Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel to detect cancer and particular types of cancer.

Abstract: The present description provides a hematological disorder (HD) assay panel for targeted detection of methylation patterns or variants specific to various hematological disorders, such as clonal hematopoiesis of indeterminate potential (CHIP) and blood cancers, such as leukemia, lymphoid neoplasms (e.g. lymphoma), multiple myeloma, and myeloid neoplasm. Further provided herein includes methods of designing, making, and using the HD assay panel for detection of various hematological disorders.

Abstract: Systems and methods described herein include detecting a presence or absence of HPV in a biological sample having cell-free nucleic acids from a subject and potentially cell-free nucleic acids from an HPV strain. Based on a detection of HPV viral nucleic acids in the biological sample, an HPV-based multiclass classifier that predicts a score for each HPV-associated cancer type is applied. The HPV-based multiclass classifier is trained on a training set of HPV-positive cancer samples. An HPV-associated cancer associated with the biological sample is determined based on the scores predicted by the HPV multiclass classifier.

Abstract: Systems and methods of identifying methylation patterns discriminating or indicating a cancer condition are provided. First and second datasets are obtained. Each dataset comprises a plurality of fragment methylation patterns determined by methylation sequencing of nucleic acids obtained from a first or second set of subjects and comprising a methylation state of each CpG site in a corresponding plurality of CpG sites. Each plurality of subjects has a respective first or second state of the cancer condition. First and second interval maps are generated for each respective dataset, each comprising a plurality of nodes characterized by a start methylation site, an end methylation site, a representation of each different fragment methylation pattern and a count of fragments.

Abstract: An allelic position variant calling method using a prior genotype probability at the allelic position is provided. A strand specific base count set in forward and reverse directions for the allelic position is obtained, using strand orientation and identity of a respective base at the allelic position in each respective nucleic acid fragment sequence that maps to the allelic position, where bases at the allelic position whose identity can be affected by conversion of cytosine to uracil do not contribute to the strand specific base count set. Respective forward and reverse strand conditional probabilities are computed for each candidate genotype for the allelic position using the strand specific base count set and sequencing error estimate. Likelihoods are computed using a combination of these conditional probabilities and the prior genotype probability. From this, a determination is made as to whether the likelihoods support a variant call at the allelic position.

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein are methods of designing, making, and using the cancer assay panel for the diagnosis of cancer.

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein are methods of designing, making, and using the cancer assay panel for the diagnosis of cancer.

Abstract: Systems and methods are provided for determining whether a test subject has a disease condition. In one aspect, the method includes determining at least first and second genotypic data constructs for a test subject, formed from data collected from first and second sample from the subject, respectively, at different times. The first and second genotypic data constructs are inputted into a model for the disease condition, thereby generating first and second model score sets for the disease condition, respectively. A test delta score set is determined based on a difference between the first and second model score sets. The test delta score set is evaluated against a plurality of reference delta score sets, to determine the disease condition of the test subject, where each reference delta score set is for a respective reference subject in a plurality of reference subjects.

Abstract: Methods and systems for detecting cancer and/or determining a cancer tissue of origin are disclosed. In some embodiments, a multiclass cancer classifier is disclosed that is trained with a plurality of biological samples containing cfDNA fragments. The analytics system derives a feature vector for each sample, and the multiclass classifier predicts a probability likelihood for each of a plurality of tissue of origin (TOO) classes. In some embodiments, the plurality of TOO classes include hematological subtypes, including both hematological malignancies and precursor conditions. In one embodiment, non-cancer samples having high tissue signal are pruned from the training sample set. In another embodiment, the analytics system stratifies samples according to tissue signal and applies binary threshold cutoffs determined for each stratum.

Abstract: Methods for screening for a cancer condition in a subject are provided. A biological sample from the subject is obtained. The sample comprises cell-free nucleic acid from the subject and potentially cell-free nucleic acid from a pathogen in a set of pathogens. The cell-free nucleic acid in the biological sample is sequenced to generate a plurality of sequence reads from the subject. A determination is made, for each respective pathogen in the set of pathogens, of a corresponding amount of the plurality of sequence reads that map to a sequence in a pathogen target reference for the respective pathogen, thereby obtaining a set of amounts of sequence reads, each respective amount of sequence reads in the set of amounts of sequence reads for a corresponding pathogen in the set of pathogens. The set of amounts of sequence reads is used to determine whether the subject has the cancer condition.

Abstract: Systems and methods are disclosed for determining tumor fraction in cell-free nucleic acid of a liquid biological sample of a subject. Sequence reads are obtained using the biological sample. The sequence reads are used to identify support for each variant in a variant set thereby determining an observed frequency of each variant in the variant set. For each respective variant in the variant set, a corresponding reference frequency for the respective variant is obtained in a reference set, where each corresponding reference frequency in the reference set is for a respective variant in an aberrant solid tissue sample obtained from the subject. The observed frequency of each respective variant in the variant set is evaluated against the observed frequency of the respective variant in the reference set thereby determining the tumor fraction in cell-free nucleic acid of the liquid biological sample.

