Methods and systems for selecting bait set targets

Abstract

Methods for selecting bait set targets are described. The methods may comprise, for example, selecting bait set targets based on determining similarity likelihoods for genomic regions, selecting bait set targets based on determining separability metrics for genomic regions, selecting bait set targets based on machine learning models for genomic regions, selecting bait set targets from CpG nucleotides, and / or selecting bait set targets based on hypermethylated regions in healthy subjects. The selected bait set targets based on any of the methods described herein can be combined with the selected bait set targets based on any of one or more other methods also described herein.

Description

METHODS AND SYSTEMS FOR SELECTING BAIT SET TARGETSCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the priority benefit of United States Provisional Patent Application Serial No. 63 / 610,889, filed December 15, 2023, the contents of which are incorporated herein by reference in its entirety.FIELD OF THE INVENTION

[0002] The present disclosure relates generally to methods and systems for selecting bait set targets based on genomic profiling data, and more specifically to methods and systems for selecting bait set targets based on methylation values from the genomic profiling data. Also described are methods of making a bait set and methods of using a bait set, including for targeted capture of nucleic acid molecules from a sample and targeted sequencing.BACKGROUND

[0003] Genomic nucleic acid molecules (e.g., DNA molecules) including derivatives of genomic nucleic acids (e.g., cell free nucleic acids or cell free DNA (cfDNA)) can be methylated. Some genomic loci (e.g., nucleotides and / or regions) tend to be differentially methylated among subjects with disease, (e.g., cancer, autoimmune diseases, metabolic disorders, neurological disorders, aging, and cardiovascular disease), relative to subjects without the disease (Jin and Liu (2018), “DNA methylation in human diseases”, Genes and Diseases 5(1): 1-8). Identifying such differentially methylated loci, however, is challenging. The challenge is heightened when selecting from the differentially methylated loci, loci that are differentially methylated at statistically confident levels, because each locus can have distinct individual propensities for being methylated. Improved methods are needed for selecting differentially methylated loci. The differentially methylated loci can be used to design sequencing bait sets, and the use of such bait sets can be relevant for generating an appropriate bait set for diagnosing and treating disease. The improved methods would improve the overall efficiency when both designing and using sequencing bait sets.BRIEF SUMMARY OF THE INVENTION

[0004] Disclosed herein are methods and systems for selecting bait set targets for designing a bait set. When analyzing, e.g., cell free DNA in relatively low quantities (which is often the case for non-tissue samples that are collected from patients in early stages of a disease), it can be increasingly difficult to determine which regions are most important to capture without sequencing the entire genome. Given the demanding resources that can be required to sequence the entire genome of the patient, those skilled in the art are faced with using enrichment techniques that can include a trade-off between the number of regions that are sequenced and the depth at which those regions are sequenced. This can be accomplished through either known hybrid capture (baits) or polymerase chain reaction (PCR) techniques that may use, e.g., a specific set of primers. The problem with these techniques is that the regions to be targeted must be known beforehand. Knowing which regions have the highest likelihood of providing meaningful information can have significant advantages to the overall sensitivity and specificity of the assay. In particular, in the case of epigenetic analysis, identifying which CpG regions are the most likely to exhibit meaningful methylation pattern information can be exceedingly difficult. Traditional methods for identifying bait sets or primer panels have suffered from statistical noise. Such noise can compromise the selecting of reliable bait set targets that can serve as biomarkers of disease. For example, multiple variables not limited to the indication of a disease, e.g., the age or sex of the subject, can affect whether a DNA nucleotide is methylated or not for the subject. Given the statistical noise associated with the methylation of DNA, the methods described herein select bait set targets based on stringent statistical criteria.

[0005] The results of any of the different bait set target selection methods described herein can be combined to generate a combined bait target set. A first exemplary bait target selection process is based on determining a similarity likelihood between the genomic regions of two groups, e.g., a disease group and a non-disease group, and selecting the genomic regions that are highly differentially methylated, according to the determined similarity likelihood (i.e., a low similarity likelihood). A second exemplary bait target selection process is based on determining separability metrics between the genomic regions of sets of groups, e.g., a cancer tissue group and a healthy tissue group versus a healthy plasma and healthy tissue group and selecting the genomic regions that are highly differentially methylated across any of the two sets of groups,according to the separability metrics. A third exemplary bait target selection process is based on using multiple machine learning models to generate from genomic regions, multiple lists of genomic regions. The multiple lists of genomic regions are then filtered, based on a separability metric computed for each list. Genomic regions that are present above some threshold number across the remaining lists are then selected. A fourth exemplary bait target selection process uses as input, nucleotide sites, such as CpG sites, that can, for example, be sparsely distributed across the genome (e.g., CpG sites from LI regions). CpG sites from disease and non-disease groups are compared, by computing for each comparison, similarity likelihoods (e.g., a Kolmogorov- Smirnov test statistic or a p-value from binomial significance testing) and separability metrics. CpG sites that are highly differential between disease and non-disease groups can be combined with CpG sites that are not differential between different non-disease groups, to select informative CpG sites. The informative CpG sites can be clustered together to derive genomic regions that can be used as bait set targets. A fifth exemplary bait target selection process identifies genomic regions that are highly methylated in healthy samples, according to a central tendency measure (e.g., a mean difference) exceeding some threshold. Additional statistical criteria can be applied for each of the exemplary bait target selection processes described herein.

[0006] For any of the described bait target selection processes, the selected genomic regions can be used as bait set targets. The bait set targets selected based on any of the methods can be combined to form a final set of bait set targets. A bait set, e.g., a sequencing bait set, can be designed based on the final set of bait set targets. The combining of bait set targets selected based on the described selection methods ensures the designing of a complete set of targets. Some targets may be difficult to select based on the analyses used by some selection methods, and thus, by combining bait set targets from diverse selection methods, a comprehensive set of targets is assembled. The methods described herein allow for the ability to detect disease signals without sequencing entire genomes or large swaths of the genome, by instead sequencing only the bait set targets that have been carefully selected by the methods.

[0007] In some aspects, disclosed herein is a method comprising: providing a plurality of nucleic acid molecules obtained from one or more samples from one or more subjects for a first group or from one or more samples from one or more subjects for a second group; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules;amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, by one or more processors, sequence read data for the plurality of sequence reads; determining, by the one or more processors, first methylation values corresponding to the sequence read data for a genomic region from the one or more samples for the first group; determining, by the one or more processors, second methylation values corresponding to the sequence read data for the genomic region from the one or more samples for the second group; determining from the first methylation values and the second methylation values, by the one or more processors, at least one similarity likelihood for the genomic region; and selecting, by the one or more processors, a bait set target corresponding to the genomic region based on the determined similarity likelihood being less than a predetermined first threshold.

[0008] In some embodiments, the method can further comprise determining, by the one or more processors, a fraction of significant samples for the genomic region, wherein the fraction of significant samples for the genomic region is a number of samples in the first group for which the similarity likelihood for the genomic region is less than the predetermined first threshold, normalized by the total number of samples in the first group; and selecting, by the one or more processors, the bait set target corresponding to the genomic region based on the fraction of significant samples for the genomic region exceeding a predetermined second threshold.

[0009] In any of the embodiments herein, the method can further comprise determining, by the one or more processors, a mean difference between the first methylation values and the second methylation values; and removing, by the one or more processors, the genomic region, based on the mean difference being less than a predetermined third threshold. In any of the embodiments herein, the sequence read data for the genomic region from one or more samples in the first group comprise sequence read data related to a disease.

[0010] In some aspects, disclosed herein is a method comprising: providing a plurality of nucleic acid molecules obtained from one or more samples from one or more subjects for one or more first groups or from one or more samples from one or more subjects for one or more second groups; ligating one or more adapters onto one or more nucleic acid molecules from the pluralityof nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, by one or more processors, sequence read data for the plurality of sequence reads; determining, by the one or more processors, first methylation values corresponding to the sequence read data for a genomic region from one or more samples for one or more first groups; determining, by the one or more processors, second methylation values corresponding to the sequence read data for the genomic region from one or more samples for one or more second groups; determining, by the one or more processors, from the first methylation values and the second methylation values, one or more separability metrics for the genomic region; and selecting, by the one or more processors, a bait set target corresponding to the genomic region based on the determined one or more separability metrics exceeding a predetermined first threshold.

[0011] In some embodiments, the method can further comprise determining, by the one or more processors, a mean difference from the first methylation values and the second methylation values; and removing the genomic region, by the one or more processors, based on the determined mean difference being less than a predetermined second threshold.

[0012] In some aspects, disclosed herein is a method comprising: providing a plurality of nucleic acid molecules obtained from one or more samples; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, by one or more processors, sequence read data for the plurality of sequence reads; receiving, by the one or more processors, one or more methylation datasets based on the sequence read data, wherein a methylation dataset in the one or more methylation datasets comprises genomic regions and their corresponding methylation values; ranking, by the one or more processors, using one or more machine learning models, the genomic regions and their corresponding methylation values, for the one or moremethylation datasets, to generate one or more lists of ranked genomic regions; determining, by the one or more processors, one or more separability metrics for the one or more lists of ranked genomic regions; selecting one or more informative lists of ranked genomic regions from the one or more lists of ranked genomic regions, by the one or more processors, based on the one or more separability metrics exceeding a predetermined separability threshold; selecting informative genomic regions from the one or more informative lists of ranked genomic regions, by the one or more processors, based on a frequency of the informative genomic regions exceeding a predetermined frequency threshold; and selecting first bait set targets, by the one or more processors, wherein the first bait set targets correspond to the informative genomic regions.

[0013] In some embodiments, the method can further comprise selecting further informative genomic regions from a plurality of informative genomic regions comprising the informative genomic regions, by the one or more processors, based on the further informative genomic regions being in at least a portion of the plurality of informative genomic regions; and selecting second bait set targets, by the one or more processors, wherein the second bait set targets correspond to the further informative genomic regions.

[0014] In some aspects, disclosed herein is a method comprising: providing a plurality of nucleic acid molecules obtained from one or more samples from one or more subjects for one or more first groups or from one or more samples from one or more subjects for one or more second groups; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, by one or more processors, sequence read data for the plurality of sequence reads; determining, by the one or more processors, disease methylation values corresponding to the sequence read data for CpG sites, for one or more samples in a disease group; determining, by the one or more processors, first non-disease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a first nondisease group; determining from the disease methylation values and the first non-disease methylation values, by the one or more processors, one or more first similarity likelihoods forthe CpG sites; and selecting from the CpG sites, disease CpG sites corresponding to the one or more first similarity likelihoods, by the one or more processors, based on the one or more first similarity likelihoods exceeding or being less than a predetermined first similarity likelihood threshold.

[0015] In some embodiments, the method can further comprise determining, by the one or more processors, from the disease methylation values and the first non-disease methylation values, one or more separability metrics for the disease CpG sites; and selecting informative disease CpG sites from the CpG sites or the disease CpG sites, by the one or more processors, based on the determined one or more separability metrics exceeding a predetermined separability metric threshold.

[0016] In any of the embodiments herein, the method can further comprise determining, by the one or more processors, a mean difference from the disease methylation values and the first nondisease methylation values; and selecting further informative disease CpG sites from the CpG sites, the disease CpG sites, or the informative disease CpG sites, by the one or more processors, based on the determined mean difference exceeding a predetermined mean difference threshold.

[0017] In any of the embodiments herein, the method can further comprise: determining, by the one or more processors, second non-disease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a second non-disease group; determining from the first non-disease methylation values and second non-disease methylation values, by the one or more processors, one or more second similarity likelihoods for the CpG sites; and selecting informative CpG sites, from the CpG sites, the disease CpG sites, the informative disease CpG sites, or the further informative disease CpG sites, based on the one or more second similarity likelihoods being less than a predetermined second similarity likelihood threshold.

[0018] In any of the embodiments herein, the method can further comprise clustering, by the one or more processors, informative CpG sites based on any two informative CpG sites being within a predetermined distance from one another, and that within the predetermined distance, at least four informative CpG sites are found, to generate one or more clusters; and selecting, by the one or more processors, bait set targets corresponding to the one or more clusters.

[0019] In some aspects, disclosed herein is a method comprising: providing a plurality of nucleic acid molecules obtained from one or more samples of one or more subjects; ligating one or moreadapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, by one or more processors, sequence read data for the plurality of sequence reads; determining, by the one or more processors, one or more methylation values corresponding to the sequence read data for a genomic region from one or more healthy samples; determining, by the one or more processors, a central tendency measure from the one or more methylation values; and selecting, by the one or more processors, a bait set target corresponding to the genomic region based on the determined central tendency measure exceeding a predetermined threshold.

[0020] In some embodiments, the central tendency measure can be the mean, median, or the mode. In any of the embodiments herein, the genomic region can be hypermethylated in the one or more healthy samples, relative to one or more disease samples.

[0021] In some aspects, disclosed herein is a method for selecting bait set targets, comprising: determining, by one or more processors, first methylation values corresponding to sequence read data for a genomic region from one or more samples in a first group; determining, by the one or more processors, second methylation values corresponding to sequence read data for the genomic region from one or more samples in a second group; determining from the first methylation values and the second methylation values, by the one or more processors, at least one similarity likelihood for the genomic region; and selecting, by the one or more processors, a bait set target corresponding to the genomic region based on the determined similarity likelihood being less than a predetermined first threshold.

[0022] In some embodiments, the method can further comprise: determining, by the one or more processors, a fraction of significant samples for the genomic region, wherein the fraction of significant samples for the genomic region is a number of samples in the first group for which the similarity likelihood for the genomic region is less than the predetermined first threshold, normalized by the total number of samples in the first group; and selecting, by the one or more processors, the bait set target corresponding to the genomic region based on the fraction of significant samples for the genomic region exceeding a predetermined second threshold.

[0023] In any of the embodiments herein, the method can further comprise determining, by the one or more processors, a mean difference between the first methylation values and the second methylation values; and removing, by the one or more processors, the genomic region, based on the mean difference being less than a predetermined third threshold.

[0024] In any of the embodiments herein, the sequence read data for the genomic region from one or more samples in the first group can comprise sequence read data related to a disease. In some embodiments, the disease can be a cancer.

[0025] In any of the embodiments herein, the sequence read data for the genomic region from one or more samples in the second group can comprise panel of normal read count data. In any of the embodiments herein, the similarity likelihood is determined by a Kolmogorov-Smirnov testing.

[0026] In some aspects, disclosed herein is a method for selecting bait set targets, comprising: determining, by one or more processors, first methylation values corresponding to sequence read data for a genomic region from one or more samples for one or more first groups; determining, by the one or more processors, second methylation values corresponding to sequence read data for the genomic region from one or more samples for one or more second groups; determining, by the one or more processors, from the first methylation values and the second methylation values, one or more separability metrics for the genomic region; and selecting, by the one or more processors, a bait set target corresponding to the genomic region based on the determined one or more separability metrics exceeding a predetermined first threshold.

[0027] In some embodiments, the method can further comprise determining, by the one or more processors, a mean difference from the first methylation values and the second methylation values; and removing the genomic region, by the one or more processors, based on the determined mean difference being less than a predetermined second threshold. In any of the embodiments herein, the method can further comprise the one or more first groups comprise a group comprising cancer tissue samples, or a group comprising healthy tissue samples. In any of the embodiments herein, the method can further comprise the one or more second groups comprise a group comprising healthy plasma samples, or a group comprising healthy tissue samples. In any of the embodiments herein, the separability metric can be an Area Under the Curve (AUC) score.

[0028] In some aspects, disclosed herein is a method for selecting bait set targets for one or more samples, comprising: receiving, by one or more processors, one or more methylation datasets, wherein a methylation dataset in the one or more methylation datasets comprises genomic regions and their corresponding methylation values; ranking, by the one or more processors, using one or more machine learning models, the genomic regions and their corresponding methylation values, for the one or more methylation datasets, to generate one or more lists of ranked genomic regions; determining, by the one or more processors, one or more separability metrics for the one or more lists of ranked genomic regions; selecting one or more informative lists of ranked genomic regions from the one or more lists of ranked genomic regions, by the one or more processors, based on the one or more separability metrics exceeding a predetermined separability threshold; selecting informative genomic regions from the one or more informative lists of ranked genomic regions, by the one or more processors, based on a frequency of the informative genomic regions exceeding a predetermined frequency threshold; and selecting first bait set targets, by the one or more processors, wherein the first bait set targets correspond to the informative genomic regions.

[0029] In some embodiments, the method can further comprise: selecting further informative genomic regions from a plurality of informative genomic regions comprising the informative genomic regions, by the one or more processors, based on the further informative genomic regions being in at least a portion of the plurality of informative genomic regions; and selecting second bait set targets, by the one or more processors, wherein the second bait set targets correspond to the further informative genomic regions.

[0030] In any of the embodiments herein, the ranking can be based on an informativeness of the genomic regions and their corresponding methylation values. In some embodiments, the informativeness can be based on the Gini score. In any of the embodiments herein, the ranking, using the one or more machine learning models, comprises k-fold cross-validation or repeated k- fold cross-validation. In any of the embodiments herein, the one or more machine learning models comprise a random forest model, a support vector machine model, or a generalized linear model.

[0031] In some aspects, disclosed herein is a method for selecting bait set targets, comprising: determining, by one or more processors, disease methylation values corresponding to sequenceread data for CpG sites, for one or more samples in a disease group; determining, by the one or more processors, first non-disease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a first non-disease group; determining from the disease methylation values and the first non-disease methylation values, by the one or more processors, one or more first similarity likelihoods for the CpG sites; and selecting from the CpG sites, disease CpG sites corresponding to the one or more first similarity likelihoods, by the one or more processors, based on the one or more first similarity likelihoods exceeding or being less than a predetermined first similarity likelihood threshold.

[0032] In some embodiments, the method can further comprise determining, by the one or more processors, from the disease methylation values and the first non-disease methylation values, one or more separability metrics for the disease CpG sites; and selecting informative disease CpG sites from the CpG sites or the disease CpG sites, by the one or more processors, based on the determined one or more separability metrics exceeding a predetermined separability metric threshold.

[0033] In any of the embodiments herein, the method can further comprise determining, by the one or more processors, a mean difference from the disease methylation values and the first non- disease methylation values; and selecting further informative disease CpG sites from the CpG sites, the disease CpG sites, or the informative disease CpG sites, by the one or more processors, based on the determined mean difference exceeding a predetermined mean difference threshold.

[0034] In any of the embodiments herein, the method can further comprise: determining, by the one or more processors, second non-disease methylation values corresponding to sequence read data for CpG sites in a region, for one or more samples in a second non-disease group; determining from the first non-disease methylation values and second non-disease methylation values, by the one or more processors, one or more second similarity likelihoods for the CpG sites; and selecting informative CpG sites, from the CpG sites, the disease CpG sites, the informative disease CpG sites, or the further informative disease CpG sites, based on the one or more second similarity likelihoods being less than a predetermined second similarity likelihood threshold; In any of the embodiments herein, the method can further comprise: clustering, by the one or more processors, informative CpG sites based on any two informative CpG sites being within a predetermined distance from one another, and that within the predetermined distance, atleast four informative CpG sites are found, to generate one or more clusters; and selecting, by the one or more processors, bait set targets corresponding to the one or more clusters. In any of the embodiments herein, the CpG sites can be from LI genomic regions.

[0035] In any of the embodiments herein, the one or more first similarity likelihoods can be determined by statistical testing. In any of the embodiments herein, the one or more second similarity likelihoods can be determined by statistical testing. In any of the embodiments herein, the statistical testing can be a binomial significance testing. In any of the embodiments herein, the statistical testing can be a Kolmogorov-Smirnov testing. In any of the embodiments herein, the one or more samples in the disease group can have cancer. In any of the embodiments herein, the sequences read data for the CpG sites for the one or more samples in the first nondisease group can comprise panel of normal read count data. In any of the embodiments herein, the sequence read data for the CpG sites for the one or more samples in the first non-disease group can be from healthy plasma or healthy tissue. In any of the embodiments herein, the sequence read data for the CpG sites for the one or more samples in the second non-disease group can comprise panel of normal read count data. In any of the embodiments herein, the sequence read data for the CpG sites for the one or more samples in the second non-disease group are from healthy plasma or healthy tissue.

[0036] In some aspects, disclosed herein is a method for selecting bait set targets, comprising: determining, by one or more processors, one or more methylation values corresponding to sequence read data for a genomic region from one or more healthy samples; determining, by the one or more processors, a central tendency measure from the one or more methylation values; and selecting, by the one or more processors, a bait set target corresponding to the genomic region based on the determined central tendency measure exceeding a predetermined threshold. In some embodiments, the central tendency measure can be the mean, median, or the mode. In any of the embodiments herein, the genomic region can be hypermethylated in the one or more healthy samples, relative to one or more disease samples.

[0037] In any of the embodiments herein, the method can further comprise: selecting a second set of bait set targets, comprising: determining, by one or more processors, first methylation values corresponding to sequence read data for a genomic region from one or more samples for one or more first groups; determining, by the one or more processors, second methylation valuescorresponding to sequence read data for the genomic region from one or more samples for one or more second groups; determining, by the one or more processors, from the first methylation values and the second methylation values, one or more separability metrics for the genomic region; and selecting, by the one or more processors, a bait set target corresponding to the genomic region based on the determined one or more separability metrics exceeding a predetermined first threshold.

[0038] In any of the embodiments herein, selecting a third set of bait set targets, comprising: receiving, by one or more processors, one or more methylation datasets, wherein a methylation dataset in the one or more methylation datasets comprises genomic regions and their corresponding methylation values; ranking, by the one or more processors, using one or more machine learning models, the genomic regions and their corresponding methylation values, for the one or more methylation datasets, to generate one or more lists of ranked genomic regions; determining, by the one or more processors, one or more separability metrics for the one or more lists of ranked genomic regions; selecting one or more informative lists of ranked genomic regions from the one or more lists of ranked genomic regions, by the one or more processors, based on the one or more separability metrics exceeding a predetermined separability threshold; selecting informative genomic regions from the one or more informative lists of ranked genomic regions, by the one or more processors, based on a frequency of the informative genomic regions exceeding a predetermined frequency threshold; and selecting first bait set targets, by the one or more processors, wherein the first bait set targets correspond to the informative genomic regions.

[0039] In any of the embodiments herein, selecting a fourth set of bait set targets, comprising: determining, by one or more processors, disease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a disease group; determining, by the one or more processors, first non-disease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a first non-disease group; determining from the disease methylation values and the first non-disease methylation values, by the one or more processors, one or more first similarity likelihoods for the CpG sites; and selecting from the CpG sites, disease CpG sites corresponding to the one or more first similarity likelihoods, by the one or more processors, based on the one or more first similarity likelihoods exceeding or being less than a predetermined first similarity likelihood threshold.

[0040] In any of the embodiments herein, the method can further comprise selecting a fifth set of bait set targets, comprising: determining, by one or more processors, one or more methylation values corresponding to sequence read data for a genomic region from one or more healthy samples; determining, by the one or more processors, a central tendency measure from the one or more methylation values; and selecting, by the one or more processors, a bait set target corresponding to the genomic region based on the determined central tendency measure exceeding a predetermined threshold. In any of the embodiments herein, a methylation value in the methylation values can be a methylation fraction value or a methylation entropy value. In some embodiments, the methylation fraction value can be a number of sequence reads that are methylated at a nucleotide site normalized by a total number of sequence reads at the nucleotide site. In some embodiments, the methylation entropy value, H, is determined by:H = ~p log2p - q log2q wherein: p is the number of sequence reads that are methylated at a nucleotide site normalized by a total number of sequence reads at the nucleotide site; and q is the number of sequence reads that are unmethylated at a nucleotide site normalized by a total number of sequence reads at the nucleotide site, and can be expressed as 1 — p.

[0041] In some aspects, disclosed herein is a method for selecting a final set of bait set targets comprising combining at least the bait set target selected from using the methods of any of the embodiments herein. In some aspects, disclosed herein is a method for selecting the final set of bait set targets comprising combining at least the first bait set targets selected from using the methods of any of the embodiments herein. In some aspects, disclosed herein is a method for selecting the final set of bait set targets comprising combining at least the second bait set targets selected from using the methods of any of the embodiments herein. In some aspects, disclosed herein is a method for selecting the final set of bait set targets comprising combining at least the bait set targets from using the methods of any of the embodiments herein. In any of the embodiments herein, the method can further comprise designing a final bait set corresponding to the final set of bait set targets. In some embodiments, the method can further comprise generating the final bait set based on the designed final bait set. In any of the embodiments herein, the final set of bait set targets can comprise a single bait set target.

[0042] In any of the embodiments herein, the final set of bait set targets can be used to determine a disease signal in the subject, wherein the disease signal is indicative of a disease status in the subject. In some embodiments, the disease signal can be indicative of the cancer, Alzheimer’s Disease, type 2 diabetes, cardiovascular disease, autoimmune diseases, or any combination thereof. In any of the embodiments herein, the one or more subjects are suspected of having or are determined to have cancer. In some embodiments, the cancer is a B cell cancer (multiple myeloma), a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, nonHodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell cancer, essential thrombocythemia, agnogenic myeloidmetaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, or a carcinoid tumor.

[0043] In some embodiments, the cancer can comprise acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed / amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR / MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, a diffuse large B-cell lymphoma, fallopian tube cancer, a follicular B-cell nonHodgkin lymphoma, a follicular lymphoma, gastric cancer, gastric cancer (HER2+), gastroesophageal junction (GEJ) adenocarcinoma, a gastrointestinal stromal tumor, a gastrointestinal stromal tumor (KIT+), a giant cell tumor of the bone, a glioblastoma, granulomatosis with polyangiitis, a head and neck squamous cell carcinoma, a hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, lupus erythematosus, a mantle cell lymphoma, medullary thyroid cancer, melanoma, a melanoma with a BRAF V600 mutation, a melanoma with a BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman's disease, multiple hematologic malignancies including Philadelphia chromosomepositive ALL and CML, multiple myeloma, myelofibrosis, a non-Hodgkin’s lymphoma, a nonresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, a nonsmall cell lung cancer, a non-small cell lung cancer (ALK+), a non-small cell lung cancer (PD- L1+), a non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), a non-small cell lung cancer (with BRAF V600E mutation), a non-small cell lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a non-small cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cellcarcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSI-H / dMMR), a squamous cell cancer of the head and neck, a squamous non-small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.

