050695_G_T Substitution
Cancer
CGPLLU174 Lung I Tumor-derived KRAS 12G>C chr12_25398285-25398285_C_A Substitution
Cancer
CGPLLU174 Lung I Hematopoietic DNMT3A 891R>W chr2_25457216-25457216_G_A Substitution
Cancer
CGPLLU174 Lung I Hematopoietic DNMT3A 715I>M chr2_25463537-25463537_G_C Substitution
Cancer
CGPLLU175 Lung I Tumor-derived TP53 179H>R chr17_7578394-7578394_T_C Substitution
cancer
CGPLLU175 Lung I Hematopoietic DNMT3A 2598−1G>A chr2_25457290-25457290_C_T Substitution
Cancer
CGPLLU175 Lung I Hematopoietic DNMT3A 755F>L chr2_25463230-25463230_A_G Substitution
Cancer
CGPLLU175 Lung I Germline ATM 337R>C chr11_108117798-108117798_C_T Substitution
Cancer
CGPLLU175 Lung I Tumor-derived ERBB4 941Q>X chr2_212288925-212288925_G_A Substitution
Cancer
CGPLLU176 Lung I Hematopoietic DNMT3A 750P>S chr2_25463245-25463245_G_A Substitution
Cancer
CGPLLU176 Lung I Hematopoietic DNMT3A 735Y>C chr2_25463289-25463289_T_C Substitution
Cancer
CGPLLU177 Lung II Tumor-derived KRAS 12G>V chr12_25398284-25398284_C_A Substitution
Cancer
CGPLLU177 Lung II Hematopoietic DNMT3A 897V>G chr2_25457197-25457197_A_C Substitution
Cancer
CGPLLU177 Lung II Hematopoietic DNMT3A 882R>C chr2_25457243-25457243_G_A Substitution
Cancer
CGPLLU177 Lung II Hematopoietic DNMT3A 2173+1G>A chr2_25463508-25463508_C_T Substitution
Cancer
CGPLLU178 Lung I Tumor-derived CDH1 251T>M chr16_68844164-68844164_C_T Substitution
Cancer
CGPLLU178 Lung I Tumor-derived PIK3CA 861Q>X chr3_178947145-178947145_C_T Substitution
Cancer
CGPLLU179 Lung I Hematopoietic DNMT3A 879N>D chr2_25457252-25457252_T_C Substitution
Cancer
CGPLLU179 Lung I Germline APC 2611T>I chr5_112179123-112179123_C_T Substitution
Cancer
CGPLLU180 Lung I Tumor-derived STK11 237D>Y chr19_1220691-1220691_G_T Substitution
Cancer
CGPLLU180 Lung I Tumor-derived TP53 293G>V chr17_7577060-7577060_C_A Substitution
Cancer
CGPLLU180 Lung I Tumor-derived TP53 282R>P chr17_7577093-7577093_C_G Substitution
Cancer
CGPLLU180 Lung I Tumor-derived TP53 177P>L chr17.fa:7578400-7578400_G_A Substitution
Cancer
CGPLLU180 Lung I Tumor-derived RB1 565S>X chr13_48955578-48955578_C_G Substitution
Cancer
CGPLLU197 Lung I Hematopoietic DNMT3A 882R>C chr2_25457243-25457243_G_A Substitution
Cancer
CGPLLU197 Lung I Hematopoietic DNMT3A 879N>D chr2_25457252-25457252_T_C Substitution
Cancer
CGPLLU198 Lung I Tumor-derived TP53 162I>N chr17_7578445-7578445_A_T Substitution
Cancer
CGPLLU198 Lung I Tumor-derived EGFR 858L>R chr7_55259515_55259515_T_G Substitution
Cancer
CGPLLU202 Lung I Tumor-derived EGFR 790T>M chr7.fa:55249071-55249071_C_T Substitution
Cancer
CGPLLU202 Lung I Tumor-derived EGFR 868E>X chr7_55259544-55259544_G_T Substitution
Cancer
CGPLLU204 Lung I Tumor-derived KIT 956R>Q chr4_55604659-55604659_G_A Substitution
Cancer
CGPLLU205 Lung II Hematopoietic DNMT3A 736R>C chr2_25463287-25463287_G_A Substitution
Cancer
CGPLLU205 Lung II Hematopoietic DNMT3A 696Q>X chr2_25463596-25463596_G_A Substitution
Cancer
CGPLLU206 Lung III Tumor-derived TP53 672+1G>A chr17_7578176-7578176_C_T Substitution
Cancer
CGPLLU206 Luna III Tumor-derived TP53 131N>S chr17_7573538-7578538_T_C Substitution
Cancer
CGPLLU207 Lung II Tumor-derived TP53 376−1G>A chr17_7578555-7578555_C_T Substitution
Cancer
CGPLLU207 Lung II Germline ALK 419F>L chr2_29606625-29606625_A_G Substitution
Cancer
CGPLLU207 Lung II Tumor-derived EGFR 790T>M chr7.fa:55249071-55249071_C_T Substitution
Cancer
CGPLLU208 Lung II Tumor-derived TP53 250P>L chr17_7577532-7577532_G_A Substitution
Cancer
CGPLLU208 Lung II Germline EGFR 224R>H chr7_55220281-55220281_G_A Substitution
Cancer
CGPLLU208 Lung II Tumor-derived EGFR 858L>R chr7_55259515_55259515_T_G Substitution
Cancer
CGPLLU208 Lung II Tumor-derived MYC 98R>W chr8_128750755-128750755_C_T Substitution
Cancer
CGPLLU209 Lung II Germline STK11 354F>L chr19_1223125-1223125_C_G Substitution
Cancer
CGPLLU209 Lung II Tumor-derived TP53 100Q>X chr17_7579389-7579389_G_A Substitution
Cancer
CGPLLU209 Lung II Tumor-derived CDKN2A 88E>X chr9_21971096-21971096_C_A Substitution
Cancer
CGPLLU209 Lung II Tumor-derived PDGFRA 921A>T chr4_55155052_55155052_G_A Substitution
Cancer
CGPLLU209 Lung II Germline EGFR 567M>V chr7_55231493-55231493_A_G Substitution
Cancer
CGPLOV10 Ovarian I Tumor-derived TP53 342R>X chr17_7574003-7574003_G_A Substitution
Cancer
CGPLOV11 Ovarian IV Tumor-derived TP53 248R>Q chr17_7577538-7577538_C_T Substitution
Cancer
CGPLOV11 Ovarian IV Germline TP53 63A>V chr17_7579499-7579499_G_A Substitution
Cancer
CGPLOV13 Ovarian IV Tumor-derived ALK 444W>C chr2_29551298-29551298_C_A Substitution
Cancer
CGPLOV13 Ovarian IV Germline PDGFRA 401A>D chr4_55136880-55136880_C_A Substitution
Cancer
CGPLOV13 Ovarian IV Tumor-derived KIT 135R>H chr4_55564516-55564516_G_A Substitution
Cancer
CGPLOV14 Ovarian I Tumor-derived HNF1A 230E>K chr12_12143484-121431484_G_A Substitution
Cancer
CGPLOV15 Ovarian III Tumor-derived TP53 278P>S chr17_7577106-7577106_G_A Substitution
Cancer
CGPLOV15 Ovarian III Tumor-derived EGFR 433H>D chr7_55225445_55225445_C_G Substitution
Cancer
CGPLOV17 Ovarian I Tumor-derived TP53 248R>Q chr17_7577538-7577538_C_T Substitution
Cancer
CGPLOV17 Ovarian I Germline PDGFRA 1071D>N chr4_55161380-55161380_G_A Substitution
Cancer
CGPLOV18 Ovarian I Germline AFC 1125V>A chr5_112174665-112174665_T_C Substitution
Cancer
CGPLOV19 Ovarian II Germline FGFR3 403K>E chr4_1806188-1806188_A_G Substitution
Cancer
CGPLOV19 Ovarian II Tumor-derived TP53 273R>H chr17_7577120-7577120_C_T Substitution
Cancer
CGPLOV19 Ovarian II Germline AR 176S>R chrX_66765516-66765516_C_A Substitution
Cancer
CGPLOV19 Ovarian II Tumor-derived APC 1378Q>X chr5_112175423-112175423_C_T Substitution
Cancer
CGPLOV20 Ovarian II Tumor-derived TP53 195I>T chr17_7578265-7578265_A_G Substitution
Cancer
CGPLOV20 Ovarian II Germline EGFR 253K>R chr7_55221714-55221714_A_G Substitution
Cancer
CGPLOV21 Ovarian IV Germline STK11 354F>L chr19_1223125-1223125_C_G Substitution
Cancer
CGPLOV21 Ovarian IV Tumor-derived TP53 275C>Y chr17_7577114-7577114_C_T Substitution
Cancer
CGPLOV21 Ovarian IV Tumor-derived ERBB4 602S>T chr2_212530114_212530114_C_G Substitution
Cancer
CGPLOV22 Ovarian III Tumor-derived TP53 193H>P chr17_7578271-7578271_T_G Substitution
Cancer
CGPLOV22 Ovarian III Tumor-derived CTNNB1 41T>A chr3_41266124-41266124_A_G Substitution
Cancer
Wild-type Fragments
25th
Minimum Percentile Mode Median
Alteration Mutant cfDNA cfDNA cfDNA cfDNA
Hotspot Detected Allele Distinct Fragment Fragment Fragment Fragment
Patient Alteration in Tissue Fraction Coverage Size (bp) Size (bp) Size (bp) Size (bp)
CGCRC291 No No 0.14% 11688 100 151 167 169
CGCRC291 Yes No 0.10% 11779 100 155 171 169
CGCRC291 Yes Yes 22.85% 11026 100 156 166 169
CGCRC291 Yes Yes 14.85% 7632 97 152 169 167
CGCRC291 No Yes 11.23% 7218 101 155 167 169
CGCRC291 Yes Yes 11.05% 10757 86 154 166 167
CGCRC291 Yes Yes 18.11% 5429 100 151 171 167
CGCRC292 Yes No 1.41% 6120 101 157 167 169
CGCRC292 Yes Yes 0.13% 10693 100 155 169 168
CGCRC292 NA Yes 31.99% 7587 97 158 166 171
CGCRC293 No No 0.35% 7672 95 159 168 170
CGCRC294 Yes Yes 0.14% 7339 84 155 166 167
CGCRC294 Yes Yes 0.13% 12054 89 159 167 170
CGCRC295 No No 0.45% 5602 101 157 164 170
CGCRC295 No Yes 0.34% 8330 100 157 166 169
CGCRC296 NA Yes 30.48% 8375 89 161 166 172
CGCRC297 NA Yes 41.39% 3580 102 159 164 170
CGCRC298 Yes Yes 0.08% 13032 100 159 168 171
CGCRC298 No No 0.11% 13475 93 158 169 170
CGCRC298 No No 0.55% 5815 100 156 168 169
CGCRC299 No Yes 0.30% 11995 100 154 164 165
CGCRC299 No Yes 0.12% 15363 96 151 166 164
CGCRC300 No No 0.15% 7487 100 162 179 173
CGCRC301 No No 0.21% 5881 100 156 169 169
CGCRC302 Yes Yes 0.05% 24784 84 153 165 164
CGCRC302 Yes Yes 0.12% 11763 95 154 165 165
CGCRC303 Yes Yes 0.08% 13967 95 159 169 171
CGCRC303 No No 0.21% 10167 81 160 169 172
CGCRC303 No No 0.17% 10845 100 160 169 172
CGCRC304 No No 0.22% 16168 90 153 167 164
CGCRC304 No No 0.27% 10502 100 152 165 163
CGCRC304 No Yes 0.43% 12987 101 154 165 165
CGCRC305 No Yes 0.11% 12507 100 159 169 171
CGCRC305 Yes No 0.19% 10301 100 156 168 166
CGCRC306 Yes No 0.12% 8594 101 157 165 169
CGCRC306 No Yes 8.02% 9437 90 159 167 171
CGCRC306 Yes Yes 7.30% 6090 100 152 163 166
CGCRC306 NA Yes 34.78% 4585 103 158 167 179
CGCRC306 No Yes 6.32% 7395 81 160 166 171
CGCRC306 Yes No 0.96% 4885 100 152 170 167
CGCRC306 NA Yes 38.70% 3700 100 159 168 171
CGCRC307 No No 0.56% 6860 100 158 170 170
CGCRC307 No Yes 0.34% 10065 95 157 168 169
CGCRC307 Yes Yes# 0.24% 7520 102 156 167 168
CGCRC307 Yes Yes 0.38% 8623 76 157 169 168
CGCRC307 Yes Yes 0.31% 10606 100 155 167 168
CGCRC307 No No 0.15% 13189 90 158 168 171
CGCRC308 Yes No 0.06% 16287 90 159 168 169
CGCRC308 NA Yes 27.69% 7729 100 160 164 170
CGCRC308 No Yes 0.11% 14067 92 157 170 169
CGCRC309 Yes Yes 2.70% 13036 85 157 170 169
CGCRC309 Yes Yes 3.00% 9084 101 157 166 168
CGCRC310 Yes Yes 0.13% 7393 100 153 165 164
CGCRC310 No Yes 0.11% 11689 100 152 166 164
CGCRC310 No Yes 0.15% 10273 100 153 166 164
CGCRC311 Yes No 0.86% 8456 94 160 171 172
CGCRC312 No Yes 0.59% 4719 100 160 165 173
CGCRC312 Yes Yes 0.47% 3391 101 157 172 170
CGCRC313 Yes Yes 0.17% 5013 100 163 166 174
CGCRC313 Yes Yes 0.07% 8150 72 161 171 174
CGCRC314 Yes Yes 0.30% 4684 100 158 165 169
CGCRC314 No Yes 2.50% 6902 85 159 165 170
CGCRC314 Yes Yes 0.38% 7229 102 158 167 170
CGCRC315 Yes Yes 0.27% 8733 94 155 167 169
CGCRC315 Yes Yes 0.25% 9623 101 158 166 170
CGCRC316 Yes Yes 6.52% 12880 100 150 166 163
CGCRC316 No Yes 5.74% 7479 93 157 164 168
CGCRC316 Yes Yes 5.47% 13682 100 149 165 162
CGCRC316 No No 0.11% 16716 85 153 166 156
CGCRC316 No Yes 0.13% 17060 100 150 166 153
CGCRC317 Yes Yes 0.36% 14587 84 152 166 154
CGCRC317 No Yes 0.23% 10483 100 152 164 155
CGCRC317 Yes No 0.29% 3497 101 149 166 153
CGCRC318 No Yes 0.25% 16436 98 158 170 170
CGCRC320 NA Yes 34.76% 6521 100 163 170 175
CGCRC320 No No 0.12% 11633 100 162 174 174
CGCRC321 No No 0.20% 6916 88 161 167 174
CGCRC321 Yes No 0.08% 9559 94 159 171 170
CGCRC321 NA Yes 41.86% 5545 100 159 172 172
CGCRC332 No Yes 19.98% 605 104 154 170 176
CGCRC333 No Yes 43.03% 1265 89 159 165 171
CGCRC333 Yes Yes 22.26% 3338 102 153 165 169
CGCRC333 No No 1.00% 3008 102 153 169 169
CGCRC334 Yes Yes 13.44% 1725 105 160 170 175
CGCRC334 NA Yes 35.28% 1168 100 159 164 174
CGCRC334 No No 3.85% 1798 103 159 166 173
CGCRC335 Yes Yes 0.32% 2411 99 155 167 157
CGCRC336 Yes Yes 75.26% 757 104 156 171 170
CGCRC336 Yes Yes 42.87% 1080 102 150 166 167
CGCRC336 No Yes 81.61% 391 102 161 165 171
CGCRC337 No No 0.12%, 6497 72 153 169 177
CGCRC337 NA Yes 46.26% 1686 100 147 170 163
CGCRC338 Yes Yes 27.03% 1408 105 153 164 166
CGCRC339 Yes Yes 1.94% 1256 105 158 168 169
CGCRC339 Yes Yes 2.35% 1639 101 158 165 172
CGCRC339 No Yes 3.14% 1143 100 154 170 167
CGCRC339 Yes Yes 1.71% 1584 108 161 171 173
CGCRC340 Yes Yes 18.26% 876 101 162 170 175
CGCRC340 Yes Yes 22.57% 796 105 159 164 174
CGPLBR38 No Yes 0.53% 9684 95 156 166 168
CGPLBR40 NA Yes 28.99% 10277 78 162 168 173
CGPLBR44 Yes Yes 1.82% 10715 99 162 171 173
CGPLBR44 No Yes 0.41% 10837 100 159 169 171
CGPLBR44 No Yes 0.13% 12640 100 159 168 171
CGPLBR48 NA Yes 34.61% 5631 100 164 170 179
CGPLBR48 No No 0.18% 12467 101 167 174 180
CGPLBR55 No No 0.18% 10527 101 158 169 169
CGPLBR55 Yes Yes 0.68% 6011 101 153 166 167
CGPLBR55 Yes Yes 0.42% 3973 101 153 166 166
CGPLBR63 NA Yes 34.82% 3405 97 165 170 176
CGPLBR67 Yes Yes 0.11% 10259 87 157 168 168
CGPLBR67 Yes Yes 0.68% 5163 100 151 167 166
CGPLBR67 No No 0.28% 6250 100 155 166 167
CGPLBR69 No No 0.29% 7558 100 159 166 170
CGPLBR69 NA Yes 41.74% 3938 101 154 169 166
CGPLBR69 NA Yes 41.66% 2387 101 157 166 168
CGPLBR70 No No 0.36% 6916 100 158 171 169
CGRLBR70 NA Yes 40.28% 3580 107 160 169 173
CGPLBR71 Yes Yes 0.10% 7930 85 156 166 158
CGPLBR72 NA Yes 44.03% 2389 100 157 160 170
CGPLBR73 No No 0.27% 11348 95 161 173 174
CGPLBR73 NA Yes 35.58% 3422 102 157 168 169
CGPLBR74 NA Yes 36.23% 3784 101 163 175 174
CGPLBR75 Yes Yes 0.14% 7290 103 162 173 172
CGPLBR76 NA Yes 36.57% 4342 104 166 171 179
CGPLBR76 Yes Yes 0.12% 11785 100 165 168 177
CGPLBR77 No Yes 2.29% 6161 100 158 166 169
CGPLBR80 No No 0.54% 3643 96 165 166 185
CGPLBR83 NA Yes 42.66% 3479 105 162 164 174
GGPLBR83 No No 0.28% 3496 103 165 170 177
CGPLBR83 NA Yes 44.91% 1748 100 164 173 175
CGPLBR86 NA Yes 42.32% 4241 98 160 168 175
CGPLBR86 NA Yes 43.38% 3096 88 160 167 174
CGPLBR87 No No 0.35% 3680 101 162 168 175
CGPLBR87 Yes No 0.31% 6180 101 163 164 175
CGPLBR87 No No 0.40% 7746 86 160 167 175
CGPLBR87 NA Yes 42.94% 2266 106 160 166 172
CGPLBR88 No No 0.13% 17537 89 185 200 223
CGPLBR88 NA Yes 31.19% 5919 101 162 172 173
CGPLBR92 No Yes 0.20% 15530 77 150 164 152
CGPLBR96 Yes No 0.10% 9893 100 159 164 171
CGPLBR96 No Yes 5.81% 8620 95 162 167 173
CGPLBR96 No No 0.60% 8036 85 162 169 175
CGPLBR97 Yes Yes 0.11% 14856 93 160 168 170
CGPLBR97 NA Yes 34.12% 5329 100 161 165 171
CGPLBR97 Yes Yes 0.13% 7010 97 158 169 170
CGPLLU144 Yes Yes 1.95% 11371 100 156 165 167
CGPLLU144 Yes Yes 5.10% 7641 100 155 167 166
CGPLLU144 No Yes 0.16% 9996 100 158 168 169
CGPLLU144 No No 0.22% 4956 101 159 166 169
CGPLLU144 Yes Yes 2.94% 8540 100 153 170 166
CGPLLU144 No No 0.18% 7648 101 156 164 166
CGPLLU146 Yes No 0.25% 5920 100 155 164 168
CGPLLU146 No Yes 1.30% 9356 100 155 166 168
CGPLLU146 No Yes 0.84% 7284 101 158 165 170
CGPLLU146 No Yes 0.87% 4183 103 160 166 170
CGPLLU146 No No 0.20% 6778 100 157 166 168
CGPLLU147 Yes No 0.15% 4807 100 155 166 170
CGPLLU147 No Yes 0.55% 5282 100 156 167 171
CGPLLU147 No Yes 0.94% 7122 100 158 174 173
CGPLLU147 NA Yes 43.47% 2825 101 160 165 173
CGPLLU162 No No 0.22% 9940 95 161 164 174
CGPLLU162 Yes Yes 0.22% 13855 87 160 174 173
CGPLLU162 No No 0.14% 11251 100 153 167 166
CGPLLU163 No No 0.21% 10805 85 159 165 173
CGPLLU163 No Yes 0.15% 20185 83 158 166 170
CGPLLU164 No Yes 1.23% 6795 91 156 161 169
CGPLLU164 NA Yes 42.52% 4561 92 157 164 169
CGPLLU164 No No 0.20% 8097 100 158 170 170
CGPLLU164 Yes No 0.10% 9241 100 155 165 157
CGPLLU164 No Yes 1.78% 10806 100 157 168 159
CGPLLU164 No Yes 1.86% 10919 100 157 168 159
CGPLLU164 No Yes 0.96% 5412 103 159 175 171
CGPLLU164 No No 0.22% 5151 101 160 166 169
CGPLLU165 NA Yes 36.62% 7448 95 155 167 167
CGPLLU165 Yes Yes 0.16% 5822 102 154 166 166
CGPLLU168 Yes Yes 0.06% 15985 97 152 165 166
CGPLLU168 No No 0.39% 11070 100 156 165 168
CGPLLU168 Yes Yes 0.07% 11063 83 157 166 169
CGPLLU174 No Yes 0.33% 5881 88 162 165 174
CGPLLU174 No Yes 0.40% 3696 100 162 167 172
CGPLLU174 Yes Yes 0.16% 4941 101 162 167 172
CGPLLU174 No Yes 0.29% 7527 100 163 168 173
CGPLLU174 No Yes 0.26% 8353 101 162 168 173
CGPLLU175 Yes Yes 8.03% 10214 100 160 166 170
CGPLLU175 No No 0.21% 9739 100 157 168 168
CGPLLU175 No Yes 0.15% 9509 100 157 165 168
CGPLLU175 NA Yes 43.84% 2710 101 157 165 167
CGPLLU175 No Yes 3.64% 6565 100 158 166 168
CGPLLU176 No Yes 0.92% 6513 101 164 168 175
CGPLLU176 No Yes 0.21% 5962 100 164 174 175
CGPLLU177 Yes Yes 2.49% 7044 102 160 165 170
CGPLLU177 No Yes 1.53% 9950 88 160 169 171
CGPLLU177 Yes No 0.29% 11233 100 160 168 171
CGPLLU177 No No 0.13% 10966 75 160 169 172
CGPLLU178 No No 0.29% 8378 100 162 176 172
CGPLLU178 No No 0.17% 7235 101 159 167 170
CGPLLU179 No Yes 0.38% 8350 103 161 169 171
CGPLLU179 NA Yes 39.91% 2609 103 162 171 173
CGPLLU180 No Yes 2.43% 6085 91 158 165 170
CGPLLU180 No Yes 2.07% 6680 92 158 164 169
CGPLLU180 No Yes 1.94% 7790 92 158 167 168
CGPLLU180 Yes No 0.08% 9036 101 160 169 171
CGPLLU180 No Yes 1.01% 4679 100 157 169 168
CGPLLU197 Yes No 0.16% 7196 102 162 166 172
CGPLLU197 No No 0.38% 7147 100 161 166 172
CGPLLU198 No Yes 0.87% 9322 97 157 165 168
CGPLLU198 Yes Yes 0.52% 8303 100 160 173 172
CGPLLU202 Yes Yes 0.05% 14197 90 151 165 166
CGPLLU202 No No 0.13% 9279 51 150 168 167
CGPLLU204 No No 0.26% 7185 100 157 165 168
CGPLLU205 No Yes 0.70% 10739 96 156 165 166
CGPLLU205 No Yes 3.47% 12065 100 154 165 165
CGPLLU206 Yes Yes 26.13% 6746 94 148 165 164
CGPLLU206 No No 0.21% 11225 100 147 167 164
CGPLLU207 Yes Yes 0.32% 11224 100 159 165 170
CGPLLU207 NA Yes 34.58% 4960 101 160 166 170
CGPLLU207 Yes No 0.09% 13216 85 161 165 172
CGPLLU208 Yes Yes 1.33% 5211 101 156 166 168
CGPLLU208 NA Yes 39.34% 5253 100 159 164 170
CGPLLU208 Yes Yes 0.86% 10233 100 160 170 171
CGPLLU208 No No 0.17% 11421 100 158 165 171
CGPLLU209 NA Yes 26.84% 11695 96 153 166 169
CGPLLU209 No Yes 9.97% 12771 94 155 163 168
CGPLLU209 Yes Yes 9.13% 16557 92 157 169 170
CGPLLU209 No Yes 9.32% 13057 97 158 167 171
CGPLLU209 NA Yes 30.41% 8521 100 155 167 169
CGPLOV10 Yes Yes 3.14% 4421 101 161 165 172
CGPLOV11 Yes Yes 0.87% 7987 100 157 164 169
CGPLOV11 NA Yes 37.77% 3782 97 160 166 171
CGPLOV13 No Yes 0.12% 12072 88 157 165 169
CGPLOV13 NA Yes 37.98% 4107 103 159 166 169
