Promoter methylation detection

Abstract

Described herein are methods of selecting therapies for personalized cancer therapy, such as diagnostics, by simultaneously detecting genomic and epigenomic attributes from a single patient sample, including quantitative promoter methylation, and uses for determining methylation patterns associated with epigenetic allele status.

Description

Cross Reference to Related Applications

[0001] This application claims the benefit of U.S. Provisional Patent Application Nos. 63 / 509,917, filed June 23, 2023, and 63 / 495,688, filed April 12, 2023, each of which is incorporated herein by reference in its entirety. Field of the invention

[0002] Described herein are methods of selecting a therapy, such as a diagnosis, for personalized cancer treatment by simultaneously interrogating genomic and epigenomic attributes, including quantitative promoter methylation, from a single patient sample. Background

[0003] Therapy selection for cancer patients is imprecise. Patterns of DNA, RNA, and proteins from patient samples are often analyzed, which can predict response to a particular therapy. The range of these biomarkers can be from single genes (e.g. EGFR, by real-time PCR) and proteins (e.g. HER2, by immunohistochemistry) to complex genomic features (e.g. tumor mutational burden, by next-generation sequencing). The testing workflow for multiple types of analytes is often separate and cannot be combined due to incompatibilities in isolation, chemistry, and quantification processes, among others. As a result, diagnostic tests cannot examine the full range of biomarkers that provide useful information, and multiple, separate tests must be performed if a multi-omic result is needed. In most cases, multiple tests are not performed due to a variety of reasons including lack of sufficient patient sample, and clinical decisions are made based on incomplete information.

[0004] There is a great need in the art for improvements in personalized medicine. By simultaneously interrogating genomic and epigenomic states of a patient sample, more accurate predictions of therapy effectiveness can be achieved.

[0005] Described herein is the simultaneous testing and incorporation of information from both genomic and epigenomic components derived from a patient sample, such a diagnostic test would be able to take into account additional information not otherwise available. Of interest are methylation states, particularly in promoter regions, including methylation patterns related to epigenetic allele status, which can explain cases where genomic alterations do not account for different effectiveness of therapies in patients, including for example, parp inhibitors (PARPi). SUMMARY

[0006] Described herein is a method comprising detecting methylation in one or more promoter regions of at least one of a plurality of genes; and generating a plurality of methylation calls to quantify methylation of the one or more promoter regions. In other embodiments, the method comprises obtaining a sample. In other embodiments, the method comprises having obtained a sample. In other embodiments, the method comprises processing the quantity of methylation of the one or more promoter regions to characterize the sample. In other embodiments, characterizing the sample comprises HRD, cancer-derived promoter methylation, familial form of colorectal cancer, or Lynch syndrome tumor type. In other embodiments, the promoter comprises a region 5 kb upstream of a transcription start site (TSS), wherein the 5 kb region is further refined using one or more of the following: custom set regions, methylation peaks found in clinical samples, and exclusion of peaks found in normal samples. In other embodiments, the TSS is defined at the transcript level. In other embodiments, the TSS is defined at the gene level. In other embodiments, the method comprises determining a ratio of the number of molecules overlapping a target region normalized by the total positive control molecules. In other embodiments, the method comprises determining the ratio comprises filtering molecules based on at least the number of overlapping CpGs. In other embodiments, quantifying methylation of the one or more promoter regions is based on the number of methylated CpGs. In other embodiments, the method comprises refining the one or more promoter regions based on at least literature annotation, common methylation peak location, and / or public datasets. In other embodiments, the genes comprise tumor suppressor genes, HRR genes, and IO genes. In other embodiments, the HRR genes comprise at least BRCA1 and BRCA2.

[0007] In other embodiments, the method comprises comparing to a minimum methylation threshold derived from a training population of samples. In other embodiments, the training samples comprise cancer-free samples. In other embodiments, the minimum methylation threshold for calling comprises at least one of the following: a minimum molecule count of 1-100, and a minimum methylation score per gene is the maximum of the following: 95 normal quantile + 8X105 or the median + 5*median absolute deviation. In other embodiments, quantifying methylation of the one or more promoter regions predicts therapy response. In other embodiments, quantifying methylation of the one or more promoter regions is combined with MSI-H status.