[0044] In some embodiments, the method can further comprise treating the one or more subjects with an anti-cancer therapy. In some embodiments, the anti-cancer therapy can comprise a targeted anti-cancer therapy. In some embodiments, the targeted anti-cancer therapy can comprise abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado- trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab- vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta), axitinib (Inlyta), belantamab mafodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), belzutifan (Welireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), brigatinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabometyx, Cometriq), canakinumab (Haris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab-rwlc (Libtayo), ceritinib (LDK378 / Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), copanlisib hydrochloride (Aliqopa), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro), daratumumab (Darzalex), daratumumab and hyaluronidase-fihj (Darzalex Faspro), darolutamide (Nubeqa), dasatinib (Sprycel), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib mesylate (Idhifa), encorafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide (Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedratinib hydrochloride (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinibmesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), iobenguane 1131 (Azedra), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu 177-dotatate (Lutathera), margetuximab-cmkb (Margenza), midostaurin (Rydapt), mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasudotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazyva), ofatumumab (Arzerra), olaparib (Lynparza), olaratumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), panobinostat (Farydak), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralsetinib (Gavreto), radium 223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), ripretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), rucaparib camsylate (Rubraca), ruxolitinib phosphate (Jakafi), sacituzumab govitecan-hziy (Trodelvy), seliciclib, selinexor (Xpovio), selpercatinib (Retevmo), selumetinib sulfate (Koselugo), siltuximab (Sylvant), sipuleucel-T (Provenge), sirolimus proteinbound particles (Fyarro), sonidegib (Odomzo), sorafenib (Nexavar), sotorasib (Lumakras), sunitinib (Sutent), tafasitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), talazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), tebentafusp-tebn (Kimmtrak), temsirolimus (Torisel), tepotinib hydrochloride (Tepmetko), tisagenlecleucel (Kymriah), tisotumab vedotin-tftv (Tivdak), tocilizumab (Actemra), tofacitinib (Xeljanz), tositumomab (Bexxar), trametinib (Mekinist), trastuzumab (Herceptin), tretinoin (Vesanoid), tivozanib hydrochloride (Fotivda), toremifene (Fareston), tucatinib (Tukysa), umbralisib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), vismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv-aflibercept (Zaltrap), or any combination thereof.

[0045] In any of the embodiments herein, the method can further comprise obtaining the one or more samples from the one or more subjects. In any of the embodiments herein, the one or more samples can comprise a tissue biopsy sample, a liquid biopsy sample, or a normal control. In some embodiments, the one or more samples is a liquid biopsy sample and can comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva. In some embodiments, the one or more samples is a liquid biopsy sample and can comprise circulating tumor cells (CTCs). In some embodiments, the one or more samples is a liquid biopsy sample and comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof. In any of the embodiments herein, the plurality of nucleic acid molecules can comprise a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules. In some embodiments, the tumor nucleic acid molecules can be derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample. In some embodiments, the sample can comprise a liquid biopsy sample, and wherein the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.

[0046] In any of the embodiments herein, the one or more adapters can comprise amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences. In any of the embodiments herein, the captured nucleic acid molecules are captured from the amplified nucleic acid molecules by hybridization to one or more bait molecules. In some embodiments, the one or more bait molecules can comprise one or more nucleic acid molecules, each comprising a region that is complementary to a region of a captured nucleic acid molecule. In any of the embodiments herein, amplifying nucleic acid molecules comprises performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique. In any of the embodiments herein, the sequencing can comprise use of a massively parallel sequencing (MPS) technique, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, direct sequencing, or Sanger sequencing technique. In some embodiments, the sequencing can comprise massively parallel sequencing, and the massively parallel sequencing technique comprises next generation sequencing (NGS).In any of the embodiments herein, the sequencer can comprise a next generation sequencer. In any of the embodiments herein, one or more of the pluralities of sequencing reads overlap one or more gene loci within one or more subgenomic intervals in the sample.

[0047] In some embodiments, the one or more gene loci can comprise between 10 and 20 loci, between 10 and 40 loci, between 10 and 60 loci, between 10 and 80 loci, between 10 and 100 loci, between 10 and 150 loci, between 10 and 200 loci, between 10 and 250 loci, between 10 and 300 loci, between 10 and 350 loci, between 10 and 400 loci, between 10 and 450 loci, between 10 and 500 loci, between 20 and 40 loci, between 20 and 60 loci, between 20 and 80 loci, between 20 and 100 loci, between 20 and 150 loci, between 20 and 200 loci, between 20 and 250 loci, between 20 and 300 loci, between 20 and 350 loci, between 20 and 400 loci, between 20 and 500 loci, between 40 and 60 loci, between 40 and 80 loci, between 40 and 100 loci, between 40 and 150 loci, between 40 and 200 loci, between 40 and 250 loci, between 40 and 300 loci, between 40 and 350 loci, between 40 and 400 loci, between 40 and 500 loci, between 60 and 80 loci, between 60 and 100 loci, between 60 and 150 loci, between 60 and 200 loci, between 60 and 250 loci, between 60 and 300 loci, between 60 and 350 loci, between 60 and 400 loci, between 60 and 500 loci, between 80 and 100 loci, between 80 and 150 loci, between 80 and 200 loci, between 80 and 250 loci, between 80 and 300 loci, between 80 and 350 loci, between 80 and 400 loci, between 80 and 500 loci, between 100 and 150 loci, between 100 and 200 loci, between 100 and 250 loci, between 100 and 300 loci, between 100 and 350 loci, between 100 and 400 loci, between 100 and 500 loci, between 150 and 200 loci, between 150 and 250 loci, between 150 and 300 loci, between 150 and 350 loci, between 150 and 400 loci, between 150 and 500 loci, between 200 and 250 loci, between 200 and 300 loci, between 200 and 350 loci, between 200 and 400 loci, between 200 and 500 loci, between 250 and 300 loci, between 250 and 350 loci, between 250 and 400 loci, between 250 and 500 loci, between 300 and 350 loci, between 300 and 400 loci, between 300 and 500 loci, between 350 and 400 loci, between 350 and 500 loci, or between 400 and 500 loci.

[0048] 92. The method of claim 90 or 91, wherein the one or more gene loci comprise ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4,BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA, CHEK1, CHEK2, CIC, CREBBP, CRKL, CSF1R, CSF3R, CTCF, CTNNA1, CTNNB1, CUL3, CUL4A, CXCR4, CYP17A1, DAXX, DDR1, DDR2, DIS3, DNMT3A, D0T1L, EED, EGFR, EMSY (Cllorf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FECN, FET1, FET3, FOXE2, FUBP1, GABRA6, GATA3, GATA4, GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, IDH1, IDH2, IGF1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KEF, KIT, KEHE6, KMT2A (MEE), KMT2D (MLL2), KRAS, LTK, LYN, MAF, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K13, MAPK1, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MET, MITF, MKNK1, MLH1, MPL, MRE11A, MSH2, MSH3, MSH6, MST1R, MTAP, MTOR, MUTYH, MYB, MYC, MYCL, MYCN, MYD88, NBN, NF1, NF2, NFE2L2, NFKBIA, NKX2-1, NOTCH1, NOTCH2, NOTCH3, NPM1, NRAS, NT5C2, NTRK1, NTRK2, NTRK3, NUTM1, P2RY8, PALB2, PARK2, PARP1, PARP2, PARP3, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, SOCS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, ZNF703, or any combination thereof.

[0049] In any of the embodiments herein, the one or more gene loci can comprise ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319,CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1-3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRp, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.

[0050] In any of the embodiments herein, the method can further comprise generating, by the one or more processors, a report indicating the final set of bait set targets. In some embodiments, the method can further comprise transmitting the report to a healthcare provider. In some embodiments, the report is transmitted via a computer network or a peer-to-peer connection. In some aspects, disclosed herein is a method for diagnosing a disease, the method comprising diagnosing a disease based on the final set of bait set targets, wherein the final set of bait set targets is determined according to the method of any of the embodiments disclosed herein.

[0051] In some aspects, disclosed herein is a method of selecting an anti-cancer therapy, the method comprising: responsive to results from the final set of bait set targets, selecting an anticancer therapy for the one or more subjects, wherein the final set of bait set targets are determined according to the method of any of embodiments disclosed herein. In some aspects, disclosed herein is method of treating a cancer in one or more subjects, comprising: responsive to results from the final set of bait set targets, administering an effective amount of anti-cancer therapy to the one or more subjects, wherein the final set of bait set targets are determined according to the method of any of the embodiments disclosed herein.

[0052] In some aspects, disclosed herein is a method for monitoring cancer progression or recurrence in a subject, the method comprising: determining first results from the final set of bait set targets in a first sample obtained from the subject at a first time point according to the method of any of the embodiments disclosed herein; determining second results from the final set of bait set targets in a second sample obtained from the subject at a second time point; and comparing the first results to the second results, thereby monitoring the cancer progression or recurrence. In some embodiments, the second results for the second sample is determined according to the method of any one of the embodiments disclosed herein.

[0053] In any of the embodiments herein, the method can further comprise selecting an anticancer therapy for the one or more subjects in response to the cancer progression. In any of theembodiments herein, the method can further comprise administering an anti-cancer therapy for the one or more subjects in response to the cancer progression. In any of the embodiments herein, the method can further comprise adjusting an anti-cancer therapy for the one or more subjects in response to the cancer progression. In any of the embodiments herein, the method can further comprise adjusting a dosage of the anti-cancer therapy or selecting a different anti-cancer therapy in response to the cancer progression. In some embodiments, the method can further comprise administering the adjusted anti-cancer therapy to the one or more subjects. In any of the embodiments herein, the first time point can be before the subject has been administered an anti-cancer therapy, and wherein the second time point is after the subject has been administered the anti-cancer therapy. In any of the embodiments herein, the subject can have a cancer, is at risk of having a cancer, is being routine tested for cancer, or is suspected of having a cancer. In any of the embodiments herein, the cancer can be a solid tumor. In any of the embodiments herein, the cancer can be a hematological cancer. In any of the embodiments herein, the anticancer therapy can comprise chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery. In any of the embodiments herein, the method can further comprise determining, identifying, or applying the results from the final set of bait set targets for the sample as a diagnostic value associated with the sample. In any of the embodiments herein, the method can further comprise generating a genomic profile for the subject based on determining the results from the final set of bait set targets.

[0054] In some embodiments, the genomic profile for the subject further can comprise results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof. In any of the embodiments, herein the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test. In any of the embodiments, the method can further comprise selecting an anti-cancer therapy, administering an anti-cancer therapy, or applying an anti-cancer therapy to the subject based on the generated genomic profile. In any of the embodiments herein, the method can further comprise determining the results from the final set of bait set targets for the one or more samples is used in making suggested treatment decisions for the subject. In any of the embodiments herein, thedetermining the results from the final set of bait set targets for the sample is used in applying or administering a treatment to the subject.

[0055] In some aspects, disclosed herein is a system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: determine, by one or more processors, first methylation values corresponding to sequence read data for a genomic region from one or more samples in a first group; determine, by the one or more processors, second methylation values corresponding to sequence read data for the genomic region from one or more samples in a second group; determine from the first methylation values and the second methylation values, by the one or more processors, at least one similarity likelihood for the genomic region; and select, by the one or more processors, a bait set target corresponding to the genomic region based on the determined similarity likelihood being less than a predetermined first threshold.

[0056] In some embodiments, the system can comprise further instructions that, when executed by the one or more processors, cause the system to: determine, by the one or more processors, a fraction of significant samples for the genomic region, wherein the fraction of significant samples for the genomic region is a number of samples in the first group for which the similarity likelihood for the genomic region is less than the predetermined first threshold, normalized by the total number of samples in the first group; and select, by the one or more processors, the bait set target corresponding to the genomic region based on the fraction of significant samples for the genomic region exceeding a predetermined second threshold. In any of the embodiments herein, the system can comprise further instructions that, when executed by the one or more processors, cause the system to: determine, by the one or more processors, a mean difference between the first methylation values and the second methylation values; and remove, by the one or more processors, the genomic region, based on the mean difference being less than a predetermined third threshold.

[0057] In some aspects, disclosed herein is a system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: determine, by one or more processors, first methylation values corresponding to sequence read data for agenomic region from one or more samples for one or more first groups; determine, by the one or more processors, second methylation values corresponding to sequence read data for the genomic region from one or more samples for one or more second groups; determine, by the one or more processors, from the first methylation values and the second methylation values, one or more separability metrics for the genomic region; and select, by the one or more processors, a bait set target corresponding to the genomic region based on the determined one or more separability metrics exceeding a predetermined first threshold.

[0058] In some embodiments, the system can comprise further instructions that, when executed by the one or more processors, cause the system to: determine, by the one or more processors, a mean difference from the first methylation values and the second methylation values; and remove the genomic region, by the one or more processors, based on the determined mean difference being less than a predetermined second threshold.

[0059] In some aspects, disclosed herein is a system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive, by one or more processors, one or more methylation datasets, wherein a methylation dataset in the one or more methylation datasets comprises genomic regions and their corresponding methylation values; rank, by the one or more processors, using one or more machine learning models, the genomic regions and their corresponding methylation values, for the one or more methylation datasets, to generate one or more lists of ranked genomic regions; determine, by the one or more processors, one or more separability metrics for the one or more lists of ranked genomic regions; select one or more informative lists of ranked genomic regions from the one or more lists of ranked genomic regions, by the one or more processors, based on the one or more separability metrics exceeding a predetermined separability threshold; select informative genomic regions from the one or more informative lists of ranked genomic regions, by the one or more processors, based on a frequency of the informative genomic regions exceeding a predetermined frequency threshold; and select first bait set targets, by the one or more processors, wherein the first bait set targets correspond to the informative genomic regions. In some embodiments, the system can comprise further instructions that, when executed by the one or more processors, cause the system to: select further informative genomic regions from aplurality of informative genomic regions comprising the informative genomic regions, by the one or more processors, based on the further informative genomic regions being in at least a portion of the plurality of informative genomic regions; and select second bait set targets, by the one or more processors, wherein the second bait set targets correspond to the further informative genomic regions.

[0060] In some aspects, disclosed herein is a system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: determine, by one or more processors, disease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a disease group; determine, by the one or more processors, first non-disease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a first non-disease group; determine from the disease methylation values and the first non-disease methylation values, by the one or more processors, one or more first similarity likelihoods for the CpG sites; and select from the CpG sites, disease CpG sites corresponding to the one or more first similarity likelihoods, by the one or more processors, based on the one or more first similarity likelihoods exceeding or being less than a predetermined first similarity likelihood threshold.

[0061] In some embodiments, the system can comprise further instructions that, when executed by the one or more processors, cause the system to: determine, by the one or more processors, from the disease methylation values and the first non-disease methylation values, one or more separability metrics for the disease CpG sites; and select informative disease CpG sites from the CpG sites or the disease CpG sites, by the one or more processors, based on the determined one or more separability metrics exceeding a predetermined separability metric threshold.

[0062] In any of the embodiments herein, the system can comprise further instructions that, when executed by the one or more processors, cause the system to: determine, by the one or more processors, a mean difference from the disease methylation values and the first non-disease methylation values; and select further informative disease CpG sites from the CpG sites, the disease CpG sites, or the informative disease CpG sites, by the one or more processors, based on the determined mean difference exceeding a predetermined mean difference threshold. In any of the embodiments herein, the system can comprise further instructions that, when executed by theone or more processors, cause the system to: determine, by the one or more processors, second non-disease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a second non-disease group; determine from the first non-disease methylation values and second non-disease methylation values, by the one or more processors, one or more second similarity likelihoods for the CpG sites; and select informative CpG sites, from the CpG sites, the disease CpG sites, the informative disease CpG sites, or the further informative disease CpG sites, based on the one or more second similarity likelihoods being less than a predetermined second similarity likelihood threshold.

[0063] In any of the embodiments herein, the system can comprise further instructions that when executed by the one or more processors, cause the system to: cluster, by the one or more processors, informative CpG sites based on any two informative CpG sites being within a predetermined distance from one another, and that within the predetermined distance, at least four informative CpG sites are found, to generate one or more clusters; and select, by the one or more processors, bait set targets corresponding to the one or more clusters.

[0064] In some aspects, disclosed herein is a system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: determine, by one or more processors, one or more methylation values corresponding to sequence read data for a genomic region from one or more healthy samples; determine, by the one or more processors, a central tendency measure from the one or more methylation values; and select, by the one or more processors, a bait set target corresponding to the genomic region based on the determined central tendency measure exceeding a predetermined threshold.

[0065] In some aspects, disclosed herein is a non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: determine, by one or more processors, first methylation values corresponding to sequence read data for a genomic region from one or more samples in a first group; determine, by the one or more processors, second methylation values corresponding to sequence read data for the genomic region from one or more samples in a second group; determine from the first methylation values and the second methylation values, by the one or more processors, at least one similarity likelihood for the 1genomic region; and select, by the one or more processors, a bait set target corresponding to the genomic region based on the determined similarity likelihood being less than a predetermined first threshold.

[0066] In some embodiments, the non-transitory computer-readable storage medium can comprise further instructions that, when executed by the one or more processors, cause the system to: determine, by the one or more processors, a fraction of significant samples for the genomic region, wherein the fraction of significant samples for the genomic region is a number of samples in the first group for which the similarity likelihood for the genomic region is less than the predetermined first threshold, normalized by the total number of samples in the first group; and select, by the one or more processors, the bait set target corresponding to the genomic region based on the fraction of significant samples for the genomic region exceeding a predetermined second threshold.

[0067] In any of the embodiments herein, the non-transitory computer-readable storage medium of can comprise further instructions that, when executed by the one or more processors, cause the system to: determine, by the one or more processors, a mean difference between the first methylation values and the second methylation values; and remove, by the one or more processors, the genomic region, based on the mean difference being less than a predetermined third threshold.

[0068] In some aspects, disclosed herein is a non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: determine, by one or more processors, first methylation values corresponding to sequence read data for a genomic region from one or more samples for one or more first groups; determine, by the one or more processors, second methylation values corresponding to sequence read data for the genomic region from one or more samples for one or more second groups; determine, by the one or more processors, from the first methylation values and the second methylation values, one or more separability metrics for the genomic region; and select, by the one or more processors, a bait set target corresponding to the genomic region based on the determined one or more separability metrics exceeding a predetermined first threshold.

[0069] In some embodiments, the non-transitory computer-readable storage medium can comprise further instructions that, when executed by the one or more processors, cause the system to: determine, by the one or more processors, a mean difference from the first methylation values and the second methylation values; and remove the genomic region, by the one or more processors, based on the determined mean difference being less than a predetermined second threshold.

[0070] In some aspects, disclosed herein is a non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive, by one or more processors, one or more methylation datasets, wherein a methylation dataset in the one or more methylation datasets comprises genomic regions and their corresponding methylation values; rank, by the one or more processors, using one or more machine learning models, the genomic regions and their corresponding methylation values, for the one or more methylation datasets, to generate one or more lists of ranked genomic regions; determine, by the one or more processors, one or more separability metrics for the one or more lists of ranked genomic regions; select one or more informative lists of ranked genomic regions from the one or more lists of ranked genomic regions, by the one or more processors, based on the one or more separability metrics exceeding a predetermined separability threshold; select informative genomic regions from the one or more informative lists of ranked genomic regions, by the one or more processors, based on a frequency of the informative genomic regions exceeding a predetermined frequency threshold; and select first bait set targets, by the one or more processors, wherein the first bait set targets correspond to the informative genomic regions.

[0071] In some embodiments, the non-transitory computer-readable storage medium can comprise further instructions that, when executed by the one or more processors, cause the system to: select further informative genomic regions from a plurality of informative genomic regions comprising the informative genomic regions, by the one or more processors, based on the further informative genomic regions being in at least a portion of the plurality of informative genomic regions; and select second bait set targets, by the one or more processors, wherein the second bait set targets correspond to the further informative genomic regions.

[0072] In some aspects, disclosed herein is a non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: determine, by one or more processors, disease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a disease group; determine, by the one or more processors, first nondisease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a first non-disease group; determine from the disease methylation values and the first non-disease methylation values, by the one or more processors, one or more first similarity likelihoods for the CpG sites; and select from the CpG sites, disease CpG sites corresponding to the one or more first similarity likelihoods, by the one or more processors, based on the one or more first similarity likelihoods exceeding or being less than a predetermined first similarity likelihood threshold.

[0073] In some embodiments, the non-transitory computer-readable storage medium can comprise further instructions that, when executed by the one or more processors, cause the system to: determine, by the one or more processors, from the disease methylation values and the first non-disease methylation values, one or more separability metrics for the disease CpG sites; and select informative disease CpG sites from the CpG sites or the disease CpG sites, by the one or more processors, based on the determined one or more separability metrics exceeding a predetermined separability metric threshold.

[0074] In any of the embodiments herein, the non-transitory computer-readable storage medium can comprise further instructions that, when executed by the one or more processors, cause the system to: determine, by the one or more processors, a mean difference from the disease methylation values and the first non-disease methylation values; and select further informative disease CpG sites from the CpG sites, the disease CpG sites, or the informative disease CpG sites, by the one or more processors, based on the determined mean difference exceeding a predetermined mean difference threshold.

[0075] In any of the embodiments herein, the non-transitory computer-readable storage medium can comprise further instructions that, when executed by the one or more processors, cause the system to: determine, by the one or more processors, second non-disease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a second non-disease group; determine from the first non-disease methylation values and second non-disease methylation values, by the one or more processors, one or more second similarity likelihoods for the CpG sites; and select informative CpG sites, from the CpG sites, the disease CpG sites, the informative disease CpG sites, or the further informative disease CpG sites, based on the one or more second similarity likelihoods being less than a predetermined second similarity likelihood threshold. In any of the embodiments herein, the non-transitory computer-readable storage medium of any of the embodiments herein can comprise further instructions that, when executed by the one or more processors, cause the system to: cluster, by the one or more processors, informative CpG sites based on any two informative CpG sites being within a predetermined distance from one another, and that within the predetermined distance, at least four informative CpG sites are found, to generate one or more clusters; and select, by the one or more processors, bait set targets corresponding to the one or more clusters.

[0076] In some aspects, disclosed herein is a non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: determine, by one or more processors, one or more methylation values corresponding to sequence read data for a genomic region from one or more healthy samples; determine, by the one or more processors, a central tendency measure from the one or more methylation values; and select, by the one or more processors, a bait set target corresponding to the genomic region based on the determined central tendency measure exceeding a predetermined threshold.INCORPORATION BY REFERENCE

[0077] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference in their entirety to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference in its entirety. In the event of a conflict between a term herein and a term in an incorporated reference, the term herein controls.BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Various aspects of the disclosed methods, devices, and systems are set forth with particularity in the appended claims. A better understanding of the features and advantages of the disclosed methods, devices, and systems will be obtained by reference to the following detailed description of illustrative embodiments and the accompanying drawings, of which:

[0079] FIG. 1 provides a first non-limiting exemplary method for selecting bait set targets based on methylation values.

[0080] FIG. 2 provides another embodiment of the first non-limiting exemplary method for selecting bait set targets based on methylation values.

[0081] FIG. 3 provides a second non-limiting exemplary method for selecting bait set targets based on methylation values.

[0082] FIG. 4 provides another embodiment of the second non-limiting exemplary method for selecting bait set targets based on methylation values.

[0083] FIG. 5 provides a third non-limiting exemplary method for selecting bait set targets based on methylation values.

[0084] FIG. 6 provides another embodiment of the third non-limiting exemplary method for selecting bait set targets based on methylation values.

[0085] FIG. 7 provides a fourth non-limiting exemplary method for selecting bait set targets based on methylation values.

[0086] FIG. 8A provides a first portion of another embodiment of the fourth non-limiting exemplary method for selecting bait set targets based on methylation values.

[0087] FIG. 8B continues the first portion of the additional embodiment of the fourth nonlimiting exemplary method for selecting bait set targets based on methylation values, in reference to FIG. 8A.

[0088] FIG. 9 provides a fifth non-limiting exemplary method for selecting bait set targets based on methylation values.

[0089] FIG. 10 provides a non-limiting exemplary method for combining selected bait set targets, based on methylation values, using two or more methods.

[0090] FIG. 11 depicts an exemplary computing device or system in accordance with one embodiment of the present disclosure.

[0091] FIG. 12 depicts an exemplary computer system or computer network, in accordance with some instances of the systems described herein.

[0092] FIG. 13 provides exemplary data for a method for selecting bait set targets based on determining similarity likelihoods of methylation fraction values.

[0093] FIG. 14 provides exemplary data for a method for selecting bait set targets based on determining separability metrics of methylation fraction values.

[0094] FIG. 15 provides exemplary data for a method for selecting bait set targets based on the methylation fraction values of CpG clusters.

[0095] FIG. 16 provides exemplary data for selecting bait set targets for colorectal cancer (CRC).

[0096] FIG. 17 provides exemplary data for selecting bait set targets for lung cancer.

[0097] FIG. 18 provides exemplary data for comparing bait set targets indicative of CRC, lung cancer and / or tissues of origin (TOO).

[0098] FIG. 19 provides exemplary data for comparing bait set targets based on different bait set target selection methods for CRC or lung cancer.

[0099] FIG. 20 provides exemplary data for bait set targets based on different bait set target selection methods for CRC or lung cancer.DETAILED DESCRIPTION

[0100] Selecting bait set targets for detecting disease, based on DNA methylation, is challenging. Described herein are methods for selecting bait set targets, to design a bait set. The methods for selecting bait set targets described herein are based on the analyses of methylation values, e.g., methylation fraction and / or methylation entropy values, for genomic regions and / or CpG sites. The selected bait set targets can be used to design and / or manufacture a final set of bait sets. The final set of bait sets can be used to identify disease signals in subjects, and accordingly, can be used to provide appropriate treatments for the subjects.

[0101] Selecting bait set targets for the detecting of disease often relies on identifying differential DNA methylation between disease subjects versus non-disease subjects. DNA methylation, however, is subject to statistical noise. Such noise can compromise the selecting of reliable bait set targets that can serve as biomarkers of disease. For example, multiple variablesnot limited to the indication of a disease, e.g., the age or sex of the subject, can affect whether a DNA nucleotide is methylated or not for the subject.

[0102] Statistical methods are required to select genomic regions that can act as informative bait set targets, despite the statistical noise, such as the effects of unwanted variables. Described herein are methods and systems for selecting bait set targets, based on statistical methods. The selected bait set targets can be used to design a bait set, e.g., a final bait set. The designed bait set can be used to detect the presence and / or progression of a disease in a subject, which can further be used to inform an appropriate therapy for the subject.