CGPLOV13 No Yes 0.35% 8427 100 161 165 171
CGPLOV14 No No 0.14% 11418 92 154 167 171
CGPLOV15 Yes Yes 3.54% 7689 102 157 164 169
CGPLOV15 No No 0.19% 7617 101 159 167 171
CGPLOV17 Yes Yes 0.32% 4463 96 155 168 163
CGPLOV17 NA Yes 44.10% 2884 110 157 170 170
CGPLOV18 NA Yes 40.81% 2945 101 159 164 169
CGPLOV19 NA Yes 23.80% 9727 95 158 167 172
CGPLOV19 Yes Yes 36.83% 4387 100 158 165 169
CGPLOV19 NA Yes 65.29% 2775 93 161 171 171
CGPLOV19 Yes Yes 46.35% 3818 102 156 170 170
CGPLOV20 Yes Yes 0.21% 5404 94 159 165 170
CGPLOV20 NA Yes 44.05% 3744 102 158 166 169
CGPLOV21 NA Yes 7.68% 21823 81 158 166 169
CGPLOV21 No Yes 2.04% 18806 101 159 165 169
CGPLOV21 No No 14.36% 10801 89 160 166 169
CGPLOV22 No Yes 0.49% 11952 100 155 165 167
CGPLOV22 Yes Yes 0.34% 12399 92 150 165 164
Wild-type Fragments Mutant Fragments
75th 25th
Mean Percentile Maximum Minimum Percentile Mode Median
cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA
Fragment Fragment Fragment Distinct Fragment Fragment Fragment Fragment
Size (bp) Size (bp) Size (bp) Coverage Size (bp) Size (bp) Size (bp) Size (bp)
179 188 400 19 100 142 233 165
182 185 400 21 132 166 182 176
180 183 400 5411 92 152 167 169
177 182 400 1903 100 148 166 166
184 185 400 1344 108 155 167 170
181 182 400 2108 100 153 166 168
176 180 400 1951 101 149 175 167
176 183 399 75 123 162 167 172
177 182 400 23 101 130 130 139
183 188 399 6863 100 160 168 173
188 186 400 34 77 154 171 170
175 179 396 9 138 147 176 171
184 185 400 21 115 145 155 159
179 185 397 30 137 149 181 162
179 182 397 44 125 155 155 169
185 188 400 8167 101 160 166 171
187 188 400 3562 102 158 168 170
184 187 399 15 93 137 127 174
183 185 400 26 137 163 166 167
181 182 397 35 118 147 176 163
172 175 400 71 133 152 170 165
169 174 400 55 130 153 165 164
189 187 399 17 149 155 326 170
176 183 400 18 156 170 174 174
169 175 397 51 108 143 268 152
166 173 397 26 118 147 153 156
184 186 400 45 116 151 168 163
185 186 400 25 157 165 191 175
185 187 400 25 124 168 180 180
167 175 394 86 121 155 169 166
167 173 397 45 124 143 197 162
170 175 398 108 126 147 162 162
190 189 400 23 131 148 145 166
182 182 399 42 138 155 155 174
189 187 399 25 126 153 176 176
192 193 400 977 101 149 189 170
173 179 391 525 102 140 168 159
181 185 399 4010 100 158 166 170
178 184 399 625 100 140 167 162
175 179 398 37 111 143 142 166
181 186 396 3184 102 159 168 172
180 183 399 47 111 148 144 169
133 184 397 39 111 146 182 162
185 184 400 24 110 146 309 182
176 180 400 32 117 146 154 157
180 184 399 43 111 143 144 177
185 187 400 29 109 140 204 159
179 182 399 20 128 152 180 163
176 184 398 7515 101 160 170 171
182 182 399 31 85 146 137 166
181 182 395 428 100 135 138 149
175 180 397 352 97 136 132 147
165 172 397 15 131 137 132 144
170 173 398 25 107 138 159 161
171 173 400 27 122 147 161 161
189 189 400 91 112 165 168 173
189 189 400 27 124 144 154 154
178 184 399 24 105 143 132 159
188 189 399 8 122 143 122 161
194 192 400 17 144 163 173 173
180 183 394 15 132 159 186 166
183 185 399 233 131 162 167 172
186 186 398 27 136 155 183 163
192 195 399 23 137 144 175 152
182 184 399 29 131 157 177 171
166 172 396 1616 100 146 164 159
175 180 400 806 96 158 169 169
165 172 399 1410 102 140 149 154
170 177 397 49 99 153 143 182
166 173 398 33 140 155 154 170
180 178 400 73 95 140 140 155
172 177 400 38 115 160 164 167
171 174 386 6 124 137 170 156
180 183 400 70 124 151 151 164
194 199 399 6586 96 162 168 175
184 188 400 41 112 172 176 177
194 198 399 35 146 168 175 175
182 184 399 20 166 180 185 191
183 186 397 5338 102 159 175 171
202 203 393 178 101 150 168 171
195 195 397 1350 104 153 163 171
185 189 400 1257 100 153 168 170
185 189 396 30 117 163 164 172
203 210 391 336 105 153 141 171
188 194 399 741 101 161 169 176
193 193 396 89 100 145 171 171
172 179 396 12 129 143 143 153
186 188 387 3559 91 155 164 173
177 183 392 873 102 149 163 164
194 200 377 1909 100 158 167 176
202 259 400 27 122 157 164 179
171 178 395 1818 103 147 169 162
178 182 374 546 102 151 166 166
179 184 397 26 132 142 138 171
195 194 400 53 117 157 166 169
176 179 397 40 124 150 169 166
188 191 390 38 107 153 180 174
205 207 399 217 102 146 144 163
196 195 397 266 111 147 150 166
186 184 400 76 123 157 171 169
179 186 400 9832 93 161 166 172
191 190 400 277 104 162 160 176
191 189 400 65 123 165 166 172
187 189 400 31 136 163 171 167
202 202 400 5286 102 166 168 181
196 201 400 102 138 166 161 179
181 182 397 30 138 158 189 185
181 181 400 64 113 158 163 167
176 179 398 27 121 163 200 171
191 192 398 2943 100 165 176 176
179 181 399 25 138 153 138 167
171 177 399 60 110 136 147 147
172 179 399 26 139 147 180 176
186 184 398 35 121 149 360 161
176 178 397 4000 103 155 166 167
176 178 385 2390 99 157 164 168
182 184 400 28 131 160 168 167
194 193 400 3545 100 161 169 173
179 180 398 15 121 146 166 166
188 187 400 2587 103 158 162 169
189 192 400 86 121 165 183 177
178 184 399 3339 101 157 165 169
179 187 391 3193 101 163 178 173
183 186 398 13 111 153 153 161
197 201 400 4140 102 166 169 179
191 194 400 16 130 143 143 157
183 183 400 209 125 154 175 170
211 230 400 41 158 176 197 186
193 193 400 3445 94 162 175 174
197 199 400 23 123 182 248 224
193 195 399 1787 100 163 163 176
204 207 400 4100 100 159 164 173
196 195 400 3096 79 159 161 173
202 203 400 73 142 178 178 184
205 203 400 23 161 168 168 171
195 196 400 170 125 158 173 173
195 192 400 2086 101 162 169 176
238 280 400 125 84 192 194 207
197 194 400 5715 108 163 154 174
172 173 398 109 78 148 149 158
196 191 399 35 119 161 172 171
189 190 400 826 102 162 166 171
194 195 400 95 135 160 161 170
184 184 400 27 128 150 150 169
179 184 399 4771 103 161 168 171
187 185 399 7 147 154 154 167
179 179 395 330 106 152 165 166
172 177 399 536 106 151 167 163
179 183 400 45 138 163 175 172
182 182 397 16 138 146 146 155
172 177 397 293 101 152 169 164
171 177 399 23 130 152 162 162
180 183 399 54 104 161 154 176
184 184 400 154 96 149 157 163
186 187 399 79 102 163 177 174
183 185 400 44 118 149 163 163
182 184 400 35 136 164 204 181
192 191 400 13 138 164 169 169
199 205 400 50 128 155 161 171
191 193 400 81 108 150 108 173
190 191 389 2597 101 159 165 172
192 197 400 58 92 173 192 192
183 189 400 74 90 147 142 167
175 178 400 37 144 163 185 172
194 202 400 61 93 164 181 181
184 186 400 66 104 158 194 174
191 190 396 101 126 155 176 176
188 185 394 4718 100 156 164 168
186 186 399 30 134 161 175 175
180 180 397 34 139 163 155 170
182 182 400 262 101 150 152 165
182 182 400 277 101 150 147 166
180 182 395 65 121 158 161 167
177 182 400 16 144 172 179 179
185 184 399 7186 100 154 167 166
181 179 394 21 108 164 164 173
177 180 400 18 111 127 127 158
179 181 400 72 121 156 173 166
177 182 400 30 106 160 174 174
200 199 399 36 131 147 143 177
184 185 392 20 144 173 266 178
182 184 395 16 147 156 156 164
186 187 399 34 159 168 168 176
186 186 396 5 116 182 182 185
185 183 399 1073 100 142 164 152
179 180 400 46 109 151 143 175
181 181 400 30 146 154 146 168
176 179 392 2742 102 154 164 166
174 180 399 298 103 140 148 150
197 194 399 67 115 164 250 173
195 194 399 19 156 165 165 185
178 182 395 189 105 138 141 150
183 185 398 227 123 160 168 169
185 184 397 53 78 161 175 175
190 188 395 50 130 161 168 168
186 187 396 28 139 150 173 170
179 184 400 24 130 153 176 170
185 185 394 48 111 154 170 168
189 187 398 2337 100 163 166 172
198 200 396 172 83 152 160 166
190 188 400 215 123 151 159 163
184 184 400 207 121 151 157 161
191 189 397 17 143 170 217 214
181 182 398 52 122 152 167 164
191 189 399 17 109 161 173 171
191 189 399 40 136 164 166 171
180 181 399 127 88 149 131 162
181 186 400 68 141 166 175 176
169 179 398 10 81 167 167 167
170 181 398 33 107 162 167 167
175 181 391 23 112 156 190 164
175 177 400 109 130 153 169 166
172 176 400 684 105 153 167 166
179 178 398 2946 100 138 157 155
175 178 399 30 121 165 165 176
187 186 400 63 140 155 154 167
181 184 400 4754 101 160 170 170
182 187 400 31 131 162 162 174
181 183 400 150 110 144 166 162
179 184 400 5290 95 159 167 169
181 186 400 140 101 155 175 167
187 190 397 20 92 141 241 168
190 192 400 8065 85 156 164 169
174 182 400 2586 101 147 165 165
185 188 400 2808 100 150 158 167
182 187 400 2227 100 154 162 171
176 183 396 8425 100 155 165 169
186 188 399 142 112 146 140 159
186 185 399 104 132 158 159 167
183 185 392 3462 101 160 173 172
182 183 399 25 94 140 140 158
177 181 399 3789 101 159 168 169
181 184 400 57 131 152 170 170
183 191 400 36 118 154 201 182
187 185 399 362 110 152 143 180
182 188 400 20 158 153 311 174
186 187 397 23 126 151 184 168
188 189 400 2980 100 158 169 170
183 183 391 2793 91 158 167 170
185 189 395 7357 100 158 175 171
184 184 398 5186 101 157 165 170
182 187 400 15595 64 159 167 170
186 185 400 6749 101 158 167 170
193 190 400 23 127 148 148 194
182 185 394 3901 101 160 167 171
179 180 400 4633 100 158 169 170
175 179 400 734 101 151 155 165
175 180 394 4022 101 159 167 168
184 182 400 117 116 156 156 172
172 176 395 65 109 145 177 167
Adjusted
Difference Difference P Value of
between between Difference
Wild-type Mutant Fragments Median Mean between
Fragments 75th Mutant and Mutant and Mutant and
Mean Mean Percentile Maximum Wild-type Wild-Type Wild-type
cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA
Fragment Fragment Fragment Fragment Fragment Fragment Fragment
Size (bp) Size (bp) Size (bp) Size (bp) Sizes (bp) Sizes (bp) Sizes
179 180 230 305 −4.0 1.54 0.475
182 191 198 309 7.0 8.33 0.250
180 186 191 399 0.0 5.89 0.000
177 177 183 383 −1.0 −0.25 0.874
184 189 131 398 1.0 5.37 0.009
181 165 187 386 1.0 3.30 0.025
176 179 182 397 0.0 2.65 0.148
176 182 190 370 3.0 5.31 0.368
177 164 155 345 −29.5 −12.79 0.000
183 186 189 400 2.0 3.13 0.002
188 177 192 335 −0.5 −11.46 0.571
175 177 176 290 4.0 1.22 0.475
184 176 175 368 −11.0 −7.99 0.052
179 182 181 369 −8.0 3.49 0.061
179 185 194 338 0.0 5.78 0.023
185 184 187 400 −1.0 −1.27 0.212
187 185 185 399 0.0 −2.62 0.114
184 173 193 261 3.0 −11.00 0.507
183 179 180 364 −3.0 −4.34 0.430
181 172 176 336 −6.0 −9.35 0.166
172 169 173 301 0.0 3.57 0.668
169 166 168 325 0.0 −2.15 0.630
189 221 301 387 −3.0 32.43 0.453
176 210 219 372 5.0 33.84 0.368
169 164 178 268 −12.0 −5.12 0.000
166 174 158 327 −9.5 8.37 0.036
184 175 177 346 −8.0 −8.84 0.057
185 207 199 350 3.0 22.93 0.465
185 189 191 338 8.0 4.06 0.154
167 168 175 309 2.0 0.46 0.445
167 166 168 377 −1.0 −0.91 0.482
170 164 174 302 −3.0 6.74 0.064
190 189 205 333 −5.0 −0.80 0.297
182 177 187 343 5.5 −4.51 0.171
189 188 229 305 7.0 −0.19 0.234
192 182 192 380 −1.0 −9.76 0.000
173 168 176 382 −7.0 −5.57 0.052
181 181 185 398 0.0 0.37 0.773
178 172 181 380 −9.0 −6.68 0.009
175 172 186 321 −1.0 −2.38 0.572
181 182 187 400 0.5 0.95 0.564
180 176 183 353 −1.0 −4.83 0.598
133 182 185 337 −7.0 −0.44 0.064
185 208 284 355 14.0 22.31 0.031
176 167 166 298 −11.0 −8.94 0.013
180 187 212 319 9.0 7.22 0.062
185 188 204 387 −12.0 3.32 0.031
179 166 180 219 −6.5 −13.04 0.155
176 177 185 400 1.0 1.08 0.166
182 167 176 316 −3.0 −14.62 0.469
181 158 166 340 −20.0 −23.47 0.000
175 149 159 326 −21.0 −26.04 0.000
165 163 171 323 −20.0 −1.73 0.000
170 175 190 299 −3.0 4.33 0.384
171 173 171 342 −3.0 2.54 0.354
189 196 192 379 1.0 6.83 0.571
189 167 172 320 −19.0 22.39 0.000
178 183 190 367 −11.0 4.97 0.054
188 168 195 241 −13.0 −19.21 0.100
194 213 261 372 −1.0 19.22 0.587
180 174 185 265 −3.0 −5.62 0.461
183 190 187 394 2.0 7.27 0.137
186 170 178 262 −7.0 −16.03 0.131
192 190 212 327 −17.0 −1.76 0.018
182 183 179 319 1.0 0.74 0.564
166 163 170 354 −3.5 −3.57 0.000
175 179 184 366 1.0 3.80 0.054
165 164 170 398 −8.0 −0.35 0.816
170 206 284 333 16.0 36.25 0.000
166 180 180 296 7.0 14.38 0.104
180 173 178 324 −9.0 −6.66 0.000
172 182 179 329 1.5 10.09 0.479
171 153 168 178 −7.5 18.98 0.411
180 182 183 385 −6.0 1.71 0.064
194 193 196 399 0.0 −1.79 0.166
184 195 195 373 3.0 11.02 0.397
194 181 186 312 1.0 −13.40 0.587
182 205 219 357 21.0 23.48 0.013
183 183 185 394 −1.0 0.03 0.984
202 198 240 357 −5.0 −4.34 0.571
195 201 258 400 0.0 5.94 0.066
185 189 202 392 1.0 4.37 0.064
185 175 179 372 3.0 −10.29 0.463
203 200 240 399 −4.0 −3.10 0.571
188 190 194 400 2.0 1.96 0.571
193 197 229 393 −2.0 3.42 0.479
172 163 166 275 −14.0 −8.99 0.084
186 195 211 398 3.0 8.92 0.001
177 177 181 400 −3.0 −0.39 0.880
194 202 242 398 5.0 7.98 0.061
202 199 231 350 2.0 −3.82 0.685
171 173 180 396 −1.0 1.92 0.372
178 180 182 381 0.0 2.87 0.416
179 183 188 351 1.5 3.29 0.572
195 192 198 336 −3.0 −2.86 0.451
176 181 176 309 −1.0 4.53 0.539
188 185 210 326 0.5 −2.59 0.576
205 188 212 360 −12.0 −17.11 0.004
196 188 204 379 −8.0 −7.53 0.208
186 182 182 346 1.0 −3.64 0.479
179 180 186 399 −1.0 1.04 0.155
191 201 200 384 3.0 9.95 0.061
191 198 192 371 1.0 7.08 0.560
187 201 199 387 −4.0 14.14 0.341
202 201 203 400 2.0 −0.88 0.587
196 199 209 372 −1.5 2.90 0.679
181 191 191 311 16.0 9.25 0.000
181 179 176 318 0.0 −2.85 0.679
176 187 190 392 5.0 10.89 0.314
191 187 192 398 0.0 −3.83 0.015
179 181 184 340 −1.0 2.00 0.571
171 161 159 327 −19.0 −9.77 0.000
172 176 184 344 9.0 3.52 0.015
186 197 195 360 −9.0 10.77 0.314
176 176 178 397 0.5 0.65 0.610
176 178 180 400 0.0 1.78 0.314
182 177 179 338 −2.0 −5.83 0.463
194 194 192 399 0.0 0.40 0.825
179 172 204 221 −2.0 −7.32 0.564
188 189 186 399 −1.0 1.12 0.598
189 189 193 373 3.0 −0.01 0.293
178 177 184 400 0.0 −1.73 0.598
179 180 186 389 −1.0 0.22 0.839
183 171 179 323 −11.0 −12.36 0.061
197 197 200 400 0.0 −0.32 0.839
191 173 173 325 −20.0 −18.40 0.000
183 196 233 357 1.0 12.55 0.025
211 215 220 374 1.0 3.72 0.603
193 194 194 399 0.0 0.65 0.714
197 232 260 359 47.0 34.97 0.000
193 192 194 400 1.0 −0.85 0.718
204 200 202 400 −2.0 −3.65 0.062
196 194 191 397 −1.0 −2.45 0.251
202 237 338 377 9.0 35.30 0.114
205 189 186 380 −4.0 −16.38 0.435
195 188 190 400 −2.0 −6.17 0.293
195 203 203 400 4.5 8.80 0.000
238 243 324 400 −16.0 5.51 0.574
197 200 196 400 1.0 2.87 0.065
172 166 173 302 −4.0 −5.94 0.190
196 191 180 390 0.0 −4.34 0.627
189 187 187 395 −2.0 −1.94 0.475
194 182 184 400 −5.0 −11.54 0.155
184 174 185 319 −1.0 −9.68 0.571
179 179 183 400 0.0 0.15 0.880
187 164 174 177 −3.0 −22.90 0.155
179 178 178 361 −1.0 −1.35 0.685
172 172 175 363 −3.0 −0.34 0.880
179 185 191 380 3.0 6.52 0.368
182 162 170 224 −14.0 −19.82 0.007
172 170 174 392 −2.0 −1.37 0.646
171 163 177 232 −4.0 −7.62 0.252
180 195 206 383 7.5 14.58 0.064
184 176 185 347 −5.5 −7.87 0.154
186 200 203 372 4.0 14.61 0.270
183 185 188 338 −7.0 1.98 0.039
182 194 203 369 13.0 11.80 0.039
192 198 173 333 −1.0 6.05 0.610
199 216 301 360 0.0 17.02 0.623
191 198 224 385 0.0 6.48 0.624
190 185 187 397 −1.0 −5.17 0.005
192 202 200 397 18.0 9.79 0.007
183 176 182 391 −6.5 −6.78 0.061
175 192 186 375 6.0 17.15 0.005
194 197 211 370 8.0 3.34 0.169
184 189 194 379 3.5 4.60 0.270
191 194 213 331 7.0 2.50 0.718
188 190 187 393 −1.0 2.54 0.113
186 190 208 339 5.0 4.07 0.302
180 178 175 349 3.0 −1.65 0.407
182 181 186 393 −4.0 −0.65 0.876
182 182 185 393 −3.0 0.36 0.926
180 186 188 338 −4.0 6.15 0.234
177 187 180 376 10.0 9.98 0.130
185 183 181 396 −1.0 −1.73 0.154
181 196 200 357 7.0 14.95 0.213
177 189 186 352 −8.0 12.47 0.179
179 183 179 396 −2.0 4.31 0.427
177 180 186 282 5.0 3.09 0.252
200 196 227 298 2.5 −4.24 0.479
184 199 215 269 6.0 15.13 0.252
182 177 169 302 −8.0 −4.82 0.119
186 206 196 365 3.0 20.55 0.415
186 201 192 329 12.0 14.52 0.263
185 157 164 346 −18.0 −27.67 0.000
179 174 183 325 7.0 −5.22 0.054
181 186 181 367 −0.5 5.19 0.568
176 176 178 387 −1.0 −0.24 0.874
174 152 162 288 −18.0 −22.25 0.000
197 187 201 366 −2.0 −9.89 0.425
195 197 199 361 10.0 2.20 0.154
178 164 175 348 −20.0 −14.58 0.000
183 185 184 396 −2.0 1.68 0.706
185 189 188 392 4.0 3.80 0.241
190 184 175 377 −4.5 −5.86 0.234
186 170 173 354 −2.5 −15.88 0.416
179 193 199 359 0.0 13.13 0.598
185 173 183 295 −3.0 −11.80 0.270
189 187 185 394 −1.0 −1.27 0.564
198 193 226 396 −4.0 −4.93 0.490
190 188 196 365 −6.0 −1.72 0.735
184 181 179 365 −7.0 −3.01 0.571
191 198 217 294 43.0 7.08 0.000
181 179 173 372 −4.5 −2.07 0.137
191 181 174 293 −1.0 −9.24 0.576
191 185 185 335 −1.0 −5.86 0.571
180 168 178 311 −6.0 −11.80 0.005
181 198 207 387 4.0 17.11 0.184
169 159 176 182 1.0 −10.20 0.589
170 174 185 322 0.0 4.57 0.636
175 175 190 349 −4.0 −0.92 0.308
175 175 178 382 0.0 −0.09 0.987
172 172 175 385 1.0 0.00 0.999
179 172 174 398 −9.0 −7.28 0.000
175 198 219 325 12.0 22.37 0.007
187 201 215 372 −3.0 13.70 0.286
181 179 181 393 0.0 −1.72 0.154
182 180 185 352 2.0 −2.26 0.494
181 176 173 385 −6.0 −5.86 0.314
179 179 184 400 −1.0 0.11 0.909
181 179 180 352 −4.5 −2.77 0.589
187 178 209 283 −3.0 −9.82 0.479
190 190 190 399 0.0 −0.08 0.942
174 169 179 386 −3.5 −4.59 0.000
185 189 200 399 −3.0 4.17 0.007
