Abstract: Classification of cancer condition, in a plurality of different cancer conditions, for a species, is provided in which, for each training subject in a plurality of training subjects, there is obtained a cancer condition and a genotypic data construct including genotypic information for the respective training subject. Genotypic constructs are formatted into corresponding vector sets comprising one or more vectors. Vector sets are provided to a network architecture including a convolutional neural network path comprising at least a first convolutional layer associated with a first filter that comprise a first set of filter weights and a scorer. Scores, corresponding to the input of vector sets into the network architecture, are obtained from the scorer. Comparison of respective scores to the corresponding cancer condition of the corresponding training subjects is used to adjust the filter weights thereby training the network architecture to classify cancer condition.

Abstract: A method of classifying a liver disease by analyzing a DNA sample, wherein the DNA sample comprises cfDNA and/or blood cell DNA, the method comprising: obtaining the DNA sample; determining CpG methylation status at CpG sites of DNA molecules of the DNA sample; identifying a methylation pattern based on the CpG methylation status of the DNA molecules; assigning to the sample a liver disease classification based on the methylation pattern.

Abstract: A method of classifying a liver disease by analyzing a DNA sample, wherein the DNA sample comprises cfDNA and/or blood cell DNA, the method comprising: obtaining the DNA sample; determining CpG methylation status at CpG sites of DNA molecules of the DNA sample; identifying a methylation pattern based on the CpG methylation status of the DNA molecules; assigning to the sample a liver disease classification based on the methylation pattern.

Abstract: Classification of cancer condition, in a plurality of different cancer conditions, for a species, is provided in which, for each training subject in a plurality of training subjects, there is obtained a cancer condition and a genotypic data construct including genotypic information for the respective training subject. Genotypic constructs are formatted into corresponding vector sets comprising one or more vectors. Vector sets are provided to a network architecture including a convolutional neural network path comprising at least a first convolutional layer associated with a first filter that comprise a first set of filter weights and a scorer. Scores, corresponding to the input of vector sets into the network architecture, are obtained from the scorer. Comparison of respective scores to the corresponding cancer condition of the corresponding training subjects is used to adjust the filter weights thereby training the network architecture to classify cancer condition.

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: Described herein are methods of preparing dual-indexed nucleic acid libraries for methylation profiling using bisulfite conversion sequencing. In various embodiments, the methods use a two-step indexing process to tag bisulfite-treated DNA with unique molecular identifiers (UMIs).

Abstract: Classification of cancer condition, in a plurality of different cancer conditions, for a species, is provided in which, for each training subject in a plurality of training subjects, there is obtained a cancer condition and a genotypic data construct including genotypic information for the respective training subject. Genotypic constructs are formatted into corresponding vector sets comprising one or more vectors. Vector sets are provided to a network architecture including a convolutional neural network path comprising at least a first convolutional layer associated with a first filter that comprise a first set of filter weights and a scorer. Scores, corresponding to the input of vector sets into the network architecture, are obtained from the scorer. Comparison of respective scores to the corresponding cancer condition of the corresponding training subjects is used to adjust the filter weights thereby training the network architecture to classify cancer condition.

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: Described herein are methods of preparing dual-indexed nucleic acid libraries for methylation profiling using bisulfite conversion sequencing. In various embodiments, the methods use a two-step indexing process to tag bisulfite-treated DNA with unique molecular identifiers (UMIs).

Abstract: Classification of cancer condition, in a plurality of different cancer conditions, for a species, is provided in which, for each training subject in a plurality of training subjects, there is obtained a cancer condition and a genotypic data construct including genotypic information for the respective training subject. Genotypic constructs are formatted into corresponding vector sets comprising one or more vectors. Vector sets are provided to a network architecture including a convolutional neural network path comprising at least a first convolutional layer associated with a first filter that comprise a first set of filter weights and a scorer. Scores, corresponding to the input of vector sets into the network architecture, are obtained from the scorer. Comparison of respective scores to the corresponding cancer condition of the corresponding training subjects is used to adjust the filter weights thereby training the network architecture to classify cancer condition.