[0103] The first statistical method determines similarity likelihoods, e.g., Kolmogorov-Smirnov statistics, from the methylation values of genomic regions of two groups. The genomic regions with similarity likelihoods lower than a predetermined threshold are selected as bait set targets. Further statistical analyses can be performed to filter the genomic regions to be selected as the bait set targets.

[0104] The second statistical method determines separability metrics, e.g., an area under curve (AUC) score from a receiver operating characteristic (ROC) curve, from the methylation values of genomic regions of two pluralities of groups. The genomic regions with separability metrics higher than a predetermined threshold are selected as bait set targets. Further statistical analyses can be performed to filter the genomic regions to be selected as the bait set targets.

[0105] The third statistical method describes using machine learning models to generate lists of ranked genomic regions. The lists of ranked genomic regions are then filtered and aggregated together, based, in part, on thresholding. The aggregated genomic regions are selected as bait set targets. Further statistical analyses can be performed to filter the genomic regions to be selected as the bait set targets.

[0106] The fourth statistical method determines similarity likelihoods and separability metrics, from the methylation values of CpG sites of two or more groups. Thresholding based on the determined similarity likelihoods and separability metrics result in informative CpG sites that are then clustered together, based on their proximities to one another. Further statistical analyses can be performed to filter the CpG sites before clustering. The clustered CpG sites are selected as bait set targets.

[0107] The fifth statistical method determine hypermethylated regions in healthy subjects. The hypermethylated regions are selected as bait set targets. Further statistical analyses can be performed to filter the hypermethylated regions to be selected as the bait set targets.

[0108] The five statistical methods described above can be combined, to determine a final set of selected bait set targets. A final bait set can be designed and / or manufactured, based on the final set of selected bait set targets.Definitions

[0109] Unless otherwise defined, all of the technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art in the field to which this disclosure belongs.

[0110] As used in this specification and the appended claims, the singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. Any reference to “or” herein is intended to encompass “and / or” unless otherwise stated.

[0111] ‘ ‘About” and “approximately” shall generally mean an acceptable degree of error for the quantity measured given the nature or precision of the measurements. Exemplary degrees of error are within 20 percent (%), typically, within 10%, and more typically, within 5% of a given value or range of values.

[0112] As used herein, the terms "comprising" (and any form or variant of comprising, such as "comprise" and "comprises"), "having" (and any form or variant of having, such as "have" and "has"), "including" (and any form or variant of including, such as "includes" and "include"), or "containing" (and any form or variant of containing, such as "contains" and "contain"), are inclusive or open-ended and do not exclude additional, un-recited additives, components, integers, elements, or method steps.

[0113] As used herein, the terms “individual,” “patient,” or “subject” are used interchangeably and refer to any single animal, e.g., a mammal (including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates) for which treatment is desired. In particular embodiments, the individual, patient, or subject herein is a human.

[0114] The terms “cancer” and “tumor” are used interchangeably herein. These terms refer to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, immortality, metastatic potential, rapid growth and proliferation rate, and certain characteristic morphological features. Cancer cells are often in the form of a tumor, but such cells can exist alone within an animal, or can be a non-tumorigenic cancer cell, such as a leukemia cell. These terms include a solid tumor, a soft tissue tumor, or a metastatic lesion. As used herein, the term “cancer” includes premalignant, as well as malignant cancers.

[0115] As used herein, “treatment” (and grammatical variations thereof such as “treat” or “treating”) refers to clinical intervention (e.g., administration of an anti-cancer agent or anticancer therapy) in an attempt to alter the natural course of the individual being treated, and can be performed either for prophylaxis or during the course of clinical pathology. Desirable effects of treatment include, but are not limited to, preventing occurrence or recurrence of disease, alleviation of symptoms, diminishment of any direct or indirect pathological consequences of the disease, preventing metastasis, decreasing the rate of disease progression, amelioration or palliation of the disease state, and remission or improved prognosis.

[0116] As used herein, the term “subgenomic interval” (or “subgenomic sequence interval”) refers to a portion of a genomic sequence.

[0117] As used herein, the term "subject interval" refers to a subgenomic interval or an expressed subgenomic interval (e.g., the transcribed sequence of a subgenomic interval).

[0118] As used herein, the terms “variant sequence” or “variant” are used interchangeably and refer to a modified nucleic acid sequence relative to a corresponding “normal” or “wild-type” sequence. In some instances, a variant sequence may be a “short variant sequence” (or “short variant”), i.e., a variant sequence of less than about 50 base pairs in length.

[0119] The terms “allele frequency” and “allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular allele relative to the total number of sequence reads for a genomic locus.

[0120] It is understood that aspects and variations of the invention described herein include “consisting” and / or “consisting essentially of’ aspects and variations.

[0121] When a range of values is provided, it is to be understood that each intervening value between the upper and lower limit of that range, and any other stated or intervening value in thatstates range, is encompassed within the scope of the present disclosure. Where the stated range includes upper or lower limits, ranges excluding either of those included limits are also included in the present disclosure.

[0122] Some of the analytical methods described herein include mapping sequences to a reference sequence, determining sequence information, and / or analyzing sequence information. It is well understood in the art that complementary sequences can be readily determined and / or analyzed, and that the description provided herein encompasses analytical methods performed in reference to a complementary sequence.

[0123] The section headings used herein are for organization purposes only and are not to be construed as limiting the subject matter described. The description is presented to enable one of ordinary skill in the art to make and use the invention and is provided in the context of a patent application and its requirements. Various modifications to the described embodiments will be readily apparent to those persons skilled in the art and the generic principles herein may be applied to other embodiments. Thus, the present invention is not intended to be limited to the embodiment shown but is to be accorded the widest scope consistent with the principles and features described herein.

[0124] The figures illustrate processes according to various embodiments. In the exemplary processes, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the exemplary processes. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0125] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.Methods for selecting bait set targets

[0126] Described herein are statistical methods for selecting bait set targets, based on DNA methylation. The bait set targets selected based on one of the statistical methods can be combined with the bait set methods selected based on another one or more of the statistical methods.Selecting bait set targets based on determining similarity likelihoods for genomic regions

[0127] The present statistical method determines similarity likelihoods, e.g., Kolmogorov- Smirnov statistics, from the methylation values of genomic regions of two groups. The genomic regions with similarity likelihoods lower than a predetermined threshold are selected as bait set targets. Further statistical analyses can be performed to filter the genomic regions to be selected as the bait set targets.

[0128] FIGS. 1 and 2 show exemplary schematics showing general processes 100 and 200 for selecting bait set targets based on methylation values. General process 200 is identical to general process 100, except that general process 200 includes additional analyses as indicated in 210, 212, 214, and 216. Processes 100 and / or 200 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, processes 100 and / or 200 performed using a client-server system, and the blocks of processes 100 and / or 200 are divided up in any manner between the server and a client device. In other examples, the blocks of processes 100 and / or 200 are divided up between the server and multiple client devices. Thus, while portions of processes 100 and / or 200 are described herein as being performed by particular devices of a client-server system, it will be appreciated that processes 100 and / or 200 are not so limited. In other examples, processes 100 and / or 200 are performed using only a client device or only multiple client devices. In processes 100 and / or 200, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the processes 100 and / or 200. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0129] At 102 in FIG. 1 or 202 in FIG. 2, first methylation values corresponding to sequence read data for a genomic region from one or more samples in a first group are determined. A methylation value from the first methylation values can include values that are determined for a single nucleotide (e.g., a CpG nucleotide). The methylation value can, for example, be based on a plurality of sequencing reads, such as a methylation fraction value. For example, from a plurality of sequence reads that overlap with a given nucleotide of a reference genome, the number of sequence reads indicating methylation on the given nucleotide can be counted. Thenumber of sequence reads indicating methylation on the given nucleotide can be normalized by the total number of sequence reads from the plurality of sequence reads, to derive the methylation fraction value. Different methylation values, such as a methylation entropy value, can be calculated based on the plurality of sequence reads for a given nucleotide. The described method of determining a methylation value for a given nucleotide, can be repeated for other nucleotides, and for other samples. To determine methylation values for a genomic region, the methylation values for each nucleotide in the genomic region, and for all samples, can be pooled together. The resulting pool of methylation values can comprise a distribution of methylation values that can be subject to statistical methods, such as comparing the distribution of methylation values to a second distribution of methylation values. The comparing can be done via a Kolmogorov-Smirnov test. The methylation values can be formatted in any number of ways that may be compatible with computational analyses. For example, the methylation values for the first group can be formatted as a matrix of methylation values, e.g., an m X n matrix where m can be the number of samples and n can be the number of features, such as the genomic regions. The methylation values can also be tidied into a long data format, such that each row of the data is limited to a single observation, i.e., each row of the data is limited to values regarding a single genomic region of a single sample. The sequence read data for the genomic region from one or more samples in the first group can comprise sequence read data related to a disease. The disease can be a cancer. The disease can also include Alzheimer’s Disease, type 2 diabetes, cardiovascular disease, autoimmune disease, or any combination thereof. The one or more samples related to the disease can include samples that appear phenotypically healthy, but coming from a subject that is diagnosed or suspected to have the disease.

[0130] At 104 in FIG. 1 or 204 in FIG. 2, second methylation values corresponding to sequence read data for a genomic region from one or more samples in a second group are determined. Similar to 102 in FIG. 1 or 204 in FIG. 2, a methylation value from the second methylation values can include values that are determined for a single nucleotide (e.g., a CpG nucleotide). The methylation value can, for example, be based on a plurality of sequencing reads, such as a methylation fraction value. For example, from a plurality of sequence reads that overlap with a given nucleotide of a reference genome, the number of sequence reads indicating methylation on the given nucleotide can be counted. The number of sequence reads indicating methylation onthe given nucleotide can be normalized by the total number of sequence reads from the plurality of sequence reads, to derive the methylation fraction value. Different methylation values, such as methylation entropy, can be calculated based on the plurality of sequence reads for a given nucleotide. The described method of determining a methylation value for a given nucleotide, can be repeated for other nucleotides, and for other samples. To determine methylation values for a genomic region, the methylation values for each nucleotide in the genomic region, and for all samples, can be pooled together. The resulting pool of methylation values can comprise a distribution of methylation values that can be subject to statistical methods, such as comparing the distribution of methylation values to the first distribution of methylation values described above. The comparing can be done via a Kolmogorov-Smirnov test. The methylation values can be formatted in any number of ways that may be compatible with computational analyses. For example, the methylation values for the first group can be formatted as a matrix of methylation values, e.g., an m X n matrix where m can be the number of samples and n can be the number of features, such as the genomic regions. The methylation values can also be tidied into a long data format, such that each row of the data is limited to a single observation, i.e., each row of the data is limited to values regarding a single genomic region of a single sample.

[0131] The sequence read data for the genomic region from one or more samples in the second group can comprise panel of normal read count data. The panel of normal read count data can include a panel of normal samples selected or curated to represent a relevant set of healthy samples, e.g., samples that are believed to not have any somatic alterations. Panel of normal read count data (i.e., panel of normal) serve to capture recurrent technical artifacts, in order to improve the analyzing of sequencing data by, for example, ruling out technical artifacts that may be observed in the comparison group, e.g., disease group. The panel of normal read count data may derive from the same methods, including analyses, used to derive the sequence read data for the CpG sites for the disease group. Methylation values can be determined from the panel of normals. The methylation values can be compared against methylation values deriving from non-panel of normal read count data, such as sequence read count data deriving from the sample of interest, e.g., the disease sample.

[0132] At 106 in FIG. 1 or 206 in FIG. 2, at least one similarity likelihood for the genomic region is determined from the first methylation values and the second methylation values. Thesimilarity likelihood can be a value that describes how similar two sets of values or two distributions of values are to one another. For example, the similarity likelihood can refer to a value that describes how likely it is to observe two sample distributions, e.g., two distributions of methylation values (such as the first methylation values and the second methylation values), given that the two sample distributions are drawn from a common population distribution. The common population distribution can be unknown, e.g., the experimenter need not know whether the common population distribution is a specific type of probability distribution, e.g., a Gaussian or a Poisson distribution. In the case of the methods described herein, the similarity likelihood can describe how likely two sample distributions — a distribution of methylation values from disease samples and a distribution of methylation values from non-disease samples — come from a common population distribution. The similarity likelihood can be determined by a Kolmogorov-Smirnov testing. The Kolmogorov-Smirnov testing can result in a Kolmogorov- Smirnov statistic, that can range in value between 0 and 1, where a higher value suggests a higher likelihood that the two sample distributions are drawn from a common population distribution. The Kolmogorov-Smirnov statistic, Dn, can be determined for two sample distributions by:Dn= sup |Fl n(x) - F2,m(x) | X wherein sup is the supremum of the set of distances between the two sample distributions. The two sample distributions expressed as two empirical distribution functions are denoted Fl n(x) and F2;m(x), and x denotes observations of a variable of interest, such as the methylation value of a genomic region. The similarity likelihood need not be determined by Kolmogorov-Smirnov testing. The similarity likelihood can be determined from other statistical analyses, such as binomial significance testing. For example, the similarity likelihood can be the p-value from binomial significance testing and can describe the probability of observing methylation at a genomic region for e.g., a disease sample, given the probability of observing methylation at the genomic region for e.g., a non-disease sample. The similarity likelihood can be subject to error correcting techniques that account for multiple comparisons arising from multiple hypothesis testing, e.g., by determining the false discovery rate and / or correcting the similarity likelihood by Bonferroni correction or Benjamini-Hochberg correction. Such correcting techniques are referred to herein as multiple testing correction.

[0133] At 108 in FIG. 1 or 208 in FIG. 2, a bait set target corresponding to the genomic region based on the determined similarity likelihood being less than a predetermined first threshold is selected. For example, if the determined similarity likelihood e.g., the Kolmogorov-Smirnov statistic for a genomic region is less than the predetermined first threshold, e.g. 0.05, following multiple testing correction, then the genomic region can be included in the set of bait set targets. The logic is that if the first methylation values, e.g., the disease methylation values, and the second methylation values, e.g., the non-disease methylation values, score a low similarity likelihood, then in the case of the Kolmogorov-Smirnov testing, the disease and non-disease methylation values are highly unlikely to come from the same population distribution.

[0134] At 210 in FIG. 2, a fraction of significant samples for the genomic region is determined. The fraction of significant samples for the genomic region can be a number of samples in the first group for which the similarity likelihood for the genomic region is less than the predetermined first threshold, normalized by the total number of samples in the first group. The fraction of significant samples should not be mistaken for the methylation values, e.g., the methylation fraction values. The methylation fraction values are determined based on dividing the number of reads possessing methylation for a nucleotide of interest, by the total number of reads that overlap with the nucleotide of interest. In contrast, the fraction of significant samples is determined downstream of determining the methylation values, e.g., from the methylation fraction values. That is, after the sample distribution for the genomic region from the first group is compared to the sample distribution for the genomic region from the second group, and a similarity likelihood (e.g., the Kolmogorov-Smirnov statistic) is determined from the comparison, the genomic region is classified as being either significant or not significant, based on whether the similarity likelihood is lower than the predetermined first threshold (e.g., 0.05). The classifying the genomic region as being either significant or not significant, based on the similarity likelihood, is repeated for multiple (e.g., all) samples. The number of samples classified as significant is then divided by the total number of samples that were classified, to determine the fraction of significant samples for the genomic region. The fraction of significant samples is determined for multiple (e.g., all) genomic regions. The outcome of the above analyses is a fraction of significant samples corresponding to each genomic region.

[0135] At 212 in FIG. 2, the bait set target corresponding to the genomic region based on the fraction of significant samples for the genomic region exceeding a predetermined second threshold is selected. That is, if the fraction of significant samples for a genomic region is greater than the predetermined second threshold, the genomic region is selected as a bait set target. The predetermined second threshold can be different, depending on the groups being compared. For example, if the first group comprises cancer tissue samples, and the second group comprises panel of normal read count data based on healthy plasma, the predetermined second threshold can range between 0.8 and 0.9. Alternatively, if the first group comprises cancer plasma samples from stage IV patients, and the second group comprises panel of normal read count data based on healthy plasma, the predetermined second threshold can be 0.5. Alternatively, if the third group comprises healthy tissue samples, and the second group comprises panel of normal read count data based on healthy plasma, the predetermined second threshold can range between 0.1 and 0.8. Comparing the healthy tissue samples against healthy plasma samples can be informative for identifying bait set targets that can distinguish healthy tissue from healthy plasma.

[0136] At 214 in FIG. 2, a mean difference between the first methylation values and the second methylation values is determined. For example, the first methylation values for a genomic region can be averaged, and the second methylation values for the genomic region can be averaged. The difference between the two averages, i.e., the mean difference, can then be determined. The mean difference can be determined for multiple (e.g., all) genomic regions.

[0137] At 216 in FIG. 2, based on the mean difference being less than a predetermined third threshold, the genomic region is removed. For example, if the mean difference between the first group and the second group of the genomic region is less than 0.2 or 0.25, the genomic region can be excluded from being a bait set target. Thresholding based on the mean difference removes genomic regions that are methylated to a similar extent between the two groups.

[0138] Process 100 or process 200 can be used to select bait set targets, based on methylation values of DNA. The selected bait set targets can be included in a final set of bait set targets. A final bait set can be designed based on the final set of bait set targets. The bait set targets selected according to process 100 or process 200 can be combined with bait set targets selected according to another method, such as the methods indicated by processes 300-900.Selecting bait set targets based on determining separability metrics for genomic regions

[0139] The present statistical method determines separability metrics, e.g., an area under curve (AUC) score from a receiver operating characteristic (ROC) curve, from the methylation values of genomic regions of two pluralities of groups. The genomic regions with separability metrics higher than a predetermined threshold are selected as bait set targets. Further statistical analyses can be performed to filter the genomic regions to be selected as the bait set targets.

[0140] FIGS. 3 and 4 show exemplary schematics showing general processes 300 and 400 for selecting bait set targets based on methylation values. General process 400 is identical to general process 300, except that general process 400 includes additional analyses as indicated in 410 and 412. Processes 300 and / or 400 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, processes 300 and / or 400 are performed using a client-server system, and the blocks of processes 300 and / or 400 are divided up in any manner between the server and a client device. In other examples, the blocks of processes 300 and / or 400 are divided up between the server and multiple client devices. Thus, while portions of processes 300 and / or 400 are described herein as being performed by particular devices of a client-server system, it will be appreciated that processes 300 and / or 400 are not so limited. In other examples, processes 300 and / or 400 are performed using only a client device or only multiple client devices. In processes 300 and / or 400, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the processes 300 and / or 400. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0141] At 302 in FIG. 3 or 402 in FIG. 4, first methylation values corresponding to sequence read data for a genomic region from one or more samples for one or more first groups are determined. A methylation value from the first methylation values can include values that are determined for a single nucleotide (e.g., a CpG nucleotide). The methylation value can, for example, be based on a plurality of sequencing reads, such as a methylation fraction value. For example, from a plurality of sequence reads that overlap with a given nucleotide of a reference genome, the number of sequence reads indicating methylation on the given nucleotide can becounted. The number of sequence reads indicating methylation on the given nucleotide can be normalized by the total number of sequence reads from the plurality of sequence reads, to derive the methylation fraction value. Different methylation values, such as a methylation entropy value, can be calculated based on the plurality of sequence reads for a given nucleotide. The described method of determining a methylation value for a given nucleotide, can be repeated for other nucleotides, and for other samples. To determine methylation values for a genomic region, the methylation values for each nucleotide in the genomic region, and for all samples, can be pooled together. The resulting pool of methylation values can comprise a distribution of methylation values that can be subject to statistical methods, such as determining a separability metric, e.g., an area under curve (AUC) score from a receiver operating characteristic (ROC) curve. The methylation values can be formatted in any number of ways that may be compatible with computational analyses. For example, the methylation values for the first group can be formatted as a matrix of methylation values, e.g., an m X n matrix where m can be the number of samples and n can be the number of features, such as the genomic regions. The methylation values can also be tidied into a long data format, such that each row of the data is limited to a single observation, i.e., each row of the data is limited to values regarding a single genomic region of a single sample.

[0142] The one or more first groups can comprise a group comprising cancer tissue samples, or a group comprising healthy tissue samples. The one or more first groups can be compared to one or more second groups. For example, if the first group comprises cancer tissue samples and healthy tissue samples, and the second group comprises healthy plasma samples and healthy tissue samples, the following pairwise comparisons can be made: cancer tissue samples versus healthy plasma samples, healthy tissue samples versus healthy plasma samples, and / or cancer tissue samples versus healthy tissue samples.

[0143] At 304 in FIG. 3 or 404 in FIG. 4, second methylation values corresponding to sequence read data for a genomic region from one or more samples for one or more second groups are determined. A methylation value from the second methylation values can include values that are determined for a single nucleotide (e.g., a CpG nucleotide). The methylation value can, for example, be based on a plurality of sequencing reads, such as a methylation fraction value. For example, from a plurality of sequence reads that overlap with a given nucleotide of a referencegenome, the number of sequence reads indicating methylation on the given nucleotide can be counted. The number of sequence reads indicating methylation on the given nucleotide can be normalized by the total number of sequence reads from the plurality of sequence reads, to derive the methylation fraction value. Different methylation values, such as a methylation entropy value, can be calculated based on the plurality of sequence reads for a given nucleotide. The described method of determining a methylation value for a given nucleotide, can be repeated for other nucleotides, and for other samples. To determine methylation values for a genomic region, the methylation values for each nucleotide in the genomic region, and for all samples, can be pooled together. The resulting pool of methylation values can comprise a distribution of methylation values that can be subject to statistical methods, such as comparing the distribution of methylation values to a second distribution of methylation values. The comparing can be done via a Kolmogorov-Smirnov test. The methylation values can be formatted in any number of ways that may be compatible with computational analyses. For example, the methylation values for the first group can be formatted as a matrix of methylation values, e.g., an m X n matrix where m can be the number of samples and n can be the number of features, such as the genomic regions. The methylation values can also be tidied into a long data format, such that each row of the data is limited to a single observation, i.e., each row of the data is limited to values regarding a single genomic region of a single sample.

[0144] The one or more second groups can comprise a group comprising healthy plasma samples, or a group comprising healthy tissue samples. The one or more second groups can be compared to the one or more first groups. For example, if the second group comprises healthy plasma samples and healthy tissues samples, and the first group comprises cancer tissue samples and healthy tissues samples, the following pairwise comparisons can be made: cancer tissue samples versus healthy plasma samples, healthy tissue samples versus healthy plasma samples, and / or cancer tissue samples versus healthy tissue samples.

[0145] At 306 in FIG. 3 or 406 in FIG. 4, one or more separability metrics are determined, from the first methylation values and the second methylation values. The separability metrics can comprise any metric that describes how well a feature can separate a dataset into two (or more) classes. For example, the separability metric can describe how well the methylation of a genomic region (or some other classifier) distinguishes whether a sample belongs in the one ormore first groups (e.g., a disease group), versus the one or more second groups (e.g., a nondisease group). The separability metric can be an Area Under the Curve (AUC) score. An AUC score is derived by determining the area under a receiver-operating characteristic (ROC) curve, and describes how well a binary classifier separates a dataset. A ROC curve plots the true positive rate of a classifier against the false positive rate of a classifier. The true positive rate is expressed as the number of true positives classified by the classifier, normalized by the sum of the number of true positives and false negatives classified by the classifier. The false positive rate is expressed as the number of false positive classified by the classifier, normalized by the sum of the number of false positive and true negatives classified by the classifier. The AUC score can range in value between 0.0 and 1.0, where a higher AUC value refers to a better performing classifier. The AUC score can be interpreted as a probability that the classifier, e.g., a genomic region, ranks a random positive observation (e.g., an observation that belongs to a class, such as a disease group) more highly than a random negative observation (e.g., an observation that does not belong to a class, such as non-disease group). The methylation values, e.g., the methylation fraction values or methylation entropy values can be used with the group labels to generate an AUC value for every genomic region. For example, for two groups, e.g., a disease group and a non-disease group, there is a list of methylation values between 0 and 1, inclusive. From the two lists of values, two histograms can be plotted, one for the disease group for a genomic region and one for the non-disease group for the genomic region. A threshold can then be incremented along the x-axis, such that at each increment, the number of true positives, false positives, true negatives, and false negatives can be determined from the two histograms, and accordingly, the true positive rates and the false positive rates can be determined at each increment. From the determined true positive and false positive rates, the ROC and AUC can be derived and plotted for the genomic region.

[0146] At 308 in FIG. 3 or 408 in FIG. 4, a bait set target corresponding to the genomic region based on the determined one or more separability metrics exceeding a predetermined first threshold is determined. For example, if the determined separability metric, e.g., the AUC score for a genomic region exceeds a predetermined first threshold, e.g., 0.90 or 0.95, for any of the pairs of groups being compared between the one or more first groups and the one or more second groups, then the genomic region can be included in the set of bait set targets. The pairs ofgroups being compared can, for example, be: cancer tissue samples versus healthy plasma samples, healthy tissue samples versus healthy plasma samples, and / or cancer tissue samples versus healthy tissue samples. The logic behind selecting for genomic regions with high AUC scores is that if the AUC score from comparing two groups based on the genomic region is high, then the genomic region can separate the two groups, e.g., a disease group versus a non-disease group, with high performance. Accordingly, the AUC score can serve as a probability that can predict how well the genomic region separates the two groups.

[0147] At 410 in FIG. 4, a mean difference from the first methylation values and the second methylation values are determined. For example, the first methylation values for a genomic region can be averaged, and the second methylation values for the genomic region can be averaged. The difference between the two averages, i.e., the mean difference, can then be determined. The mean difference can be determined for multiple (e.g., all) genomic regions.

[0148] At 412 in FIG. 4, the genomic region based on the determined mean difference being less than a predetermined second threshold is removed. For example, if the mean difference between the one or more first groups and the one or more second groups of the genomic region is less than 0.2 or 0.25, the genomic region can be excluded from being a bait set target. Thresholding based on the mean difference removes genomic regions that are methylated to a similar extent between the two groups being compared from the one or more first groups and the one or more second groups.

[0149] Process 300 or process 400 can be used to select bait set targets, based on methylation values of DNA. The selected bait set targets can be included in a final set of bait set targets. A final bait set can be designed based on the final set of bait set targets. The bait set targets selected according to process 300 or process 400 can be combined with bait set targets selected according to another method, such as the methods indicated by processes 100-200 and / or processes 500-900.Selecting bait set targets based on machine learning models for genomic regions

[0150] The present statistical method describes using machine learning models to generate lists of ranked genomic regions. The lists of ranked genomic regions are then filtered and aggregated together, based, in part, on thresholding. The aggregated genomic regions are selected as bait settargets. Further statistical analyses can be performed to filter the genomic regions to be selected as the bait set targets.