182 183 190 398 0.0 1.00 0.564
176 176 184 400 0.0 0.54 0.568
186 180 193 352 −13.0 −5.41 0.463
186 189 180 331 −2.0 3.05 0.657
183 184 187 396 1.0 0.82 0.576
182 159 163 341 −11.0 −23.47 0.027
177 176 181 395 0.0 −0.66 0.576
181 179 184 327 −1.0 −2.41 0.568
183 187 201 328 11.0 3.60 0.114
187 207 268 389 11.0 20.70 0.000
182 198 209 311 3.0 15.25 0.475
185 185 185 328 −1.0 −1.49 0.571
188 187 189 398 0.0 −0.84 0.637
183 181 182 389 1.0 −2.30 0.171
185 182 187 399 −1.0 −2.37 0.008
184 185 186 400 1.0 1.72 0.240
182 181 185 397 −1.0 −1.39 0.245
186 185 187 400 0.0 −0.52 0.702
193 222 292 378 24.0 29.58 0.027
182 182 185 398 2.0 0.32 0.821
179 185 187 400 1.0 6.16 0.000
175 176 178 366 −4.0 0.48 0.823
175 172 178 399 −1.0 −2.84 0.000
184 199 184 399 5.0 15.08 0.084
172 181 181 306 3.0 9.11 0.293
TABLE 4
APPENDIX - D: Summary of whole genome cfDNA analyses
Analysis Patient Read Total Bases High Quality
Patient Timepoint type Type Length Sequenced Bases Analyzed Coverage
CGCRC291 Preoperative WGS Colorectal 100 7232125000 4695396600 1.86
treatment naïve Cancer
CGCRC292 Preoperative WGS Colorectal 100 6794092800 4471065400 1.77
treatment naïve Cancer
CGCRC293 Preoperative WGS Colorectal 100 8373899600 5686176000 2.26
treatment naïve Cancer
CGCRC294 Preoperative WGS Colorectal 100 8081312000 5347045800 2.12
treatment naïve Cancer
CGCRC296 Preoperative WGS Colorectal 100 10072029200 6770998200 2.69
treatment naïve Cancer
CGCRC299 Preoperative WGS Colorectal 100 10971591600 7632723200 3.03
treatment naïve Cancer
CGCRC300 Preoperative WGS Colorectal 100 9894332600 6699951000 2.66
treatment naïve Cancer
CGCRC301 Preoperative WGS Colorectal 100 7857346200 5021002000 1.99
treatment naïve Cancer
CGCRC302 Preoperative WGS Colorectal 100 11671913000 8335275800 3.31
treatment naïve Cancer
CGCRC304 Preoperative WGS Colorectal 100 19011739200 12957614200 5.14
treatment naïve Cancer
CGCRC305 Preoperative WGS Colorectal 100 7177341400 4809957200 1.91
treatment naïve Cancer
CGCRC306 Preoperative WGS Colorectal 100 8302233200 5608043600 2.23
treatment naïve Cancer
CGCRC307 Preoperative WGS Colorectal 100 8034729400 5342620000 2.12
treatment naïve Cancer
CGCRC308 Preoperative WGS Colorectal 100 8670084800 5934037200 2.35
treatment naïve Cancer
CGCRC311 Preoperative WGS Colorectal 100 6947634400 4704601800 1.87
treatment naïve Cancer
CGCRC315 Preoperative WGS Colorectal 100 5205544000 3419565400 1.36
treatment naïve Cancer
CGCRC316 Preoperative WGS Colorectal 100 6405388600 4447534800 1.76
treatment naïve Cancer
CGCRC317 Preoperative WGS Colorectal 100 6060390400 4104616600 1.63
treatment naïve Cancer
CGCRC318 Preoperative WGS Colorectal 100 6848768600 4439404800 1.76
treatment naïve Cancer
CGCRC319 Preoperative WGS Colorectal 100 10545294400 7355181600 2.92
treatment naïve Cancer
CGCRC320 Preoperative WGS Colorectal 100 5961999200 3945054000 1.57
treatment naïve Cancer
CGCRC321 Preoperative WGS Colorectal 100 8248095400 5614355000 2.23
treatment naïve Cancer
CGCRC333 Preoperative WGS Colorectal 100 10540267600 6915490600 2.74
treatment naïve Cancer
CGCRC336 Preoperative WGS Colorectal 100 10675581800 7087691800 2.81
treatment naïve Cancer
CGCRC338 Preoperative WGS Colorectal 100 13788172600 3970308600 3.56
treatment naïve Cancer
CGCRC341 Preoperative WGS Colorectal 100 10753467600 7311539200 2.90
treatment naïve Cancer
CGCRC342 Preoperative WGS Colorectal 100 11836966000 7552793200 3.00
treatment naïve Cancer
CGH14 Human adult elutriated WGS Healthy 100 36525427600 24950300200 9.90
lymphocytes
CGH15 Human adult elutriated WGS Healthy 100 29930855000 23754049400 9.43
lymphocytes
CGLU316 Pre-treatment, Day −53 WGS Lung 100 10354123200 6896471400 2.74
Cancer
CGLU316 Pre-treatment, Day −4 WGS Lung 100 7870039200 5254938800 2.09
Cancer
CGLU316 Post-treatment, Day 18 WGS Lung 100 8155322000 5416262400 2.15
Cancer
CGLU316 Post-treatment, Day 87 WGS Lung 100 9442310400 6087893400 2.42
Cancer
CGLU344 Pre-treatment, Day −21 WGS Lung 100 8728318600 5769097200 2.29
Cancer
CGLU344 Pre-treatment, Day 0 WGS Lung 100 11710249400 7826902600 3.11
Cancer
CGLU344 Post-treatment, Day 0.1875 WGS Lung 100 11569683000 7654701600 3.04
Cancer
CGLU344 Post-treatment, Day 59 WGS Lung 100 11042459200 6320138800 2.51
Cancer
CGLU369 Pre-treatment, Day −2 WGS Lung 100 8630932800 5779595800 2.29
Cancer
CGLU369 Post-treatment, Day 12 WGS Lung 100 9227709600 6136755200 2.44
Cancer
CGLU369 Post-treatment, Day 68 WGS Lung 100 7995282600 5239077200 2.08
Cancer
CGLU369 Post-treatment, Day 110 WGS Lung 100 8750541000 5626139000 2.23
Cancer
CGLU373 Pre-treatment, Day −2 WGS Lung 100 11746059600 7547485800 3.00
Cancer
CGLU373 Post-treatment, Day 0.125 WGS Lung 100 13801136800 9255579400 3.67
Cancer
CGLU373 Post-treatment, Day 7 WGS Lung 100 11537896800 7654111200 3.04
Cancer
CGLU373 Post-treatment, Day 47 WGS Lung 100 8046326400 5397702400 2.14
Cancer
CGPLBR100 Preoperative WGS Breast 100 8440532400 5729474800 2.27
treatment naïve Cancer
CGPLBR101 Preoperative WGS Breast 100 9786253600 6673495200 2.65
treatment naïve Cancer
CGPLBR102 Preoperative WGS Breast 100 8664980400 5669781600 2.25
treatment naïve Cancer
CGPLBR103 Preoperative WGS Breast 100 9846936200 6662883400 2.64
treatment naïve Cancer
CGPLBR104 Preoperative WGS Breast 100 9443375400 6497061000 2.58
treatment naïve Cancer
CGPLBR12 Preoperative WGS Breast 100 7017577800 4823327400 1.91
treatment naïve Cancer
CGPLBR18 Preoperative WGS Breast 100 10309652800 7130386000 2.83
treatment naïve Cancer
CGPLBR23 Preoperative WGS Breast 100 9034484800 6219625800 2.47
treatment naïve Cancer
CGPLBR24 Preoperative WGS Breast 100 9891454200 6601857400 2.62
treatment naïve Cancer
CGPLBR28 Preoperative WGS Breast 100 7997607200 5400803200 2.14
treatment naïve Cancer
CGPLBR30 Preoperative WGS Breast 100 8502597200 5885822400 2.34
treatment naïve Cancer
CGPLBR31 Preoperative WGS Breast 100 12660085600 8551995600 3.39
treatment naïve Cancer
CGPLBR32 Preoperative WGS Breast 100 8773498600 5839034600 2.32
treatment naïve Cancer
CGPLBR33 Preoperative WGS Breast 100 10931742800 6967030600 2.76
treatment naïve Cancer
CGPLBR34 Preoperative WGS Breast 100 10861398600 7453225800 2.96
treatment naïve Cancer
CGPLBR35 Preoperative WGS Breast 100 9180193600 6158440200 2.44
treatment naïve Cancer
CGPLBR36 Preoperative WGS Breast 100 9159948400 6091817800 2.42
treatment naïve Cancer
CGPLBR37 Preoperative WGS Breast 100 10307505800 6929530600 2.75
treatment naïve Cancer
CGPLBR38 Preoperative WGS Breast 100 9983824000 6841725400 2.71
treatment naïve Cancer
CGPLBR40 Preoperative WGS Breast 100 10148823800 7024345400 2.79
treatment naïve Cancer
CGPLBR41 Preoperative WGS Breast 100 11168192000 7562945800 3.00
treatment naïve Cancer
CGPLBR45 Preoperative WGS Breast 100 8793780600 6011109400 2.39
treatment naïve Cancer
CGPLBR46 Preoperative WGS Breast 100 7228607600 4706130000 1.87
treatment naïve Cancer
CGPLBR47 Preoperative WGS Breast 100 7906911400 5341655000 2.12
treatment naïve Cancer
CGPLBR48 Preoperative WGS Breast 100 6992032000 4428636200 1.76
treatment naïve Cancer
CGPLBR49 Preoperative WGS Breast 100 7311195000 4559460200 1.81
treatment naïve Cancer
CGPLBR50 Preoperative WGS Breast 100 11107960600 7582776600 3.01
treatment naïve Cancer
CGPLBR51 Preoperative WGS Breast 100 8393547400 5102069000 2.02
treatment naïve Cancer
CGPLBR52 Preoperative WGS Breast 100 9491894800 6141729000 2.44
treatment naïve Cancer
CGPLBR55 Preoperative WGS Breast 100 9380109800 6518855200 2.59
treatment naïve Cancer
CGPLBR56 Preoperative WGS Breast 100 12191816800 8293011200 3.29
treatment naïve Cancer
CGPLBR57 Preoperative WGS Breast 100 9847584400 6713638000 2.66
treatment naïve Cancer
CGPLBR59 Preoperative WGS Breast 100 7476477000 5059878200 2.01
treatment naïve Cancer
CGPLBR60 Preoperative WGS Breast 100 6531354600 4331253800 1.72
treatment naïve Cancer
CGPLBR61 Preoperative WGS Breast 100 9311029200 6430920800 2.55
treatment naïve Cancer
CGPLBR63 Preoperative WGS Breast 100 8971949000 6044009600 2.40
treatment naïve Cancer
CGPLBR65 Preoperative WGS Breast 100 7197301400 4835015200 1.92
treatment naïve Cancer
CGPLBR68 Preoperative WGS Breast 100 10003774000 6974918800 2.77
treatment naïve Cancer
CGPLBR69 Preoperative WGS Breast 100 10080881800 6903459200 2.74
treatment naïve Cancer
CGPLBR70 Preoperative WGS Breast 100 8824002800 6002533800 2.38
treatment naïve Cancer
CGPLBR71 Preoperative WGS Breast 100 10164136800 6994668600 2.78
treatment naïve Cancer
CGPLBR72 Preoperative WGS Breast 100 18416841400 12328783000 4.89
treatment naïve Cancer
CGPLBR73 Preoperative WGS Breast 100 10281460200 7078613200 2.81
treatment naïve Cancer
CGPLBR76 Preoperative WGS Breast 100 10105270400 6800705000 2.70
treatment naïve Cancer
CGPLBR81 Preoperative WGS Breast 100 5087126000 3273367200 1.30
treatment naïve Cancer
CGPLBR82 Preoperative WGS Breast 100 10576496600 7186662600 2.85
treatment naïve Cancer
CGPLBR83 Preoperative WGS Breast 100 8977124400 5947525000 2.36
treatment naïve Cancer
CGPLBR84 Preoperative WGS Breast 100 6272538600 4066870600 1.61
treatment naïve Cancer
CGPLBR87 Preoperative WGS Breast 100 8460954800 5375710200 2.13
treatment naïve Cancer
CGPLBR88 Preoperative WGS Breast 100 8665810400 5499898200 2.18
treatment naïve Cancer
CGPLBR90 Preoperative WGS Breast 100 6663469200 4392442400 1.74
treatment naïve Cancer
CGPLBR91 Preoperative WGS Breast 100 10933002400 7647842000 3.03
treatment naïve Cancer
CGPLBR92 Preoperative WGS Breast 100 10392674000 6493598000 2.58
treatment naïve Cancer
CGPLBR93 Preoperative WGS Breast 100 5659836000 3931106800 1.56
treatment naïve Cancer
CGPLH189 Preoperative WGS Healthy 100 11400610400 7655568800 3.04
treatment naïve
CGPLH190 Preoperative WGS Healthy 100 11444671600 7581175200 3.01
treatment naïve
CGPLH192 Preoperative WGS Healthy 100 12199010800 8126804800 3.22
treatment naïve
CGPLH193 Preoperative WGS Healthy 100 10201897600 6635285400 2.63
treatment naïve
CGPLH194 Preoperative WGS Healthy 100 11005087400 7081652600 2.81
treatment naïve
CGPLH196 Preoperative WGS Healthy 100 12891462800 8646881800 3.43
treatment naïve
CGPLH197 Preoperative WGS Healthy 100 11961841600 8052855200 3.20
treatment naïve
CGPLH198 Preoperative WGS Healthy 100 13605489000 8885716000 3.53
treatment naïve
CGPLH199 Preoperative WGS Healthy 100 1818090200 5615316000 2.23
treatment naïve
CGPLH200 Preoperative WGS Healthy 100 14400027600 9310342000 3.69
treatment naïve
CGPLH201 Preoperative WGS Healthy 100 6208766800 4171848400 1.66
treatment naïve
CGPLH202 Preoperative WGS Healthy 100 11282922800 7363530600 2.92
treatment naïve
CGPLH203 Preoperative WGS Healthy 100 13540689600 9068747600 3.60
treatment naïve
CGPLH205 Preoperative WGS Healthy 100 10343537800 6696988600 2.66
treatment naïve
CGPLH208 Preoperative WGS Healthy 100 12796300000 8272073400 3.28
treatment naïve
CGPLH209 Preoperative WGS Healthy 100 13123035400 8531813600 3.39
treatment naïve
CGPLH210 Preoperative WGS Healthy 100 10184218800 6832204600 2.71
treatment naïve
CGPLH211 Preoperative WGS Healthy 100 14655260200 8887067600 3.53
treatment naïve
CGPLH300 Preoperative WGS Healthy 100 7062083400 4553351200 1.81
treatment naïve
CGPLH307 Preoperative WGS Healthy 100 7239128200 4547697200 1.80
treatment naïve
CGPLH308 Preoperative WGS Healthy 100 8512551400 5526653600 2.19
treatment naïve
CGPLH309 Preoperative WGS Healthy 100 11664474200 7431836600 2.95
treatment naïve
CGPLH310 Preoperative WGS Healthy 100 11045691000 7451506200 2.96
treatment naïve
CGPLH311 Preoperative WGS Healthy 100 10406803200 6786479600 2.69
treatment naïve
CGPLH314 Preoperative WGS Healthy 100 10371343800 6925866600 2.75
treatment naïve
CGPLH315 Preoperative WGS Healthy 100 9508538400 6208744600 2.46
treatment naïve
CGPLH316 Preoperative WGS Healthy 100 10131063600 6891181000 2.73
treatment naïve
CGPLH317 Preoperative WGS Healthy 100 8364314400 5302232600 2.10
treatment naïve
CGPLH319 Preoperative WGS Healthy 100 8780528200 5585897000 2.22
treatment naïve
CGPLH320 Preoperative WGS Healthy 100 8956232600 5784619200 2.30
treatment naïve
CGPLH322 Preoperative WGS Healthy 100 9563837800 6445517800 2.56
treatment naïve
CGPLH324 Preoperative WGS Healthy 100 6765038600 4469201600 1.77
treatment naïve
CGPLH325 Preoperative WGS Healthy 100 8008213400 5099262800 2.02
treatment naïve
CGPLH326 Preoperative WGS Healthy 100 9554226200 6112544800 2.43
treatment naïve
CGPLH327 Preoperative WGS Healthy 100 8239168800 5351280200 2.12
treatment naïve
CGPLH328 Preoperative WGS Healthy 100 7197086800 4516894800 1.79
treatment naïve
CGPLH329 Preoperative WGS Healthy 100 8921554800 5493709800 2.18
treatment naïve
CGPLH330 Preoperative WGS Healthy 100 10693603400 7077793600 2.81
treatment naïve
CGPLH331 Preoperative WGS Healthy 100 8982792000 5538096200 2.20
treatment naïve
CGPLH333 Preoperative WGS Healthy 100 7856985400 5178829600 2.06
treatment naïve
CGPLH335 Preoperative WGS Healthy 100 9370663400 6035739400 2.40
treatment naïve
CGPLH336 Preoperative WGS Healthy 100 8002498200 5340331400 2.12
treatment naïve
CGPLH337 Preoperative WGS Healthy 100 7399022000 4954467600 1.97
treatment naïve
CGPLH338 Preoperative WGS Healthy 100 8917121600 6170927200 2.45
treatment naïve
CGPLH339 Preoperative WGS Healthy 100 8591130800 5866411400 2.33
treatment naïve
CGPLH340 Preoperative WGS Healthy 100 8046351000 5368062000 2.13
treatment naïve
CGPLH341 Preoperative WGS Healthy 100 7914788600 5200304800 2.06
treatment naïve
CGPLH342 Preoperative WGS Healthy 100 8633473000 5701972400 2.26
treatment naïve
CGPLH343 Preoperative WGS Healthy 100 6694769800 4410670800 1.75
treatment naïve
CGPLH344 Preoperative WGS Healthy 100 7628192400 4961476600 1.97
treatment naïve
CGPLH345 Preoperative WGS Healthy 100 7121569400 4747223000 1.88
treatment naïve
CGPLH346 Preoperative WGS Healthy 100 7707924600 4873321600 1.93
treatment naïve
CGPLH35 Preoperative WGS Healthy 100 47305985200 4774186200 12.63
treatment naïve
CGPLH350 Preoperative WGS Healthy 100 9745839800 6054055200 2.40
treatment naïve
CGPLH351 Preoperative WGS Healthy 100 13317435800 6714465000 3.46
treatment naïve
CGPLH352 Preoperative WGS Healthy 100 7659351600 4752309400 1.89
treatment naïve
CGPLH353 Preoperative WGS Healthy 100 8435782400 5275098200 2.09
treatment naïve
CGPLH354 Preoperative WGS Healthy 100 8018644000 4857577600 1.93
treatment naïve
CGPLH355 Preoperative WGS Healthy 100 8624675800 5709726400 2.27
treatment naïve
CGPLH356 Preoperative WGS Healthy 100 8817952800 5729595200 2.27
treatment naïve
CGPLH357 Preoperative WGS Healthy 100 11931696200 7690004400 3.05
treatment naïve
CGPLH358 Preoperative WGS Healthy 100 12802561200 8451274800 3.35
treatment naïve
CGPLH36 Preoperative WGS Healthy 100 40173545600 3974810400 10.52
treatment naïve
CGPLH360 Preoperative WGS Healthy 100 7280078400 4918566200 1.95
treatment naïve
CGPLH361 Preoperative WGS Healthy 100 7493498400 4966813800 1.97
treatment naïve
CGPLH362 Preoperative WGS Healthy 100 11345644200 7532133600 2.99
treatment naïve
CGPLH363 Preoperative WGS Healthy 100 6117382800 3965952400 1.57
treatment naïve
CGPLH364 Preoperative WGS Healthy 100 10823498400 7195657000 2.86
treatment naïve
CGPLH365 Preoperative WGS Healthy 100 5938367400 3954556200 1.57
treatment naïve
CGPLH366 Preoperative WGS Healthy 100 7063168600 4731853000 1.88
treatment naïve
CGPLH367 Preoperative WGS Healthy 100 7119631800 4627888200 1.84
treatment naïve
CGPLH368 Preoperative WGS Healthy 100 7726718400 4975233400 1.97
treatment naïve
CGPLH369 Preoperative WGS Healthy 100 10967584200 7130956800 2.83
treatment naïve
CGPLH37 Preoperative WGS Healthy 100 45970545400 4591328800 12.15
treatment naïve
CGPLH370 Preoperative WGS Healthy 100 9237170600 6106373800 2.42
treatment naïve
CGPLH371 Preoperative WGS Healthy 100 8077798800 5237070600 2.08
treatment naïve
CGPLH380 Preoperative WGS Healthy 100 14049589200 8614241200 3.42
treatment naïve
CGPLH381 Preoperative WGS Healthy 100 16743792000 10767882800 4.27
treatment naïve
CGPLH382 Preoperative WGS Healthy 100 18474025200 12276437200 4.87
treatment naïve
CGPLH383 Preoperative WGS Healthy 100 13215954000 8430420600 3.35
treatment naïve
CGPLH384 Preoperative WGS Healthy 100 8481814000 5463636200 2.17
treatment naïve
CGPLH385 Preoperative WGS Healthy 100 9596118800 6445445600 2.56
treatment naïve
CGPLH386 Preoperative WGS Healthy 100 7399540400 4915484800 1.95
treatment naïve
CGPLH387 Preoperative WGS Healthy 100 6860332600 4339724400 1.72
treatment naïve
CGPLH388 Preoperative WGS Healthy 100 8679705600 5463945400 2.17
treatment naïve
CGPLH389 Preoperative WGS Healthy 100 7266863600 4702386000 1.87
treatment naïve
CGPLH390 Preoperative WGS Healthy 100 7509035600 4913901800 1.95
treatment naïve