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel for diagnosis of cancer.

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel for diagnosis of cancer.

Abstract: Systems and methods are disclosed for determining a cell source fraction in a biological sample of a test subject. Nucleic acid fragments are obtained from a biological sample, comprising cell-free nucleic acid, of the test subject. A methylation state is obtained for each nucleic acid fragment in a first plurality of nucleic acid fragments. Each respective nucleic acid fragment is individually assigned a first score, thereby obtaining a first plurality of scores. Each respective score represents a likelihood that the corresponding nucleic acid fragment was obtained from a cell-free nucleic acid molecule associated with the first cell source. The first plurality of scores is transformed into a first plurality of counts, each count in the first plurality of counts being for a methylation site in a first predetermined set of methylation sites. A first cell source fraction for the test subject is estimated using the first plurality of counts.

Abstract: In various embodiments, an analytics system uses models to determine features and classification of disease states. A disease state can indicate presence or absence of cancer, a cancer type, or a cancer tissue of origin. The models can include a binary classifier and a tissue of origin classifier. The analytics system can process sequence reads from test biological samples to generate data for training the classifiers. The analytics system can also use combinations of machine learning techniques to train the models, which can include a multilayer perceptron. In some embodiments, the analytics system uses methylation information to train the models to determine predictions regarding disease state.

Abstract: Systems and methods for cancer subject tumor fraction estimation comprise obtaining a first plurality of nucleic acid fragment sequences from the subject's liquid biological sample. The first plurality of sequences represent cell-free nucleic acids in the liquid sample. A second plurality of nucleic acid fragment sequences is obtained from the subject's tumor sample. The second plurality of sequences represent nucleic acid molecules in the tumor. Smoothed noise rates, each determined using nucleic acid fragment sequences from non-cancer samples mapping to a corresponding allele position in a plurality of allele positions, are obtained. Variant allele counts and coverages are determined for the allele positions using the first plurality of sequences. Solid variant allele fractions are determined for the plurality of allele positions using the second plurality of sequences.

Abstract: A system and method for determining a presence of cancer in a test sample from a test subject comprising a set of fragments of deoxyribonucleic acid (DNA). The fragments may be identified through probabilistic analyses or identified when determined to be hypermethylated or hypomethylated. The system generates a test feature vector with a score for each CpG site for use in a trained model. The score is based on a number of the fragments in the test sample that overlap the CpG site. The system inputs the test feature vector into the trained model. The trained model has a function that generates a cancer prediction based on the test feature vector and a set of classification parameters. The cancer prediction for the test sample may include a cancer prediction value for each cancer type that describes a likelihood the test sample is of that particular cancer type.

Abstract: A method and system for determining one or more sources of a cell free deoxyribonucleic acid (cfDNA) test sample from a test subject. The cfDNA test sample contains a plurality of deoxyribonucleic acid (DNA) molecules with numerous CpG sites that may be methylated or unmethylated. A trained deconvolution model comprises a plurality of methylation parameters, including a methylation level at each CpG site for each source, and a function relating a sample vector as input and a source of origin prediction as output. The method generates a test sample vector comprising a site methylation metric relating to DNA molecules from the test sample that are methylated at that CpG site. The method inputs the test sample vector into the trained deconvolution model to generate a source of origin prediction indicating a predicted DNA molecule contribution of each source.

Abstract: Technical solutions for classifying patients with respect to multiple cancer classes are provided. The classification can be done using cell-free whole genome sequencing information from subjects. A reference set of subjects is used to train classifiers to recognize genomic markers that distinguish such cancer classes. The classifier training includes dividing the reference genome into a set of non-overlapping bins, applying a dimensionality reduction method to obtain a feature set, and using the feature set to train classifiers. For subjects with unknown cancer class, the trained classifiers provide probabilities or likelihoods that the subject has a respective cancer class for each cancer in a set of cancer classes. The present disclosure thus describes methods to improve the screening and detection of cancer class from among several cancer classes. This serves to facilitate early and appropriate treatment for subjects afflicted with cancer.

Abstract: Methods and systems for detecting positive, neutral, or negative selection at a locus include obtaining a test sample of cell-free nucleic acids from a subject, preparing a sequencing library of the cell-free nucleic acids, sequencing the library to obtain a plurality of sequence reads, analyzing the sequence reads to detect and quantify one or more somatic mutations at the locus, determining a selection coefficient for the locus, and comparing the selection coefficient with a threshold value to detect positive, neutral, or negative selection at the locus.