[0151] FIGS. 5 and 6 show exemplary schematics showing general processes 500 and 600 for selecting bait set targets based on methylation values. General process 600 is identical to general process 500, except that general process 600 includes additional analyses as indicated in 614 and 616. Processes 500 and / or 600 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, processes 500 and / or 600 are performed using a client-server system, and the blocks of processes 500 and / or 600 are divided up in any manner between the server and a client device. In other examples, the blocks of processes 500 and / or 600 are divided up between the server and multiple client devices. Thus, while portions of processes 500 and / or 600 are described herein as being performed by particular devices of a client-server system, it will be appreciated that processes 500 and / or 600 are not so limited. In other examples, processes 500 and / or 600 are performed using only a client device or only multiple client devices. In processes 500 and / or 600, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the processes 500 and / or 600. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0152] At 502 in FIG. 5 or 602 in FIG. 6, one or more methylation datasets are received, wherein a methylation dataset in the one or more methylation datasets comprises genomic regions and their corresponding methylation values. The one or more methylation datasets can derive from one or more samples from a disease group and / or one or more samples from a nondisease group. A methylation dataset in the one or more methylation datasets can include methylation values from multiple groups, such as a disease group and a non-disease group. The methylation dataset can be formatted as a matrix of the methylation values, e.g., an m X n matrix where m can be the number of samples and n can be the number of features, such as the genomic regions. The methylation values can also be tidied into a long data format, such that each row of the data is limited to a single observation, i.e., each row of the data is limited to values regarding a single genomic region of a single sample. In the case that the methylation dataset is a matrix of the methylation values, the matrix can derive from the horizontal orvertical concatenation of multiple matrices, such as a matrix deriving from a disease group concatenated to a matrix deriving from a non-disease group.

[0153] The methylation values comprised in the one or more methylation datasets can include values that are determined for single nucleotides (e.g., CpG nucleotides), such as values based on a plurality of sequencing reads. For example, from a plurality of sequence reads that overlap with a given nucleotide of a reference genome, the number of sequence reads indicating methylation on the given nucleotide can be counted. The number of sequence reads indicating methylation on the given nucleotide can be normalized by the total number of sequence reads from the plurality of sequence reads, to derive the methylation fraction value. Different methylation values, such as a methylation entropy value, can also be calculated based on the plurality of sequence reads for a given nucleotide. The described method of determining a methylation value for a given nucleotide, can be repeated for other nucleotides, and for other samples. To determine methylation values for a genomic region, the methylation values for each nucleotide in the genomic region, and for all samples, can be pooled together. The resulting pool of methylation values can comprise a distribution of methylation values that can be subject to statistical methods, such as input into one or more machine learning models.

[0154] At 504 in FIG. 5 or 604 in FIG. 6, the genomic regions and their corresponding methylation values are ranked, for the one or more methylation datasets, using the one or more machine learning models, to generate one or more lists of ranked genomic regions. For example, a copy of a methylation dataset, e.g., a m X n matrix of samples by genomic regions, can be inputted into each of the one or more machine learning models. For each of the one or more machine learning models, the input copy of the methylation dataset is subject to a cross- validation technique. As part of the cross-validation technique, the copy of the methylation dataset is split into a training fraction comprising random methylation values from the methylation dataset, and a test fraction comprising different random methylation values from the methylation dataset (and optionally, a validation fraction comprising other different random methylation values). The splitting of the methylation dataset allows for the ranking of the genomic regions, by the one or more machine learning models. The ranking, using the one or more machine learning models, can comprise A- fold cross-validation or repeated A- fold cross- validation. A- fold cross-validation involves splitting the data, e.g., the methylation dataset, basedon a number of “folds”, k, determined by the user. Each fold contains a random subset of data from the dataset, and each fold contains the same or approximately the same length of data from the dataset. Of the k folds, one of the folds is assigned to be the test fraction. The remaining folds (i.e., k — 1 folds) are assigned to be the training fraction. The number of iterations, i.e., the number of times the model is trained, is k times, where for each iteration, a different fold of the assigned k folds is the training fraction. In Zc- fold cross-validation, k is both the number of folds and the number of iterations. Repeated A- Ibid cross-validation (i.e., iterative A- lb Id cross- validation) is a variant of A- Ibid cross validation. In repeated A- lb Id cross-validation, k is still the number of folds, but k can be a different value from the number of iterations, i.e., the number of times the model is trained, n is the number of iterations, and n need not equal k. That is, in addition to assigning one of the k folds to be the test fraction, and the remaining folds (i.e., k - 1 folds) to be the training fraction, the dataset is shuffled for n iterations. As a result, when using repeated A- lb Id cross-validation, the total number of times the model can be trained is n X k. For example, if k is 5, and n is 10, then the model can be trained 50 times, where each training is trained on 80% (i.e., 4 out of 5 folds) of the total dataset. Other cross-validation techniques can alternatively be used on the methylation dataset. Cross-validation prevents any particular assigned training fraction, such as a training fraction resulting from an unusual random selection, from having an outsized impact on the training of the machine learning model. The crossvalidating and the training of the machine learning model outputs, in the case of repeated A- Ibid cross-validation, n X k lists of ranked genomic regions. The ranking can be based on an informativeness of the genomic regions and their corresponding methylation values. The informativeness can be assigned from the machine learning model and can be, for example, based on the amount of Shannon information gained when using a genomic region to decide whether it belongs to a group, e.g., a disease group or a non-disease group. The informativeness can be based on the Gini score. For example, the Gini score can be used to determine the mean decrease in the Gini score, in a random forest machine learning model. The Gini score (i.e., the Gini index or the Gini impurity) can be understood as the degree or probability of a particular feature, such as a genomic region, being wrongly classified, e.g. into a group, when the particular feature is randomly chosen. The Gini score can range in value between 0 and 1. If the Gini index for a genomic region is 0, then that genomic region certainly belongs in a group, e.g.,a disease group. Conversely, if the Gini index for a genomic region is 1, then the genomic region belonging to a group is the result of random chance. Each of the lists of the ranked genomic regions can be ranked according to the Gini index, such that for each list, only the genomic features with the lowest Gini indices are selected. For example, if the cross-validating and training of the machine learning model outputs 50 lists of ranked genomic regions, where each list contains 300 genomic regions ranked according to the Gini index, each list can be filtered by the Gini index, such that only the top 100 genomic features are selected (i.e., the genomic features with the lowest 100 Gini indices). The result would be 50 lists of ranked genomic regions, where each list no longer contains 300 genomic regions, but rather, 100 genomic regions. Other statistical scores relating to each of the lists of ranked genomic regions can be determined, such as the AUC value corresponding to the trained machine learning model that outputs a list of ranked genomic regions. The one or more machine learning models can comprise a random forest model, a support vector machine model, or a generalized linear model. The use of multiple machine learning models increases the number of lists of ranked genomic regions that are outputted. For example, if 3 machine learning models and repeated A- fold cross- validation with k = 5 folds and n = 10 iterations are used, then 3 x 5 x 10 = 150 trained machine learning models and 150 corresponding lists of ranked genomic regions are outputted, and for each genomic region for each of the lists, a probability of the genomic region belonging to a group can be provided. If desired, 150 AUC scores corresponding to the 150 trained machine learning models and 150 lists of ranked genomic regions can also be determined. If each of the example output 150 lists of ranked genomic regions has a length of 300 genomic regions, and each genomic region is assigned a probability of belonging in a disease or a nondisease group, then the total output data can be formatted as 150 matrices, where each matrix is 300 X 2 (genomic regions by groups), and each element of a matrix in the 150 matrices is a probability of the genomic feature belonging in a group. Each 300 X 2 can be truncated to a 100 X 2 matrix of genomic regions by groups, based on the ranked informativeness of each of the 300 genomic regions. A random forest model is a meta estimator that fits decision tree classifiers on various sub-samples of the dataset, e.g., methylation dataset, and uses a majority vote, in the case of classification, to classify a feature, e.g., a genomic region, into a group, e.g., a disease group. A support vector machine (SVM) model is a classifier model that fits ahyperplane or a set of hyperplanes onto the (high-dimensional) dataset, e.g., the methylation dataset, such that the hyperplane or set of hyperplane divvies the dataset into groups, e.g., a disease group and a non-disease group. Generalized linear models (GLMs) constitute a class of linear models where the dependent variable is linearly related to the explanatory variables via a specified link function. GLMs can be described in terms of the link function, which links a linear predictor with a probability distribution parameter, for a probability distribution of interest. The linear predictor is a linear combination of the parameters and explanatory variables. Linear regression is an example of a GLM, where the dependent variable is normally distributed. GLMs can output a probability that a given genomic feature belongs to a group, e.g., a disease group. Regardless of which of the one or more machine learning models are used when using the methods described herein, the cross-validating and the training of each machine learning model results in a plurality of lists of ranked genomic regions that can be used as candidate bait set targets and can be subject to further analyses, as described below.

[0155] At 506 in FIG. 5 or 606 in FIG. 6, one or more separability metrics for the one or more lists of ranked genomic regions are determined. The separability metrics can comprise any metric that describes how well a feature can separate a dataset into two (or more) classes. For example, the separability metric can describe how well a machine learning model, given a training fraction from a methylation dataset, classifies genomic regions as belonging in either a disease group or a non-disease group. The separability metric can be an AUC score, i.e., the AUC of a ROC curve. The AUC score describes how well a binary classifier separates a dataset. A ROC curve plots the true positive rate of a classifier against the false positive rate of a classifier. The true positive rate is expressed as the number of true positives classified by the classifier, normalized by the sum of the number of true positives and false negatives classified by the classifier. The false positive rate is expressed as the number of false positive classified by the classifier, normalized by the sum of the number of false positive and true negatives classified by the classifier. The AUC score can range in value between 0.0 and 1.0, where a higher AUC value refers to a better performing classifier. The AUC score can be interpreted as a probability that the classifier, e.g., a machine learning model trained on a training fraction of the methylation dataset, ranks a random positive observation (e.g., a genomic region belonging a class, such as a disease group) more highly than a random negative observation (e.g., a genomicregion that does not belong to the class). A separability metric, e.g., AUC score, can be assigned to each of the trained machine learning models, and thus, to each model’s output list of ranked genomic regions. For example, if 150 models are trained, then 150 corresponding lists of ranked genomic regions and 150 corresponding AUC scores are outputted.

[0156] At 508 in FIG. 5 or 608 in FIG. 6, one or more informative lists of ranked genomic regions from the one or more lists of ranked genomic regions based on the one or more separability metrics exceeding a predetermined separability threshold are selected. That is, of the one or more lists of ranked genomic regions, only the top lists are selected, based on each list’s corresponding separability metric, e.g., AUC score, exceeding the predetermined separability threshold. The predetermined separability threshold can range, for example, from 0.90 to 0.95. For example, if 150 lists of ranked genomic regions are outputted from 150 models, then only the 150 lists with corresponding AUC scores of at least the threshold, e.g., 0.9, are selected, which can, for example, result in 30 models. The 30 models are the informative lists of ranked genomic regions. The informative lists of ranked genomic regions are the lists of ranked genomic regions that remain after thresholding based on the predetermined separability threshold.

[0157] At 510 in FIG. 5 or 610 in FIG. 6, informative genomic regions from the one or more informative lists of ranked genomic regions based on a frequency of the informative genomic regions exceeding a predetermined frequency threshold are selected. From the reduced number of lists of ranked genomic regions that remain after thresholding, i.e., the informative lists of ranked genomic regions, a single list of informative genomic regions is selected, i.e., the informative genomic regions are selected. Selecting the informative genomic regions comprises setting a predetermined frequency threshold, e.g., 50%, and determining if a genomic region in the informative lists of ranked genomic regions appears at least 50% of the time (i.e., appears at least 50% of the time across the lists of ranked genomic regions). The genomic regions that appear more frequently than the predetermined frequency threshold are selected for downstream analyses, as described below. For example, in the case that there are 30 informative lists of ranked genomic regions, where each informative list includes 100 genomic regions, the 30 informative lists can be collapsed into a single list of 22 genomic regions, if each of the 22 genomic regions appears across the 30 informative lists (i.e., 30 X 100 = 3000 total genomicregions, including repeats) more than 1500 times, provided that the frequency threshold is 50% (50% of 3000 = 1500).

[0158] At 512 in FIG. 5 or 612 in FIG. 6, first bait set targets are selected, wherein the first bait set targets correspond to the informative genomic regions. For example, if the informative genomic regions comprise 22 genomic regions, then the first bait set targets can be the 22 genomic regions. The first bait set targets can be further filtered, for example, by excluding targets featuring motifs that may compromise efficient manufacturing, such as tandem repeats of GC-rich regions. The first bait set targets can be used for informing the design and / or generation of a final set of bait set targets.

[0159] At 614 in FIG. 6, further informative genomic regions from a plurality of informative genomic regions comprising the informative genomic regions, based on the further informative genomic regions being in at least a portion of the plurality of informative genomic regions. In some cases, the disease group that the bait set targets are aiming to identify from a sample can be decomposed into further disease ontologies. For example, lung cancer samples can be decomposed into lung adenocarcinoma, lung squamous cell carcinoma, and the combination of both. Different disease ontologies, such as the different lung cancers, may be different enough that selecting bait set targets based on just one disease ontology, e.g., lung adenocarcinoma, may not be sufficient for selecting bait set targets that can identify the broader disease, e.g., lung cancer. To select bait set targets that can be informative across disease ontologies of a disease, the methods described herein can use the informative genomic regions for each disease ontology of a disease, i.e., the plurality of informative genomic regions, e.g., lists of informative genomic regions. For example, if the disease comprises 6 disease ontologies, and accordingly, 6 corresponding lists of informative genomic regions are selected, where the total number of genomic regions across the 6 lists is 120 genomic regions (including repeats), then genomic regions that occur in the 120 genomic regions at least a threshold number of times, i.e., a portion of the plurality of informative genomic regions, can be selected. The selected genomic regions can also be the union of the genomic regions across the plurality of informative genomic regions. The selected genomic regions are the further informative genomic regions.

[0160] At 616 in FIG. 6, second bait set targets wherein the second bait set targets correspond to the further informative genomic regions are selected. For example, if the further informativegenomic regions comprise 18 genomic regions, then the second bait set targets can be the 18 genomic regions. The second bait set targets can be further filtered, for example, by excluding targets featuring motifs that may compromise efficient manufacturing, such as tandem repeats of GC-rich regions. The second bait set targets can be used for informing the design and / or generation of a final set of bait set targets.

[0161] Process 500 or process 600 can be used to select bait set targets, based on methylation values of DNA. The selected bait set targets can be included in a final set of bait set targets. A final bait set can be designed based on the final set of bait set targets. The bait set targets selected according to process 500 or process 600 can be combined with bait set targets selected according to another method, such as the methods indicated by processes 100-400 and / or processes 700-900.Selecting bait set targets from CpG nucleotides

[0162] The present statistical method determines similarity likelihoods and separability metrics, from the methylation values of CpG sites of two or more groups. Thresholding based on the determined similarity likelihoods and separability metrics result in informative CpG sites that are then clustered together, based on their proximities to one another. Further statistical analyses can be performed to filter the CpG sites before clustering. The clustered CpG sites are selected as bait set targets.

[0163] FIGS. 7, 8A and 8B show exemplary schematics showing general processes 700 and 800 for selecting bait set targets based on methylation values. General process 800 is identical to general process 700, except that general process 800 includes additional analyses as indicated in 810-826. Processes 700 and / or 800 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, processes 700 and / or 800 are performed using a client-server system, and the blocks of processes 700 and / or 800 are divided up in any manner between the server and a client device. In other examples, the blocks of processes 700 and / or 800 are divided up between the server and multiple client devices. Thus, while portions of processes 700 and / or 800 are described herein as being performed by particular devices of a client-server system, it will be appreciated that processes 700 and / or 800 are not so limited. In other examples, processes 700 and / or 800 are performed using only a client device oronly multiple client devices. In processes 700 and / or 800, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the processes 700 and / or 800. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0164] At 702 in FIG. 7 or 802 in FIG. 8, disease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a disease group, are determined. The one or more samples in the disease group can have cancer. The cancer can be lung cancer, colorectal cancer, or any other kind of cancer. The disease methylation values can relate to the disease, such as cancer. A methylation value from the disease methylation values can include values that are determined for a single nucleotide (e.g., a CpG nucleotide). The methylation value can, for example, be based on a plurality of sequencing reads, such as a methylation fraction value. For example, from a plurality of sequence reads that overlap with a given nucleotide of a reference genome, the number of sequence reads indicating methylation on the given nucleotide can be counted. The number of sequence reads indicating methylation on the given nucleotide can be normalized by the total number of sequence reads from the plurality of sequence reads, to derive the methylation fraction value. Different methylation values, such as a methylation entropy value, can be calculated based on the plurality of sequence reads for a given nucleotide. The described method of determining a methylation value for a given nucleotide, can be repeated for other nucleotides, and for other samples. To determine methylation values for a CpG site, the methylation values for for all samples, can be pooled together. The resulting pool of methylation values can comprise a distribution of methylation values that can be subject to statistical methods, such as comparing the distribution of methylation values to a second distribution of methylation values. The comparing can be done via a Kolmogorov-Smirnov test or a binomial significance test. The methylation values can be formatted in any number of ways that may be compatible with computational analyses. For example, the methylation values for the first group can be formatted as a matrix of methylation values, e.g., an m X n matrix where m can be the number of samples and n can be the number of features, such as the CpG sites. The methylation values can also be tidied into a long data format, such that each row of the data islimited to a single observation, i.e., each row of the data is limited to values regarding a single CpG site of a single sample.

[0165] The CpG sites can be from LI genomic regions. LI (i.e., LINE-1) genomic regions are a family of related class I transposable elements in the DNA of some organisms. They comprise long interspersed nuclear elements (LINEs). LI elements comprise approximately 17% of the human genome and are tightly regulated in the germline by epigenetic modifications, such as DNA methylation and histone modifications. The CpG sites of LI regions are sparsely distributed across the genome. Accordingly, the methods described herein can apply to not only CpG sites from LI genomic regions, but CpG sites in general — particularly for sparsely distributed CpG sites. The methods described herein relate to selecting CpG sites and clustering the CpG sites into genomic regions, which can be used as bait set targets, from which a bait set can be designed. The present statistical method contrasts with the other four methods described herein, because the other four methods select genomic regions from already identified genomic regions, rather than clustering selected CpG sites into genomic regions.

[0166] At 704 in FIG. 7 or 804 in FIG. 8, first non-disease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a first non-disease group are determined. A methylation value from the first non-disease methylation values can include values that are determined for a single nucleotide (e.g., a CpG nucleotide). The methylation value can, for example, be based on a plurality of sequencing reads, such as a methylation fraction value. For example, from a plurality of sequence reads that overlap with a given nucleotide of a reference genome, the number of sequence reads indicating methylation on the given nucleotide can be counted. The number of sequence reads indicating methylation on the given nucleotide can be normalized by the total number of sequence reads from the plurality of sequence reads, to derive the methylation fraction value. Different methylation values, such as a methylation entropy value, can be calculated based on the plurality of sequence reads for a given nucleotide. The described method of determining a methylation value for a given nucleotide, can be repeated for other nucleotides, and for other samples. To determine methylation values for a CpG site, the methylation values for all samples, can be pooled together. The resulting pool of methylation values can comprise a distribution of methylation values that can be subject to statistical methods, such as comparing the distribution of methylation values to a seconddistribution of methylation values. The comparing can be done via a Kolmogorov-Smirnov test or a binomial significance test. The methylation values can be formatted in any number of ways that may be compatible with computational analyses. For example, the methylation values for the first group can be formatted as a matrix of methylation values, e.g., an m X n matrix where m can be the number of samples and n can be the number of features, such as the CpG sites. The methylation values can also be tidied into a long data format, such that each row of the data is limited to a single observation, i.e., each row of the data is limited to values regarding a single CpG site of a single sample.

[0167] The sequences read data for the CpG sites for the one or more samples in the first nondisease group comprise panel of normal read count data. The panel of normal read count data can include a panel of normal samples selected or curated to represent a relevant set of healthy samples, e.g., samples that are believed to not have any somatic alterations. Panel of normal read count data (i.e., panel of normal) serve to capture recurrent technical artifacts, in order to improve the analyzing of sequencing data by, for example, ruling out technical artifacts that may be observed in the comparison group, e.g., disease group. The panel of normal read count data may derive from the same methods, including analyses, used to derive the sequence read data for the CpG sites for the disease group. The sequence read data for the CpG sites for the one or more samples in the first non-disease group can be from healthy plasma or healthy tissue.

[0168] At 706 in FIG. 7 or 806 in FIG. 8, from the disease methylation values and the first non-disease methylation values, one or more first similarity likelihoods for the CpG sites are determined. The similarity likelihood can be a value that describes how similar two sets of values or two distributions of values are to one another. For example, the similarity likelihood can refer to a value that describes how likely it is to observe two sample distributions, e.g., two distributions of methylation values (such as the disease methylation values and the first non- disease methylation values), given that the two sample distributions are drawn from a common population distribution. The common population distribution can be unknown, e.g., the experimenter need not know whether the common population distribution is a specific type of probability distribution, e.g., a Gaussian or a Poisson distribution. In the case of the methods described herein, the similarity likelihood can describe how likely two sample distributions — a distribution of methylation values from disease samples and a distribution of methylation valuesfrom the first non-disease samples — come from a common population distribution. The one or more first similarity likelihoods can be determined by statistical testing. The statistical testing can be a Kolmogorov-Smirnov testing. The Kolmogorov-Smirnov testing can result in a Kolmogorov-Smirnov statistic, that can range in value between 0 and 1, where a higher value suggests a higher likelihood that the two sample distributions are drawn from a common population distribution. The Kolmogorov-Smirnov statistic, Dn, can be determined for two sample distributions by:Dn= sup |Fl n(x) - F2,m(x) | X wherein, sup is the supremum of the set of distances between the two sample distributions. The two sample distributions expressed as two empirical distribution functions are denoted Fl n(x) and F2;m(x), and x denotes observations of a variable of interest, such as the methylation value of a genomic region.

[0169] The similarity likelihood need not be determined by Kolmogorov-Smirnov testing. The similarity likelihood can be determined from other statistical analyses, such as statistical testing. The statistical testing can be a binomial significance testing. For example, the similarity likelihood can be the p-value from binomial significance testing and can describe the probability of observing methylation at a genomic region for e.g., a disease sample, given the probability of methylation at the genomic region for e.g., a non-disease sample. The p-value from binomial significance testing can be expressed as:wherein, z is an index of summation; x is the amount of methylation seen at the CpG site in a group, e.g., the disease group; F(x) is the probability of observing at least the amount of methylation seen at the CpG site in the group; n is the total observed number of sequence reads with methylation at the CpG site in the group; p is the probability of methylation at the CpG site in a comparison group, e.g., the first non-disease group or the second non-disease group; and q is 1 — p, i.e., q is the complementary probability of p. The p-value from binomial significance testing can be understood as the probability of observing at least the amount of methylation across reads and / or samples in the group, e.g., the disease group, given the probability of methylation at the CpG site in a comparison group, e.g., the first non-disease group, i.e., p.

[0170] The similarity likelihood can be subject to error correcting techniques that account for multiple comparisons arising from multiple hypothesis testing, e.g., by determining the false discovery rate and / or correcting the similarity likelihood by Bonferroni correction or Benjamini- Hochberg correction. Such correcting techniques are referred to herein as multiple testing correction.

[0171] At 708 in FIG. 7 or 808 in FIG. 8, disease CpG sites corresponding to the one or more first similarity likelihoods are selected, based on the one or more first similarity likelihoods exceeding or being less than a predetermined first similarity likelihood threshold. For example, the determined first similarity likelihood from the one or more determined first similarity likelihoods can be based on a p-value from binomial significance testing. Binomial significance testing can classify a CpG site as significantly different between a disease group and a nondisease group, based on a p-value that derives from the comparison between the two groups, and the p-value being less than a threshold value. If the disease CpG site is frequently classified across samples as being significantly different, e.g., in at least 90% of cases for colorectal disease samples or at least 80% of cases for lung cancer disease samples, then that disease CpG site is selected.. Similarly, for example, when the determined first similarity likelihood from the one or more determined first similarity likelihoods is a Kolmogorov-Smirnov (K-S) statistic, then if the K-S statistic for a CpG site is less than the predetermined first threshold, e.g. 0.05, following multiple testing correction, then the CpG site can be included in the set of bait set targets. The logic is that if, for example, the disease methylation values, and the first non-disease methylation values score a low similarity likelihood, then in the case of the Kolmogorov- Smirnov testing, the disease and the first non-disease methylation values are highly unlikely to come from the same population distribution.

[0172] At 810 in FIG. 8, one or more separability metrics for the disease CpG sites are determined from the disease methylation values and the first non-disease methylation values. The separability metrics of the one or more separability metrics can comprise any metric that describes how well a feature can separate a dataset into two (or more) classes. For example, the separability metric can describe how well the methylation of a CpG site (or some other classifier) distinguishes whether a sample belongs in the one or more first groups (e.g., the disease group), versus the one or more second groups (e.g., the first non-disease group). Theseparability metric can be an Area Under the Curve (AUC) score. An AUC score is derived by determining the area under a receiver-operating characteristic (ROC) curve, and describes how well a binary classifier separates a dataset. A ROC curve plots the true positive rate of a classifier against the false positive rate of a classifier. The true positive rate is expressed as the number of true positives classified by the classifier, normalized by the sum of the number of true positives and false negatives classified by the classifier. The false positive rate is expressed as the number of false positive classified by the classifier, normalized by the sum of the number of false positive and true negatives classified by the classifier. The AUC score can range in value between 0.0 and 1.0, where a higher AUC value refers to a better performing classifier. The AUC score can be interpreted as a probability that the classifier, e.g., a CpG site, ranks a random positive observation (e.g., an observation that belongs to a class, such as the disease group) more highly than a random negative observation (e.g., an observation that does not belong to the class). The methylation values, e.g., the methylation fraction values or methylation entropy values can be used with the group labels to generate an AUC value for every CpG site. For example, for two groups, e.g., a disease group for a CpG site and a non-disease group for the CpG site, there is a list of methylation values between 0 and 1, inclusive. From the two lists of values, two histograms can be plotted, one for the disease group and one for the non-disease group. A threshold can then be incremented along the x-axis, such that at each increment, the number of true positives, false positives, true negatives, and false negatives can be determined from the two histograms, and accordingly, the true positive rates and the false positive rates can be determined at each increment. From the determined true positive and false positive rates, the ROC and AUC can be derived and plotted for the CpG site.