CGPLH391 Preoperative WGS Healthy 100 7252286000 4702404800 1.87
treatment naïve
CGPLH392 Preoperative WGS Healthy 100 7302618200 4722407000 1.87
treatment naïve
CGPLH393 Preoperative WGS Healthy 100 8879138000 5947871800 2.36
treatment naïve
CGPLH394 Preoperative WGS Healthy 100 8737031000 5599777400 2.22
treatment naïve
CGPLH395 Preoperative WGS Healthy 100 7783904800 4907146000 1.95
treatment naïve
CGPLH396 Preoperative WGS Healthy 100 7585567200 5076638200 2.01
treatment naïve
CGPLH398 Preoperative WGS Healthy 100 13001418200 8607025000 3.42
treatment naïve
CGPLH399 Preoperative WGS Healthy 100 9867699200 5526646000 2.19
treatment naïve
CGPLH400 Preoperative WGS Healthy 100 10573939000 6290438200 2.50
treatment naïve
CGPLH401 Preoperative WGS Healthy 100 9415150000 6139638000 2.44
treatment naïve
CGPLH402 Preoperative WGS Healthy 100 5541458000 2972027800 1.18
treatment naïve
CGPLH403 Preoperative WGS Healthy 100 6470913200 3549772600 1.41
treatment naïve
CGPLH404 Preoperative WGS Healthy 100 7369651800 4120205000 1.64
treatment naïve
CGPLH405 Preoperative WGS Healthy 100 7360239000 4293522600 1.70
treatment naïve
CGPLH406 Preoperative WGS Healthy 100 6028125400 3426007400 1.36
treatment naïve
CGPLH407 Preoperative WGS Healthy 100 7073375200 4079286800 1.62
treatment naïve
CGPLH408 Preoperative WGS Healthy 100 8006103200 5121285600 2.03
treatment naïve
CGPLH409 Preoperative WGS Healthy 100 7343124600 4432335600 1.76
treatment naïve
CGPLH410 Preoperative WGS Healthy 100 7551842000 4818779600 1.91
treatment naïve
CGPLH411 Preoperative WGS Healthy 100 6119676400 3636478400 1.44
treatment naïve
CGPLH412 Preoperative WGS Healthy 100 7960821200 4935752200 1.96
treatment naïve
CGPLH413 Preoperative WGS Healthy 100 7623405400 4827888400 1.92
treatment naïve
CGPLH414 Preoperative WGS Healthy 100 7381312400 4743337200 1.88
treatment naïve
CGPLH415 Preoperative WGS Healthy 100 7240754200 4162208800 1.65
treatment naïve
CGPLH416 Preoperative WGS Healthy 100 7745658600 4670226000 1.85
treatment naïve
CGPLH417 Preoperative WGS Healthy 100 7627498600 4403085600 1.75
treatment naïve
CGPLH418 Preoperative WGS Healthy 100 9090285000 5094814000 2.02
treatment naïve
CGPLH419 Preoperative WGS Healthy 100 7914120200 5078389800 2.02
treatment naïve
CGPLH42 Preoperative WGS Healthy 100 39492040600 3901039400 10.32
treatment naïve
CGPLH420 Preoperative WGS Healthy 100 7014307800 4711393600 1.87
treatment naïve
CGPLH422 Preoperative WGS Healthy 100 9103972800 6053559800 2.40
treatment naïve
CGPLH423 Preoperative WGS Healthy 100 10154714200 6128800200 2.43
treatment naïve
CGPLH424 Preoperative WGS Healthy 100 11002394000 6573756000 2.61
treatment naïve
CGPLH425 Preoperative WGS Healthy 100 14681352600 9272557000 3.68
treatment naïve
CGPLH426 Preoperative WGS Healthy 100 8336731000 5177430800 2.05
treatment naïve
CGPLH427 Preoperative WGS Healthy 100 8242924400 5632991800 2.24
treatment naïve
CGPLH428 Preoperative WGS Healthy 100 8512550400 5604756600 2.22
treatment naïve
CGPLH429 Preoperative WGS Healthy 100 8369802800 5477121400 2.17
treatment naïve
CGPLH43 Preoperative WGS Healthy 100 38513193400 3815698400 10.10
treatment naïve
CGPLH430 Preoperative WGS Healthy 100 10357365400 6841611000 2.71
treatment naïve
CGPLH431 Preoperative WGS Healthy 100 7599875800 5006909000 1.99
treatment naïve
CGPLH432 Preoperative WGS Healthy 100 7932532400 4932304200 1.96
treatment naïve
CGPLH434 Preoperative WGS Healthy 100 10417028600 6965998800 2.76
treatment naïve
CGPLH435 Preoperative WGS Healthy 100 8747793800 5677115200 2.25
treatment naïve
CGPLH436 Preoperative WGS Healthy 100 7990589400 5228737800 2.07
treatment naïve
CGPLH437 Preoperative WGS Healthy 100 10156991200 6935537200 2.75
treatment naïve
CGPLH438 Preoperative WGS Healthy 100 9473604000 6445455600 2.56
treatment naïve
CGPLH439 Preoperative WGS Healthy 100 8303723400 5439877200 2.16
treatment naïve
CGPLH440 Preoperative WGS Healthy 100 9055233800 6018631400 2.39
treatment naïve
CGPLH441 Preoperative WGS Healthy 100 10290682000 6896415200 2.74
treatment naïve
CGPLH442 Preoperative WGS Healthy 100 9876551600 6591249800 2.62
treatment naïve
CGPLH443 Preoperative WGS Healthy 100 9837225800 6360740800 2.52
treatment naïve
CGPLH444 Preoperative WGS Healthy 100 9199271400 5755941600 2.28
treatment naïve
CGPLH445 Preoperative WGS Healthy 100 8089236400 5218259800 2.07
treatment naïve
CGPLH446 Preoperative WGS Healthy 100 7890664200 5181606000 2.06
treatment naïve
CGPLH447 Preoperative WGS Healthy 100 7775775000 5120239800 2.03
treatment naïve
CGPLH448 Preoperative WGS Healthy 100 8686964800 5605079200 2.22
treatment naïve
CGPLH449 Preoperative WGS Healthy 100 8604545400 5527726600 2.19
treatment naïve
CGPLH45 Preoperative WGS Healthy 100 39029653000 3771601200 9.98
treatment naïve
CGPLH450 Preoperative WGS Healthy 100 8428254800 5439950000 2.16
treatment naïve
CGPLH451 Preoperative WGS Healthy 100 8128977600 5186265600 2.06
treatment naïve
CGPLH452 Preoperative WGS Healthy 100 6474313400 4216316400 1.67
treatment naïve
CGPLH453 Preoperative WGS Healthy 100 9831832800 6224917600 2.47
treatment naïve
CGPLH455 Preoperative WGS Healthy 100 7373753000 4593473600 1.82
treatment naïve
CGPLH456 Preoperative WGS Healthy 100 8455416200 5457148200 2.17
treatment naïve
CGPLH457 Preoperative WGS Healthy 100 8647618000 5534503800 2.20
treatment naïve
CGPLH458 Preoperative WGS Healthy 100 6633156400 4415186000 1.75
treatment naïve
CGPLH459 Preoperative WGS Healthy 100 8361048200 5497193800 2.18
treatment naïve
CGPLH46 Preoperative WGS Healthy 100 35361484600 3516232800 9.30
treatment naïve
CGPLH460 Preoperative WGS Healthy 100 6788835400 4472282800 1.77
treatment naïve
CGPLH463 Preoperative WGS Healthy 100 8534880800 5481759200 2.18
treatment naïve
CGPLH464 Preoperative WGS Healthy 100 6692520000 4184463400 1.66
treatment naïve
CGPLH465 Preoperative WGS Healthy 100 7772884600 4878430800 1.94
treatment naïve
CGPLH466 Preoperative WGS Healthy 100 9056275000 5830877400 2.31
treatment naïve
CGPLH467 Preoperative WGS Healthy 100 9331419200 4585861000 1.82
treatment naïve
CGPLH468 Preoperative WGS Healthy 100 9334067400 6314830400 2.51
treatment naïve
CGPLH469 Preoperative WGS Healthy 100 7376691000 4545246600 1.80
treatment naïve
CGPLH47 Preoperative WGS Healthy 100 38485647600 3534883600 9.35
treatment naïve
CGPLH470 Preoperative WGS Healthy 100 7899727600 5221650600 2.07
treatment naïve
CGPLH471 Preoperative WGS Healthy 100 9200430600 6102371000 2.42
treatment naïve
CGPLH472 Preoperative WGS Healthy 100 8143742400 5399946600 2.14
treatment naïve
CGPLH473 Preoperative WGS Healthy 100 8123924600 5419825400 2.15
treatment naïve
CGPLH474 Preoperative WGS Healthy 100 8853071400 6084059400 2.41
treatment naïve
CGPLH475 Preoperative WGS Healthy 100 8115374000 5291718000 2.10
treatment naïve
CGPLH476 Preoperative WGS Healthy 100 8163162600 5096869600 2.02
treatment naïve
CGPLH477 Preoperative WGS Healthy 100 8350093200 5465468600 2.17
treatment naïve
CGPLH478 Preoperative WGS Healthy 100 8259642200 5406516200 2.15
treatment naïve
CGPLH479 Preoperative WGS Healthy 100 8027598600 5417376800 2.15
treatment naïve
CGPLH48 Preoperative WGS Healthy 100 42232410000 4165893400 11.02
treatment naïve
CGPLH480 Preoperative WGS Healthy 100 7832983200 5020127000 1.99
treatment naïve
CGPLH481 Preoperative WGS Healthy 100 7578518800 4883280800 1.94
treatment naïve
CGPLH482 Preoperative WGS Healthy 100 8279364800 5652263600 2.24
treatment naïve
CGPLH483 Preoperative WGS Healthy 100 8660338800 5823859200 2.31
treatment naïve
CGPLH484 Preoperative WGS Healthy 100 8445420000 5794328000 2.30
treatment naïve
CGPLH485 Preoperative WGS Healthy 100 8371255400 5490207800 2.18
treatment naïve
CGPLH486 Preoperative WGS Healthy 100 8216712200 5506871000 2.19
treatment naïve
CGPLP487 Preoperative WGS Healthy 100 7936294200 5309250200 2.11
treatment naïve
CGPLH488 Preoperative WGS Healthy 100 8355603600 5453160000 2.16
treatment naïve
CGPLH49 Preoperative WGS Healthy 100 33912191800 3310056000 8.76
treatment naïve
CGPLH490 Preoperative WGS Healthy 100 7768712400 5175567800 2.05
treatment naïve
CGPLH491 Preoperative WGS Healthy 100 9070904000 6011275000 2.39
treatment naïve
CGPLH492 Preoperative WGS Healthy 100 7208727200 4753213800 1.89
treatment naïve
CGPLH493 Preoperative WGS Healthy 100 10542882600 7225870800 2.87
treatment naïve
CGPLH494 Preoperative WGS Healthy 100 10908197600 7046645000 2.80
treatment naïve
CGPLH495 Preoperative WGS Healthy 100 8945040400 5891697800 2.34
treatment naïve
CGPLH496 Preoperative WGS Healthy 100 10859723400 7549608000 3.00
treatment naïve
CGPLH497 Preoperative WGS Healthy 100 9630507400 6473162800 2.57
treatment naïve
CGPLH498 Preoperative WGS Healthy 100 10060232600 6744622800 2.68
treatment naïve
CGPLH499 Preoperative WGS Healthy 100 10221293600 6951282800 2.76
treatment naïve
CGPLH50 Preoperative WGS Healthy 100 41243860600 4073272800 10.78
treatment naïve
CGPLH500 Preoperative WGS Healthy 100 9703168200 6239893800 2.48
treatment naïve
CGPLH501 Preoperative WGS Healthy 100 9104779800 6161602800 2.45
treatment naïve
CGPLH502 Preoperative WGS Healthy 100 8514467400 5290881400 2.10
treatment naïve
CGPLH503 Preoperative WGS Healthy 100 9019992200 6100383400 2.42
treatment naïve
CGPLH504 Preoperative WGS Healthy 100 9320330200 6199750200 2.46
treatment naïve
CGPLH505 Preoperative WGS Healthy 100 7499497400 4914559000 1.95
treatment naïve
CGPLH506 Preoperative WGS Healthy 100 10526142000 6963312600 2.76
treatment naïve
CGPLH507 Preoperative WGS Healthy 100 9091018400 6146678600 2.44
treatment naïve
CGPLH508 Preoperative WGS Healthy 100 10989315600 7360201400 2.92
treatment naïve
CGPLH509 Preoperative WGS Healthy 100 9729084600 6702691600 2.66
treatment naïve
CGPLH51 Preoperative WGS Healthy 100 35967451400 3492833200 9.24
treatment naïve
CGPLH510 Preoperative WGS Healthy 100 11162691600 7626795400 3.03
treatment naïve
CGPLH511 Preoperative WGS Healthy 100 11888619600 8110427600 3.22
treatment naïve
CGPLH512 Preoperative WGS Healthy 100 10726438400 7110078000 2.82
treatment naïve
CGPLH513 Preoperative WGS Healthy 100 10701564200 7155271400 2.84
treatment naïve
CGPLH514 Preoperative WGS Healthy 100 8822067000 5958773800 2.36
treatment naïve
CGPLH515 Preoperative WGS Healthy 100 7792074800 5317464600 2.11
treatment naïve
CGPLH516 Preoperative WGS Healthy 100 8642620000 5846439400 2.32
treatment naïve
CGPLH517 Preoperative WGS Healthy 100 11915929600 8013937000 3.18
treatment naïve
CGPLH518 Preoperative WGS Healthy 100 12804517400 8606661600 3.42
treatment naïve
CGPLH519 Preoperative WGS Healthy 100 11513222200 7922798400 3.14
treatment naïve
CGPLH52 Preoperative WGS Healthy 100 49247304200 4849631400 12.83
treatment naïve
CGPLH520 Preoperative WGS Healthy 100 8942102400 6030683400 2.39
treatment naïve
CGPLH54 Preoperative WGS Healthy 100 45399346400 4466164600 11.82
treatment naïve
CGPLH55 Preoperative WGS Healthy 100 42547725000 4283337600 11.33
treatment naïve
CGPLH56 Preoperative WGS Healthy 100 33460308000 3226338000 8.53
treatment naïve
CGPLH57 Preoperative WGS Healthy 100 36504735200 3509125000 9.28
treatment naïve
CGPLH59 Preoperative WGS Healthy 100 39642810600 3820011000 10.11
treatment naïve
CGPLH625 Preoperative WGS Healthy 100 6408225000 4115487600 1.63
treatment naïve
CGPLH626 Preoperative WGS Healthy 100 9915193600 6391657000 2.54
treatment naïve
CGPLH63 Preoperative WGS Healthy 100 37447047600 3506737000 9.28
treatment naïve
CGPLH639 Preoperative WGS Healthy 100 8158965800 5216049600 2.07
treatment naïve
CGPLH64 Preoperative WGS Healthy 100 34275506800 3264508000 8.63
treatment naïve
CGPLH640 Preoperative WGS Healthy 100 8058876800 5333551800 2.12
treatment naïve
CGPLH642 Preoperative WGS Healthy 100 7545555600 4909732800 1.95
treatment naïve
CGPLH643 Preoperative WGS Healthy 100 7865776800 5254772000 2.09
treatment naïve
CGPLH644 Preoperative WGS Healthy 100 6890139000 4599387400 1.83
treatment naïve
CGPLH646 Preoperative WGS Healthy 100 7757219400 5077408200 2.01
treatment naïve
CGPLH75 Preoperative WGS Healthy 100 23882926000 2250344400 5.95
treatment naïve
CGPLH76 Preoperative WGS Healthy 100 30631483600 3086042200 8.16
treatment naïve
CGPLH77 Preoperative WGS Healthy 100 31651741400 3041290200 8.04
treatment naïve
CGPLH78 Preoperative WGS Healthy 100 31165831200 3130079800 8.28
treatment naïve
CGPLH79 Preoperative WGS Healthy 100 31935043000 3128408200 8.27
treatment naïve
CGPLH80 Preoperative WGS Healthy 100 32965093000 3311371800 8.76
treatment naïve
CGPLH81 Preoperative WGS Healthy 100 27035311200 2455084400 6.49
treatment naïve
CGPLH82 Preoperative WGS Healthy 100 28447051200 2893358200 7.65
treatment naïve
CGPLH83 Preoperative WGS Healthy 100 26702240200 2459494000 6.50
treatment naïve
CGPLH84 Preoperative WGS Healthy 100 25176861400 2524467400 6.68
treatment naïve
CGPLLU13 Pre-treatment, Day −2 WGS Lung 100 9126585600 5915061800 2.35
Cancer
CGPLLU13 Post-treatment, Day 5 WGS Lung 100 7739120200 5071745800 2.01
Cancer
CGPLLU13 Post-treatment, Day 28 WGS Lung 100 9081585400 5764371600 2.29
Cancer
CGPLLU13 Post-treatment, Day 91 WGS Lung 100 9576557000 6160760200 2.44
Cancer
CGPLLU14 Pre-treatment, Day −38 WGS Lung 100 13659198400 9033455800 3.58
Cancer
CGPLLU14 Pre-treatment, Day −16 WGS Lung 100 7178855800 4856648600 1.93
Cancer
CGPLLU14 Pre-treatment, Day −3 WGS Lung 100 7653473000 4816193600 1.91
Cancer
CGPLLU14 Pre-treatment, Day 0 WGS Lung 100 7851997400 5193256600 2.06
Cancer
CGPLLU14 Post-treatment, Day 0.33 WGS Lung 100 7193040800 4869701600 1.93
Cancer
CGPLLU14 Post-treatment, Day 7 WGS Lung 100 7102050000 4741432600 1.88
Cancer
CGPLLU144 Preoperative WGS Lung 100 4934013600 3415936400 1.36
treatment naïve Cancer
CGPLLU147 Preoperative WGS Lung 100 24409561000 2118672800 5.61
treatment naïve Cancer
CGPLLU161 Preoperative WGS Lung 100 8998813400 6016145000 2.39
treatment naïve Cancer
CGPLLU162 Preoperative WGS Lung 100 9709792400 6407866400 2.54
treatment naïve Cancer
CGPLLU163 Preoperative WGS Lung 100 9150620200 6063569800 2.41
treatment naïve Cancer
CGPLLU165 Preoperative WGS Lung 100 28374436400 2651138600 7.01
treatment naïve Cancer
CGPLLU168 Preoperative WGS Lung 100 5692739400 3695191000 1.47
treatment naïve Cancer
CGPLLU169 Preoperative WGS Lung 100 9093975600 5805320800 2.30
treatment naïve Cancer
CGPLLU175 Preoperative WGS Lung 100 33794816800 3418750400 9.04
treatment naïve Cancer
CGPLLU176 Preoperative WGS Lung 100 8778553800 5794950200 2.30
treatment naïve Cancer
CGPLLU177 Preoperative WGS Lung 100 3734614800 2578696200 1.02
treatment naïve Cancer
CGPLLU180 Preoperative WGS Lung 100 28305936600 2756034200 7.29
treatment naïve Cancer
CGPLLU198 Preoperative WGS Lung 100 23244959200 2218577200 5.86
treatment naïve Cancer
CGPLLU202 Preoperative WGS Lung 100 21110128200 1831279400 4.84
treatment naïve Cancer
CGPLLU203 Preoperative WGS Lung 100 4304235600 2896429000 1.15
treatment naïve Cancer
CGPLLU205 Preoperative WGS Lung 100 10502467000 7386984800 2.93
treatment naïve Cancer
CGPLLU206 Preoperative WGS Lung 100 21888248200 2026666000 5.36
treatment naïve Cancer
CGPLLU207 Preoperative WGS Lung 100 10806230600 7363049000 2.92
treatment naïve Cancer
CGPLLU208 Preoperative WGS Lung 100 7795426800 5199545800 2.06
treatment naïve Cancer
CGPLLU209 Preoperative WGS Lung 100 26174542000 2621961800 6.93
treatment naïve Cancer
CGPLLU244 Pre-treatment, Day −7 WGS Lung 100 9967531400 6704365800 2.66
Cancer
CGPLLU244 Pre-treatment, Day −1 WGS Lung 100 9547119200 5785172600 2.30
Cancer
CGPLLU244 Post-treatment Day 6 WGS Lung 100 9535898600 6452174000 2.56
Cancer
CGPLLU244 Post-treatment, Day 62 WGS Lung 100 8783628600 5914149000 2.35
Cancer
CGPLLU245 Pre-treatment, Day −32 WGS Lung 100 10025823200 6313303800 2.51
Cancer
CGPLLU245 Pre-treatment, Day 0 WGS Lung 100 9462480400 6612867800 2.62
Cancer
CGPLLU245 Post-treatment, Day 7 WGS Lung 100 9143825000 6431013200 2.55
Cancer
CGPLLU245 Post-treatment, Day 21 WGS Lung 100 9072713800 6368533000 2.53
Cancer
CGPLLU246 Pre-treatment, Day −21 WGS Lung 100 9579787000 6458003400 2.56
Cancer
CGPLLU246 Pre-treatment, Day 0 WGS Lung 100 9512703600 6440535600 2.56
Cancer
CGPLLU246 Post-treatment, Day 9 WGS Lung 100 9512646000 6300939200 2.50
Cancer
CGPLLU246 Post-treatment, Day 42 WGS Lung 100 11136103000 7358747400 2.92
Cancer
CGPLLU264 Pre-treatment, Day −1 WGS Lung 100 9196005000 6239803600 2.48
Cancer
CGPLLU264 Post-treatment, Day 6 WGS Lung 100 8247416600 5600454200 2.22
Cancer
CGPLLU264 Post-treatment, Day 27 WGS Lung 100 8681022200 5856109000 2.32
Cancer
CGPLLU264 Post-treatment, Day 69 WGS Lung 100 8931976400 5974246000 2.37
Cancer
CGPLLU265 Pre-treatment, Day 0 WGS Lung 100 9460534000 6111185200 2.43
Cancer