[0008] In other embodiments, the therapy comprises one or more of an immune checkpoint inhibitor, a poly (ADP-ribose) polymerase (PARP) inhibitor, a kinase inhibitor, or an aromatase inhibitor, or a PI3K and mTOR inhibitor. In other embodiments, the immune checkpoint inhibitor is Pembrolizumab. In other embodiments, the method comprises the poly (ADP-ribose) polymerase (PARP) inhibitor Olaparib or Talazoparib. In other embodiments, the therapy is a combination of a PI3K and mTOR inhibitor with a poly (ADP-ribose) polymerase (PARP) inhibitor. In other embodiments, the PI3K and mTOR inhibitor is Gedatolisib and the poly (ADP-ribose) polymerase (PARP) inhibitor is Talazoparib.

[0009] Described herein is a method comprising determining a promoter region of at least one of a plurality of genes, each gene obtained from a plurality of samples, determining a methylation score for the promoter region to produce a plurality of methylation calls and / or a quantification of promoter methylation, processing the plurality of methylation calls to produce a prediction that the test sample exhibits a genomic state.

[0010] Described herein is a method comprising determining promoter regions of a plurality of genes, each gene obtained from a plurality of samples; determining methylation scores for the promoter regions to produce a plurality of methylation calls and / or quantification of promoter methylation; processing the plurality of methylation calls to produce a prediction that a test sample exhibits a genomic state. In other embodiments, the genomic state comprises HRD, cancer-derived promoter methylation, familial form of colorectal cancer, or Lynch syndrome tumor type. In other embodiments, the promoter comprises a region 5 kb upstream of the TSS, wherein the 5 kb region is further refined using one or more of the following: custom set regions, methylation peaks found in clinical samples, and exclusion of peaks found in normal samples. In other embodiments, the TSS is defined at the transcript level. In other embodiments, the TSS is defined at the gene level. In other embodiments, the methylation score is determined as a ratio of the number of molecules overlapping the target region normalized by the total positive control molecules. In other embodiments, molecules supporting the methylation score are filtered based on at least the number of overlapping CpGs. In other embodiments, the promoter region is refined based on at least literature annotation, common methylation peak location, and / or public datasets. In other embodiments, the genes comprise tumor suppressor genes, HRR genes, and IO genes. In other embodiments, the HRR genes comprise at least BRCA1 and BRCA2. In other embodiments, the calls comprise deriving a minimum methylation threshold from a population of training samples. In other embodiments, the training samples comprise cancer-free samples. In other embodiments, the minimum methylation threshold for the calls comprises: a minimum molecule count per gene of 1-100 minimum and / or a maximum methylation score of: 95 normal quantile + 8X105 or median + 5*median absolute deviation. In other embodiments, the promoter methylation calls in combination with MSI-H status predict therapy response. In other embodiments, the therapy comprises one or more of an immune checkpoint inhibitor, a poly (ADP-ribose) polymerase (PARP) inhibitor, a kinase inhibitor, or an aromatase inhibitor, or a PI3K and mTOR inhibitor. In other embodiments, the immune checkpoint inhibitor is Pembrolizumab. In other embodiments, the poly (ADP-ribose) polymerase (PARP) inhibitor is Olaparib or Talazoparib. In other embodiments, the therapy is a combination of a PI3K and mTOR inhibitor with a poly (ADP-ribose) polymerase (PARP) inhibitor. In other embodiments, the PI3K and mTOR inhibitor is Gedatolisib and the poly (ADP-ribose) polymerase (PARP) inhibitor is Talazoparib.

[0011] Described herein is a method comprising determining promoter regions of BRCA1 and BRCA2 each obtained from more than one sample, determining a methylation score for the promoter regions to generate more than one methylation call, processing the more than one methylation call to generate a prediction that the patient exhibits biallelic loss of BRCA1 or BRCA2.

[0012] Described herein is a method comprising determining promoter regions of BRCA1 and BRCA2 each obtained from more than one sample, determining a methylation score for the promoter regions to generate more than one methylation call, processing the more than one methylation call to generate a prediction that the patient exhibits biallelic loss of BRCA1 or BRCA2, determining that the patient is a candidate for treatment with a PARPi.