[0173] At 812 in FIG. 8, informative disease CpG sites from the CpG sites or the disease CpG sites based on the determined one or more separability metrics exceeding a predetermined separability metric threshold are selected. For example, if the determined separability metric, e.g., the AUC score for a CpG site exceeds the predetermined separability metric threshold, e.g., 0.99 for colorectal cancer samples or 0.95 for lung cancer samples, for any of the pairs of groups being compared between the one or more first groups and the one or more second groups, then the CpG site can be included in the set of bait set targets. The pairs of groups being compared can, for example, be: cancer tissue samples versus healthy plasma samples, healthy tissuesamples versus healthy plasma samples, and / or cancer tissue samples versus healthy tissue samples. The logic behind selecting for CpG sites with high AUC scores is that if the AUC score from comparing two groups based on the CpG site is high, then the CpG site can separate the two groups, e.g., a disease group versus a non-disease group, with high performance. Accordingly, the AUC score can serve as a probability that can predict how well the CpG site separates the two groups.

[0174] At 814 in FIG. 8, a mean difference from the disease methylation values and the first non-disease methylation values are determined. For example, the disease methylation values for a CpG site can be averaged, and the first non-disease methylation values for the CpG site can be averaged. The difference between the two averages, i.e., the mean difference, can then be determined. The mean difference can be determined for multiple (e.g., all) CpG sites.

[0175] At 816 in FIG. 8, further informative disease CpG sites from the CpG sites, the disease CpG sites, or the informative disease CpG sites based on the determined mean difference exceeding a predetermined mean difference threshold are selected. For example, if the mean difference between the disease methylation values and the first non-disease methylation values for a CpG site is greater than 0.3, the CpG site can be selected as a bait set target. The selected CpG sites are the further informative disease CpG sites. The mean difference can be computed at any point in the bait set target selection process, e.g., from the CpG sites, the disease CpG sites, or the informative disease CpG sites. Thresholding based on the mean difference removes CpG sites that are methylated to a similar extent between the two groups being compared from the one or more first groups and the one or more second groups. The determined mean difference can be negative.

[0176] At 818 in FIG. 8, second non-disease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a second non-disease group, are determined. A methylation value from the second non-disease methylation values can include values that are determined for a single nucleotide (e.g., a CpG nucleotide). The methylation value can, for example, be based on a plurality of sequencing reads, such as a methylation fraction value. For example, from a plurality of sequence reads that overlap with a given nucleotide of a reference genome, the number of sequence reads indicating methylation on the given nucleotide can be counted. The number of sequence reads indicating methylation on the given nucleotide can benormalized by the total number of sequence reads from the plurality of sequence reads, to derive the methylation fraction value. Different methylation values, such as a methylation entropy value, can be calculated based on the plurality of sequence reads for a given nucleotide. The described method of determining a methylation value for a given nucleotide, can be repeated for other nucleotides, and for other samples. To determine methylation values for a genomic region, the methylation values for each nucleotide in the genomic region, and for all samples, can be pooled together. The resulting pool of methylation values can comprise a distribution of methylation values that can be subject to statistical methods, such as comparing the distribution of methylation values to a second distribution of methylation values. The comparing can be done via a Kolmogorov-Smirnov test or a binomial significance test. The methylation values can be formatted in any number of ways that may be compatible with computational analyses. For example, the methylation values for the first group can be formatted as a matrix of methylation values, e.g., an m X n matrix where m can be the number of samples and n can be the number of features, such as the CpG sites. The methylation values can also be tidied into a long data format, such that each row of the data is limited to a single observation, i.e., each row of the data is limited to values regarding a single CpG site of a single sample.

[0177] The sequence read data for the CpG sites for the one or more samples in the second nondisease group can comprise panel of normal read count data. The panel of normal read count data can include a panel of normal samples selected or curated to represent a relevant set of healthy samples, e.g., samples that are believed to not have any somatic alterations. Panel of normal read count data (i.e., panel of normal) serve to capture recurrent technical artifacts, in order to improve the analyzing of sequencing data by, for example, ruling out technical artifacts that may be observed in the comparison group, e.g., disease group. The panel of normal read count data may derive from the same methods, including analyses, used to derive the sequence read data for the CpG sites for the disease group. Methylation values can be determined from the panel of normal. The methylation values can be compared against methylation values deriving from non-panel of normal read count data, such as sequence read count data deriving from the sample of interest, e.g., the second non-disease group sample. The sequence read data for the CpG sites for the one or more samples in the second non-disease group can be from healthy plasma or healthy tissue.

[0178] At 820 in FIG. 8, one or more second similarity likelihoods for the CpG sites are determined from the first non-disease methylation values and second non-disease methylation values. The similarity likelihood can be a value that describes how similar two sets of values or two distributions of values are to one another. For example, the similarity likelihood can refer to a value that describes how likely it is to observe two sample distributions, e.g., two distributions of methylation values (such as the first non-disease methylation values and the second non- disease methylation values), given that the two sample distributions are drawn from a common population distribution. The common population distribution can be unknown, e.g., the experimenter need not know whether the common population distribution is a specific type of probability distribution, e.g., a Gaussian or a Poisson distribution. In the case of the methods described herein, the similarity likelihood can describe how likely two sample distributions — a distribution of methylation values from the first non-disease samples and a distribution of methylation values from the second non-disease samples — come from a common population distribution. The one or more second similarity likelihoods can be determined by statistical testing. The statistical testing can be a Kolmogorov-Smirnov testing. The Kolmogorov-Smirnov testing can result in a Kolmogorov-Smirnov statistic, that can range in value between 0 and 1, where a higher value suggests a higher likelihood that the two sample distributions are drawn from a common population distribution. The Kolmogorov-Smirnov statistic, Dn, can be determined for two sample distributions by:Dn= sup |Fl n(x) - F2,m(x) | X wherein, sup is the supremum of the set of distances between the two sample distributions. The two sample distributions expressed as two empirical distribution functions are denoted Fl n(x) and F2;m(x), and x denotes observations of a variable of interest, such as the methylation value of a genomic region.

[0179] The similarity likelihood need not be determined by Kolmogorov-Smirnov testing. The similarity likelihood can be determined from other statistical analyses, such as statistical testing. The statistical testing can be a binomial significance testing. For example, the similarity likelihood can be the p-value from binomial significance testing and can describe the probability of observing methylation at a genomic region for e.g., a first non-disease sample, given theprobability of methylation at the genomic region for e.g., a second non-disease sample. The p- value from binomial significance testing can be expressed as:wherein, z is an index of summation; x is the amount of methylation seen at the CpG site in a group, e.g., the first non-disease group; P(x) is the probability of observing at least the amount of methylation seen at the CpG site in the first non-disease group; n is the total observed number of sequence reads with methylation at the CpG site in the group; p is the probability of methylation at the CpG site in a comparison group, e.g., the second non-disease group; and q is 1 — p, i.e., q is the complementary probability of p. The p- value from binomial significance testing can be understood as the probability of observing at least the amount of methylation across reads and / or samples in the group, e.g., the first non-disease group, given the probability of methylation at the CpG site in a comparison group, e.g., the second non-disease group, i.e., p.

[0180] At 822 in FIG. 8, informative CpG sites from the CpG sites, the disease CpG sites, informative disease CpG sites, or the further informative disease CpG sites are selected, based on the one or more second similarity likelihoods being less than a predetermined second similarity likelihood threshold. For example, the determined second similarity likelihood from the one or more determined second similarity likelihoods can be based on a p-value from binomial significance testing. Binomial significance testing can classify a CpG site as significantly different between the first non-disease group and the second non-disease group, based on a p-value that derives from the comparison between the two groups, and the p-value being less than a threshold value. If the CpG site (e.g., the CpG site, the disease CpG site, the informative disease CpG site, or the further informative disease CpG site) is infrequently classified across samples as being significantly different, e.g., in less than 5% of panel of normal samples, then that disease CpG site is selected. Similarly, for example, when the determined second similarity likelihood from the one or more determined second similarity likelihoods is a Kolmogorov-Smirnov (K-S) statistic, then if the K-S statistic for a CpG site is greater than the predetermined second threshold, e.g. 0.95, following multiple testing correction, then the CpG site can be included in the set of bait set targets. The logic is that if, for example, the first non- disease methylation values, and the second non-disease methylation values score a highsimilarity likelihood, then in the case of the Kolmogorov-Smirnov testing, the disease and the first non-disease methylation values are likely to come from the same population distribution, and be common to non-disease samples, in general. In the case that the first non-disease group is healthy tissue and the second non-disease group is healthy plasma, detecting unexpected tissue DNA in the healthy plasma can be considered a signal of disease.

[0181] At 824 in FIG. 8, informative CpG sites are clustered based on any two informative CpG sites being within a predetermined distance from one another, and that within the predetermined distance, at least four informative CpG sites are found, to generate one or more clusters. The clustering can be understood as a rolling window analysis, where the window is sized at the predetermined distance. The predetermined distance can be 300 base pairs long. If at least four informative CpG sites are found in the window, then the nucleotides included in the span of the window can be included in the cluster. The window can continue rolling across the genome, such that every time the windows moves (by one nucleotide), and at least four informative CpG sites are found within the window, the nucleotides within the window can be added to the cluster, provided that a cluster was generated at the window’s previous position. In doing so, a cluster longer than the window size, e.g., longer than 300 base pairs can be generated.

[0182] At 826 in FIG. 8, bait set targets corresponding to the one or more clusters are selected. The clusters can be understood as genomic regions. The bait set targets can be further filtered, for example, by excluding targets featuring motifs that may compromise efficient manufacturing, such as tandem repeats of GC-rich regions. The bait set targets can be used for informing the design and / or generation of a final set of bait set targets.

[0183] Process 700 or process 800 can be used to select bait set targets, based on methylation values of DNA. The selected bait set targets can be included in a final set of bait set targets. A final bait set can be designed based on the final set of bait set targets. The bait set targets selected according to process 700 or process 800 can be combined with bait set targets selected according to another method, such as the methods indicated by processes 100-600 and / or process 900.Selecting bait set targets based on hypermethylated regions in healthy subjects

[0184] The present statistical method determine hypermethylated regions in healthy subjects. The hypermethylated regions are selected as bait set targets. Further statistical analyses can be performed to filter the hypermethylated regions to be selected as the bait set targets.

[0185] FIG. 9 shows exemplary schematics showing general process 900 for selecting bait set targets based on methylation values. Process 900 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 900 is performed using a client-server system, and the blocks of process 900 are divided up in any manner between the server and a client device. In other examples, the blocks of process 900 are divided up between the server and multiple client devices. Thus, while portions of process 900 are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 900 are not so limited. In other examples, process 900 is performed using only a client device or only multiple client devices. In process 900, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 900. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0186] At 902 in FIG. 9, one or more methylation values corresponding to sequence read data for a genomic region from one or more healthy samples are determined. The one or more methylation values from the one or more healthy samples (e.g., healthy plasma samples) can include values that are determined for a single nucleotide (e.g., a CpG nucleotide). The methylation value can, for example, be based on a plurality of sequencing reads, such as a methylation fraction value. For example, from a plurality of sequence reads that overlap with a given nucleotide of a reference genome, the number of sequence reads indicating methylation on the given nucleotide can be counted. The number of sequence reads indicating methylation on the given nucleotide can be normalized by the total number of sequence reads from the plurality of sequence reads, to derive the methylation fraction value. Different methylation values, such as a methylation entropy value, can be calculated based on the plurality of sequence reads for a given nucleotide. The described method of determining a methylation value for a given nucleotide, can be repeated for other nucleotides, and for other samples. To determinemethylation values for a genomic region, the methylation values for each nucleotide in the genomic region, and for all samples, can be pooled together. The resulting pool of methylation values can comprise a distribution of methylation values that can be subject to statistical methods. The methylation values can be formatted in any number of ways that may be compatible with computational analyses. For example, the methylation values for the first group can be formatted as a matrix of methylation values, e.g., an m X n matrix where m can be the number of samples and n can be the number of features, such as the genomic regions. The methylation values can also be tidied into a long data format, such that each row of the data is limited to a single observation, i.e., each row of the data is limited to values regarding a single genomic region of a single sample. The one or more healthy samples can include healthy plasma samples. Moreover, the sequence read data from which the methylation values derive can include longer reads, i.e., longer fragments that are sequenced.

[0187] The genomic region can be hypermethylated in the one or more healthy samples, relative to one or more disease samples. For example, the genomic region can have a high aggregate methylation value (e.g., a high methylation fraction value or a high methylation entropy value) across the one or more healthy samples. The aggregate methylation value can be determined based on a central tendency measure.

[0188] At 904 in FIG. 9, a central tendency measure from the one or more methylation values is determined. The central tendency measure can be the mean, median, or the mode. For example, the arithmetic mean of the methylation values can be determined for a genomic region from one or more healthy samples. The median of the methylation values can additionally and / or alternatively be determined for the hypermethylated genomic region. The median can be useful for identifying and optionally excluding outliers from the methylation values of a given genomic region. The central tendency measure of the methylation fraction values from the one or more healthy samples can be used to determine whether the genomic region can be considered to be hypermethylated.

[0189] At 906 in FIG. 9, a bait set target corresponding to the genomic region based on the determined central tendency measure exceeding a predetermined threshold is selected. For example, if the determined central tendency measure, e.g., the arithmetic mean of themethylation values, for a genomic region is greater than the predetermined threshold, e.g., 0.9, then the genomic region can be considered to be hypermethylated in healthy samples.

[0190] Process 900 can be used to select bait set targets, based on methylation values of DNA. The selected bait set targets can be included in a final set of bait set targets. A final bait set can be designed based on the final set of bait set targets. The bait set targets selected according to process 900 can be combined with bait set targets selected according to another method, such as the methods indicated by processes 100-800.Selecting bait set targets based on the combining of different bait selection methods

[0191] FIG. 10 shows exemplary schematics showing general process 1000 for selecting bait set targets based on methylation values. Process 1000 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 1000 can be performed using a client-server system, and the blocks of process 1000 can be divided up in any manner between the server and a client device. In other examples, the blocks of process 1000 can be divided up between the server and multiple client devices. Thus, while portions of process 1000 are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 1000 are not so limited. In other examples, process 1000 is performed using only a client device or only multiple client devices. In process 1000, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 1000. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0192] In some aspects, described herein is a method for selecting a final set of bait set targets comprising combining at least the bait set target or the bait set targets selected from using two or more of the methods described herein. That is, the bait set targets selected based on the above methods can be combined into a final set of bait set targets. The combining can comprise performing a union of the genomic regions across the two or more methods used to select the bait set targets. In doing so, redundant genomic regions can be eliminated from the final set of bait set targets. Further filtering of the genomic regions can optionally be performed on the final set of bait set targets. For example, genomic regions can be excluded based on knownconfounding effects according to the scientific literature, e.g., if the genomic region is highly rich in GC nucleotides and / or comprises several tandem repeats, the genomic region may not be suitable for manufacturing a final bait set that corresponds to the final set of bait set targets.

[0193] The combining can further comprise designing a final bait set corresponding to the final set of bait set targets. The final bait set sequences an comprise oligonucleotide sequences that overlap, at least in part, with the bait set targets of the final set. The combining can further comprise generating the final bait set based on the designed final bait set. The generating of the designed final bait set can comprise chemical oligonucleotide synthesis with a scalable silicon- based manufacturing platform, to generate probe panels for comprehensive genomic profiling. The final bait set can be generated by a third party. The final set of bait set targets can comprise a single bait set target. In rare cases, combining the bait set targets derived from two or more of the statistical methods can result in the selection of a single unique bait set target.

[0194] The above five statistical methods are based on receiving methylation values, e.g., first methylation values and / or second methylation values. The methylation values can include values based on a plurality of sequencing reads. A methylation value in the methylation values can be a methylation fraction value or a methylation entropy value. The methylation fraction value can be a number of sequence reads that are methylated at a nucleotide site normalized by a total number of sequence reads at the nucleotide site. The methylation entropy values can also be derived from the number of sequence reads that are methylated and / or not methylated. The methylation entropy values can be understood to be a measure of the variation or disorder of a set of methylation values, and need not necessarily be understood as a value for methylation. The methylation entropy value, H, can be determined by:H = ~p log2p - q log2q wherein: p is the number of sequence reads that are methylated at a nucleotide site normalized by a total number of sequence reads at the nucleotide site; and q is the number of sequence reads that are unmethylated at a nucleotide site normalized by a total number of sequence reads at the nucleotide site, and can be expressed as 1 — p. The log2refers to a base 2 logarithm.

[0195] Multiple types of methylation values can be used for any one of the five statistical methods. The methylation values for any of the given statistical methods, however, cannot comprise a mix of different types of methylation values. For example, a matrix of methylationvalues to be used for the first statistical method cannot comprise a mix of both methylation fraction values and methylation entropy values. The combining of the bait set target or targets from the two or more statistical methods, however, can be based on multiples types of methylation values. For example, bait set targets selected based on methylation fraction values received by the first statistical method can be combined with bait set targets selected based on methylation entropy values received by the second statistical method.

[0196] The final set of bait set targets can be used to determine a disease signal in the subject, wherein the disease signal can be indicative of a disease status in the subject. For example, the detection of hypo- and / or hyper-methylated genomic regions corresponding to the final set of bait set targets can indicate the disease status. The disease status can be, for example, a stage in the progression of the disease, such as a prognostic stage, e.g., stage IV, in the case of cancer. The disease signal can be indicative of the cancer, Alzheimer’s Disease, type 2 diabetes, cardiovascular disease, autoimmune diseases, or any combination thereof.

[0197] Designing and / or applying the final set of bait set targets may further comprise one or more of the steps of: (i) obtaining the sample from the subject (e.g., a subject suspected of having or determined to have cancer), (ii) extracting nucleic acid molecules (e.g., a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules) from the sample, (iii) ligating one or more adapters to the nucleic acid molecules extracted from the sample (e.g., one or more amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences), (iv) performing a methylation conversion reaction to convert, e.g., non-methylated cytosine to uracil, (v) amplifying the nucleic acid molecules (e.g., using a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique), (vi) capturing nucleic acid molecules from the amplified nucleic acid molecules (e.g., by hybridization to one or more bait molecules, where the bait molecules each comprise one or more nucleic acid molecules that each comprising a region that is complementary to a region of a captured nucleic acid molecule), (vii) sequencing the nucleic acid molecules extracted from the sample (or library proxies derived therefrom) using, e.g., a next-generation (massively parallel) sequencing technique, a whole genome sequencing (WGS) technique, a whole exome sequencing technique, a targeted sequencing technique, a direct sequencing technique, or a Sanger sequencing technique) using, e.g., a next-generation (massively parallel) sequencer, and (viii) generating, displaying, transmitting, and / or delivering a report (e.g., an electronic, web-based, or paper report) to the subject (or patient), a caregiver, a healthcare provider, a physician, an oncologist, an electronic medical record system, a hospital, a clinic, a third-party payer, an insurance company, or a government office. In some instances, the report comprises output from the methods described herein. In some instances, all or a portion of the report may be displayed in the graphical user interface of an online or webbased healthcare portal. In some instances, the report is transmitted via a computer network or peer-to-peer connection.

[0198] In some instances, the disclosed methods may be used to select bait set targets by assessing methylation values in at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 20, at least 30, at least 40, or more than 40 gene loci.

[0199] In some instances, the disclosed methods may be used to identify variants that can alter methylation patterns, in genes or non-coding regions that regulate the expression of those genes. Similarly, the disclosed methods can be used to identify methylation patterns (e.g., hypermethylation or hypomethylation of a genomic region or CpG for samples in a group, relative to another group) in the genes or the non-coding regions that regulate the expression of those genes. The variants ant / or methylation patterns can be in the ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA, CHEK1, CHEK2, CIC, CREBBP, CRKL, CSF1R, CSF3R, CTCF, CTNNA1, CTNNB1, CUL3, CUL4A, CXCR4, CYP17A1, DAXX, DDR1, DDR2, DIS3, DNMT3A, DOT1L, EED, EGFR, EMSY (Cllorf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLT1, FLT3, FOXL2, FUBP1, GABRA6, GATA3, GATA4,GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, IDH1, IDH2, IGF1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KEL, KIT, KLHL6, KMT2A (MLL), KMT2D (MLL2), KRAS, LTK, LYN, MAF, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K13, MAPK1, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MET, MITF, MKNK1, MLH1, MPL, MRE11A, MSH2, MSH3, MSH6, MST1R, MTAP, MTOR, MUTYH, MYB, MYC, MYCL, MYCN, MYD88, NBN, NF1, NF2, NFE2L2, NFKBIA, NKX2-1, NOTCH1, NOTCH2, NOTCH3, NPM1, NRAS, NT5C2, NTRK1, NTRK2, NTRK3, NUTM1, P2RY8, PALB2, PARK2, PARP1, PARP2, PARP3, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, SOCS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, or ZNF703 gene locus, or any combination thereof.

[0200] In some instances, the disclosed methods may be used to identify variants that can alter methylation patterns, in genes or non-coding regions that regulate the expression of those genes. Similarly, the disclosed methods can be used to identify methylation patterns (e.g., hypermethylation or hypomethylation of a genomic region or CpG for samples in a group, relative to another group) in the genes or the non-coding regions that regulate the expression of those genes. The genes variants can be in the ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1-3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRp, PD-L1, PI3K5, PIGF, PTCH, RAF,RANKL, RET, R0S1, SLAMF7, VEGF, VEGFA, or VEGFB gene locus, or any combination thereof.Methods of use

[0201] The disclosed methods may be used with any of a variety of samples. For example, in some instances, the sample may comprise a tissue biopsy sample, a liquid biopsy sample, or a normal control. In some instances, the sample may be a liquid biopsy sample and may comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva. In some instances, the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs). In some instances, the sample may be a liquid biopsy sample and may comprise cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.

[0202] In some instances, the nucleic acid molecules extracted from a sample may comprise a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules. In some instances, the tumor nucleic acid molecules may be derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules may be derived from a normal portion of the heterogeneous tissue biopsy sample. In some instances, the sample may comprise a liquid biopsy sample, and the tumor nucleic acid molecules may be derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample while the non- tumor nucleic acid molecules may be derived from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.

[0203] In some instances, the disclosed methods for designing a bait set based on the selected bait set targets may be used to diagnose (or as part of a diagnosis of) the presence of disease or other condition (e.g., cancer, genetic disorders (such as Down Syndrome and Fragile X), neurological disorders, or any other disease type where detection of variants, e.g., copy number alternations, are relevant to diagnosing, treating, or predicting said disease) in a subject (e.g., a patient). In some instances, the disclosed methods may be applicable to diagnosis of any of a variety of cancers as described elsewhere herein.

[0204] In some instances, the disclosed methods for designing a bait set based on the selected bait set targets may be used to predict genetic disorders in fetal DNA. e.g., for invasive or non- invasive prenatal testing). For example, sequence read data obtained by sequencing fetal DNAextracted from samples obtained using invasive amniocentesis, chorionic villus sampling (cVS), or fetal umbilical cord sampling techniques, or obtained using non-invasive sampling of cell-free DNA (cfDNA) samples (which comprises a mix of maternal cfDNA and fetal cfDNA), may be processed according to the disclosed methods to identify variants, e.g., copy number alterations, associated with, e.g., Down Syndrome (trisomy 21), trisomy 18, trisomy 13, and extra or missing copies of the X and Y chromosomes.

[0205] In some instances, the disclosed methods for designing a bait set based on the selected bait set targets may be used to design a bait set for the detection, diagnosis, treatment selection, monitoring or analysis of patients having or suspected of having cardiovascular disease (e.g., congenital hear defects) or neurological diseases (e.g., Alzheimer’s Disease, Parkinson’s Disease, etc.)

[0206] In some instances, the disclosed methods for designing a bait set based on the selected bait set targets may be used to select a subject (e.g., a patient) for a clinical trial. In some instances, patient selection for clinical trials based on, e.g., selection of the bait set target across one or more genomic loci, may accelerate the development of targeted therapies and improve the healthcare outcomes for treatment decisions.

[0207] In some instances, the disclosed methods for designing a bait set based on the selected bait set targets may be used to select an appropriate therapy or treatment (e.g., an anti-cancer therapy or anti-cancer treatment) for a subject. In some instances, for example, the anti-cancer therapy or treatment may comprise use of a poly (ADP-ribose) polymerase inhibitor (PARPi), a platinum compound, chemotherapy, radiation therapy, a targeted therapy (e.g., immunotherapy), surgery, or any combination thereof.

[0208] In some instances, the targeted therapy (or anti-cancer target therapy) may comprise abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado- trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab- vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta), axitinib (Inlyta), belantamab mafodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), belzutifan (Welireg), bevacizumab (Avastin), bexarotene(Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), brigatinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabometyx, Cometriq), canakinumab (Haris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab-rwlc (Libtayo), ceritinib (LDK378 / Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), copanlisib hydrochloride (Aliqopa), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro), daratumumab (Darzalex), daratumumab and hyaluronidase-fihj (Darzalex Faspro), darolutamide (Nubeqa), dasatinib (Sprycel), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib mesylate (Idhifa), encorafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide (Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedratinib hydrochloride (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinib mesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), iobenguane 1131 (Azedra), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu 177-dotatate (Lutathera), margetuximab-cmkb (Margenza), midostaurin (Rydapt), mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasudotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazyva), ofatumumab (Arzerra), olaparib (Lynparza), olaratumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), panobinostat (Farydak), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralsetinib (Gavreto), radium 223 dichloride(Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), ripretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), rucaparib camsylate (Rubraca), ruxolitinib phosphate (Jakafi), sacituzumab govitecan-hziy (Trodelvy), seliciclib, selinexor (Xpovio), selpercatinib (Retevmo), selumetinib sulfate (Koselugo), siltuximab (Sylvant), sipuleucel-T (Provenge), sirolimus proteinbound particles (Fyarro), sonidegib (Odomzo), sorafenib (Nexavar), sotorasib (Lumakras), sunitinib (Sutent), tafasitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), talazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), tebentafusp-tebn (Kimmtrak), temsirolimus (Torisel), tepotinib hydrochloride (Tepmetko), tisagenlecleucel (Kymriah), tisotumab vedotin-tftv (Tivdak), tocilizumab (Actemra), tofacitinib (Xeljanz), tositumomab (Bexxar), trametinib (Mekinist), trastuzumab (Herceptin), tretinoin (Vesanoid), tivozanib hydrochloride (Fotivda), toremifene (Fareston), tucatinib (Tukysa), umbralisib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), vismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv-aflibercept (Zaltrap), or any combination thereof.