CGPLLU265 Post-treatment, Day 3 WGS Lung 100 8051601200 4984166600 1.98
Cancer
CGPLLU265 Post-treatment, Day 7 WGS Lung 100 8082224600 5110092600 2.03
Cancer
CGPLLU265 Post-treatment, Day 84 WGS Lung 100 8368637400 5369526400 2.13
Cancer
CGPLLU266 Pre-treatment, Day 0 WGS Lung 100 8583766400 5846473600 2.32
Cancer
CGPLL266 Post-treatment, Day 16 WGS Lung 100 8795793600 5984531400 2.37
Cancer
CGPLLU266 Post-treatment, Day 83 WGS Lung 100 9157947600 6227735000 2.47
Cancer
CGPLLU266 Post-treatment, Day 328 WGS Lung 100 7299455400 5049379000 2.00
Cancer
CGPLLU267 Pre-treatment, Day −1 WGS Lung 100 10658657800 6892067000 2.73
Cancer
CGPLLU267 Post-treatment, Day 34 WGS Lung 100 8492833400 5101097800 2.02
Cancer
CGPLLU267 Post-treatment, Day 90 WGS Lung 100 12030314800 7757930400 3.08
Cancer
CGPLLU269 Pre-treatment, Day 0 WGS Lung 100 9170168000 5830454400 2.31
Cancer
CGPLLU269 Post-treatment, Day 9 WGS Lung 100 8905640400 5298461400 2.10
Cancer
CGPLLU269 Post-treatment, Day 28 WGS Lung 100 8455306600 5387927400 2.14
Cancer
CGPLLU271 Post-treatment, Day 259 WGS Lung 100 8112060400 5404979000 2.14
Cancer
CGPLLU271 Pre-treatment, Day 0 WGS Lung 100 13150818200 8570453400 3.40
Cancer
CGPLLU271 Post-treatment, Day 6 WGS Lung 100 9008880600 5854051400 2.32
Cancer
CGPLLU271 Post-treatment, Day 20 WGS Lung 100 8670913000 5461577000 2.17
Cancer
CGPLLU271 Post-treatment, Day 104 WGS Lung 100 8887441400 5609039000 2.23
Cancer
CGPLLU43 Pre-treatment, Day −1 WGS Lung 100 8407811200 5203486400 2.06
Cancer
CGPLLU43 Post-treatment, Day 6 WGS Lung 100 9264335200 5626714400 2.23
Cancer
CGPLLU43 Post-treatment, Day 27 WGS Lung 100 8902283000 5485656200 2.18
Cancer
CGPLLU43 Post-treatment, Day 83 WGS Lung 100 9201509200 5075084200 2.33
Cancer
CGPLLU86 Pre-treatment, Day 0 WGS Lung 100 9152729200 6248173200 2.48
Cancer
CGPLLU86 Post-treatment, Day 0.5 WGS Lung 100 6703253000 4663026800 1.85
Cancer
CGPLLU86 Post-treatment, Day 7 WGS Lung 100 6590121400 4559562400 1.81
Cancer
CGPLLU86 Post-treatment, Day 17 WGS Lung 100 8653551800 5900136000 2.34
Cancer
CGPLLU88 Pre-treatment, Day 0 WGS Lung 100 8096528000 5505475400 2.18
Cancer
CGPLLU88 Post-treatment, Day 7 WGS Lung 100 8283192200 5784217600 2.30
Cancer
CGPLLU88 Post-treatment, Day 297 WGS Lung 100 9297110800 6407258000 2.54
Cancer
CGPLLU89 Pre-treatment, Day 0 WGS Lung 100 7842145200 5356095400 2.13
Cancer
CGPLLU89 Post-treatment, Day 7 WGS Lung 100 7234220200 4930375200 1.96
Cancer
CGPLLU89 Post-treatment, Day 22 WGS Lung 100 6242889800 4057361000 1.61
Cancer
CGPLOV11 Preoperative WGS Ovarian 100 8985130400 5871959600 2.33
treatment naïve Cancer
CGPLOV12 Preoperative WGS Ovarian 100 9705820000 6430505400 2.55
treatment naïve Cancer
CGPLOV13 Preoperative WGS Ovarian 100 10307949490 7029712000 2.79
treatment naïve Cancer
CGPLOV15 Preoperative WGS Ovarian 100 8472829400 5562142400 2.21
treatment naïve Cancer
CGPLOV16 Preoperative WGS Ovarian 100 10977781000 7538581600 2.99
treatment naïve Cancer
CGPLOV19 Preoperative WGS Ovarian 100 8800876200 5855304000 2.32
treatment naïve Cancer
CGPLOV20 Preoperative WGS Ovarian 100 8714443600 5695165800 2.26
treatment naïve Cancer
CGPLOV21 Preoperative WGS Ovarian 100 10180394800 7120260400 2.83
treatment naïve Cancer
CGPLOV22 Preoperative WGS Ovarian 100 10107760000 6821916800 2.71
treatment naïve Cancer
CGPLOV23 Preoperative WGS Ovarian 100 10643399800 7206330800 2.86
treatment naïve Cancer
CGPLOV24 Preoperative WGS Ovarian 100 6780929000 4623300400 1.83
treatment naïve Cancer
CGPLOV25 Preoperative WGS Ovarian 100 7817548600 5359975200 2.13
treatment naïve Cancer
CGPLOV26 Preoperative WGS Ovarian 100 11763101400 8178024400 3.25
treatment naïve Cancer
CGPLOV28 Preoperative WGS Ovarian 100 9522546400 6259423400 2.48
treatment naïve Cancer
CGPLOV31 Preoperative WGS Ovarian 100 9104831200 6109358400 2.42
treatment naïve Cancer
CGPLOV32 Preoperative WGS Ovarian 100 9222073600 6035150000 2.39
treatment naïve Cancer
CGPLOV37 Preoperative WGS Ovarian 100 8898328600 5971018200 2.37
treatment naïve Cancer
CGPLOV38 Preoperative WGS Ovarian 100 8756025200 5861536600 2.33
treatment naïve Cancer
CGPLOV40 Preoperative WGS Ovarian 100 9709391600 6654707200 2.64
treatment naïve Cancer
CGPLOV41 Preoperative WGS Ovarian 100 8923625000 5973070400 2.37
treatment naïve Cancer
CGPLOV42 Preoperative WGS Ovarian 100 10719380400 7353214200 2.92
treatment naïve Cancer
CGPLOV43 Preoperative WGS Ovarian 100 10272189000 6423288600 2.55
treatment naïve Cancer
CGPLOV44 Preoperative WGS Ovarian 100 9861862600 6769185800 2.69
treatment naïve Cancer
CGPLOV46 Preoperative WGS Ovarian 100 8788956400 5789863400 2.30
treatment naïve Cancer
CGPLOV47 Preoperative WGS Ovarian 100 9380561800 6480763600 2.57
treatment naïve Cancer
CGPLOV48 Preoperative WGS Ovarian 100 9258552600 6380106400 2.53
treatment naïve Cancer
CGPLOV49 Preoperative WGS Ovarian 100 8787025400 6134503600 2.43
treatment naïve Cancer
CGPLOV50 Preoperative WGS Ovarian 100 10144154400 6984721400 2.77
treatment naïve Cancer
CGPLPA112 Preoperative WGS Pancreatic 100 12740651400 9045622000 3.59
treatment naïve Cancer
CGPLPA113 Preoperative WGS Duodenal 100 8802479000 5909030800 2.34
treatment naïve Cancer
CGPLPA114 Preoperative WGS Bile Duct 100 8792313600 6019061000 2.39
treatment naïve Cancer
CGPLPA115 Preoperative WGS Bile Duct 100 8636551400 5958809000 2.36
treatment naïve Cancer
CGPLPA117 Preoperative WGS Bile Duct 100 9128885200 6288833200 2.50
treatment naïve Cancer
CGPLPA118 Preoperative WGS Bile Duct 100 7931485800 5407532800 2.15
treatment naïve Cancer
CGPLPA122 Preoperative WGS Bile Duct 100 10888985000 7530118800 2.99
treatment naïve Cancer
CGPLPA124 Preoperative WGS Bile Duct 100 8562012400 5860171000 2.33
treatment naïve Cancer
CGPLPA125 Preoperative WGS Bile Duct 100 9715576600 6390321000 2.54
treatment naïve Cancer
CGPLPA126 Preoperative WGS Bile Duct 100 8056768800 5651600800 2.24
treatment naïve Cancer
CGPLPA127 Preoperative WGS Bile Duct 100 8000301000 5382987600 2.14
treatment naïve Cancer
CGPLPA128 Preoperative WGS Bile Duct 100 6165751600 4256521400 1.69
treatment naïve Cancer
CGPLPA129 Preoperative WGS Bile Duct 100 7143147400 4917370400 1.95
treatment naïve Cancer
CGPLPA130 Preoperative WGS Bile Duct 100 5664035000 3603919400 1.43
treatment naïve Cancer
CGPLPA131 Preoperative WGS Bile Duct 100 8292982000 5844942000 2.32
treatment naïve Cancer
CGPLPA134 Preoperative WGS Bile Duct 100 7088917000 5048887600 2.00
treatment naïve Cancer
CGPLPA135 Preoperative WGS Bile Duct 100 8759665600 5800618200 2.30
treatment naïve Cancer
CGPLPA136 Preoperative WGS Bile Duct 100 7539715800 5248227600 2.08
treatment naïve Cancer
CGPLPA137 Preoperative WGS Bile Duct 100 8391815400 5901273800 2.34
treatment naïve Cancer
CGPLPA139 Preoperative WGS Bile Duct 100 8992280200 6328314400 2.51
treatment naïve Cancer
CGPLPA14 Preoperative WGS Pancreatic 100 8787706200 5731317600 2.27
treatment naïve Cancer
CGPLPA140 Preoperative WGS Bile Duct 100 16365641800 11216732000 4.45
treatment naïve Cancer
CGPLPA141 Preoperative WGS Bile Duct 100 15086298000 10114790200 4.01
treatment naïve Cancer
CGPLPA15 Preoperative WGS Pancreatic 100 8255566800 5531677600 2.20
treatment naïve Cancer
CGPLPA155 Preoperative WGS Bile Duct 100 9457155800 6621881800 2.63
treatment naïve Cancer
CGPLPA156 Preoperative WGS Pancreatic 100 9345385800 6728653000 2.67
treatment naïve Cancer
CGPLPA165 Preoperative WGS Bile Duct 100 8356604600 5829895800 2.31
treatment naïve Cancer
CGPLPA168 Preoperative WGS Bile Duct 100 10355661600 7048115500 2.80
treatment naïve Cancer
CGPLPA17 Preoperative WGS Pancreatic 100 8073547400 4687808000 1.86
treatment naïve Cancer
CGPLPA184 Preoperative WGS Bile Duct 100 9014218400 6230922200 2.47
treatment naïve Cancer
CGPLPA187 Preoperative WGS Bile Duct 100 8883536200 6140874400 2.44
treatment naïve Cancer
CGPLPA23 Preoperative WGS Pancreatic 100 9335452000 6246525400 2.48
treatment naïve Cancer
CGPLPA25 Preoperative WGS Pancreatic 100 10077515400 6103322200 2.42
treatment naïve Cancer
CGPLPA26 Preoperative WGS Pancreatic 100 8354272400 5725781000 2.27
treatment naïve Cancer
CGPLPA28 Preoperative WGS Pancreatic 100 8477461600 5688846800 2.26
treatment naïve Cancer
CGPLPA33 Preoperative WGS Pancreatic 100 7287615600 4596723800 1.82
treatment naïve Cancer
CGPLPA34 Preoperative WGS Pancreatic 100 6122902400 4094828000 1.62
treatment naïve Cancer
CGPLPA37 Preoperative WGS Pancreatic 100 12714888200 8527779200 3.38
treatment naïve Cancer
CGPLPA38 Preoperative WGS Pancreatic 100 8525500600 5501341400 2.18
treatment naïve Cancer
CGPLPA39 Preoperative WGS Pancreatic 100 10602663600 6812333000 2.70
treatment naïve Cancer
CGPLPA40 Preoperative WGS Pancreatic 100 9083670000 5394717800 2.14
treatment naïve Cancer
CGPLPA42 Preoperative WGS Pancreatic 100 5972126600 3890395200 1.54
treatment naïve Cancer
CGPLPA46 Preoperative WGS Pancreatic 100 4720090200 2626298800 1.04
treatment naïve Cancer
CGPLPA47 Preoperative WGS Pancreatic 100 7317385800 4543833000 1.80
treatment naïve Cancer
CGPLPA48 Preoperative WGS Pancreatic 100 7553856200 5022695600 1.99
treatment naïve Cancer
CGPLPA52 Preoperative WGS Pancreatic 100 5655875000 3551861600 1.41
treatment naïve Cancer
CGPLPA53 Preoperative WGS Pancreatic 100 9504749000 6323344800 2.51
treatment naïve Cancer
CGPLPA58 Preoperative WGS Pancreatic 100 8088090200 5118138200 2.03
treatment naïve Cancer
CGPLPA59 Preoperative WGS Pancreatic 100 14547364600 9617778600 3.82
treatment naïve Cancer
CGPLPA67 Preoperative WGS Pancreatic 100 8222177400 5351172600 2.12
treatment naïve Cancer
CGPLPA69 Preoperative WGS Pancreatic 100 7899181400 5006114800 1.99
treatment naïve Cancer
CGPLPA71 Preoperative WGS Pancreatic 100 7349620400 4955417400 1.97
treatment naïve Cancer
CGPLPA74 Preoperative WGS Pancreatic 100 6666371400 4571394200 1.81
treatment naïve Cancer
CGPLPA76 Preoperative WGS Pancreatic 100 9755658600 6412606800 2.54
treatment naïve Cancer
CGPLPA85 Preoperative WGS Pancreatic 100 10856223000 7309498600 2.90
treatment naïve Cancer
CGPLPA86 Preoperative WGS Pancreatic 100 8744365400 5514523200 2.19
treatment naïve Cancer
CGPLPA92 Preoperative WGS Pancreatic 100 8073791200 5390492800 2.14
treatment naïve Cancer
CGPLPA93 Preoperative WGS Pancreatic 100 10390273000 7186589400 2.85
treatment naïve Cancer
CGPLPA94 Preoperative WGS Pancreatic 100 11060347600 7641336400 3.03
treatment naïve Cancer
CGPLPA95 Preoperative WGS Pancreatic 100 12416627200 7206503800 2.86
treatment naïve Cancer
CGST102 Preoperative WGS Gastric 100 6637004600 4545072600 1.80
treatment naïve cancer
CGST11 Preoperative WGS Gastric 100 9718427800 6259679600 2.48
treatment naïve cancer
CGST110 Preoperative WGS Gastric 100 9319661600 6359317400 2.52
treatment naïve cancer
CGST114 Preoperative WGS Gastric 100 6865213000 4841171600 1.92
treatment naïve cancer
CGST13 Preoperative WGS Gastric 100 9284554800 6360843800 2.52
treatment naïve cancer
CGST131 Preoperative WGS Gastric 100 5924382000 3860677200 1.53
treatment naïve cancer
CGST141 Preoperative WGS Gastric 100 8486380800 5860491000 2.33
treatment naïve cancer
CGST16 Preoperative WGS Gastric 100 13820725800 9377828000 3.72
treatment naïve cancer
CGST18 Preoperative WGS Gastric 100 7781288000 5278862400 2.09
treatment naïve cancer
CGST21 Preoperative WGS Gastric 100 7171165400 4103970800 1.63
treatment naïve cancer
CGST26 Preoperative WGS Gastric 100 8983961800 6053405600 2.40
treatment naïve cancer
CGST28 Preoperative WGS Gastric 100 9683035400 6745116400 2.68
treatment naïve cancer
CGST30 Preoperative WGS Gastric 100 8584086600 5741416000 2.28
treatment naïve cancer
CGST32 Preoperative WGS Gastric 100 8568194600 5783369200 2.29
treatment naïve cancer
CGST33 Preoperative WGS Gastric 100 9351699600 6448718400 2.56
treatment naïve cancer
CGST38 Preoperative WGS Gastric 100 8409876400 5770989200 2.29
treatment naïve cancer
CGST39 Preoperative WGS Gastric 100 10573763000 7597016000 3.01
treatment naïve cancer
CGST41 Preoperative WGS Gastric 100 9434854200 6609415400 2.62
treatment naïve cancer
CGST45 Preoperative WGS Gastric 100 8203868600 5625223000 2.23
treatment naïve cancer
CGST47 Preoperative WGS Gastric 100 8938597600 6178990600 2.45
treatment naïve cancer
CGST48 Preoperative WGS Gastric 100 9106628800 6517085200 2.59
treatment naïve cancer
CGST53 Preoperative WGS Gastric 100 9005374200 5854996200 2.32
treatment naïve cancer
CGST58 Preoperative WGS Gastric 100 10020368600 6133458400 2.43
treatment naïve cancer
CGST67 Preoperative WGS Gastric 100 9198135600 5911071000 2.35
treatment naïve cancer
CGST77 Preoperative WGS Gastric 100 8228789400 5119116800 2.03
treatment naïve cancer
CGST80 Preoperative WGS Gastric 100 10596963400 7283152800 2.89
treatment naïve cancer
CGST81 Preoperative WGS Gastric 100 8494881200 5838064000 2.32
treatment naïve cancer
TABLE 5
APPENDIX E: High coverage whole genome cfDNA analyses of healthy individuals and lung cancer patients
Correlation Correlation of Correction
of Fragment GC Corrected of Fragment Correlation
Ratio Profile Fragment Ratio Ratio Profile of Fragment
to Median Profile to to Median Ratio
Median Fragment Median Fragment Fragment Profile to
cfDNA Ratio Profile Ratio Profile Ratio Lymphocyte
Patient Analysis Stage at Fragment of Healthy of Healthy Profile of Nucleosome
Patient Type Type Timepoint Diagnosis Size (bp) Individuals Individuals Lymphocytes Distances
CGPLH75 Healthy WGS Preoperative NA 168 0.977 0.952 0.920 −0.886
treatment naïve
CGPLH77 Healthy WGS Preoperative NA 166 0.970 0.960 0.904 −0.912
treatment naïve
CGPLH80 Healthy WGS Preoperative NA 168 0.955 0.949 0.960 −0.917
treatment naïve
CGPLH81 Healthy WGS Preoperative NA 167 0.949 0.953 0.869 −0.883
treatment naïve
CGPLH82 Healthy WGS Preoperative NA 166 0.969 0.949 0.954 −0.917
treatment naïve
CGPLH83 Healthy WGS Preoperative NA 167 0.949 0.939 0.919 −0.904
treatment naïve
CGPLH84 Healthy WGS Preoperative NA 168 0.967 0.948 0.951 −0.913
treatment naïve
CGPLH52 Healthy WGS Preoperative NA 167 0.946 0.968 0.952 −0.924
treatment naïve
CGPLH35 Healthy WGS Preoperative NA 166 0.981 0.973 0.945 −0.921
treatment naïve
CGPLH37 Healthy WGS Preoperative NA 168 0.968 0.970 0.951 −0.922
treatment naïve
CGPLH54 Healthy WGS Preoperative NA 167 0.968 0.976 0.948 −0.925
treatment naïve
CGPLH55 Healthy WGS Preoperative NA 166 0.947 0.964 0.948 −0.917
treatment naïve
CGPLH48 Healthy WGS Preoperative NA 168 0.959 0.965 0.960 −0.923
treatment naïve
CGPLH50 Healthy WGS Preoperative NA 167 0.960 0.968 0.952 −0.921
treatment naïve
CGPLH36 Healthy WGS Preoperative NA 168 0.955 0.954 0.955 −0.919
treatment naïve
CGPLH42 Healthy WGS Preoperative NA 167 0.973 0.963 0.948 −0.918
treatment naïve
CGPLH43 Healthy WGS Preoperative NA 166 0.952 0.958 0.953 −0.928
treatment naïve
CGPLH69 Healthy WGS Preoperative NA 168 0.970 0.965 0.951 −0.925
treatment naïve
CGPLH45 Healthy WGS Preoperative NA 168 0.965 0.950 0.949 −0.911
treatment naïve
CGPLH47 Healthy WGS Preoperative NA 167 0.952 0.944 0.954 −0.921
treatment naïve
CGPLH46 Healthy WGS Preoperative NA 168 0.966 0.965 0.953 −0.923
treatment naïve
CGPLH63 Healthy WGS Preoperative NA 168 0.977 0.968 0.939 −0.920
treatment naïve
CGPLH51 Healthy WGS Preoperative NA 168 0.935 0.955 0.957 −0.914
treatment naïve
CGPLH57 Healthy WGS Preoperative NA 169 0.965 0.954 0.955 −0.917
treatment naïve
CGPLH49 Healthy WGS Preoperative NA 168 0.958 0.951 0.950 −0.924
treatment naïve
CGPLH56 Healthy WGS Preoperative NA 166 0.940 0.957 0.959 −0.911
treatment naïve
CGPLH64 Healthy WGS Preoperative NA 169 0.960 0.940 0.949 −0.918
treatment naïve
CGPLH78 Healthy WGS Preoperative NA 166 0.956 0.936 0.958 −0.911
treatment naïve
CGPLH79 Healthy WGS Preoperative NA 168 0.960 0.957 0.953 −0.917
treatment naïve
CGPLH76 Healthy WGS Preoperative NA 167 0.969 0.965 0.953 −0.917
treatment naïve
CGPLLU175 Lung WGS Preoperative I 165 0.316 0.284 0.244 −0.262
Cancer treatment naïve
CGPLLU180 Lung WGS Preoperative I 166 0.907 0.846 0.826 −0.819
Cancer treatment naïve
CGPLLU198 Lung WGS Preoperative I 166 0.972 0.946 0.928 −0.911
Cancer treatment naïve
CGPLLU202 Lung WGS Preoperative I 163 0.821 0.605 0.905 −0.843
Cancer treatment naïve
CGPLLU165 Lung WGS Preoperative II 163 0.924 0.961 0.815 −0.851
Cancer treatment naïve
CGPLLU209 Lung WGS Preoperative II 163 0.578 0.526 0.513 −0.534
Cancer treatment naïve
CGPLLU147 Lung WGS Preoperative III 166 0.953 0.919 0.939 −0.912
Cancer treatment naïve
CGPLLU206 Lung WGS Preoperative III 158 0.488 0.343 0.460 −0.481
Cancer treatment naïve
TABLE 6
APPENDIX F: Monitoring response to therapy using whole genome analyses of cfDNA fragmentation profiles and targeted mutations analyses
Correlation
or Fragment Correlation
Ratio Profile of Fragment
to Median Ratio