Abstract: Methods and systems for determining a subject's likelihood of responding to a treatment by assessing the subject's cell-free DNA (cfDNA) sample include receiving sequence data gathered from sequencing the cfDNA sample, generating a feature matrix of values that correspond to synonymous and nonsynonymous mutations detected in the sequence data, and predicting, based on analysis of the feature matrix at a TMB prediction model, a tumor mutational burden (TMB) for a tissue of interest at the subject. The predicted TMB is evaluated to determine whether a set of criteria indicating a likely response to treatment is met. The set of criteria can include criterion(s) that are met when the predicted TMB is high, when the predicted TMB corresponds to a predicted tumoral heterogeneity indicative of homogeneous tissue, when the predicted TMB corresponds to a tumor fraction indicative of a positive responder, or any combination thereof.

Abstract: A system generates a cancer detection panel. The system is configured to generate an assay having a minimized size and number of genomic regions while still detecting the presence of cancer at or above a specific performance threshold. To select the genomic regions for the panel, the system employs a classification model. The classification model receives a set of genomic regions that may be associated with disease presence. The model then determines a sensitivity score for each genomic region and ranks the regions according to their score. The sensitivity score is based on a likelihood that variations in the genomic region are indicative of cancer. The model then selects genomic regions for the panel based on their rank. The model only selects as many genomic indicators as are needed for desired detection performance. The genomic regions can be associated with solid or liquid cancers, viral regions, or cancer hotspots.

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 for determining whether a subject has a disease condition in a set of disease conditions are provided. The method includes obtaining a test dataset that comprises a first plurality of bin values obtained for a first plurality of bins collectively representing a first portion of a reference genome, and a second plurality of bin values obtained for a second plurality of bins collectively representing a second portion of the reference genome. The first and second plurality of bin values are derived from a targeted sequencing of a plurality of nucleic acids that are enriched using a plurality of probes. A plurality of copy number values are determined from the first and second plurality of bin values. The copy number values are inputted into a trained classifier, thereby determining whether the subject has a disease condition.

Abstract: Systems and methods for determining consensus base calls in nucleic acid sequencing are provided. A sequencing dataset is obtained corresponding to a plurality of base reads for a first base position within a plurality of base positions of a target nucleic acid molecule. The sequencing dataset includes at least two features, for each base read of the plurality of base reads. The at least two features are selected from among the features: a nucleotide base, a read quality score, a strand identifier, a trinucleotide context of the base read, and a confidence score associated with the trinucleotide context. The sequencing dataset is transformed into a feature tensor representing a distribution of the plurality of features in the sequencing dataset. The feature tensor is assessed with a classifier to determine a consensus base call for the first base position. The consensus base call comprises a predicted nucleotide base.

Abstract: Systems and methods for determining a tumor fraction for a subject are provided. A plurality of bin values is obtained. Each respective bin value in the plurality of bin values corresponds to a bin in a plurality of bins. Each bin represents a corresponding region of a reference genome. The plurality of bin values is derived from a first biological sample of the subject. A plurality of copy number values is determined at least in part from the plurality of bins values. A plurality of allele frequencies for a plurality of alleles is derived from a second biological sample of the subject. At least the plurality of copy number values and the plurality of allele frequencies, or a plurality of features derived therefrom, are applied to a reference model, thereby determining the tumor fraction of the subject.

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: Classification of cancer condition, in a plurality of different cancer conditions, for a species, is provided in which, for each training subject in a plurality of training subjects, there is obtained a cancer condition and a genotypic data construct including genotypic information for the respective training subject. Genotypic constructs are formatted into corresponding vector sets comprising one or more vectors. Vector sets are provided to a network architecture including a convolutional neural network path comprising at least a first convolutional layer associated with a first filter that comprise a first set of filter weights and a scorer. Scores, corresponding to the input of vector sets into the network architecture, are obtained from the scorer. Comparison of respective scores to the corresponding cancer condition of the corresponding training subjects is used to adjust the filter weights thereby training the network architecture to classify cancer condition.

Abstract: A predictive cancer model generates a cancer prediction for an individual of interest by analyzing values of one or more types of features that are derived from cfDNA obtained from the individual. Specifically, cfDNA from the individual is sequenced to generate sequence reads using one or more physical assays, examples of which include a small variant sequencing assay, whole genome sequencing assay, and methylation sequencing assay. The sequence reads of the physical assays are processed through corresponding computational analyses to generate each of small variant features, whole genome features, and methylation features. The values of features can be provided to a predictive cancer model that generates a cancer prediction. In some embodiments, the values of different types of features can be separately provided into different predictive models. Each separate predictive model can output a score that can serve as input into an overall model that outputs the cancer prediction.

Abstract: A system, method and computer program product for analyzing data of high dimensionality (e.g., sequence reads of nucleic acid samples in connection with a disease condition) are provided.