[0209] In some instances, the disclosed methods for designing a bait set based on the selected bait set targets may be used in treating a disease (e.g., a cancer) in a subject. For example, in response to selecting a bait set target using any of the methods disclosed herein, an effective amount of an anti-cancer therapy or anti-cancer treatment may be administered to the subject.

[0210] In some instances, the disclosed methods for designing a bait set based on the selected bait set targets may be used for monitoring disease progression or recurrence (e.g., cancer or tumor progression or recurrence) in a subject. For example, in some instances, the methods may be used to determine first results from the final set of bait set targets in a first sample obtained from the subject at a first time point, and used to determine second results from the final set of bait set targets in a second sample obtained from the subject at a second time point, where comparison of the first determination of the first results and the second determination of the second results allows one to monitor disease progression or recurrence. In some instances, the first time point is chosen before the subject has been administered a therapy or treatment, and the second time point is chosen after the subject has been administered the therapy or treatment.

[0211] In some instances, the disclosed methods may be used for adjusting a therapy or treatment (e.g., an anti-cancer treatment or anti-cancer therapy) for a subject, e.g., by adjusting a treatment dose and / or selecting a different treatment in response to a change in the results from using the final set of bait set targets.

[0212] In some instances, the results from using the final set of bait set targets using the disclosed methods may be used as a prognostic or diagnostic indicator associated with the sample. For example, in some instances, the prognostic or diagnostic indicator may comprise an indicator of the presence of a disease (e.g., cancer) in the sample, an indicator of the probability that a disease (e.g., cancer) is present in the sample, an indicator of the probability that the subject from which the sample was derived will develop a disease (e.g., cancer) (i.e., a risk factor), or an indicator of the likelihood that the subject from which the sample was derived will respond to a particular therapy or treatment.

[0213] In some instances, the disclosed methods for selecting bait set targets may be implemented as part of a genomic profiling process that comprises identification of the presence of variant sequences at one or more gene loci in a sample derived from a subject as part of detecting, monitoring, predicting a risk factor, or selecting a treatment for a particular disease, e.g., cancer. In some instances, the variant panel selected for genomic profiling may comprise the detection of variant sequences at a selected set of gene loci. In some instances, the variant panel selected for genomic profiling may comprise detection of variant sequences at a number of gene loci through comprehensive genomic profiling (CGP), which is a next- generation sequencing (NGS) approach used to assess hundreds of genes (including relevant cancer biomarkers) in a single assay. Inclusion of the disclosed methods for selecting bait set targets as part of a genomic profiling process (or inclusion of the output from the disclosed methods for selecting bait set targets as part of the genomic profile of the subject) can improve the validity of, e.g., disease detection calls and treatment decisions, made on the basis of the genomic profile by, for example, independently confirming the presence of a bait set target in a given patient sample.

[0214] In some instances, a genomic profile may comprise information on the presence of genes (or variant sequences thereof), copy number variations, epigenetic traits, proteins (or modifications thereof), and / or other biomarkers in an individual’s genome and / or proteome, aswell as information on the individual’s corresponding phenotypic traits and the interaction between genetic or genomic traits, phenotypic traits, and environmental factors.

[0215] In some instances, a genomic profile for the subject may comprise results from a comprehensive genomic profiling (CGP) test, a nucleic acid sequencing-based test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.

[0216] In some instances, the method can further include administering or applying a treatment or therapy (e.g., an anti-cancer agent, anti-cancer treatment, or anti-cancer therapy) to the subject based on the generated genomic profile. An anti-cancer agent or anti-cancer treatment may refer to a compound that is effective in the treatment of cancer cells. Examples of anti-cancer agents or anti-cancer therapies include, but not limited to, alkylating agents, antimetabolites, natural products, hormones, chemotherapy, radiation therapy, immunotherapy, surgery, or a therapy configured to target a defect in a specific cell signaling pathway, e.g., a defect in a DNA mismatch repair (MMR) pathway.Samples

[0217] The disclosed methods and systems may be used with any of a variety of samples (also referred to herein as specimens) comprising nucleic acids (e.g., DNA or RNA) that are collected from a subject (e.g., a patient). Examples of a sample include, but are not limited to, a tumor sample, a tissue sample, a biopsy sample e.g., a tissue biopsy, a liquid biopsy, or both), a blood sample (e.g., a peripheral whole blood sample), a blood plasma sample, a blood serum sample, a lymph sample, a saliva sample, a sputum sample, a urine sample, a gynecological fluid sample, a circulating tumor cell (CTC) sample, a cerebral spinal fluid (CSF) sample, a pericardial fluid sample, a pleural fluid sample, an ascites (peritoneal fluid) sample, a feces (or stool) sample, or other body fluid, secretion, and / or excretion sample (or cell sample derived therefrom). In certain instances, the sample may be frozen sample or a formalin-fixed paraffin-embedded (FFPE) sample.

[0218] In some instances, the sample may be collected by tissue resection (e.g., surgical resection), needle biopsy, bone marrow biopsy, bone marrow aspiration, skin biopsy, endoscopicbiopsy, fine needle aspiration, oral swab, nasal swab, vaginal swab or a cytology smear, scrapings, washings or lavages (such as a ductal lavage or bronchoalveolar lavage), etc.

[0219] In some instances, the sample is a liquid biopsy sample, and may comprise, e.g., whole blood, blood plasma, blood serum, urine, stool, sputum, saliva, or cerebrospinal fluid. In some instances, the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs). In some instances, the sample may be a liquid biopsy sample and may comprise cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.

[0220] In some instances, the sample may comprise one or more premalignant or malignant cells. Premalignant, as used herein, refers to a cell or tissue that is not yet malignant but is poised to become malignant. In certain instances, the sample may be acquired from a solid tumor, a soft tissue tumor, or a metastatic lesion. In certain instances, the sample may be acquired from a hematologic malignancy or pre-malignancy. In other instances, the sample may comprise a tissue or cells from a surgical margin. In certain instances, the sample may comprise tumor-infiltrating lymphocytes. In some instances, the sample may comprise one or more non- malignant cells. In some instances, the sample may be, or is part of, a primary tumor or a metastasis (e.g., a metastasis biopsy sample). In some instances, the sample may be obtained from a site (e.g., a tumor site) with the highest percentage of tumor (e.g., tumor cells) as compared to adjacent sites (e.g., sites adjacent to the tumor). In some instances, the sample may be obtained from a site (e.g., a tumor site) with the largest tumor focus (e.g., the largest number of tumor cells as visualized under a microscope) as compared to adjacent sites (e.g., sites adjacent to the tumor).

[0221] In some instances, the disclosed methods may further comprise analyzing a primary control (e.g., a normal tissue sample). In some instances, the disclosed methods may further comprise determining if a primary control is available and, if so, isolating a control nucleic acid (e.g., DNA) from said primary control. In some instances, the sample may comprise any normal control (e.g., a normal adjacent tissue (NAT)) if no primary control is available. In some instances, the sample may be or may comprise histologically normal tissue. In some instances, the method includes evaluating a sample, e.g., a histologically normal sample e.g., from a surgical tissue margin) using the methods described herein. In some instances, the disclosed methods may further comprise acquiring a sub-sample enriched for non-tumor cells, e.g., bymacro-dissecting non-tumor tissue from said NAT in a sample not accompanied by a primary control. In some instances, the disclosed methods may further comprise determining that no primary control and no NAT is available, and marking said sample for analysis without a matched control.

[0222] In some instances, samples obtained from histologically normal tissues (e.g., otherwise histologically normal surgical tissue margins) may still comprise a genetic alteration such as a variant sequence as described herein. The methods may thus further comprise re-classifying a sample based on the presence of the detected genetic alteration. In some instances, multiple samples (e.g., from different subjects) are processed simultaneously.

[0223] The disclosed methods and systems may be applied to the analysis of nucleic acids extracted from any of variety of tissue samples (or disease states thereof), e.g., solid tissue samples, soft tissue samples, metastatic lesions, or liquid biopsy samples. Examples of tissues include, but are not limited to, connective tissue, muscle tissue, nervous tissue, epithelial tissue, and blood. Tissue samples may be collected from any of the organs within an animal or human body. Examples of human organs include, but are not limited to, the brain, heart, lungs, liver, kidneys, pancreas, spleen, thyroid, mammary glands, uterus, prostate, large intestine, small intestine, bladder, bone, skin, etc.

[0224] In some instances, the nucleic acids extracted from the sample may comprise deoxyribonucleic acid (DNA) molecules. Examples of DNA that may be suitable for analysis by the disclosed methods include, but are not limited to, genomic DNA or fragments thereof, mitochondrial DNA or fragments thereof, cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA). Cell-free DNA (cfDNA) is comprised of fragments of DNA that are released from normal and / or cancerous cells during apoptosis and necrosis, and circulate in the blood stream and / or accumulate in other bodily fluids. Circulating tumor DNA (ctDNA) is comprised of fragments of DNA that are released from cancerous cells and tumors that circulate in the blood stream and / or accumulate in other bodily fluids.

[0225] In some instances, DNA is extracted from nucleated cells from the sample. In some instances, a sample may have a low nucleated cellularity, e.g., when the sample is comprised mainly of erythrocytes, lesional cells that contain excessive cytoplasm, or tissue with fibrosis. Insome instances, a sample with low nucleated cellularity may require more, e.g., greater, tissue volume for DNA extraction.

[0226] In some instances, the nucleic acids extracted from the sample may comprise ribonucleic acid (RNA) molecules. Examples of RNA that may be suitable for analysis by the disclosed methods include, but are not limited to, total cellular RNA, total cellular RNA after depletion of certain abundant RNA sequences (e.g., ribosomal RNAs), cell-free RNA (cfRNA), messenger RNA (mRNA) or fragments thereof, the poly(A)-tailed mRNA fraction of the total RNA, ribosomal RNA (rRNA) or fragments thereof, transfer RNA (tRNA) or fragments thereof, and mitochondrial RNA or fragments thereof. In some instances, RNA may be extracted from the sample and converted to complementary DNA (cDNA) using, e.g., a reverse transcription reaction. In some instances, the cDNA is produced by random-primed cDNA synthesis methods. In other instances, the cDNA synthesis is initiated at the poly(A) tail of mature mRNAs by priming with oligo(dT)-containing oligonucleotides. Methods for depletion, poly (A) enrichment, and cDNA synthesis are well known to those of skill in the art.

[0227] In some instances, the sample may comprise a tumor content (e.g., comprising tumor cells or tumor cell nuclei), or a non-tumor content (e.g., immune cells, fibroblasts, and other non-tumor cells). In some instances, the tumor content of the sample may constitute a sample metric. In some instances, the sample may comprise a tumor content of at least 5-50%, 10-40%, 15-25%, or 20-30% tumor cell nuclei. In some instances, the sample may comprise a tumor content of at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, or at least 50% tumor cell nuclei. In some instances, the percent tumor cell nuclei (e.g., sample fraction) is determined (e.g., calculated) by dividing the number of tumor cells in the sample by the total number of all cells within the sample that have nuclei. In some instances, for example when the sample is a liver sample comprising hepatocytes, a different tumor content calculation may be required due to the presence of hepatocytes having nuclei with twice, or more than twice, the DNA content of other, e.g., non-hepatocyte, somatic cell nuclei. In some instances, the sensitivity of detection of a genetic alteration, e.g., a variant sequence, or a determination of, e.g., micro satellite instability, may depend on the tumor content of the sample. For example, a sample having a lower tumor content can result in lower sensitivity of detection for a given size sample.

[0228] In some instances, as noted above, the sample comprises nucleic acid e.g., DNA, RNA (or a cDNA derived from the RNA), or both), e.g., from a tumor or from normal tissue. In certain instances, the sample may further comprise a non-nucleic acid component, e.g., cells, protein, carbohydrate, or lipid, e.g., from the tumor or normal tissue.Subjects

[0229] In some instances, the sample is obtained (e.g., collected) from a subject (e.g., patient) with a condition or disease (e.g., a hyperproliferative disease or a non-cancer indication) or suspected of having the condition or disease. In some instances, the hyperproliferative disease is a cancer. In some instances, the cancer is a solid tumor or a metastatic form thereof. In some instances, the cancer is a hematological cancer, e.g., a leukemia or lymphoma.

[0230] In some instances, the subject has a cancer or is at risk of having a cancer. For example, in some instances, the subject has a genetic predisposition to a cancer (e.g., having a genetic mutation that increases his or her baseline risk for developing a cancer). In some instances, the subject has been exposed to an environmental perturbation (e.g., radiation or a chemical) that increases his or her risk for developing a cancer. In some instances, the subject is in need of being monitored for development of a cancer. In some instances, the subject is in need of being monitored for cancer progression or regression, e.g., after being treated with an anti-cancer therapy (or anti-cancer treatment). In some instances, the subject is in need of being monitored for relapse of cancer. In some instances, the subject is in need of being monitored for minimum residual disease (MRD). In some instances, the subject has been, or is being treated, for cancer. In some instances, the subject has not been treated with an anti-cancer therapy (or anti-cancer treatment).

[0231] In some instances, the subject (e.g., a patient) is being treated, or has been previously treated, with one or more targeted therapies. In some instances, e.g., for a patient who has been previously treated with a targeted therapy, a post-targeted therapy sample (e.g., specimen) is obtained (e.g., collected). In some instances, the post-targeted therapy sample is a sample obtained after the completion of the targeted therapy.

[0232] In some instances, the patient has not been previously treated with a targeted therapy. In some instances, e.g., for a patient who has not been previously treated with a targeted therapy,the sample comprises a resection, e.g., an original resection, or a resection following recurrence (e.g., following a disease recurrence post-therapy).Cancers

[0233] In some instances, the sample is acquired from a subject having a cancer. Exemplary cancers include, but are not limited to, B cell cancer (e.g., multiple myeloma), melanomas, breast cancer, lung cancer (such as non-small cell lung carcinoma or NSCLC), bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain or central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine or endometrial cancer, cancer of the oral cavity or pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel or appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, cancer of hematological tissues, adenocarcinomas, inflammatory myofibroblastic tumors, gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, non-Hodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endothelio sarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell cancers, essential thrombocythemia, agnogenic myeloidmetaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, carcinoid tumors, and the like.

[0234] In some instances, the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed / amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR and MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, a diffuse large B- cell lymphoma, fallopian tube cancer, a follicular B-cell non-Hodgkin lymphoma, a follicular lymphoma, gastric cancer, gastric cancer (HER2+), a gastroesophageal junction (GEJ) adenocarcinoma, a gastrointestinal stromal tumor, a gastrointestinal stromal tumor (KIT+), a giant cell tumor of the bone, a glioblastoma, granulomatosis with polyangiitis, a head and neck squamous cell carcinoma, a hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, lupus erythematosus, a mantle cell lymphoma, medullary thyroid cancer, melanoma, a melanoma with a BRAF V600 mutation, a melanoma with a BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman's disease, multiple hematologic malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma, myelofibrosis, a non-Hodgkin’ s lymphoma, a nonresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, a non-small cell lung cancer, a non-small cell lung cancer (ALK+), a non-small cell lung cancer (PD-L1+), a non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), a non-small cell lung cancer (with BRAF V600E mutation), a non-small cell lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a non- small cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissuesarcoma, a solid tumor (MSI-H / dMMR), a squamous cell cancer of the head and neck, a squamous non- small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.

[0235] In some instances, the cancer is a hematologic malignancy (or premaligancy). As used herein, a hematologic malignancy refers to a tumor of the hematopoietic or lymphoid tissues, e.g., a tumor that affects blood, bone marrow, or lymph nodes. Exemplary hematologic malignancies include, but are not limited to, leukemia (e.g., acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myelogenous leukemia (CML), hairy cell leukemia, acute monocytic leukemia (AMoL), chronic myelomonocytic leukemia (CMML), juvenile myelomonocytic leukemia (JMML), or large granular lymphocytic leukemia), lymphoma (e.g., AIDS-related lymphoma, cutaneous T-cell lymphoma, Hodgkin lymphoma (e.g., classical Hodgkin lymphoma or nodular lymphocyte- predominant Hodgkin lymphoma), mycosis fungoides, non-Hodgkin lymphoma (e.g., B-cell non-Hodgkin lymphoma (e.g., Burkitt lymphoma, small lymphocytic lymphoma (CLL / SLL), diffuse large B-cell lymphoma, follicular lymphoma, immunoblastic large cell lymphoma, precursor B-lymphoblastic lymphoma, or mantle cell lymphoma) or T-cell non-Hodgkin lymphoma (mycosis fungoides, anaplastic large cell lymphoma, or precursor T-lymphoblastic lymphoma)), primary central nervous system lymphoma, Sezary syndrome, Waldenstrom macroglobulinemia), chronic myeloproliferative neoplasm, Langerhans cell histiocytosis, multiple myeloma / plasma cell neoplasm, myelodysplastic syndrome, or myelodysplastic / myeloproliferative neoplasm.Nucleic acid extraction and processing

[0236] DNA or RNA may be extracted from tissue samples, biopsy samples, blood samples, or other bodily fluid samples using any of a variety of techniques known to those of skill in the art (see, e.g., Example 1 of International Patent Application Publication No. WO 2012 / 092426; Tan, et al. (2009), “DNA, RNA, and Protein Extraction: The Past and The Present”, J. Biomed. Biotech. 2009:574398; the technical literature for the Maxwell® 16 LEV Blood DNA Kit (Promega Corporation, Madison, WI); and the Maxwell 16 Buccal Swab LEV DNA Purification Kit Technical Manual (Promega Literature #TM333, January 1, 2011, Promega Corporation,Madison, WI)). Protocols for RNA isolation are disclosed in, e.g., the Maxwell® 16 Total RNA Purification Kit Technical Bulletin (Promega Literature #TB351, August 2009, Promega Corporation, Madison, WI).

[0237] A typical DNA extraction procedure, for example, comprises (i) collection of the fluid sample, cell sample, or tissue sample from which DNA is to be extracted, (ii) disruption of cell membranes (i.e., cell lysis), if necessary, to release DNA and other cytoplasmic components, (iii) treatment of the fluid sample or lysed sample with a concentrated salt solution to precipitate proteins, lipids, and RNA, followed by centrifugation to separate out the precipitated proteins, lipids, and RNA, and (iv) purification of DNA from the supernatant to remove detergents, proteins, salts, or other reagents used during the cell membrane lysis step.

[0238] Disruption of cell membranes may be performed using a variety of mechanical shear (e.g., by passing through a French press or fine needle) or ultrasonic disruption techniques. The cell lysis step often comprises the use of detergents and surfactants to solubilize lipids the cellular and nuclear membranes. In some instances, the lysis step may further comprise use of proteases to break down protein, and / or the use of an RNase for digestion of RNA in the sample.

[0239] Examples of suitable techniques for DNA purification include, but are not limited to, (i) precipitation in ice-cold ethanol or isopropanol, followed by centrifugation (precipitation of DNA may be enhanced by increasing ionic strength, e.g., by addition of sodium acetate), (ii) phenol-chloroform extraction, followed by centrifugation to separate the aqueous phase containing the nucleic acid from the organic phase containing denatured protein, and (iii) solid phase chromatography where the nucleic acids adsorb to the solid phase (e.g., silica or other) depending on the pH and salt concentration of the buffer.

[0240] In some instances, cellular and histone proteins bound to the DNA may be removed either by adding a protease or by having precipitated the proteins with sodium or ammonium acetate, or through extraction with a phenol-chloroform mixture prior to a DNA precipitation step.

[0241] In some instances, DNA may be extracted using any of a variety of suitable commercial DNA extraction and purification kits. Examples include, but are not limited to, the QIAamp (for isolation of genomic DNA from human samples) and DNAeasy (for isolation of genomic DNAfrom animal or plant samples) kits from Qiagen (Germantown, MD) or the Maxwell® and ReliaPrep™ series of kits from Promega (Madison, WI).

[0242] As noted above, in some instances the sample may comprise a formalin-fixed (also known as formaldehyde-fixed, or paraformaldehyde-fixed), paraffin-embedded (FFPE) tissue preparation. For example, the FFPE sample may be a tissue sample embedded in a matrix, e.g., an FFPE block. Methods to isolate nucleic acids e.g., DNA) from formaldehyde- or paraformaldehyde-fixed, paraffin-embedded (FFPE) tissues are disclosed in, e.g., Cronin, et al., (2004) Am J Pathol. 164(l):35-42; Masuda, et al., (1999) Nucleic Acids Res. 27 (22): 4436-4443; Specht, et al., (2001) Am J Pathol. 158(2):419-429; the Ambion RecoverAll™ Total Nucleic Acid Isolation Protocol (Ambion, Cat. No. AM1975, September 2008); the Maxwell® 16 FFPE Plus LEV DNA Purification Kit Technical Manual (Promega Literature #TM349, February 2011); the E.Z.N.A.® FFPE DNA Kit Handbook (OMEGA bio-tek, Norcross, GA, product numbers D3399-00, D3399-01, and D3399-02, June 2009); and the QIAamp® DNA FFPE Tissue Handbook (Qiagen, Cat. No. 37625, October 2007). For example, the RecoverAll™ Total Nucleic Acid Isolation Kit uses xylene at elevated temperatures to solubilize paraffin- embedded samples and a glass-fiber filter to capture nucleic acids. The Maxwell® 16 FFPE Plus LEV DNA Purification Kit is used with the Maxwell® 16 Instrument for purification of genomic DNA from 1 to 10 pm sections of FFPE tissue. DNA is purified using silica-clad paramagnetic particles (PMPs), and eluted in low elution volume. The E.Z.N.A.® FFPE DNA Kit uses a spin column and buffer system for isolation of genomic DNA. QIAamp® DNA FFPE Tissue Kit uses QIAamp® DNA Micro technology for purification of genomic and mitochondrial DNA.

[0243] In some instances, the disclosed methods may further comprise determining or acquiring a yield value for the nucleic acid extracted from the sample and comparing the determined value to a reference value. For example, if the determined or acquired value is less than the reference value, the nucleic acids may be amplified prior to proceeding with library construction. In some instances, the disclosed methods may further comprise determining or acquiring a value for the size (or average size) of nucleic acid fragments in the sample, and comparing the determined or acquired value to a reference value, e.g., a size (or average size) of at least 100, 200, 300, 400,500, 600, 700, 800, 900, or 1000 base pairs (bps). In some instances, one or more parameters described herein may be adjusted or selected in response to this determination.

[0244] After isolation, the nucleic acids are typically dissolved in a slightly alkaline buffer, e.g., Tris-EDTA (TE) buffer, or in ultra-pure water. In some instances, the isolated nucleic acids (e.g., genomic DNA) may be fragmented or sheared by using any of a variety of techniques known to those of skill in the art. For example, genomic DNA can be fragmented by physical shearing methods, enzymatic cleavage methods, chemical cleavage methods, and other methods known to those of skill in the art. Methods for DNA shearing are described in Example 4 in International Patent Application Publication No. WO 2012 / 092426. In some instances, alternatives to DNA shearing methods can be used to avoid a ligation step during library preparation.Library preparation

[0245] In some instances, the nucleic acids isolated from the sample may be used to construct a library (e.g., a nucleic acid library as described herein). In some instances, the nucleic acids are fragmented using any of the methods described above, optionally subjected to repair of chain end damage, and optionally ligated to synthetic adapters, primers, and / or barcodes (e.g., amplification primers, sequencing adapters, flow cell adapters, substrate adapters, sample barcodes or indexes, and / or unique molecular identifier sequences), size-selected (e.g., by preparative gel electrophoresis), and / or amplified (e.g., using PCR, a non-PCR amplification technique, or an isothermal amplification technique). In some instances, the fragmented and adapter-ligated group of nucleic acids is used without explicit size selection or amplification prior to hybridization-based selection of target sequences. In some instances, the nucleic acid is amplified by any of a variety of specific or non-specific nucleic acid amplification methods known to those of skill in the art. In some instances, the nucleic acids are amplified, e.g., by a whole-genome amplification method such as random-primed strand-displacement amplification. Examples of nucleic acid library preparation techniques for next-generation sequencing are described in, e.g., van Dijk, et al. (2014), Exp. Cell Research 322:12 - 20, and Illumina’s genomic DNA sample preparation kit.

[0246] In some instances, the resulting nucleic acid library may contain all or substantially all of the complexity of the genome. The term “substantially all” in this context refers to the possibility that there can in practice be some unwanted loss of genome complexity during the initial steps of the procedure. The methods described herein also are useful in cases where the nucleic acid library comprises a portion of the genome, e.g., where the complexity of the genome is reduced by design. In some instances, any selected portion of the genome can be used with a method described herein. For example, in certain embodiments, the entire exome or a subset thereof is isolated. In some instances, the library may include at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic DNA. In some instances, the library may consist of cDNA copies of genomic DNA that includes copies of at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic DNA. In certain instances, the amount of nucleic acid used to generate the nucleic acid library may be less than 5 micrograms, less than 1 microgram, less than 500 ng, less than 200 ng, less than 100 ng, less than 50 ng, less than 10 ng, less than 5 ng, or less than 1 ng.

[0247] In some instances, a library (e.g., a nucleic acid library) includes a collection of nucleic acid molecules. As described herein, the nucleic acid molecules of the library can include a target nucleic acid molecule (e.g., a tumor nucleic acid molecule, a reference nucleic acid molecule and / or a control nucleic acid molecule; also referred to herein as a first, second and / or third nucleic acid molecule, respectively). The nucleic acid molecules of the library can be from a single subject or individual. In some instances, a library can comprise nucleic acid molecules derived from more than one subject (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30 or more subjects). For example, two or more libraries from different subjects can be combined to form a library having nucleic acid molecules from more than one subject (where the nucleic acid molecules derived from each subject are optionally ligated to a unique sample barcode corresponding to a specific subject). In some instances, the subject is a human having, or at risk of having, a cancer or tumor.