Progression- Fragment Profile to Maximum
free Ratio Profile Lymphocyte Mutant
Patient Survival of Healthy Nucleosome Targeted Allele
Patient Type Analysis Type Timepoint Stage (months) Individuals Distances Mutation Fraction
CGPLLU14 Lung Targeted Mutation Pre-treatment, IV 15.4 0.941 −0.841 EGFR 861L>Q 0.89%
Cancer Analysis and WGS Day −38
CGPLLU14 Lung Targeted Mutation Pre-treatment, IV 15.4 0.933 −0.833 EGFR 861L>Q 0.18%
Cancer Analysis and WGS Day −16
CGPLLU14 Lung Targeted Mutation Pre-treatment, IV 15.4 0.908 −0.814 EGFR 719G>S 0.49%
Cancer Analysis and WGS Day 3
CGPLLU14 Lung Targeted Mutation Pre-treatment, IV 15.4 0.883 −0.752 EGFR 861L>Q 1.39%
Cancer Analysis and WGS Day 0
CGPLLU14 Lung Targeted Mutation Post-treatment, IV 15.4 0.820 −0.692 EGFR 719G>S 1.05%
Cancer Analysis and WGS Day 0.33
CGPLLU14 Lung Targeted Mutation Post-treatment, IV 15.4 0.927 −0.887 EGFR 861L>Q 0.00%
Cancer Analysis and WGS Day 7
CGPLLU88 Lung Targeted Mutation Pre-treatment, IV 18.0 0.657 −0.584 EGFR 9.06%
Cancer Analysis and WGS Day 0 745KELREA>T
CGPLLU88 Lung Targeted Mutation Post-treatment, IV 18.0 0.939 −0.799 EGFR 790T>M 0.15%
Cancer Analysis and WGS Day 7
CGPLLU88 Lung Targeted Mutation Post-treatment, IV 18.0 0.946 −0.869 EGFR 0.93%
Cancer Analysis and WGS Day 297 745KELREA>T
CGPLLU244 Lung Targeted Mutation Pre-treatment, IV 1.2 0.850 −0.706 EGFR 858L>R 4.98%
Cancer Analysis and WGS Day −7
CGPLLU244 Lung Targeted Mutation Pre-treatment, IV 1.2 0.867 −0.764 EGFR 62L>R 3.41%
Cancer Analysis and WGS Day −1
CGPLLU244 Lung Targeted Mutation Post-treatment, IV 1.2 0.703 −0.639 EGFR 858L>R 5.57%
Cancer Analysis and WGS Day 6
GGPLLU244 Lung Targeted Mutation Post-treatment, IV 1.2 0.659 −0.660 EGFR 858L>R 11.80%
Cancer Analysis and WGS Day 82
CGPLLU245 Lung Targeted Mutation Pre-treatment, IV 1.7 0.871 −0.724 EGFR 10.60%
Cancer Analysis and WGS Day −32 745KELREA>K
CGPLLU245 Lung Targeted Mutation Pre-treatment, IV 1.7 0.736 −0.608 EGFR 14.10%
Cancer Analysis and WGS Day 0 745KELREA>K
CGPLLU245 Lung Targeted Mutation Post-treatment, IV 1.7 0.731 −0.559 EGFR 6.56%
Cancer Analysis and WGS Day 7 745KELREA>K
CGPLLU245 Lung Targeted Mutation Post-treatment, IV 1.7 0.613 −0.426 EGFR 10.69%
Cancer Analysis and WGS Day 21 745KELREA>K
CGPLLU246 Lung Targeted Mutation Pre-treatment, IV 1.3 0.897 −0.757 EGFR 790T>M 0.49%
Cancer Analysis and WGS Day −21
CGPLLU246 Lung Targeted Mutation Pre-treatment, IV 1.3 0.469 0.376 EGFR 858L>R 6.17%
Cancer Analysis and WGS Day 0
CGPLLU246 Lung Targeted Mutation Post-treatment, IV 1.3 0.874 −0.746 EGFR 858L>R 1.72%
Cancer Analysis and WGS Day 9
CGPLLU246 Lung Targeted Mutation Post-treatment, IV 1.3 0.775 −0.665 EGFR 858L>R 5.29%
Cancer Analysis and WGS Day 42
CGPLLU86 Lung Targeted Mutation Pre-treatment, IV 12.4 0.817 −0.630 EGFR 0.00%
Cancer Analysis and WGS Day 0 746ELREATS>D
CGPLLU86 Lung Targeted Mutation Post-treatment, IV 12.4 0.916 −0.811 EGER 0.19%
Cancer Analysis and WGS Day 0.5 746ELREATS>D
CGPLLU86 Lung Targeted Mutation Post-treatment, IV 12.4 0.859 −0.694 EGFR 0.00%
Cancer Analysis and WGS Day 7 746ELREATS>D
CGPLLU86 Lung Targeted Mutation Post-treatment, IV 12.4 0.932 −0.848 EGFR 0.00%
Cancer Analysis and WGS Day 17 746ELREATS>D
CGPLLU89 Lung Targeted Mutation Pre-treatment, IV 6.7 0.864 −0.729 EGFR 0.42%
Cancer Analysis and WGS Day 0 747LREATS>—
CGPLLU89 Lung Targeted Mutation Post-treatment, IV 6.7 0.908 −0.803 EGFR 0.20%
Cancer Analysis and WGS Day 7 747LREATS>—
CGPLLU89 Lung Targeted Mutation Post-treatment, IV 6.7 0.853 −0.881 EGFR 0.00%
Cancer Analysis and WGS Day 22 747LREATS>—
CGLU316 Lung Targeted Mutation Pre-treatment, IV 1.4 0.331 −0.351 EGFR L861Q 15.72%
Cancer Analysis and WGS Day −53
CGLU316 Lung Targeted Mutation Pre-treatment, IV 1.4 0.225 −0.253 EGFR L861Q 45.67%
Cancer Analysis and WGS Day −4
CGLU316 Lung Targeted Mutation Post-treatment, IV 1.4 0.336 −0.364 EGFR G719A 33.38%
Cancer Analysis and WGS Day 18
CGLU316 Lung Targeted Mutation Post-treatment, IV 1.4 0.340 −0.364 EGFR L861Q 66.01%
Cancer Analysis and WGS Day 87
CGLU344 Lung Targeted Mutation Pre-treatment, IV Ongoing 0.935 −0.818 EGFR 0.00%
Cancer Analysis and WGS Day −21 E746_A750del
CGLU344 Lung Targeted Mutation Pre-treatment, IV Ongoing 0.919 −0.774 EGFR 0.22%
Cancer Analysis and WGS Day 0 E746_A750del
CGLU344 Lung Targeted Mutation Post-treatment, IV Ongoing 0.953 −0.860 EGFR 0.40%
Cancer Analysis and WGS Day 0.1675 E746_A750del
CGLU344 Lung Targeted Mutation Post-treatment, IV Ongoing 0.944 −0.832 EGFR 0.00%
Cancer Analysis and WGS Day 59 E746_A750del
CGLU369 Lung Targeted Mutation Pre-treatment, IV 7.5 0.825 −0.826 EGFR L858R 20.61%
Cancer Analysis and WGS Day −2
CGLU369 Lung Targeted Mutation Post-treatment, IV 7.5 0.950 −0.903 EGFR L858R 0.22%
Cancer Analysis and WGS Day 12
CGLU369 Lung Targeted Mutation Post-treatment, IV 7.5 0.945 −0.889 EGFR L858R 0.16%
Cancer Analysis and WGS Day 68
CGLU369 Lung Targeted Mutation Post-treatment, IV 7.5 0.886 −0.883 EGFR L858R 0.10%
Cancer Analysis and WGS Day 110
CGLU373 Lung Targeted Mutation Pre-treatment, IV Ongoing 0.922 −0.804 EGFR 0.82%
Cancer Analysis and WGS Day −2 E746_A750del
CGLU373 Lung Targeted Mutation Post-treatment, IV Ongoing 0.959 −0.853 EGFR 0.00%
Cancer Analysis and WGS Day 0.125 E746_A750del
CGLU373 Lung Targeted Mutation Post-treatment, IV Ongoing 0.967 −0.886 EGFR 0.15%
Cancer Analysis and WGS Day 7 E746_A750del
CGLU373 Lung Targeted Mutation Post-treatment, IV Ongoing 0.951 −0.890 EGFR 0.00%
Cancer Analysis and WGS Day 47 E746_A750del
CGPLLU13 Lung Targeted Mutation Pre-treatment, IV 1.5 0.425 −0.400 EGFR 7.66%
Cancer Analysis and WGS Day −2 E746_A750del
CGPLLU13 Lung Targeted Mutation Post-treatment, IV 1.5 0.272 −0.257 EGFR 13.10%
Cancer Analysis and WGS Day 5 E746_A750del
CGPLLU13 Lung Targeted Mutation Post-treatment, IV 1.5 0.584 −0.536 EGFR 6.09%
Cancer Analysis and WGS Day 28 E746_A750del
CGPLLU13 Lung Targeted Mutation Post-treatment, IV 1.5 0.530 −0.513 EGFR 9.28%
Cancer Analysis and WGS Day 91 E746_A750del
CGPLLU264 Lung Targeted Mutation Pre-treatment, IV Ongoing 0.946 −0.824 EGFR D761N 0.00%
Cancer Analysis and WGS Day −1
CGPLLU264 Lung Targeted Mutation Post-treatment, IV Ongoing 0.927 −0.788 EGFR D761N 0.16%
Cancer Analysis and WGS Day 6
CGPLLU264 Lung Targeted Mutation Post-treatment, IV Ongoing 0.962 −0.856 EGFR D761N 0.00%
Cancer Analysis and WGS Day 27
CGPLLU264 Lung Targeted Mutation Post-treatment, IV Ongoing 0.960 −0.894 EGFR D76IN 0.00%
Cancer Analysis and WGS Day 69
CGPLLU265 Lung Targeted Mutation Pre-treatment, IV Ongoing 0.953 −0.859 EGFR L858R 0.21%
Cancer Analysis and WGS Day 0
CGPLLU265 Lung Targeted Mutation Post-treatment, IV Ongoing 0.949 −0.842 EGFR L858R 0.21%
Cancer Analysis and WGS Day 3
CGPLLU265 Lung Targeted Mutation Post-treatment, IV Ongoing 0.955 −0.844 EGFR T790M 0.21%
Cancer Analysis and WGS Day 7
CGPLLU265 Lung Targeted Mutation Post-treatment, IV Ongoing 0.946 −0.825 EGFR L858R 0.00%
Cancer Analysis and WGS Day 84
CGPLLU266 Lung Targeted Mutation Pre-treatment, IV 9.6 0.961 −0.904 NA 0.00%
Cancer Analysis and WGS Day 0
CGPLLU266 Lung Targeted Mutation Post-treatment, IV 9.6 0.959 −0.886 NA 0.00%
Cancer Analysis and WGS Day 16
CGPLLU266 Lung Targeted Mutation Post-treatment, IV 9.6 0.961 −0.880 NA 0.00%
Cancer Analysis and WGS Day 83
CGPLLU266 Lung Targeted Mutation Post-treatment, IV 9.6 0.958 −0.855 NA 0.00%
Cancer Analysis and WGS Day 328
CGPLLU267 Lung Targeted Mutation Pre-treatment, IV 3.9 0.919 −0.863 EGFR L858R 1.93%
Cancer Analysis and WGS Lay −1
CGPLLU267 Lung Targeted Mutation Post-treatment, IV 3.9 0.363 −0.889 EGFR L858R 0.14%
Cancer Analysis and WGS Day 34
CGPLLU267 Lung Targeted Mutation Post-treatment, IV 3.9 0.962 −0.876 EGFR L858R 0.38%
Cancer Analysis and WGS Day 90
CGPLLU269 Lung Targeted Mutation Pre-treatment, IV Ongoing 0.951 −0.864 EGFR L858R 0.10%
Cancer Analysis and WGS Day 0
CGPLLU269 Lung Targeted Mutation Post-treatment, IV Ongoing 0.941 −0.894 EGFR L858R 0.00%
Cancer Analysis and WGS Day 9
CGPLLU269 Lung Targeted Mutation Post-treatment, IV Ongoing 0.957 −0.876 EGFR L858R 0.00%
Cancer Analysis and WGS Day 28
CGPLLU271 Lung Targeted Mutation Pre-treatment, IV 8.2 0.371 −0.284 EGFR 3.36%
Cancer Analysis and WGS Day 0 E746_A750del
CGPLLU271 Lung Targeted Mutation Post-treatment, IV 8.2 0.947 0.826 EGFR 0.17%
Cancer Analysis and WGS Day 6 E746_A750del
CGPLLU271 Lung Targeted Mutation Post-treatment, IV 8.2 0.952 −0.839 EGFR 0.00%
Cancer Analysis and WGS Day 20 E746_A750del
CGPLLU271 Lung Targeted Mutation Post-treatment, IV 8.2 0.944 −0.810 EGFR 0.00%
Cancer Analysis and WGS Day 104 E746_A750del
CGPLLU271 Lung Targeted Mutation Post-treatment, IV 8.2 0.950 −0.831 EGFR 0.44%
Cancer Analysis and WGS Day 259 E746_A750del
CGPLLU43 Lung Targeted Mutation Pre-treatment, IV Ongoing 0.944 −0.903 NA 0.00%
Cancer Analysis and WGS Day −1
CGPLLU43 Lung Targeted Mutation Post-treatment, IV Ongoing 0.956 −0.899 NA 0.00%
Cancer Analysis and WGS Day 6
CGPLLU43 Lung Targeted Mutation Post-treatment, IV Ongoing 0.959 −0.901 NA 0.00%
Cancer Analysis and WGS Day 27
CGPLLU43 Lung Targeted Mutation Post-treatment, IV Ongoing 0.965 −0.896 NA 0.00%
Cancer Analysis and WGS Day 83
TABLE 7
APPENDIX G: Whole genome cfDNA analyses in healthy individuals and cancer patients
Correlation
of Fragment
Ratio Profile
to Median
Median Fragment
cfDNA Ratio Profile
Patient Stage at Fragment of Healthy
Patient Type Analysis Type Timepoint Diagnosis Size (bp) Individuals
CGCRC291 Colorectal Targeted Mutation Preoperative V 163 0.1972
Cancer Analysis and WGS treatment naïve
CGCRC292 Colorectal Targeted Mutation Preoperative V 168 0.7804
Cancer Analysis and WGS treatment naïve
CGCRC293 Colorectal Targeted Mutation Preoperative V 166 0.9335
Cancer Analysis and WGS treatment naïve
CGCRC294 Colorectal Targeted Mutation Preoperative II 166 0.6531
Cancer Analysis and WGS treatment naïve
CGCRC295 Colorectal Targeted Mutation Preoperative II 166 0.8161
Cancer Analysis and WGS treatment naïve
CGCRC299 Colorectal Targeted Mutation Preoperative I 162 0.7325
Cancer Analysis and WGS treatment naïve
CGCRC300 Colorectal Targeted Mutation Preoperative I 167 0.9382
Cancer Analysis and WGS treatment naïve
CGCRC301 Colorectal Targeted Mutation Preoperative I 165 0.8252
Cancer Analysis and WGS treatment naïve
CGCRC302 Colorectal Targeted Mutation Preoperative II 163 0.7499
Cancer Analysis and WGS treatment naïve
CGCRC304 Colorectal Targeted Mutation Preoperative II 162 0.4642
Cancer Analysis and WGS treatment naïve
CGCRC305 Colorectal Targeted Mutation Preoperative II 165 0.8909
Cancer Analysis and WGS treatment naïve
CGCRG306 Colorectal Targeted Mutation Preoperative II 165 0.8523
Cancer Analysis and WGS treatment naïve
CGCRC307 Colorectal Targeted Mutation Preoperative II 165 0.9140
Cancer Analysis and WGS treatment naïve
CGCRC306 Colorectal Targeted Mutation Preoperative III 165 0.8734
Cancer Analysis and WGS treatment naïve
CGCRC311 Colorectal Targeted Mutation Preoperative I 166 0.8535
Cancer Analysis and WGS treatment naïve
CGCRC315 Colorectal Targeted Mutation Preoperative III 167 0.8083
Cancer Analysis and WGS treatment naïve
CGCRC316 Colorectal Targeted Mutation Preoperative III 161 0.1546
Cancer Analysis and WGS treatment naïve
CGCRC317 Colorectal Targeted Mutation Preoperative III 163 0.6242
Cancer Analysis and WGS treatment naïve
CGCRC318 Colorectal Targeted Mutation Preoperative I 166 0.8824
Cancer Analysis and WGS treatment naïve
CGCRC319 Colorectal Targeted Mutation Preoperative III 160 0.5979
Cancer Analysis and WGS treatment naïve
CGCRC320 Colorectal Targeted Mutation Preoperative I 167 0.7949
Cancer Analysis and WGS treatment naïve
CGCRC321 Colorectal Targeted Mutation Preoperative I 164 0.7604
Cancer Analysis and WGS treatment naïve
CGCRC333 Colorectal Targeted Mutation Preoperative V 163 0.4263
Cancer Analysis and WGS treatment naïve
CGCRC335 Colorectal Targeted Mutation Preoperative V 162 0.6466
Cancer Analysis and WGS treatment naïve
CGCRC338 Colorectal Targeted Mutation Preoperative V 162 0.7740
Cancer Analysis and WGS treatment naïve
CGCRC341 Colorectal Targeted Mutation Preoperative V 164 0.8995
Cancer Analysis and WGS treatment naïve
CGCRC342 Colorectal Targeted Mutation Preoperative V 158 0.2524
Cancer Analysis and WGS treatment naïve
CGPLBR100 Breast Targeted Mutation Preoperative III 166 0.9440
Cancer Analysis and WGS treatment naïve
CGPLBR101 Breast Targeted Mutation Preoperative II 169 0.8664
Cancer Analysis and WGS treatment naïve
CGPLBR102 Breast Targeted Mutation Preoperative II 169 0.9617
Cancer Analysis and WGS treatment naïve
CGPLBR103 Breast Targeted Mutation Preoperative II 168 0.9498
Cancer Analysis and WGS treatment naïve
CGPLBR104 Breast Targeted Mutation Preoperative II 167 0.8490
Cancer Analysis and WGS treatment naïve
CGPLBR12 Breast WGS Preoperative III 164 0.8350
Cancer treatment naïve
CGPLBR18 Breast WGS Preoperative III 163 0.8411
Cancer treatment naïve
CGPLBR23 Breast WGS Preoperative II 166 0.9714
Cancer treatment naïve
CGPLBR24 Breast WGS Preoperative II 156 0.8402
Cancer treatment naïve
CGPLBR26 Breast WGS Preoperative III 165 0.9584
Cancer treatment naïve
CGPLBR30 Breast WGS Preoperative II 161 0.6951
Cancer treatment naïve
CGPLBR31 Breast WGS Preoperative II 167 0.9719
Cancer treatment naïve
CGPLBR32 Breast WGS Preoperative II 165 0.9590
Cancer treatment naïve
CGPLBR33 Breast WGS Preoperative II 166 0.9706
Cancer treatment naïve
CGPLBR34 Breast WGS Preoperative II 163 0.3735
Cancer treatment naïve
CGPLBR35 Breast WGS Preoperative II 168 0.9655
Cancer treatment naïve
CGPLBP36 Breast WGS Preoperative II 169 0.9394
Cancer treatment naïve
CGPLBR37 Breast WGS Preoperative II 167 0.9591
Cancer treatment naïve
CGPLBR38 Breast Targeted Mutation Preoperative I 165 0.9105
Cancer Analysis and WGS treatment naïve
CGPLBR40 Breast Targeted Mutation Preoperative III 167 0.9273
Cancer Analysis and WGS treatment naïve
CGPLBR41 Breast Targeted Mutation Preoperative III 168 0.9626
Cancer Analysis and WGS treatment naïve
CGPLBR45 Breast WGS Preoperative II 164 0.9615
Cancer treatment naïve
CGPLBR46 Breast WGS Preoperative III 168 0.9322
Cancer treatment naïve
CGPLBR47 Breast WGS Preoperative I 166 0.9461
Cancer treatment naïve
CGPLBR48 Breast Targeted Mutation Preoperative II 169 0.7686
Cancer Analysis and WGS treatment naïve
CGPLBR49 Breast Targeted Mutation Preoperative II 171 0.8867
Cancer Analysis and WGS treatment naïve
CGPLBR50 Breast WGS Preoperative I 160 0.8593
Cancer treatment naïve
CGPLBR51 Breast WGS Preoperative II 165 0.9353
Cancer treatment naïve
CGPLBR52 Breast WGS Preoperative III 164 0.8688
Cancer treatment naïve
CGPLBR55 Breast Targeted Mutation Preoperative III 165 0.9634
Cancer Analysis and WGS treatment naïve
CGPLBR56 Breast WGS Preoperative II 163 0.9459
Cancer treatment naïve
CGPLBR57 Breast Targeted Mutation Preoperative III 166 0.9672
Cancer Analysis and WGS treatment naïve
CGPLBR59 Breast Targeted Mutation Preoperative I 168 0.9438
Cancer Analysis and WGS treatment naïve
CGPLBR60 Breast WGS Preoperative II 167 0.9479
Cancer treatment naïve
CGPLBR61 Breast Targeted Mutation Preoperative II 165 0.9611
Cancer Analysis and WGS treatment naïve
CGPLBR63 Breast Targeted Mutation Preoperative II 168 0.9555
Cancer Analysis and WGS treatment naïve
CGPLBR65 Breast WGS Preoperative II 167 0.9506
Cancer treatment naïve
CGPLBR68 Breast Targeted Mutation Preoperative III 163 0.9154
Cancer Analysis and WGS treatment naïve
CGPLBR69 Breast Targeted Mutation Preoperative II 165 0.9460
Cancer Analysis and WGS treatment naïve
CGPLBR70 Breast Targeted Mutation Preoperative II 168 0.9651
Cancer Analysis and WGS treatment naïve
CGPLBR71 Breast Targeted Mutation Preoperative II 165 0.9577
Cancer Analysis and WGS treatment naïve
CGPLBR72 Breast Targeted Mutation Preoperative II 167 0.9786
Cancer Analysis and WGS treatment naïve
CGPLBR73 Breast Targeted Mutation Preoperative II 167 0.9576
Cancer Analysis and WGS treatment naïve
CGPLBR76 Breast Targeted Mutation Preoperative II 170 0.9410
Cancer Analysis and WGS treatment naïve
CGPLBR81 Breast WGS Preoperative II 170 0.9043
Cancer treatment naïve
CGPLBR82 Breast Targeted Mutation Preoperative I 166 0.9254
Cancer Analysis and WGS treatment naïve
CGPLBR83 Breast Targeted Mutation Preoperative II 169 0.9451
Cancer Analysis and WGS treatment naïve
CGPLBR84 Breast WGS Preoperative III 169 0.9315
Cancer treatment naïve
CGPLBR87 Breast Targeted Mutation Preoperative II 166 0.9154
Cancer Analysis and WGS treatment naïve
CGPLBR88 Breast Targeted Mutation Preoperative II 169 0.9370
Cancer Analysis and WGS treatment naïve
CGPLBR90 Breast WGS Preoperative II 169 0.9002
Cancer treatment naïve
CGPLBR91 Breast Targeted Mutation Preoperative III 164 0.7955
Cancer Analysis and WGS treatment naïve
CGPLBR92 Breast Targeted Mutation Preoperative II 162 0.6774
Cancer Analysis and WGS treatment naïve
CGPLBR93 Breast Targeted Mutation Preoperative II 164 0.8773
Cancer Analysis and WGS treatment naïve
CGPLH189 Healthy WGS Preoperative NA 168 0.9325
treatment naïve
CGPLH190 Healthy WGS Preoperative NA 167 0.9433
treatment naïve
CGPLH192 Healthy WGS Preoperative NA 167 0.9646
treatment naïve
CGPLH193 Healthy WGS Preoperative NA 167 0.5423
treatment naïve
CGPLH194 Healthy WGS Preoperative NA 168 0.9567
treatment naïve
CGPLH196 Healthy WGS Preoperative NA 167 0.9709
treatment naïve
CGPLH197 Healthy WGS Preoperative NA 166 0.9605
treatment naïve
CGPLH198 Healthy WGS Preoperative NA 167 0.9238
treatment naïve
CGPLH199 Healthy WGS Preoperative NA 165 0.9618
treatment naïve
CGPLH200 Healthy WGS Preoperative NA 167 0.9183
treatment naïve
CGPLH201 Healthy WGS Preoperative NA 168 0.9548
treatment naïve
CGPLH202 Healthy WGS Preoperative NA 168 0.9471
treatment naïve
CGPLH203 Healthy WGS Preoperative NA 167 0.9534
treatment naïve
CGPLH205 Healthy WGS Preoperative NA 168 0.9075
treatment naïve
CGPLH208 Healthy WGS Preoperative NA 168 0.9422
treatment naïve
CGPLH209 Healthy WGS Preoperative NA 169 0.9556
treatment naïve
CGPLH210 Healthy WGS Preoperative NA 169 0.9447
treatment naïve
CGPLH211 Healthy WGS Preoperative NA 169 0.5538
treatment naïve
CGPLH300 Healthy WGS Preoperative NA 168 0.9019
treatment naïve
CGPLH307 Healthy WGS Preoperative NA 168 0.9576
treatment naïve
CGPLH308 Healthy WGS Preoperative NA 168 0.9481
treatment naïve
CGPLH309 Healthy WGS Preoperative NA 168 0.9672
treatment naïve
CGPLN310 Healthy WGS Preoperative NA 165 0.9547
treatment naïve
CGPLH311 Healthy WGS Preoperative NA 167 0.9302
treatment naïve
CGPLH314 Healthy WGS Preoperative NA 167 0.9482
treatment naïve
CGPLH315 Healthy WGS Preoperative NA 167 0.8659
treatment naïve
CGPLH316 Healthy WGS Preoperative NA 165 0.9374
treatment naïve
CGPLH317 Healthy WGS Preoperative NA 169 0.9542
treatment naïve
CGPLH319 Healthy WGS Preoperative NA 167 0.9578
treatment naïve
CGPLR320 Healthy WGS Preoperative NA 164 0.8913
treatment naïve
CGPLH322 Healthy WGS Preoperative NA 167 0.8751
treatment naïve
CGPLH324 Healthy WGS Preoperative NA 169 0.9519
treatment naïve
CGPLH325 Healthy WGS Preoperative NA 167 0.9124
treatment naïve
CGPLH326 Healthy WGS Preoperative NA 166 0.9574
treatment naïve
CGPLH327 Healthy WGS Preoperative NA 168 0.9533
treatment naïve
CGPLH328 Healthy WGS Preoperative NA 166 0.9643
treatment naïve
CGPLH329 Healthy WGS Preoperative NA 167 0.9609
treatment naïve
CGPLH330 Healthy WGS Preoperative NA 167 0.9118
treatment naïve
CGPLH331 Healthy WGS Preoperative NA 166 0.9679
treatment naïve
CGPLH333 Healthy WGS Preoperative NA 169 0.9474
treatment naïve
CGPLH335 Healthy WGS Preoperative NA 167 0.8909
treatment naïve
CGPLH336 Healthy WGS Preoperative NA 169 0.9248
treatment naïve
CGPLH337 Healthy WGS Preoperative NA 167 0.9533
treatment naïve
CGPLH338 Healthy WGS Preoperative NA 165 0.9388
treatment naïve
CGPLH339 Healthy WGS Preoperative NA 167 0.9396
treatment naïve
CGPLH340 Healthy WGS Preoperative NA 167 0.9488
treatment naïve
CGPLH341 Healthy WGS Preoperative NA 166 0.9533
treatment naïve
CGPLH342 Healthy WGS Preoperative NA 166 0.7858
treatment naïve
CGPLH343 Healthy WGS Preoperative NA 167 0.9421
treatment naïve
CGPLH344 Healthy WGS Preoperative NA 169 0.9192
treatment naïve
CGPLH345 Healthy WGS Preoperative NA 169 0.9345
treatment naïve
CGPLH346 Healthy WGS Preoperative NA 169 0.9475
treatment naïve
CGPLH350 Healthy WGS Preoperative NA 171 0.9570
treatment naïve
CGPLH351 Healthy WGS Preoperative NA 166 0.8176
treatment naïve
CGPLH352 Healthy WGS Preoperative NA 168 0.9521
treatment naïve
CGPLH353 Healthy WGS Preoperative NA 167 0.9435
treatment naïve
CGPLH354 Healthy WGS Preoperative NA 168 0.9481
treatment naïve
CGPLH355 Healthy WGS Preoperative NA 167 0.9613
treatment naïve
CGPLH356 Healthy WGS Preoperative NA 165 0.9474
treatment naïve
CGPLH357 Healthy WGS Preoperative NA 167 0.9255
treatment naïve
CGPLH358 Healthy WGS Preoperative NA 167 0.7777
treatment naïve
CGPLH360 Healthy WGS Preoperative NA 166 0.8500
treatment naïve
CGPLH361 Healthy WGS Preoperative NA 167 0.9261
treatment naïve
CGPLH362 Healthy WGS Preoperative NA 167 0.9236
treatment naïve
CGPLH363 Healthy WGS Preoperative NA 167 0.9488
treatment naïve
CGPLH364 Healthy WGS Preoperative NA 168 0.9311
treatment naïve
CGPLH365 Healthy WGS Preoperative NA 165 0.9371
treatment naïve
CGPLH366 Healthy WGS Preoperative NA 167 0.9536
treatment naïve
CGPLH367 Healthy WGS Preoperative NA 166 0.8748
treatment naïve
CGPLH368 Healthy WGS Preoperative NA 169 0.9490
treatment naïve
CGPLH369 Healthy WGS Preoperative NA 167 0.9428
treatment naïve
CGPLH370 Healthy WGS Preoperative NA 167 0.9642
treatment naïve
CGPLH371 Healthy WGS Preoperative NA 168 0.9621
treatment naïve
CGPLH380 Healthy WGS Preoperative NA 170 0.9662
treatment naïve
CGPLH381 Healthy WGS Preoperative NA 169 0.9541
treatment naïve
CGPLH382 Healthy WGS Preoperative NA 167 0.9380
treatment naïve
CGPLH383 Healthy WGS Preoperative NA 168 0.9700
treatment naïve
CGPLH384 Healthy WGS Preoperative NA 169 0.8061
treatment naïve
CGPLH385 Healthy WGS Preoperative NA 167 0.8666
treatment naïve
CGPLH386 Healthy WGS Preoperative NA 167 0.6920
treatment naïve
CGPLH387 Healthy WGS Preoperative NA 169 0.9583
treatment naïve
CGPLH388 Healthy WGS Preoperative NA 167 0.9348
treatment naïve
CGPLH389 Healthy WGS Preoperative NA 168 0.9409
treatment naïve
CGPLH390 Healthy WGS Preoperative NA 167 0.9216
treatment naïve
CGPLH391 Healthy WGS Preoperative NA 166 0.9334
treatment naïve
CGPLH392 Healthy WGS Preoperative NA 167 0.9165
treatment naïve
CGPLH393 Healthy WGS Preoperative NA 169 0.9256
treatment naïve
CGPLH394 Healthy WGS Preoperative NA 167 0.9257
treatment naïve
CGPLH395 Healthy WGS Preoperative NA 166 0.8611
treatment naïve
CGPLH396 Healthy WGS Preoperative NA 167 0.7884
treatment naïve
CGPLH398 Healthy WGS Preoperative NA 167 0.9463
treatment naïve
CGPLH399 Healthy WGS Preoperative NA 169 0.8780
treatment naïve
CGPLH400 Healthy WGS Preoperative NA 168 0.6662
treatment naïve
CGPLH401 Healthy WGS Preoperative NA 167 0.9428
treatment naïve
CGPLH402 Healthy WGS Preoperative NA 167 0.9353
treatment naïve
CGPLH403 Healthy WGS Preoperative NA 168 0.9329
treatment naïve
CGPLH404 Healthy WGS Preoperative NA 169 0.9402
treatment naïve
CGPLH405 Healthy WGS Preoperative NA 166 0.9579
treatment naïve
CGPLH406 Healthy WGS Preoperative NA 167 0.8188
treatment naïve
CGPLH407 Healthy WGS Preoperative NA 169 0.9527
treatment naïve
CGPLH408 Healthy WGS Preoperative NA 167 0.9584
treatment naïve
CGPLH409 Healthy WGS Preoperative NA 198 0.9220
treatment naïve
CGPLH410 Healthy WGS Preoperative NA 168 0.9102
treatment naïve
CGPLH411 Healthy WGS Preoperative NA 167 0.9392
treatment naïve
CGPLH412 Healthy WGS Preoperative NA 167 0.9561
treatment naïve
CGPLH413 Healthy WGS Preoperative NA 167 0.9461
treatment naïve
CGPLH414 Healthy WGS Preoperative NA 168 0.9258
treatment naïve
CGPLH415 Healthy WGS Preoperative NA 169 0.9217
treatment naïve
CGPLH416 Healthy WGS Preoperative NA 167 0.9672
treatment naïve
CGPLH417 Healthy WGS Preoperative NA 168 0.9578
treatment naïve
CGPLH418 Healthy WGS Preoperative NA 169 0.9376
treatment naïve
CGPLH419 Healthy WGS Preoperative NA 167 0.9228
treatment naïve
CGPLH420 Healthy WGS Preoperative NA 169 0.9164
treatment naïve
CGPLH422 Healthy WGS Preoperative NA 166 0.9069
treatment naïve
CGPLH423 Healthy WGS Preoperative NA 169 0.9606
treatment naïve
CGPLH424 Healthy WGS Preoperative NA 167 0.9553
treatment naïve
CGPLH425 Healthy WGS Preoperative NA 168 0.9722
treatment naïve
CGPLH426 Healthy WGS Preoperative NA 168 0.9560
treatment naïve
CGPLH427 Healthy WGS Preoperative NA 167 0.9594
treatment naïve
CGPLH428 Healthy WGS Preoperative NA 167 0.9591
treatment naïve
CGPLH429 Healthy WGS Preoperative NA 168 0.9358
treatment naïve
CGPLH430 Healthy WGS Preoperative NA 167 0.9639
treatment naïve
CGPLH431 Healthy WGS Preoperative NA 167 0.9570
treatment naïve
CGPLH432 Healthy WGS Preoperative NA 168 0.9485
treatment naïve
CGPLH434 Healthy WGS Preoperative NA 168 0.9571
treatment naïve
CGPLH435 Healthy WGS Preoperative NA 170 0.9133
treatment naïve
CGPLH436 Healthy WGS Preoperative NA 168 0.9360
treatment naïve
CGPLH437 Healthy WGS Preoperative NA 170 0.9445
treatment naïve
CGPLH438 Healthy WGS Preoperative NA 170 0.9537
treatment naïve
CGPLM439 Healthy WGS Preoperative NA 171 0.9547
treatment naïve
CGPLH440 Healthy WGS Preoperative NA 169 0.9562
treatment naïve
CGPLH441 Healthy WGS Preoperative NA 167 0.9660
treatment naïve
CGPLH442 Healthy WGS Preoperative NA 167 0.9569
treatment naïve
CGPLH443 Healthy WGS Preoperative NA 170 0.9431
treatment naïve
CGPLH444 Healthy WGS Preoperative NA 171 0.9429
treatment naïve
CGPLH445 Healthy WGS Preoperative NA 171 0.9446
treatment naïve
CGPLH446 Healthy WGS Preoperative NA 167 0.9502
treatment naïve
CGPLH447 Healthy WGS Preoperative NA 169 0.9421
treatment naïve
CGPLH448 Healthy WGS Preoperative NA 167 0.9553
treatment naïve
CGPLH449 Healthy WGS Preoperative NA 167 0.9550
treatment naïve
CGPLH450 Healthy WGS Preoperative NA 167 0.9572
treatment naïve
CGPLH451 Healthy WGS Preoperative NA 169 0.9548
treatment naïve
CGPLH452 Healthy WGS Preoperative NA 167 0.9498
treatment naïve
CGPLH453 Healthy WGS Preoperative NA 166 0.9572
treatment naïve
CGPLH455 Healthy WGS Preoperative NA 166 0.9526
treatment naïve
CGPLH450 Healthy WGS Preoperative NA 166 0.9507
treatment naïve
CGPLH457 Healthy WGS Preoperative NA 167 0.9429
treatment naïve
CGPLH458 Healthy WGS Preoperative NA 167 0.9511
treatment naïve
CGPLH459 Healthy WGS Preoperative NA 168 0.9609
treatment naïve
CGPLH460 Healthy WGS Preoperative NA 168 0.9331
treatment naïve
CGPLH463 Healthy WGS Preoperative NA 167 0.9506
treatment naïve
CGPLH464 Healthy WGS Preoperative NA 170 0.9133
treatment naïve
CGPLH465 Healthy WGS Preoperative NA 167 0.9251
treatment naïve
CGPLH466 Healthy WGS Preoperative NA 167 0.9679
treatment naïve
CGPLH467 Healthy WGS Preoperative NA 168 0.9273
treatment naïve
CGPLH468 Healthy WGS Preoperative NA 167 0.8553
treatment naïve
CGPLH469 Healthy WGS Preoperative NA 169 0.8225
treatment naïve
CGPLH470 Healthy WGS Preoperative NA 168 0.9073
treatment naïve
CGPLH471 Healthy WGS Preoperative NA 167 0.9354
treatment naïve
CGPLH472 Healthy WGS Preoperative NA 166 0.8509
treatment naïve
CGPLH473 Healthy WGS Preoperative NA 167 0.9206
treatment naïve
CGPLH474 Healthy WGS Preoperative NA 168 0.8474
treatment naïve
CGPLH475 Healthy WGS Preoperative NA 167 0.9155
treatment naïve
CGPLH476 Healthy WGS Preoperative NA 169 0.8807
treatment naïve
CGPLH477 Healthy WGS Preoperative NA 169 0.9129
treatment naïve
CGPLH478 Healthy WGS Preoperative NA 167 0.9588
treatment naïve
CGPLN479 Healthy WGS Preoperative NA 167 0.9503
treatment naïve
CGPLH480 Healthy WGS Preoperative NA 169 0.9522
treatment naïve
CGPLH481 Healthy WGS Preoperative NA 168 0.9568
treatment naïve
CGPLH482 Healthy WGS Preoperative NA 168 0.9379
treatment naïve
CGPLH483 Healthy WGS Preoperative NA 168 0.9518
treatment naïve
CGPLH484 Healthy WGS Preoperative NA 166 0.9630
treatment naïve
CGPLH485 Healthy WGS Preoperative NA 166 0.9547
treatment naïve
CGPLH486 Healthy WGS Preoperative NA 169 0.9199
treatment naïve
CGPLH487 Healthy WGS Preoperative NA 169 0.9575
treatment naïve
CGPLH488 Healthy WGS Preoperative NA 167 0.9618
treatment naïve
CGPLH490 Healthy WGS Preoperative NA 167 0.8950
treatment naïve
CGPLH491 Healthy WGS Preoperative NA 168 0.9631
treatment naïve
CGPLH492 Healthy WGS Preoperative NA 170 0.9335
treatment naïve
CGPLH493 Healthy WGS Preoperative NA 168 0.8718
treatment naïve
CGPLH494 Healthy WGS Preoperative NA 169 0.9623
treatment naïve
CGPLH495 Healthy WGS Preoperative NA 166 0.8777
treatment naïve
CGPLH496 Healthy WGS Preoperative NA 166 0.8788
treatment naïve
CGPLH497 Healthy WGS Preoperative NA 167 0.9576
treatment naïve
CGPLH498 Healthy WGS Preoperative NA 167 0.9526
treatment naïve
CGPLH499 Healthy WGS Preoperative NA 167 0.9733
treatment naïve
CGPLH500 Healthy WGS Preoperative NA 168 0.9542
treatment naïve
CGPLH501 Healthy WGS Preoperative NA 169 0.9526
treatment naïve
CGPLH502 Healthy WGS Preoperative NA 167 0.9512
treatment naïve
CGPLH503 Healthy WGS Preoperative NA 169 0.8947
treatment naïve
CGPLH504 Healthy WGS Preoperative NA 167 0.9561
treatment naïve
CGPLH505 Healthy WGS Preoperative NA 166 0.9554
treatment naïve
CGPLH506 Healthy WGS Preoperative NA 167 0.9733
treatment naïve
CGPLH507 Healthy WGS Preoperative NA 168 0.9222
treatment naïve
CGPLH508 Healthy WGS Preoperative NA 167 0.9674
treatment naïve
CGPLH509 Healthy WGS Preoperative NA 167 0.9475
treatment naïve
CGPLH510 Healthy WGS Preoperative NA 167 0.9459
treatment naïve
CGPLH511 Healthy WGS Preoperative NA 166 0.9714
treatment naïve
CGPLH512 Healthy WGS Preoperative NA 168 0.9442
treatment naïve
CGPLH513 Healthy WGS Preoperative NA 166 0.9705
treatment naïve
CGPLH514 Healthy WGS Preoperative NA 167 0.9690
treatment naïve
CGPLH515 Healthy WGS Preoperative NA 167 0.9568
treatment naïve
CGPLH516 Healthy WGS Preoperative NA 166 0.9508
treatment naïve
CGPLH517 Healthy WGS Preoperative NA 168 0.9635
treatment naïve
CGPLH518 Healthy WGS Preoperative NA 168 0.9647
treatment naïve
CGPLH519 Healthy WGS Preoperative NA 166 0.9366
treatment naïve
CGPLH520 Healthy WGS Preoperative NA 166 0.3649
treatment naïve
CGPLH625 Healthy WGS Preoperative NA 166 0.8766
treatment naïve
CGPLH626 Healthy WGS Preoperative NA 170 0.9011
treatment naïve
CGPLH639 Healthy WGS Preoperative NA 165 0.9482
treatment naïve
CGPLH640 Healthy WGS Preoperative NA 166 0.9131
treatment naïve
CGPLH642 Healthy WGS Preoperative NA 167 0.9641
treatment naïve
CGPLH643 Healthy WGS Preoperative NA 169 0.9450
treatment naïve
CGPLH644 Healthy WGS Preoperative NA 170 0.9398
treatment naïve
CGPLH646 Healthy WGS Preoperative NA 172 0.9296
treatment naïve
CGPLLU144 Lung Targeted Mutation Preoperative II 164 0.8702
Cancer Analysis and WGS treatment naïve
CGPLLU161 Lung Targeted Mutation Preoperative II 165 0.9128
Cancer Analysis and WGS treatment naïve
CGPLLU162 Lung Targeted Mutation Preoperative II 165 0.7753
Cancer Analysis and WGS treatment naïve
CGPLLU163 Lung Targeted Mutation Preoperative II 166 0.4770
Cancer Analysis and WGS treatment naïve
CGPLLU168 Lung Targeted Mutation Preoperative I 163 0.9164
Cancer Analysis and WGS treatment naïve
CGPLLU169 Lung Targeted Mutation Preoperative I 163 0.9326
Cancer Analysis and WGS treatment naïve
CGPLLU176 Lung Targeted Mutation Preoperative I 168 0.9572
Cancer Analysis and WGS treatment naïve
CGPLLU177 Lung Targeted Mutation Preoperative II 166 0.8472
Cancer Analysis and WGS treatment naïve
CGPLLU203 Lung Targeted Mutation Preoperative II 164 0.9119
Cancer Analysis and WGS treatment naïve
CGPLLU205 Lung Targeted Mutation Preoperative II 163 0.9518
Cancer Analysis and WGS treatment naïve
CGPLLU207 Lung Targeted Mutation Preoperative II 166 0.9344
Cancer Analysis and WGS treatment naïve
CGPLLU208 Lung Targeted Mutation Preoperative II 164 0.9091
Cancer Analysis and WGS treatment naïve
CGPLOV11 Ovarian Targeted Mutation Preoperative V 166 0.8902
Cancer Analysis and WGS treatment naïve
CGPLOV12 Ovarian Targeted Mutation Preoperative I 167 0.8779
Cancer Analysis and WGS treatment naïve
CGPLOV13 Ovarian Targeted Mutation Preoperative V 166 0.7560
Cancer Analysis and WGS treatment naïve
CGPLOV15 Ovarian Targeted Mutation Preoperative III 155 0.8585
Cancer Analysis and WGS treatment naïve
CGPLOV18 Ovarian Targeted Mutation Preoperative III 165 0.9052
Cancer Analysis and WGS treatment naïve
CGPLOV19 Ovarian Targeted Mutation Preoperative II 165 0.7854
Cancer Analysis and WGS treatment naïve
CGPLOV20 Ovarian Targeted Mutation Preoperative II 165 0.8711
Cancer Analysis and WGS treatment naïve
CGPLOV21 Ovarian Targeted Mutation Preoperative V 167 0.8942
Cancer Analysis and WGS treatment naïve
CGPLOV22 Ovarian Targeted Mutation Preoperative III 164 0.8944
Cancer Analysis and WGS treatment naïve
CGPLOV23 Ovarian Targeted Mutation Preoperative I 169 0.8510
Cancer Analysis and WGS treatment naïve
CGPLOV24 Ovarian Targeted Mutation Preoperative I 166 0.9449
Cancer Analysis and WGS treatment naïve
CGPLOV25 Ovarian Targeted Mutation Preoperative I 166 0.9590
Cancer Analysis and WGS treatment naïve
CGPLOV26 Ovarian Targeted Mutation Preoperative I 161 0.8148
Cancer Analysis and WGS treatment naïve
CGPLOV28 Ovarian Targeted Mutation Preoperative I 167 0.9635
Cancer Analysis and WGS treatment naïve