[0248] In some instances, the library (or a portion thereof) may comprise one or more subgenomic intervals. In some instances, a subgenomic interval can be a single nucleotide position, e.g., a nucleotide position for which a variant at the position is associated (positively or negatively) with a tumor phenotype. In some instances, a subgenomic interval comprises morethan one nucleotide position. Such instances include sequences of at least 2, 5, 10, 50, 100, 150, 250, or more than 250 nucleotide positions in length. Subgenomic intervals can comprise, e.g., one or more entire genes (or portions thereof), one or more exons or coding sequences (or portions thereof), one or more introns (or portion thereof), one or more microsatellite region (or portions thereof), or any combination thereof. A subgenomic interval can comprise all or a part of a fragment of a naturally occurring nucleic acid molecule, e.g., a genomic DNA molecule. For example, a subgenomic interval can correspond to a fragment of genomic DNA which is subjected to a sequencing reaction. In some instances, a subgenomic interval is a continuous sequence from a genomic source. In some instances, a subgenomic interval includes sequences that are not contiguous in the genome, e.g., subgenomic intervals in cDNA can include exonexon junctions formed as a result of splicing. In some instances, the subgenomic interval comprises a tumor nucleic acid molecule. In some instances, the subgenomic interval comprises a non-tumor nucleic acid molecule.Target capture reagents

[0249] The methods described herein may comprise contacting a nucleic acid library with a plurality of target capture reagents in order to select and capture a plurality of specific target sequences (e.g., gene sequences or fragments thereof) for analysis. In some instances, a target capture reagent (i.e., a molecule which can bind to and thereby allow capture of a target molecule) is used to select the subject intervals to be analyzed. For example, a target capture reagent can be a bait molecule, e.g., a nucleic acid molecule e.g., a DNA molecule or RNA molecule) which can hybridize to (i.e., is complementary to) a target molecule, and thereby allows capture of the target nucleic acid. In some instances, the target capture reagent, e.g., a bait molecule (or bait sequence), is a capture oligonucleotide (or capture probe). In some instances, the target nucleic acid is a genomic DNA molecule, an RNA molecule, a cDNA molecule derived from an RNA molecule, a microsatellite DNA sequence, and the like. In some instances, the target capture reagent is suitable for solution-phase hybridization to the target. In some instances, the target capture reagent is suitable for solid-phase hybridization to the target. In some instances, the target capture reagent is suitable for both solution-phase and solid-phase hybridization to the target. The design and construction of target capture reagents is described inmore detail in, e.g., International Patent Application Publication No. WO 2020 / 236941, the entire content of which is incorporated herein by reference.

[0250] The methods described herein provide for optimized sequencing of a large number of genomic loci (e.g., genes or gene products (e.g., mRNA), micro satellite loci, etc.) from samples (e.g., cancerous tissue specimens, liquid biopsy samples, and the like) from one or more subjects by the appropriate selection of target capture reagents to select the target nucleic acid molecules to be sequenced. In some instances, a target capture reagent may hybridize to a specific target locus, e.g., a specific target gene locus or fragment thereof. In some instances, a target capture reagent may hybridize to a specific group of target loci, e.g., a specific group of gene loci or fragments thereof. In some instances, a plurality of target capture reagents comprising a mix of target- specific and / or group- specific target capture reagents may be used.

[0251] In some instances, the number of target capture reagents (e.g., bait molecules) in the plurality of target capture reagents (e.g., a bait set) contacted with a nucleic acid library to capture a plurality of target sequences for nucleic acid sequencing is greater than 10, greater than 50, greater than 100, greater than 200, greater than 300, greater than 400, greater than 500, greater than 600, greater than 700, greater than 800, greater than 900, greater than 1,000, greater than 1,250, greater than 1,500, greater than 1,750, greater than 2,000, greater than 3,000, greater than 4,000, greater than 5,000, greater than 10,000, greater than 25,000, or greater than 50,000.

[0252] In some instances, the overall length of the target capture reagent sequence can be between about 70 nucleotides and 1000 nucleotides. In one instance, the target capture reagent length is between about 100 and 300 nucleotides, 110 and 200 nucleotides, or 120 and 170 nucleotides, in length. In addition to those mentioned above, intermediate oligonucleotide lengths of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length can be used in the methods described herein. In some embodiments, oligonucleotides of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, or 230 bases can be used.

[0253] In some instances, each target capture reagent sequence can include: (i) a target- specific capture sequence (e.g., a gene locus or micro satellite locus- specific complementary sequence), (ii) an adapter, primer, barcode, and / or unique molecular identifier sequence, and (iii) universal tails on one or both ends. As used herein, the term "target capture reagent" can refer to thetarget- specific target capture sequence or to the entire target capture reagent oligonucleotide including the target- specific target capture sequence.

[0254] In some instances, the target- specific capture sequences in the target capture reagents are between about 40 nucleotides and 1000 nucleotides in length. In some instances, the targetspecific capture sequence is between about 70 nucleotides and 300 nucleotides in length. In some instances, the target- specific sequence is between about 100 nucleotides and 200 nucleotides in length. In yet other instances, the target- specific sequence is between about 120 nucleotides and 170 nucleotides in length, typically 120 nucleotides in length. Intermediate lengths in addition to those mentioned above also can be used in the methods described herein, such as target- specific sequences of about 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length, as well as target- specific sequences of lengths between the above- mentioned lengths.

[0255] In some instances, the target capture reagent may be designed to select a subject interval containing one or more rearrangements, e.g., an intron containing a genomic rearrangement. In such instances, the target capture reagent is designed such that repetitive sequences are masked to increase the selection efficiency. In those instances where the rearrangement has a known juncture sequence, complementary target capture reagents can be designed to recognize the juncture sequence to increase the selection efficiency.

[0256] In some instances, the disclosed methods may comprise the use of target capture reagents designed to capture two or more different target categories, each category having a different target capture reagent design strategy. In some instances, the hybridization-based capture methods and target capture reagent compositions disclosed herein may provide for the capture and homogeneous coverage of a set of target sequences, while minimizing coverage of genomic sequences outside of the targeted set of sequences. In some instances, the target sequences may include the entire exome of genomic DNA or a selected subset thereof. In some instances, the target sequences may include, e.g., a large chromosomal region (e.g., a whole chromosome arm). The methods and compositions disclosed herein provide different target capture reagents for achieving different sequencing depths and patterns of coverage for complex sets of target nucleic acid sequences.

[0257] Typically, DNA molecules are used as target capture reagent sequences, although RNA molecules can also be used. In some instances, a DNA molecule target capture reagent can be single stranded DNA (ssDNA) or double- stranded DNA (dsDNA). In some instances, an RNA- DNA duplex is more stable than a DNA-DNA duplex and therefore provides for potentially better capture of nucleic acids.

[0258] In some instances, the disclosed methods comprise providing a selected set of nucleic acid molecules (e.g., a library catch) captured from one or more nucleic acid libraries. For example, the method may comprise: providing one or a plurality of nucleic acid libraries, each comprising a plurality of nucleic acid molecules (e.g., a plurality of target nucleic acid molecules and / or reference nucleic acid molecules) extracted from one or more samples from one or more subjects; contacting the one or a plurality of libraries (e.g., in a solution-based hybridization reaction) with one, two, three, four, five, or more than five pluralities of target capture reagents (e.g., oligonucleotide target capture reagents) to form a hybridization mixture comprising a plurality of target capture reagent / nucleic acid molecule hybrids; separating the plurality of target capture reagent / nucleic acid molecule hybrids from said hybridization mixture, e.g., by contacting said hybridization mixture with a binding entity that allows for separation of said plurality of target capture reagent / nucleic acid molecule hybrids from the hybridization mixture, thereby providing a library catch (e.g., a selected or enriched subgroup of nucleic acid molecules from the one or a plurality of libraries).

[0259] In some instances, the disclosed methods may further comprise amplifying the library catch (e.g., by performing PCR). In other instances, the library catch is not amplified.

[0260] In some instances, the target capture reagents can be part of a kit which can optionally comprise instructions, standards, buffers or enzymes or other reagents.Hybridization conditions

[0261] As noted above, the methods disclosed herein may include the step of contacting the library e.g., the nucleic acid library) with a plurality of target capture reagents to provide a selected library target nucleic acid sequences (i.e., the library catch). The contacting step can be effected in, e.g., solution-based hybridization. In some instances, the method includes repeating the hybridization step for one or more additional rounds of solution-based hybridization. In someinstances, the method further includes subjecting the library catch to one or more additional rounds of solution-based hybridization with the same or a different collection of target capture reagents.

[0262] In some instances, the contacting step is effected using a solid support, e.g., an array. Suitable solid supports for hybridization are described in, e.g., Albert, T.J. et al. (2007) Nat. Methods 4(11):903-5; Hodges, E. et al. (2007) Nat. Genet. 39(12): 1522-7; and Okou, D.T. et al. (2007) Nat. Methods 4(11 ):907-9, the contents of which are incorporated herein by reference in their entireties.

[0263] Hybridization methods that can be adapted for use in the methods herein are described in the art, e.g., as described in International Patent Application Publication No. WO 2012 / 092426. Methods for hybridizing target capture reagents to a plurality of target nucleic acids are described in more detail in, e.g., International Patent Application Publication No. WO 2020 / 236941, the entire content of which is incorporated herein by reference.Sequencing methods

[0264] The methods and systems disclosed herein can be used in combination with, or as part of, a method or system for sequencing nucleic acids (e.g., a next- generation sequencing system) to generate a plurality of sequence reads that overlap one or more gene loci within a subgenomic interval in the sample and thereby determine, e.g., gene allele sequences at a plurality of gene loci. “Next-generation sequencing” (or “NGS”) as used herein may also be referred to as “massively parallel sequencing” (or “MPS”), and refers to any sequencing method that determines the nucleotide sequence of either individual nucleic acid molecules (e.g., as in single molecule sequencing) or clonally expanded proxies for individual nucleic acid molecules in a high throughput fashion (e.g., wherein greater than 103, 104, 105or more than 105molecules are sequenced simultaneously).

[0265] Next-generation sequencing methods are known in the art, and are described in, e.g., Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46, which is incorporated herein by reference. Other examples of sequencing methods suitable for use when implementing the methods and systems disclosed herein are described in, e.g., International Patent Application Publication No. WO 2012 / 092426. In some instances, the sequencing may comprise, forexample, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, or direct sequencing. In some instances, sequencing may be performed using, e.g., Sanger sequencing. In some instances, the sequencing may comprise a paired-end sequencing technique that allows both ends of a fragment to be sequenced and generates high-quality, alignable sequence data for detection of, e.g., genomic rearrangements, repetitive sequence elements, gene fusions, and novel transcripts.

[0266] The disclosed methods and systems may be implemented using sequencing platforms such as the Roche 454, Illumina Solexa, ABI-SOLiD, ION Torrent, Complete Genomics, Pacific Bioscience, Helicos, and / or the Polonator platform. In some instances, sequencing may comprise Illumina MiSeq sequencing. In some instances, sequencing may comprise Illumina HiSeq sequencing. In some instances, sequencing may comprise Illumina NovaSeq sequencing. Optimized methods for sequencing a large number of target genomic loci in nucleic acids extracted from a sample are described in more detail in, e.g., International Patent Application Publication No. WO 2020 / 236941, the entire content of which is incorporated herein by reference.

[0267] In certain instances, the disclosed methods comprise one or more of the steps of: (a) acquiring a library comprising a plurality of normal and / or tumor nucleic acid molecules from a sample; (b) simultaneously or sequentially contacting the library with one, two, three, four, five, or more than five pluralities of target capture reagents under conditions that allow hybridization of the target capture reagents to the target nucleic acid molecules, thereby providing a selected set of captured normal and / or tumor nucleic acid molecules (i.e., a library catch); (c) separating the selected subset of the nucleic acid molecules (e.g., the library catch) from the hybridization mixture, e.g., by contacting the hybridization mixture with a binding entity that allows for separation of the target capture reagent / nucleic acid molecule hybrids from the hybridization mixture, (d) sequencing the library catch to acquiring a plurality of reads (e.g., sequence reads) that overlap one or more subject intervals (e.g., one or more target sequences) from said library catch that may comprise a mutation (or alteration), e.g., a variant sequence comprising a somatic mutation or germline mutation; (e) aligning said sequence reads using an alignment method as described elsewhere herein; and / or (f) assigning a nucleotide value for a nucleotide position inthe subject interval e.g., calling a mutation using, e.g., a Bayesian method or other method described herein) from one or more sequence reads of the plurality.

[0268] In some instances, acquiring sequence reads for one or more subject intervals may comprise sequencing at least 1, at least 5, at least 10, at least 20, at least 30, at least 40, at least 50, at least 100, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400, at least 450, at least 500, at least 550, at least 600, at least 650, at least 700, at least 750, at least 800, at least 850, at least 900, at least 950, at least 1,000, at least 1,250, at least 1,500, at least 1,750, at least 2,000, at least 2,250, at least 2,500, at least 2,750, at least 3,000, at least 3,500, at least 4,000, at least 4,500, or at least 5,000 loci, e.g., genomic loci, gene loci, microsatellite loci, etc. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing a subject interval for any number of loci within the range described in this paragraph, e.g., for at least 2,850 gene loci.

[0269] In some instances, acquiring a sequence read for one or more subject intervals comprises sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of at least 20 bases, at least 30 bases, at least 40 bases, at least 50 bases, at least 60 bases, at least 70 bases, at least 80 bases, at least 90 bases, at least 100 bases, at least 120 bases, at least 140 bases, at least 160 bases, at least 180 bases, at least 200 bases, at least 220 bases, at least 240 bases, at least 260 bases, at least 280 bases, at least 300 bases, at least 320 bases, at least 340 bases, at least 360 bases, at least 380 bases, or at least 400 bases. In some instances, acquiring a sequence read for the one or more subject intervals may comprise sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of any number of bases within the range described in this paragraph, e.g., a sequence read length (or average sequence read length) of 56 bases.

[0270] In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx or more coverage (or depth) on average. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx, at least 150x, at least 200x, at least 250x, at least 500x, at least 750x, at least l,000x, at least 1,500 x, at least 2,000x, at least 2,500x, at least 3,000x, at least 3,500x, at least 4,000x, at least 4,500x, at least 5,000x, at least 5,500x, or at least 6,000x or more coverage (or depth) on average. In some instances, acquiring a sequence read for one or more subjectintervals may comprise sequencing with an average coverage (or depth) having any value within the range of values described in this paragraph, e.g., at least 160x.

[0271] In some instances, acquiring a read for the one or more subject intervals comprises sequencing with an average sequencing depth having any value ranging from at least lOOx to at least 6,000x for greater than about 90%, 92%, 94%, 95%, 96%, 97%, 98%, or 99% of the gene loci sequenced. For example, in some instances acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 125x for at least 99% of the gene loci sequenced. As another example, in some instances acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 4,100x for at least 95% of the gene loci sequenced.

[0272] In some instances, the relative abundance of a nucleic acid species in the library can be estimated by counting the relative number of occurrences of their cognate sequences (e.g., the number of sequence reads for a given cognate sequence) in the data generated by the sequencing experiment.

[0273] In some instances, the disclosed methods and systems provide nucleotide sequences for a set of subject intervals (e.g., gene loci), as described herein. In certain instances, the sequences are provided without using a method that includes a matched normal control (e.g., a wild-type control) and / or a matched tumor control (e.g., primary versus metastatic).

[0274] In some instances, the level of sequencing depth as used herein (e.g., an X-fold level of sequencing depth) refers to the number of reads (e.g., unique reads) obtained after detection and removal of duplicate reads (e.g., PCR duplicate reads). In other instances, duplicate reads are evaluated, e.g., to support detection of copy number alteration (CNAs).Alignment

[0275] Alignment is the process of matching a read with a location, e.g., a genomic location or locus. In some instances, NGS reads may be aligned to a known reference sequence (e.g., a wild-type sequence). In some instances, NGS reads may be assembled de novo. Methods of sequence alignment for NGS reads are described in, e.g., Trapnell, C. and Salzberg, S.L. Nature Biotech., 2009, 27:455-457. Examples of de novo sequence assemblies are described in, e.g., Warren R., et al., Bioinformatics, 2007, 23:500-501; Butler, J. et al., Genome Res., 2008, 18:810-820; and Zerbino, D.R. and Birney, E., Genome Res., 2008, 18:821-829. Optimizationof sequence alignment is described in the art, e.g., as set out in International Patent Application Publication No. WO 2012 / 092426. Additional description of sequence alignment methods is provided in, e.g., International Patent Application Publication No. WO 2020 / 236941, the entire content of which is incorporated herein by reference.

[0276] Misalignment e.g., the placement of base-pairs from a short read at incorrect locations in the genome), e.g., misalignment of reads due to sequence context (e.g., the presence of repetitive sequence) around an actual cancer mutation can lead to reduction in sensitivity of mutation detection, can lead to a reduction in sensitivity of mutation detection, as reads for the alternate allele may be shifted off the histogram peak of alternate allele reads. Other examples of sequence context that may cause misalignment include short-tandem repeats, interspersed repeats, low complexity regions, insertions - deletions (indels), and paralogs. If the problematic sequence context occurs where no actual mutation is present, misalignment may introduce artifactual reads of “mutated” alleles by placing reads of actual reference genome base sequences at the wrong location. Because mutation-calling methods for multigene analysis should be sensitive to even low-abundance mutations, sequence misalignments may increase false positive discovery rates and / or reduce specificity.

[0277] In some instances, the methods and systems disclosed herein may integrate the use of multiple, individually-tuned, alignment methods to optimize base-calling performance in sequencing methods, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci. In some instances, the disclosed methods and systems may comprise the use of one or more global alignment methods. In some instances, the disclosed methods and systems may comprise the use of one or more local alignment methods. Examples of alignment methods that may be used include, but are not limited to, the Burrows-Wheeler Alignment (BWA) software bundle (see, e.g., Li, et al. (2009), “Fast and Accurate Short Read Alignment with Burrows-Wheeler Transform”, Bioinformatics 25:1754-60; Li, et al. (2010), Fast and Accurate Long-Read Alignment with Burrows-Wheeler Transform”, Bioinformatics epub. PMID: 20080505), the Smith-Waterman method (see, e.g., Smith, et al. (1981), "Identification of Common Molecular Subsequences", J. Molecular Biology 147(1): 195-197), the Striped Smith-Waterman method (see, e.g., Farrar (2007), “Striped Smith-Waterman Speeds Database Searches Six Times OverOther SIMD Implementations”, Bioinformatics 23(2): 156-161), the Needleman-Wunsch method (Needleman, et al. (1970) "A General Method Applicable to the Search for Similarities in the Amino Acid Sequence of Two Proteins", J. Molecular Biology 48(3):443-53), or any combination thereof.

[0278] In some instances, the methods and systems disclosed herein may also comprise the use of a sequence assembly method, e.g., the Arachne sequence assembly method (see, e.g., Batzoglou, et al. (2002), “ARACHNE: A Whole-Genome Shotgun Assembler”, Genome Res. 12:177-189).

[0279] In some instances, the alignment method used to analyze sequence reads is not individually customized or tuned for detection of different variants (e.g., point mutations, insertions, deletions, and the like) at different genomic loci. In some instances, different alignment methods are used to analyze reads that are individually customized or tuned for detection of at least a subset of the different variants detected at different genomic loci. In some instances, different alignment methods are used to analyze reads that are individually customized or tuned to detect each different variant at different genomic loci. In some instances, tuning can be a function of one or more of: (i) the genetic locus (e.g., gene loci, micro satellite locus, or other subject interval) being sequenced, (ii) the tumor type associated with the sample, (iii) the variant being sequenced, or (iv) a characteristic of the sample or the subject. The selection or use of alignment conditions that are individually tuned to a number of specific subject intervals to be sequenced allows optimization of speed, sensitivity, and specificity. The method is particularly effective when the alignment of reads for a relatively large number of diverse subject intervals are optimized.

[0280] In some instances, the method includes the use of an alignment method optimized for rearrangements in combination with other alignment methods optimized for subject intervals not associated with rearrangements.

[0281] In some instances, the methods disclosed herein further comprise selecting or using an alignment method for analyzing, e.g., aligning, a sequence read, wherein said alignment method is a function of, is selected responsive to, or is optimized for, one or more of: (i) tumor type, e.g., the tumor type in the sample; (ii) the location (e.g., a gene locus) of the subject interval being sequenced; (iii) the type of variant (e.g., a point mutation, insertion, deletion, substitution, copynumber variation (CNV), rearrangement, or fusion) in the subject interval being sequenced; (iv) the site (e.g., nucleotide position) being analyzed; (v) the type of sample (e.g., a sample described herein); and / or (vi) adjacent sequence(s) in or near the subject interval being evaluated (e.g., according to the expected propensity thereof for misalignment of the subject interval due to, e.g., the presence of repeated sequences in or near the subject interval).

[0282] In some instances, the methods disclosed herein allow for the rapid and efficient alignment of troublesome reads, e.g., a read having a rearrangement. Thus, in some instances where a read for a subject interval comprises a nucleotide position with a rearrangement, e.g., a translocation, the method can comprise using an alignment method that is appropriately tuned and that includes: (i) selecting a rearrangement reference sequence for alignment with a read, wherein said rearrangement reference sequence aligns with a rearrangement (in some instances, the reference sequence is not identical to the genomic rearrangement); and (ii) comparing, e.g., aligning, a read with said rearrangement reference sequence.

[0283] In some instances, alternative methods may be used to align troublesome reads. These methods are particularly effective when the alignment of reads for a relatively large number of diverse subject intervals is optimized. By way of example, a method of analyzing a sample can comprise: (i) performing a comparison (e.g., an alignment comparison) of a read using a first set of parameters (e.g., using a first mapping method, or by comparison with a first reference sequence), and determining if said read meets a first alignment criterion (e.g., the read can be aligned with said first reference sequence, e.g., with less than a specific number of mismatches); (ii) if said read fails to meet the first alignment criterion, performing a second alignment comparison using a second set of parameters, (e.g., using a second mapping method, or by comparison with a second reference sequence); and (iii) optionally, determining if said read meets said second criterion (e.g., the read can be aligned with said second reference sequence, e.g., with less than a specific number of mismatches), wherein said second set of parameters comprises use of, e.g., said second reference sequence, which, compared with said first set of parameters, is more likely to result in an alignment with a read for a variant (e.g., a rearrangement, insertion, deletion, or translocation).

[0284] In some instances, the alignment of sequence reads in the disclosed methods may be combined with a mutation calling method as described elsewhere herein. As discussed herein,reduced sensitivity for detecting actual mutations may be addressed by evaluating the quality of alignments (manually or in an automated fashion) around expected mutation sites in the genes or genomic loci (e.g., gene loci) being analyzed. In some instances, the sites to be evaluated can be obtained from databases of the human genome (e.g., the HG19 human reference genome) or cancer mutations (e.g., COSMIC). Regions that are identified as problematic can be remedied with the use of a method selected to give better performance in the relevant sequence context, e.g., by alignment optimization (or re-alignment) using slower, but more accurate alignment methods such as Smith-Waterman alignment. In cases where general alignment methods cannot remedy the problem, customized alignment approaches may be created by, e.g., adjustment of maximum difference mismatch penalty parameters for genes with a high likelihood of containing substitutions; adjusting specific mismatch penalty parameters based on specific mutation types that are common in certain tumor types (e.g. C~^T in melanoma); or adjusting specific mismatch penalty parameters based on specific mutation types that are common in certain sample types (e.g. substitutions that are common in FFPE).

[0285] Reduced specificity (increased false positive rate) in the evaluated subject intervals due to misalignment can be assessed by manual or automated examination of all mutation calls in the sequencing data. Those regions found to be prone to spurious mutation calls due to misalignment can be subjected to alignment remedies as discussed above. In cases where no remedy is found possible, “mutations” from the problem regions can be classified or screened out from the panel of targeted loci.Alignment of Methyl-Seq Sequence Reads

[0286] In some instances, the methods may include the use of an alignment method optimized for aligning sequence reads for DNA that has been converted using, e.g., a bisulfite reaction, to convert unmethylated cytosine residues to uracil (which is interpreted as a thymine in sequencing results). In some instances, sequence reads may be aligned to two genomes in silico, e.g., converted and unconverted versions of the reference genome, using such alignment tools. Methylation occurs primarily at CpG sites, but may also occur less frequently at non-CpG sites (e.g., CHG or CHH sites).

[0287] In some instances, the sequence read data may be obtained using a nucleic acid sequencing method comprising the use of a bisulfite- or enzymatic-conversion reaction (e.g., during library preparation) to convert non-methylated cytosine to uracil (see, e.g., Li, et al. (2011), “DNA Methylation Detection: Bisulfite Genomic Sequencing Analysis”, Methods Mol. Biol. 791:11-21).

[0288] In some instances, the sequence read data may be obtained using a nucleic acid sequencing method comprising the use of alternative chemical and / or enzymatic reactions (e.g., during library preparation) to convert non-methylated cytosine to uracil (or to convert methylated cytosine to dihydrouracil). For example, enzymatic deamination of non-methylated cytosine using APOBEC to form uracil can be performed using, e.g., the Enzymatic Methyl-seq Kit from New England BioLabs (Ipswich, MA) which uses prior treatment with ten-eleven translocation methylcytosine dioxygenase 2 (TET2) to oxidize 5-mC and 5-hmC, thereby providing greater protection of the methylated cytosine from deamination by APOBEC). Liu, et al. (2019) recently described a bisulfite-free and base-level-resolution sequencing-based method, TET-Assisted Pyridine borane Sequencing (TAPS), for detection of 5mC and 5hmC. The method combines ten-eleven translocation methylcytosine dioxygenase (TET)-mediated oxidation of 5mC and 5hmC to 5-carboxylcytosine (5caC) with pyridine borane reduction of 5caC to dihydrouracil (DHU). Subsequent PCR amplification converts DHU to thymine, thereby enabling conversion of methylated cytosines to thymine (Liu, et al. (2019), “Bisulfite-Free Direct Detection of 5 -Methylcytosine and 5-Hydroxymethylcytosine at Base Resolution”, Nature Biotechnology, vol. 37, pp. 424-429).

[0289] In some instances, the sequence read data may be obtained using a nucleic acid sequencing method comprising the use of Methylated DNA Immunoprecipitation (MeDIP).