CGPLOV31 Ovarian Targeted Mutation Preoperative III 167 0.9461
Cancer Analysis and WGS treatment naïve
CGPLOV32 Ovarian Targeted Mutation Preoperative I 168 0.9582
Cancer Analysis and WGS treatment naïve
CGPLOV37 Ovarian Targeted Mutation Preoperative I 170 0.9397
Cancer Analysis and WGS treatment naïve
CGPLOV38 Ovarian Targeted Mutation Preoperative I 166 0.5779
Cancer Analysis and WGS treatment naïve
CGPLOV40 Ovarian Targeted Mutation Preoperative V 170 0.6097
Cancer Analysis and WGS treatment naïve
CGPLOV41 Ovarian Targeted Mutation Preoperative V 167 0.9403
Cancer Analysis and WGS treatment naïve
CGPLOV42 Ovarian Targeted Mutation Preoperative I 166 0.9265
Cancer Analysis and WGS treatment naïve
CGPLOV43 Ovarian Targeted Mutation Preoperative I 167 0.9626
Cancer Analysis and WGS treatment naïve
CGPLOV44 Ovarian Targeted Mutation Preoperative I 164 0.9536
Cancer Analysis and WGS treatment naïve
CGPLOV45 Ovarian Targeted Mutation Preoperative I 166 0.9622
Cancer Analysis and WGS treatment naïve
CGPLOV47 Ovarian Targeted Mutation Preoperative I 165 0.9704
Cancer Analysis and WGS treatment naïve
CGPLOV48 Ovarian Targeted Mutation Preoperative I 167 0.9675
Cancer Analysis and WGS treatment naïve
CGPLOV49 Ovarian Targeted Mutation Preoperative III 164 0.8998
Cancer Analysis and WGS treatment naïve
CGPLOV50 Ovarian Targeted Mutation Preoperative III 165 0.9682
Cancer Analysis and WGS treatment naïve
CGPLPA112 Pancreatic WGS Preoperative II 164 0.8914
Cancer treatment naïve
CGPLPA113 Duodenal WGS Preoperative I 170 0.8751
Cancer treatment naïve
CGPLPA114 Bile Duct WGS Preoperative II 166 0.9098
Cancer treatment naïve
CGPLPA115 Bile Duct WGS Preoperative V 165 0.8053
Cancer treatment naïve
CGPLPA117 Bile Duct WGS Preoperative II 165 0.9395
Cancer treatment naïve
CGPLPA118 Bile Duct Targeted Mutation Preoperative I 157 0.9406
Cancer Analysis and WGS treatment naïve
CGPLPA122 Bile Duct Targeted Mutation Preoperative II 164 0.8231
Cancer Analysis and WGS treatment naïve
CGPLPA124 Bile Duct Targeted Mutation Preoperative II 166 0.9108
Cancer Analysis and WGS treatment naïve
CGPLPA125 Bile Duct WGS Preoperative II 165 0.9675
Cancer treatment naïve
CGPLPA126 Bile Duct Targeted Mutation Preoperative II 166 0.9155
Cancer Analysis and WGS treatment naïve
CGPLPA127 Bile Duct WGS Preoperative V 167 0.8916
Cancer treatment naïve
CGPLPA128 Bile Duct Targeted Mutation Preoperative II 167 0.9262
Cancer Analysis and WGS treatment naïve
CGPLPA129 Bile Duct Targeted Mutation Preoperative II 166 0.9220
Cancer Analysis and WGS treatment naïve
CGPLPA130 Bile Duct Targeted Mutation Preoperative II 169 0.8586
Cancer Analysis and WGS treatment naïve
CGPLPA131 Bile Duct Targeted Mutation Preoperative II 165 0.7707
Cancer Analysis and WGS treatment naïve
CGPLPA134 Bile Duct Targeted Mutation Preoperative II 160 0.7502
Cancer Analysis and WGS treatment naïve
CGPLPA135 Bile Duct WGS Preoperative I 165 0.9495
Cancer treatment naïve
CGPLPA136 Bile Duct Targeted Mutation Preoperative II 164 0.9289
Cancer Analysis and WGS treatment naïve
CGPLPA137 Bile Duct WGS Preoperative II 166 0.9568
Cancer treatment naïve
CGPLPA139 Bile Duct WGS Preoperative V 166 0.9511
Cancer treatment naïve
CGPLPA14 Pancreatic WGS Preoperative II 167 0.8718
Cancer treatment naïve
CGPLPA140 Bile Duct Targeted Mutation Preoperative II 166 0.9215
Cancer Analysis and WGS treatment naïve
CGPLPA141 Bile Duct WGS Preoperative II 165 0.3172
Cancer treatment naïve
CGPLPA15 Pancreatic WGS Preoperative II 167 0.9111
Cancer treatment naïve
CGPLPA155 Bile Duct WGS Preoperative II 165 0.9496
Cancer treatment naïve
CGPLPA156 Pancreatic WGS Preoperative II 167 0.9479
Cancer treatment naïve
CGPLPA165 Bile Duct WGS Preoperative I 168 0.9596
Cancer treatment naïve
CGPLPA168 Bile Duct WGS Preoperative II 162 0.7838
Cancer treatment naïve
CGPLPA17 Pancreatic WGS Preoperative II 166 0.8624
Center treatment naïve
CGPLPA184 Bile Duct WGS Preoperative II 165 0.9100
Cancer treatment naïve
CGPLPA187 Bile Duct WGS Preoperative II 165 0.8577
Cancer treatment naïve
CGPLPA23 Pancreatic WGS Preoperative II 165 0.7887
Cancer treatment naïve
CGPLPA25 Pancreatic WGS Preoperative II 166 0.9549
Cancer treatment naïve
CGPLPA26 Pancreatic WGS Preoperative II 166 0.9598
Cancer treatment naïve
CGPLPA28 Pancreatic WGS Preoperative II 165 0.9069
Cancer treatment naïve
CGPLPA33 Pancreatic WGS Preoperative II 166 0.8361
Cancer treatment naïve
CGPLPA34 Pancreatic WGS Preoperative II 168 0.9846
Cancer treatment naïve
CGPLPA37 Pancreatic WGS Preoperative II 165 0.8840
Cancer treatment naïve
CGPLPA38 Pancreatic WGS Preoperative II 167 0.8746
Cancer treatment naïve
CGPLPA39 Pancreatic WGS Preoperative II 167 0.8562
Cancer treatment naïve
CGPLPA40 Pancreatic WGS Preoperative II 165 0.8563
Cancer treatment naïve
CGPLPA42 Pancreatic WGS Preoperative II 167 0.9126
Cancer treatment naïve
CGPLPA46 Pancreatic WGS Preoperative II 169 0.8274
Cancer treatment naïve
CGPLPA47 Pancreatic WGS Preoperative II 166 0.8376
Cancer treatment naïve
CGPLPA48 Pancreatic WGS Preoperative I 167 0.9391
Cancer treatment naïve
CGPLPA52 Pancreatic WGS Preoperative II 167 0.9452
Cancer treatment naïve
CGPLPA53 Pancreatic WGS Preoperative I 163 0.9175
Cancer treatment naïve
CGPLPA58 Pancreatic WGS Preoperative II 165 0.9587
Cancer treatment naïve
CGPLPA59 Pancreatic WGS Preoperative II 163 0.9230
Cancer treatment naïve
CGPLPA67 Pancreatic WGS Preoperative II 165 0.9574
Cancer treatment naïve
CGPLPA69 Pancreatic WGS Preoperative I 168 0.9172
Cancer treatment naïve
CGPLPA71 Pancreatic WGS Preoperative II 167 0.9424
Cancer treatment naïve
CGPLPA74 Pancreatic WGS Preoperative II 165 0.9688
Cancer treatment naïve
CGPLPA78 Pancreatic WGS Preoperative II 163 0.9681
Cancer treatment naïve
CGPLPA85 Pancreatic WGS Preoperative II 165 0.9137
Cancer treatment naïve
CGPLPA86 Pancreatic WGS Preoperative II 165 0.8875
Cancer treatment naïve
CGPLPA92 Pancreatic WGS Preoperative II 167 0.9389
Cancer treatment naïve
CGPLPA93 Pancreatic WGS Preoperative II 166 0.8585
Cancer treatment naïve
CGPLPA94 Pancreatic WGS Preoperative II 162 0.9365
Cancer treatment naïve
CGPLPA95 Pancreatic WGS Preoperative II 163 0.8542
Cancer treatment naïve
CGST102 Gastric Targeted Mutation Preoperative II 167 0.9496
cancer Analysis and WGS treatment naïve
CGST11 Gastric WGS Preoperative IV 169 0.9419
cancer treatment naïve
CGST110 Gastric Targeted Mutation Preoperative II 167 0.9626
cancer Analysis and WGS treatment naïve
CGST114 Gastric Targeted Mutation Preoperative II 164 0.9535
cancer Analysis and WGS treatment naïve
CGST13 Gastric Targeted Mutation Preoperative II 166 0.9369
cancer Analysis and WGS treatment naïve
CGST131 Gastric WGS Preoperative II 171 0.9428
cancer treatment naïve
CGST141 Gastric Targeted Mutation Preoperative II 168 0.9621
cancer Analysis and WGS treatment naïve
CGST16 Gastric Targeted Mutation Preoperative II 166 0.7804
cancer Analysis and WGS treatment naïve
CGST18 Gastric Targeted Mutation Preoperative II 169 0.9523
cancer Analysis and WGS treatment naïve
CGST21 Gastric WGS Preoperative II 165 −0.4778
cancer treatment naïve
CGST26 Gastric WGS Preoperative IV 166 0.9554
cancer treatment naïve
CG3T28 Gastric Targeted Mutation Preoperative X 169 0.9076
cancer Analysis and WGS treatment naïve
CGST30 Gastric Targeted Mutation Preoperative II 169 0.9246
cancer Analysis and WGS treatment naïve
CGST32 Gastric Targeted Mutation Preoperative II 169 0.9431
cancer Analysis and WGS treatment naïve
CGST33 Gastric Targeted Mutation Preoperative I 168 0.7999
cancer Analysis and WGS treatment naïve
CGST38 Gastric WGS Preoperative 0 168 0.9368
cancer treatment naïve
CGST39 Gastric Targeted Mutation Preoperative IV 164 0.8742
cancer Analysis and WGS treatment naïve
CGST41 Gastric Targeted Mutation Preoperative IV 168 0.8194
cancer Analysis and WGS treatment naïve
CGST45 Gastric Targeted Mutation Preoperative II 168 0.9576
cancer Analysis and WGS treatment naïve
CGST47 Gastric Targeted Mutation Preoperative I 168 0.9611
cancer Analysis and WGS treatment naïve
CGST48 Gastric Targeted Mutation Preoperative IV 167 0.7469
cancer Analysis and WGS treatment naïve
CGST53 Gastric WGS Preoperative 0 173 0.0019
cancer treatment naïve
CGST58 Gastric Targeted Mutation Preoperative II 169 0.9470
cancer Analysis and WGS treatment naïve
CGST67 Gastric WGS Preoperative I 170 0.9352
cancer treatment naïve
CGST77 Gastric WGS Preoperative IV 170 0.00438
cancer treatment naïve
CGST80 Gastric Targeted Mutation Preoperative II 168 0.9313
cancer Analysis and WGS treatment naïve
CGST81 Gastric Targeted Mutation Preoperative I 168 0.9480
cancer Analysis and WGS treatment naïve
Correlation of
GC Corrected
Fragment Mutant
Ratio Profile Alelle
to Median Fraction Fraction
Fragment of Reads Detected Detected Detected
Ratio Profile Mapped to using using using
of Healthy Mitochondrial DELFI DELFI (95% DELFI (98% Targeted
Patient Individuals Genome Scene specificity) specificity) sequencing*
CGCRC291 0.5268 0.0484% 0.9976 Y Y 22.85%
CGCRC232 0.8835 0.0270% 0.7299 Y N 1.41%
CGCRC293 0.9206 0.0748% 0.5534 N N 0.35%
CGCRC294 0.8904 0.0188% 0.8757 Y Y 0.17%
CGCRC295 0.8895 0.0369% 0.9951 Y Y ND
CGCRC299 0.9268 0.0392% 0.9648 Y Y ND
CGCRC300 0.0303 0.0235% 0.4447 N N ND
CGCRC301 0.9151 0.0310% 0.2190 N N 0.21%
CGCRC302 0.9243 0.0112% 0.9897 Y Y 0.12%
CGCRC304 0.9360 0.0393% 0.9358 Y Y 0.27%
CGCRC305 0.9250 0.0120% 0.3988 Y Y 0.19%
CGCRG306 0.8186 0.0781% 0.9486 Y Y 8.02%
CGCRC307 0.9342 0.0181% 0.7042 Y N 0.58%
CGCRC306 0.9324 0.0078% 0.9082 Y Y 0.11%
CGCRC311 0.9156 0.0173% 0.1887 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 3.11%
CGCRC320 0.9101 0.0383% 0.8019 Y Y 0.84%
CGCRC321 0.9021 0.0829% 0.9759 Y Y 0.20%
CGCRC333 0.4355 0.4284% 0.9974 Y Y 43.03%
CGCRC335 0.6856 0.1154% 0.9887 Y Y 81.61%
CGCRC338 0.7573 0.1436% 0.9976 Y Y 36.00%
CGCRC341 0.9191 0.0197% 0.9670 Y Y ND
CGCRC342 0.1345 0.1732% 0.9987 Y Y 30.72%
CGPLBR100 0.8945 0.1234% 0.8684 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 —
CGPLBR26 0.9120 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 0.0306% 0.9815 Y Y —
CGPLBR33 0.9432 0.0817% 0.2853 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 0.9495 0.0516% 0.0314 N N —
CGPLBR38 0.0349 0.1352% 0.8983 Y Y 0.53%
CGPLBR40 0.9244 0.0923% 0.9846 Y Y ND
CGPLBR41 0.9346 0.0544% 0.9416 Y Y 0.32%
CGPLBR45 0.9286 0.0296% 0.3860 N N
CGPLBR46 0.9005 0.0345% 0.7270 Y N —
CGPLBR47 0.2028 0.0591% 0.8247 Y Y —
CGPLBR48 0.8246 0.0504% 0.9973 Y Y 0.18%
CGPLBR49 0.7887 0.0377% 0.9946 Y Y ND
CGPLBR50 0.8332 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% 0.7863 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 3.12%
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 0.8359 0.0274% 0.9901 Y Y —
CGPLBR87 0.8797 0.0294% 0.9988 Y Y 0.45%
CGPLBR88 0.8547 0.0181% 0.9988 Y Y 0.38%
CGPLBR90 0.8330 0.0417% 0.9687 Y Y
CGPLBR91 0.9408 0.0799% 0.8710 Y Y ND
CGPLBR92 0.8835 0.1042% 0.9866 Y Y 0.20%
CGPLBP93 0.9072 0.0352% 0.7253 Y N ND
CGPLH189 0.8947 0.0591% 0.1748 N N —
CGPLH190 0.9369 0.1193% 0.5188 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% 0.5564 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 0.9352 0.0311% 0.0185 N N —
CGPLH309 0.9451 0.0226% 0.0441 N N —
CGPLN310 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% 0.1330 N N —
CGPLH319 0.9189 0.0263% 0.2232 N N —
CGPLR320 0.9166 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 0.9396 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 0.0159% 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 0.9288 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 0.9312 0.0427% 0.2437 N N —
CGPLH357 0.9340 0.0217% 0.0070 N N
CGPLH358 0.9372 0.0174% 0.1451 N N —
CGPLH360 0.8775 0.3395% 0.0048 N N —
CGPLH361 0.9283 0.0266% 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% 0.9683 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 0.9245 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 0.6522% 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 0.0573% 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 0.0359% 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% 0.0284 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% 0.0469 N N —
CGPLH420 0.9108 0.0187% 0.0420 N N —
CGPLH422 0.9006 0.0209% 0.0324 N N —
CGPLH423 0.9289 0.0832% 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% 0.0369 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 0.3245 0.0156% 0.0136 N N —
CGPLH438 0.9138 0.0169% 0.1041 N N —
CGPLM439 0.9028 0.0225% 0.0078 N N —
CGPLH440 0.9295 0.0330% 0.0887 N N —
CGPLH441 0.9430 0.0178% 0.0085 N N
CGPLH442 0.9406 0.0169% 0.0582 N N —
CGPLH443 0.8801 0.0207% 0.0578 N N —
CGPLH444 0.9066 0.6464% 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 0.8181 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 —
CGPLH450 0.9098 0.0207% 0.0365 N N —
CGPLH457 0.9022 0.0298% 0.0354 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% 0.0121 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 0.2228 0.0715% 0.0327 N N —
CGPLH471 0.9333 0.0153% 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% 0.5246 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% 0.0828 N N —
CGPLN479 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 0.9298 0.0409% 0.0903 N N —
CGPLH490 0.8794 0.0432% 0.0424 N N —
CGPLH491 0.9332 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 0.9398 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 —
CGPLH502 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% 0.1889 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% 0.0378 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 0.9298 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% 0.0944 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 0.9376 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 0.8681 0.0423% 0.9892 Y Y 5.10%
CGPLLU161 0.9187 0.0273% 0.9955 Y Y 0.20%
CGPLLU162 0.8836 0.1410% 0.9986 Y Y 0.22%
CGPLLU163 0.3033 0.0724% 0.9940 Y Y 0.21%
CGPLLU168 0.8842 0.0712% 0.9861 Y Y 0.07%
CGPLLU169 0.9189 0.0846% 0.9866 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% 0.9677 Y Y ND
CGPLLU207 0.9379 0.0421% 0.9908 Y Y 0.32%
CGPLLU208 0.8342 0.0815% 0.9273 Y Y 1.33%
CGPLOV11 0.8872 0.0463% 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%
CGPLOV18 0.9046 0.0400% 0.9983 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% 0.9951 Y Y 14.36%
CGPLOV22 0.9355 0.0251% 0.9775 Y V 0.49%
CGPLOV23 0.8850 0.1520% 0.9916 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 0.0647% 0.8160 Y Y ND
CGPLOV31 0.9293 0.1605% 0.9795 Y Y ND
CGPLOV32 0.9338 0.1351% 0.8609 Y Y ND
CGPLOV37 0.8831 0.0986% 0.9849 Y Y 0.29%
CGPLOV38 0.6502 0.0490% 0.9990 Y Y 4.89%
CGPLOV40 0.8127 0.6145% 0.9983 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%
CGPLOV45 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 0.0807% 0.9955 Y Y ND
CGPLPA112 0.0429 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 0.8310 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 0.0612% 0.9839 Y Y ND
CGPLPA130 0.8499 0.1005% 0.9895 Y Y ND
CGPLPA131 0.9195 0.0780% 0.9885 Y Y 3.21%
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% 0.9761 Y Y 0.21%
CGPLPA141 0.9381 0.0920% 0.9988 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 —
CGPLPA165 0.9309 0.0555% 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% 0.9675 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 0.8938 0.0546% 0.9036 Y Y —
CGPLPA33 0.8553 0.0894% 0.9367 Y Y —
CGPLPA34 0.8885 0.0439% 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 0.8363 0.0283% 0.3544 N N —
CGPLPA46 0.7525 1.0982% 0.9952 Y Y —
CGPLPA47 0.8439 0.1596% 0.9346 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 0.0459% 0.1245 N N —
CGPLPA71 0.8888 0.0479% 0.0524 N N —
CGPLPA74 0.9372 0.0292% 0.0108 N N —
CGPLPA78 0.9441 0.0345% 0.0897 N N —
CGPLPA85 0.9337 0.0363% 0.0508 N N —
CGPLPA86 0.8042 0.7564% 0.9864 Y Y —
CGPLPA92 0.9003 0.1459% 0.7061 Y N —
CGPLPA93 0.8023 0.6250% 0.9978 Y Y —
CGPLPA94 0.9433 0.0160% 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% 0.5593 N N ND
CGST13 0.9156 0.0321% 0.9754 Y Y ND
CGST131 0.8886 0.2752% 0.9409 Y Y —
CGST141 0.9206 0.0338% 0.2008 N N ND
CGST16 0.8355 0.1744% 0.9974 Y Y 0.93%
CGST18 0.9111 0.0299% 0.3842 N N 0.14%
CGST21 0.2687 0.2299% 0.9910 Y Y —
CGST26 0.9140 0.0399% 0.5009 N N —
CG3T28 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% 0.9612 Y Y 2.99%
CGST33 0.7770 0.0799% 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% 0.9263 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 0.0596% 0.9705 Y Y ND
CGST67 0.8853 0.3245% 0.9002 Y Y —
CGST77 0.8295 0.1851% 0.9981 Y Y —
CGST80 0.8846 0.0490% 0.9513 Y Y 1.04%
CGST81 0.8851 0.0138% 0.9748 Y Y 0.20%
*ND 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.