[0290] Examples of alignment tools optimized for aligning sequence reads for converted DNA include, but are not limited to, NovoAlign (Novocraft Technologies, Selangor, Malaysia), and the Bismark tool (Krueger, et al. (2011), “Bismark: A Flexible Aligner and Methylation Caller for Bisulfite-Seq Applications”, Bioinformatics 27(11): 1571- 1572).Mutation calling

[0291] Base calling refers to the raw output of a sequencing device, e.g., the determined sequence of nucleotides in an oligonucleotide molecule. Mutation calling refers to the process ofselecting a nucleotide value, e.g., A, G, T, or C, for a given nucleotide position being sequenced. Typically, the sequence reads (or base calling) for a position will provide more than one value, e.g., some reads will indicate a T and some will indicate a G. Mutation calling is the process of assigning a correct nucleotide value, e.g., one of those values, to the sequence. Although it is referred to as “mutation” calling, it can be applied to assign a nucleotide value to any nucleotide position, e.g., positions corresponding to mutant alleles, wild-type alleles, alleles that have not been characterized as either mutant or wild-type, or to positions not characterized by variability.

[0292] The mutation calling can be done on the molecules converted for methylation sequencing (e.g., molecules converted via bisulfite conversion or enzymatic conversion), as used in the methods described herein. The mutation calling can be done on unconverted molecules, e.g., molecules not used for methyl- sequencing, such as molecules that are part of a parallel work stream and / or are part of the same assay as the molecules being methyl-sequenced as part of the methods described herein.

[0293] In some instances, the disclosed methods may comprise the use of customized or tuned mutation calling methods or parameters thereof to optimize performance when applied to sequencing data, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci (e.g., gene loci, micro satellite regions, etc.) in samples, e.g., samples from a subject having cancer.Optimization of mutation calling is described in the art, e.g., as set out in International Patent Application Publication No. WO 2012 / 092426.

[0294] Methods for mutation calling can include one or more of the following: making independent calls based on the information at each position in the reference sequence (e.g., examining the sequence reads; examining the base calls and quality scores; calculating the probability of observed bases and quality scores given a potential genotype; and assigning genotypes (e.g., using Bayes’ rule)); removing false positives (e.g., using depth thresholds to reject SNPs with read depth much lower or higher than expected; local realignment to remove false positives due to small indels); and performing linkage disequilibrium (LD) / imputation- based analysis to refine the calls.

[0295] Equations used to calculate the genotype likelihood associated with a specific genotype and position are described in, e.g., Li, H. and Durbin, R. Bioinformatics, 2010; 26(5): 589-95.The prior expectation for a particular mutation in a certain cancer type can be used when evaluating samples from that cancer type. Such likelihood can be derived from public databases of cancer mutations, e.g., Catalogue of Somatic Mutation in Cancer (COSMIC), HGMD (Human Gene Mutation Database), The SNP Consortium, Breast Cancer Mutation Data Base (BIC), and Breast Cancer Gene Database (BCGD).

[0296] Examples of LD / imputation based analysis are described in, e.g., Browning, B.L. and Yu, Z. Am. J. Hum. Genet. 2009, 85(6):847-61. Examples of low-coverage SNP calling methods are described in, e.g., Li, Y., et al., Annu. Rev. Genomics Hum. Genet. 2009, 10:387- 406.

[0297] After alignment, detection of substitutions can be performed using a mutation calling method (e.g., a Bayesian mutation calling method) which is applied to each base in each of the subject intervals, e.g., exons of a gene or other locus to be evaluated, where presence of alternate alleles is observed. This method will compare the probability of observing the read data in the presence of a mutation with the probability of observing the read data in the presence of basecalling error alone. Mutations can be called if this comparison is sufficiently strongly supportive of the presence of a mutation.

[0298] An advantage of a Bayesian mutation detection approach is that the comparison of the probability of the presence of a mutation with the probability of base-calling error alone can be weighted by a prior expectation of the presence of a mutation at the site. If some reads of an alternate allele are observed at a frequently mutated site for the given cancer type, then presence of a mutation may be confidently called even if the amount of evidence of mutation does not meet the usual thresholds. This flexibility can then be used to increase detection sensitivity for even rarer mutations / lower purity samples, or to make the test more robust to decreases in read coverage. The likelihood of a random base-pair in the genome being mutated in cancer is ~le-6. The likelihood of specific mutations occurring at many sites in, for example, a typical multigenic cancer genome panel can be orders of magnitude higher. These likelihoods can be derived from public databases of cancer mutations (e.g., COSMIC).

[0299] Indel calling is a process of finding bases in the sequencing data that differ from the reference sequence by insertion or deletion, typically including an associated confidence score or statistical evidence metric. Methods of indel calling can include the steps of identifyingcandidate indels, calculating genotype likelihood through local re-alignment, and performing LD-based genotype inference and calling. Typically, a Bayesian approach is used to obtain potential indel candidates, and then these candidates are tested together with the reference sequence in a Bayesian framework.

[0300] Methods to generate candidate indels are described in, e.g., McKenna, A., et al., Genome Res. 2010; 20(9): 1297-303; Ye, K., et al., Bioinformatics, 2009; 25(21):2865-71; Lunter, G., and Goodson, M., Genome Res. 2011; 21(6):936-9; and Li, H., et al. (2009), Bioinformatics 25(16):2078-9.

[0301] Methods for generating indel calls and individual-level genotype likelihoods include, e.g., the Dindel method (Albers, C.A., et al., Genome Res. 2011 ;21 (6) :961-73). For example, the Bayesian EM method can be used to analyze the reads, make initial indel calls, and generate genotype likelihoods for each candidate indel, followed by imputation of genotypes using, e.g., QCALL (Le S.Q. and Durbin R. Genome Res. 2011 ;21(6):952-60). Parameters, such as prior expectations of observing the indel can be adjusted e.g., increased or decreased), based on the size or location of the indels.

[0302] Methods have been developed that address limited deviations from allele frequencies of 50% or 100% for the analysis of cancer DNA. (see, e.g., SNVMix -Bioinformatics. 2010 March 15; 26(6): 730-736.) Methods disclosed herein, however, allow consideration of the possibility of the presence of a mutant allele at frequencies (or allele fractions) ranging from 1% to 100% (i.e., allele fractions ranging from 0.01 to 1.0), and especially at levels lower than 50%. This approach is particularly important for the detection of mutations in, for example, low-purity FFPE samples of natural (multi-clonal) tumor DNA.

[0303] In some instances, the mutation calling method used to analyze sequence reads is not individually customized or fine-tuned for detection of different mutations at different genomic loci. In some instances, different mutation calling methods are used that are individually customized or fine-tuned for at least a subset of the different mutations detected at different genomic loci. In some instances, different mutation calling methods are used that are individually customized or fine-tuned for each different mutant detected at each different genomic loci. The customization or tuning can be based on one or more of the factors described herein, e.g., the type of cancer in a sample, the gene or locus in which the subject interval to besequenced is located, or the variant to be sequenced. This selection or use of mutation calling methods individually customized or fine-tuned for a number of subject intervals to be sequenced allows for optimization of speed, sensitivity and specificity of mutation calling.

[0304] In some instances, a nucleotide value is assigned for a nucleotide position in each of X unique subject intervals using a unique mutation calling method, and X is at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 200, at least 300, at least 400, at least 500, at least 1000, at least 1500, at least 2000, at least 2500, at least 3000, at least 3500, at least 4000, at least 4500, at least 5000, or greater. The calling methods can differ, and thereby be unique, e.g., by relying on different Bayesian prior values.

[0305] In some instances, assigning said nucleotide value is a function of a value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.

[0306] In some instances, the method comprises assigning a nucleotide value (e.g., calling a mutation) for at least 10, 20, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1,000 nucleotide positions, wherein each assignment is a function of a unique value (as opposed to the value for the other assignments) which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.

[0307] In some instances, assigning said nucleotide value is a function of a set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a specified frequency (e.g., 1%, 5%, 10%, etc.) and / or if the variant is absent (e.g., observed in the reads due to base-calling error alone).

[0308] In some instances, the mutation calling methods described herein can include the following: (a) acquiring, for a nucleotide position in each of said X subject intervals: (i) a first value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type X; and (ii) a second set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a frequency (e.g., 1%, 5%, 10%, etc.) and / or if the variant is absent (e.g., observed in the reads due to base-calling error alone); and (b)responsive to said values, assigning a nucleotide value e.g., calling a mutation) from said reads for each of said nucleotide positions by weighing, e.g., by a Bayesian method described herein, the comparison among the values in the second set using the first value (e.g., computing the posterior probability of the presence of a mutation), thereby analyzing said sample.

[0309] Additional description of exemplary nucleic acid sequencing methods, mutation calling methods, and methods for analysis of genetic variants is provided in, e.g., U.S. Patent No. 9,340,830, U.S. Patent No. 9,792,403, U.S. Patent No. 11,136,619, U.S. Patent No. 11,118,213, and International Patent Application Publication No. WO 2020 / 236941, the entire contents of each of which is incorporated herein by reference.Methylation Status Calling

[0310] In some instances, the methods described herein may comprise the use of a methylation status calling method, e.g., to call the methylation status of the CpG sites based on the sequence reads and fragments (complementary pairs of forward and reverse sequence reads) derived from DNA that has been subjected to a chemical or enzymatic conversion reaction, e.g., to convert unmethylated cytosine residues to uracil (which is interpreted as a thymine in sequencing results). Examples of such methylation status calling tools include, but are not limited to, the Bismark tool (Krueger, et al. (2011), “Bismark: A Flexible Aligner and Methylation Caller for Bisulfite-Seq Applications”, Bioinformatics 27(11): 1571-1572), TARGOMICS (Garinet, et al. (2017), “Calling Chromosome Alterations, DNA Methylation Statuses, and Mutations in Tumors by Simple Targeted Next-Generation Sequencing - A Solution for Transferring Integrated Pangenomic Studies into Routine Practice?”, J. Molecular Diagnostics 19(5):776-787), Bicycle (Grana, et al. (2018) “Bicycle: A Bioinformatics Pipeline to Analyze Bisulfite Sequencing Data”, Bioinformatics 34(8): 1414-5), SMAP (Gao, et al. (2015), “SMAP: A Streamlined Methylation Analysis Pipeline for Bisulfite Sequencing”, Gigascience 4:29), and MeDUSA (Wilson, et al. (2016), “Computational Analysis and Integration of MeDIP-Seq Methylome Data”, in: Kulski JK, editor, Next Generation Sequencing: Advances, Applications and Challenges. Rijeka: InTech, p. 153-69). See also, Rauluseviciute, et al. (2019), “DNA Methylation Data by Sequencing: Experimental Approaches and Recommendations for Tools and Pipelines for Data Analysis”, Clinical Epigenetics 11:193.Systems

[0311] Also disclosed herein are systems designed to implement any of the disclosed methods for selecting bait set targets based on one or more samples from one or more subjects.

[0312] The systems may comprise, e.g., one or more processors, and a memory unit communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: determine, by one or more processors, first methylation values corresponding to sequence read data for a genomic region from one or more samples in a first group; determine, by the one or more processors, second methylation values corresponding to sequence read data for the genomic region from one or more samples in a second group; determine from the first methylation values and the second methylation values, by the one or more processors, at least one similarity likelihood for the genomic region; and select, by the one or more processors, a bait set target corresponding to the genomic region based on the determined similarity likelihood being less than a predetermined first threshold. The systems may comprise further instructions that, when executed by the one or more processors, cause the system to: determine, by the one or more processors, a fraction of significant samples for the genomic region, wherein the fraction of significant samples for the genomic region is a number of samples in the first group for which the similarity likelihood for the genomic region is less than the predetermined first threshold, normalized by the total number of samples in the first group; and select, by the one or more processors, the bait set target corresponding to the genomic region based on the fraction of significant samples for the genomic region exceeding a predetermined second threshold.

[0313] In addition, the systems may comprise, e.g., one or more processors, and a memory unit communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: determine, by one or more processors, first methylation values corresponding to sequence read data for a genomic region from one or more samples for one or more first groups; determine, by the one or more processors, second methylation values corresponding to sequence read data for the genomic region from one or more samples for one or more second groups; determine, by the one or more processors, from the first methylation values and the second methylation values, one or moreseparability metrics for the genomic region; and select, by the one or more processors, a bait set target corresponding to the genomic region based on the determined one or more separability metrics exceeding a predetermined first threshold. The systems may comprise further instructions that, when executed by the one or more processors, cause the system to: determine, by the one or more processors, a mean difference from the first methylation values and the second methylation values; and remove the genomic region, by the one or more processors, based on the determined mean difference being less than a predetermined second threshold.

[0314] The systems may comprise, e.g., one or more processors, and a memory unit communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive, by one or more processors, one or more methylation datasets, wherein a methylation dataset in the one or more methylation datasets comprises genomic regions and their corresponding methylation values; rank, by the one or more processors, using one or more machine learning models, the genomic regions and their corresponding methylation values, for the one or more methylation datasets, to generate one or more lists of ranked genomic regions; determine, by the one or more processors, one or more separability metrics for the one or more lists of ranked genomic regions; select one or more informative lists of ranked genomic regions from the one or more lists of ranked genomic regions, by the one or more processors, based on the one or more separability metrics exceeding a predetermined separability threshold; select informative genomic regions from the one or more informative lists of ranked genomic regions, by the one or more processors, based on a frequency of the informative genomic regions exceeding a predetermined frequency threshold; and select first bait set targets, by the one or more processors, wherein the first bait set targets correspond to the informative genomic regions. The systems may comprise further instructions that, when executed by the one or more processors, cause the system to: select further informative genomic regions from a plurality of informative genomic regions comprising the informative genomic regions, by the one or more processors, based on the further informative genomic regions being in at least a portion of the plurality of informative genomic regions; and select second bait set targets, by the one or more processors, wherein the second bait set targets correspond to the further informative genomic regions.I l l

[0315] The systems may comprise, e.g., one or more processors, and a memory unit communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: determine, by one or more processors, disease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a disease group; determine, by the one or more processors, first nondisease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a first non-disease group; determine from the disease methylation values and the first non-disease methylation values, by the one or more processors, one or more first similarity likelihoods for the CpG sites; and select from the CpG sites, disease CpG sites corresponding to the one or more first similarity likelihoods, by the one or more processors, based on the one or more first similarity likelihoods exceeding or being less than a predetermined first similarity likelihood threshold. The systems may comprise further instructions that, when executed by the one or more processors, cause the system to: determine, by the one or more processors, from the disease methylation values and the first non-disease methylation values, one or more separability metrics for the disease CpG sites; and select informative disease CpG sites from the CpG sites or the disease CpG sites, by the one or more processors, based on the determined one or more separability metrics exceeding a predetermined separability metric threshold. The systems may comprise further instructions that, when executed by the one or more processors, cause the system to: determine, by the one or more processors, a mean difference from the disease methylation values and the first non-disease methylation values; and select further informative disease CpG sites from the CpG sites, the disease CpG sites, or the informative disease CpG sites, by the one or more processors, based on the determined mean difference exceeding a predetermined mean difference threshold. The systems may comprise further instructions that, when executed by the one or more processors, cause the system to: determine, by the one or more processors, second non-disease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a second non-disease group; determine from the first non- disease methylation values and second non-disease methylation values, by the one or more processors, one or more second similarity likelihoods for the CpG sites; and select informative CpG sites, from the CpG sites, the disease CpG sites, the informative disease CpG sites, or the further informative disease CpG sites, based on the one or more second similarity likelihoodsbeing less than a predetermined second similarity likelihood threshold. The systems may comprise further instructions that, when executed by the one or more processors, cause the system to: cluster, by the one or more processors, informative CpG sites based on any two informative CpG sites being within a predetermined distance from one another, and that within the predetermined distance, at least four informative CpG sites are found, to generate one or more clusters; and select, by the one or more processors, bait set targets corresponding to the one or more clusters.

[0316] The systems may comprise, e.g., one or more processors, and a memory unit communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: determine, by one or more processors, one or more methylation values corresponding to sequence read data for a genomic region from one or more healthy samples; determine, by the one or more processors, a central tendency measure from the one or more methylation values; and select, by the one or more processors, a bait set target corresponding to the genomic region based on the determined central tendency measure exceeding a predetermined threshold.

[0317] In some instances, the disclosed systems may further comprise a sequencer, e.g., a next generation sequencer (also referred to as a massively parallel sequencer). Examples of next generation (or massively parallel) sequencing platforms include, but are not limited to, Roche / 454’s Genome Sequencer (GS) FLX system, Illumina / Solexa’ s Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 sequencing systems, Life / APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, ThermoFisher Scientific’s Ion Torrent Genexus system, or Pacific Biosciences’ PacBio® RS system.

[0318] In some instances, the disclosed systems may be used for selecting bait set targets in any of a variety of samples as described herein (e.g., a tissue sample, biopsy sample, hematological sample, or liquid biopsy sample derived from the subject).

[0319] In some instances, the plurality of gene loci for which sequencing data is processed to select bait set targets may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 gene loci.

[0320] In some instance, the nucleic acid sequence data is acquired using a next generation sequencing technique (also referred to as a massively parallel sequencing technique) having aread-length of less than 400 bases, less than 300 bases, less than 200 bases, less than 150 bases, less than 100 bases, less than 90 bases, less than 80 bases, less than 70 bases, less than 60 bases, less than 50 bases, less than 40 bases, or less than 30 bases.

[0321] In some instances, the selection of bait set targets is used to select, initiate, adjust, or terminate a treatment for cancer in the subject (e.g., a patient) from which the sample was derived, as described elsewhere herein.

[0322] In some instances, the disclosed systems may further comprise sample processing and library preparation workstations, microplate-handling robotics, fluid dispensing systems, temperature control modules, environmental control chambers, additional data storage modules, data communication modules (e.g., Bluetooth®, WiFi, intranet, or internet communication hardware and associated software), display modules, one or more local and / or cloud-based software packages (e.g., instrument / system control software packages, sequencing data analysis software packages), etc., or any combination thereof. In some instances, the systems may comprise, or be part of, a computer system or computer network as described elsewhere herein.Computer systems and networks

[0323] FIG. 11 illustrates an example of a computing device or system in accordance with one embodiment. Device 1100 can be a host computer connected to a network. Device 900 can be a client computer or a server. As shown in FIG. 11, device 1100 can be any suitable type of microprocessor-based device, such as a personal computer, workstation, server or handheld computing device (portable electronic device) such as a phone or tablet. The device can include, for example, one or more processor(s) 1110, input devices 1120, output devices 1130, memory or storage devices 1140, communication devices 1160, and nucleic acid sequencers 1170. Software module 1150 residing in memory or storage device 1140 may comprise, e.g., an operating system as well as software for executing the methods described herein. Input device 1120 and output device 1130 can generally correspond to those described herein, and can either be connectable or integrated with the computer.

[0324] Input device 1120 can be any suitable device that provides input, such as a touch screen, keyboard or keypad, mouse, or voice-recognition device. Output device 1130 can be any suitable device that provides output, such as a touch screen, haptics device, or speaker.

[0325] Storage device 1140 can be any suitable device that provides storage (e.g., an electrical, magnetic or optical memory including a RAM (volatile and non-volatile), cache, hard drive, or removable storage disk). Communication device 1160 can include any suitable device capable of transmitting and receiving signals over a network, such as a network interface chip or device. The components of the computer can be connected in any suitable manner, such as via a wired media (e.g., a physical system bus 1180, Ethernet connection, or any other wire transfer technology) or wirelessly (e.g., Bluetooth®, Wi-Fi®, or any other wireless technology).

[0326] Software module 1150, which can be stored as executable instructions in storage device 940 and executed by processor(s) 1110, can include, for example, an operating system and / or the processes that embody the functionality of the methods of the present disclosure (e.g., as embodied in the devices as described herein).

[0327] Software module 1150 can also be stored and / or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described herein, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a computer-readable storage medium can be any medium, such as storage device 1140, that can contain or store processes for use by or in connection with an instruction execution system, apparatus, or device. Examples of computer-readable storage media may include memory units like hard drives, flash drives and distribute modules that operate as a single functional unit. Also, various processes described herein may be embodied as modules configured to operate in accordance with the embodiments and techniques described above. Further, while processes may be shown and / or described separately, those skilled in the art will appreciate that the above processes may be routines or modules within other processes.

[0328] Software module 1150 can also be propagated within any transport medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a transport medium can be any medium that can communicate, propagate or transport programming for use by or in connection with an instruction execution system, apparatus, ordevice. The transport readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic or infrared wired or wireless propagation medium.

[0329] Device 1100 may be connected to a network (e.g., network 1204, as shown in FIG. 12 and / or described below), which can be any suitable type of interconnected communication system. The network can implement any suitable communications protocol and can be secured by any suitable security protocol. The network can comprise network links of any suitable arrangement that can implement the transmission and reception of network signals, such as wireless network connections, T1 or T3 lines, cable networks, DSL, or telephone lines.

[0330] Device 1100 can be implemented using any operating system, e.g., an operating system suitable for operating on the network. Software module 1150 can be written in any suitable programming language, such as C, C++, Java, Python, and / or R. In various embodiments, application software embodying the functionality of t...

Claims

CLAIMSWhat is claimed is:

1. A method for selecting bait set targets, comprising: determining, by one or more processors, first methylation values corresponding to sequence read data for a genomic region from one or more samples in a first group; determining, by the one or more processors, second methylation values corresponding to sequence read data for the genomic region from one or more samples in a second group; determining from the first methylation values and the second methylation values, by the one or more processors, at least one similarity likelihood or one or more separability metrics for the genomic region; and selecting, by the one or more processors, a bait set target corresponding to the genomic region based on the determined similarity likelihood being less than a predetermined first threshold or the determined one or more separability metrics exceeding a predetermined first threshold.

2. The method of claim 1, further comprising: determining, by the one or more processors, a fraction of significant samples for the genomic region, wherein the fraction of significant samples for the genomic region is a number of samples in the first group for which the similarity likelihood for the genomic region is less than the predetermined first threshold, normalized by the total number of samples in the first group; and selecting, by the one or more processors, the bait set target corresponding to the genomic region based on the fraction of significant samples for the genomic region exceeding a predetermined second threshold.

3. The method of claim 1, further comprising: determining, by the one or more processors, a mean difference between the first methylation values and the second methylation values; andremoving, by the one or more processors, the genomic region, based on the mean difference being less than a predetermined third threshold.

4. The method of claim 1, wherein the sequence read data for the genomic region from one or more samples in the first group comprise sequence read data related to a disease.

5. The method of claim 4, wherein the disease is a cancer.

6. A method for selecting bait set targets for one or more samples, comprising: receiving, by one or more processors, one or more methylation datasets, wherein a methylation dataset in the one or more methylation datasets comprises genomic regions and their corresponding methylation values; ranking, by the one or more processors, using one or more machine learning models, the genomic regions and their corresponding methylation values, for the one or more methylation datasets, to generate one or more lists of ranked genomic regions; determining, by the one or more processors, one or more separability metrics for the one or more lists of ranked genomic regions; selecting one or more informative lists of ranked genomic regions from the one or more lists of ranked genomic regions, by the one or more processors, based on the one or more separability metrics exceeding a predetermined separability threshold; selecting informative genomic regions from the one or more informative lists of ranked genomic regions, by the one or more processors, based on a frequency of the informative genomic regions exceeding a predetermined frequency threshold; and selecting first bait set targets, by the one or more processors, wherein the first bait set targets correspond to the informative genomic regions.

7. The method of claim 6, further comprising: selecting further informative genomic regions from a plurality of informative genomic regions comprising the informative genomic regions, by the one or more processors, based onthe further informative genomic regions being in at least a portion of the plurality of informative genomic regions; and selecting second bait set targets, by the one or more processors, wherein the second bait set targets correspond to the further informative genomic regions.

8. The method of claim 6, wherein the ranking is based on an informativeness of the genomic regions and their corresponding methylation values.

9. A method for selecting bait set targets, comprising: determining, by one or more processors, disease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a disease group; determining, by the one or more processors, first non-disease methylation values corresponding to sequence read data for CpG sites, for one or more samples in a first nondisease group; determining from the disease methylation values and the first non-disease methylation values, by the one or more processors, one or more first similarity likelihoods for the CpG sites; and selecting from the CpG sites, disease CpG sites corresponding to the one or more first similarity likelihoods, by the one or more processors, based on the one or more first similarity likelihoods exceeding or being less than a predetermined first similarity likelihood threshold.

10. The method of claim 9, further comprising: determining, by the one or more processors, from the disease methylation values and the first non-disease methylation values, one or more separability metrics for the disease CpG sites; and selecting informative disease CpG sites from the CpG sites or the disease CpG sites, by the one or more processors, based on the determined one or more separability metrics exceeding a predetermined separability metric threshold.

11. The method of claim 9, further comprising: determining, by the one or more processors, a mean difference from the disease methylation values and the first non-disease methylation values; and selecting further informative disease CpG sites from the CpG sites, the disease CpG sites, or the informative disease CpG sites, by the one or more processors, based on the determined mean difference exceeding a predetermined mean difference threshold.

12. The method of claim 9, further comprising: determining, by the one or more processors, second non-disease methylation values corresponding to sequence read data for CpG sites in a region, for one or more samples in a second non-disease group; determining from the first non-disease methylation values and second non-disease methylation values, by the one or more processors, one or more second similarity likelihoods for the CpG sites; and selecting informative CpG sites, from the CpG sites, the disease CpG sites, the informative disease CpG sites, or the further informative disease CpG sites, based on the one or more second similarity likelihoods being less than a predetermined second similarity likelihood threshold.

13. The method of claim 9, further comprising: clustering, by the one or more processors, informative CpG sites based on any two informative CpG sites being within a predetermined distance from one another, and that within the predetermined distance, at least four informative CpG sites are found, to generate one or more clusters; and selecting, by the one or more processors, bait set targets corresponding to the one or more clusters.

14. The method of claim 9, wherein the CpG sites are from LI genomic regions.

15. The method of claim 9, wherein the one or more first similarity likelihoods are determined by statistical testing.

16. The method of claim 9, wherein the sequences read data for the CpG sites for the one or more samples in the first non-disease group comprise panel of normal read count data.

17. The method of claim 9, wherein the sequence read data for the CpG sites for the one or more samples in the first non-disease group are from healthy plasma or healthy tissue.

18. A method for selecting bait set targets, comprising: determining, by one or more processors, one or more methylation values corresponding to sequence read data for a genomic region from one or more healthy samples; determining, by the one or more processors, a central tendency measure from the one or more methylation values; and selecting, by the one or more processors, a bait set target corresponding to the genomic region based on the determined central tendency measure exceeding a predetermined threshold.

19. The method of claim 18, wherein the central tendency measure is the mean, median, or the mode.

20. The method of claim 18, wherein the genomic region is hypermethylated in the one or more healthy samples, relative to one or more disease samples.

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