[0013] Described herein is a method comprising determining promoter regions of BRCA1 and BRCA2 each obtained from more than one sample, determining a methylation score for the promoter regions to generate more than one methylation call, processing the more than one methylation call to generate a prediction that the patient exhibits biallelic loss of BRCA1 or BRCA2, determining that the patient is a candidate for treatment with a PARPi.

[0014] Described herein is a method comprising determining promoter regions of MLH1, each promoter region obtained from more than one sample, determining a methylation score for the promoter regions to generate more than one promoter methylation call, determining from genomic data that the patient is BRAF V600E positive, wherein detecting promoter methylation in a BRAF V600E positive patient identifies the patient as one who can be at risk for a hereditary / familial form of colorectal cancer or a Lynch syndrome associated tumor type.

[0015] Described herein is a method comprising: obtaining, by a computing system having one or more hardware processors and memory, sequencing reads originating from a sample of a subject, determining one or more categorical zones corresponding to a plurality of genes included in the sample; and determining a methylation level of the one or more categorical zones by generating a quantitative measure of the sequencing reads originating from the sample of the subject. In other embodiments, the method comprises obtaining the sample. In other embodiments, the method comprises having the sample obtained. In other embodiments, the method comprises processing the methylation level of the one or more categorical zones to characterize the sample. In other embodiments, characterizing the sample comprises determining a HRD status, a promoter methylation associated with cancer. In other embodiments, the quantitative measure comprises determining a ratio of a number of molecules overlapping the categorical zone exhibiting a threshold amount of methylated cytosine normalized by a total positive control molecule. In other embodiments, the quantitative measure is compared to a predetermined threshold to determine a methylation status of the one or more categorical zones. In other embodiments, determining the ratio comprises filtering the molecules based at least on the threshold amount of methylated cytosine. In other embodiments, determining the methylation level of the one or more categorical zones is based on a number of methylated CpGs. In other embodiments, the categorical zone comprises a promoter region. In other embodiments, the one or more categorical zones individually correspond to a genomic region where a rate of cytosine methylation in the nucleic acid genomic region derived from cells obtained from a subject in which cancer is present is different than a rate of cytosine methylation in the nucleic acid genomic region derived from cells obtained from a subject in which cancer is not present. In other embodiments, the plurality of samples and the additional sample comprise cell-free nucleic acid. In other embodiments, the method comprises a training process by the computing system to generate the model using training data, wherein the training process comprises determining, by the computing system, one or more additional weights of an individual sample included in the training data based on an indication of cancer of the individual sample being within a threshold confidence level. In other embodiments, the indication of cancer of the individual sample is outside the threshold confidence level, and the method comprises: applying, by the computing system during the training process, a penalty to the weight of the individual sample. The method comprises: training, by the computing system and using one or more machine learning algorithms, the training process for one or more first iterations using a portion of the training data for the model; and generating, by the computing system, first output data of the model based on the one or more first iterations of the training process, the first output data corresponding to one or more first additional indications of presence of cancer in a first individual subject of the plurality of subjects, the first individual subject corresponding to the portion of the training data.In other embodiments, the method includes combining, by the computing system, the first output data and the training data to produce further training data; performing, by the computing system, one or more second iterations of the training process for the model using a portion of the further training data; and generating, by the computing system, second output data for the model based on the one or more second iterations of the training process, the second output data indicating one or more second further indications of the presence of cancer in a second single subject of the plurality of subjects, the second single subject corresponding to the portion of the further training data. In other embodiments, the weight of the single classification region of the plurality of classification regions is determined based on the first output data and the second output data. In other embodiments, the method includes determining, by the computing system, that a number of indications of the presence of cancer determined during the one or more iterations of the training process is at least a threshold value for the one or more samples included in the training data; and determining, by the computing system, that the modification of the one or more weights for the model is not modified or is modified by a minimum amount. In other embodiments, the method includes determining, by the computing system, that a further number of indications of the presence of cancer determined during the one or more iterations of the training process is less than a threshold value for one or more further samples included in the training data; and determining, by the computing system, that the modification of the one or more further weights for the model is modified by an amount that exceeds the minimum amount. In other embodiments, the method includes combining a plurality of nucleic acids derived from at least one of blood or tissue of a subject with a solution comprising an amount of a methyl binding domain (MBD) protein to produce a nucleic acid-MBD protein solution; and performing a plurality of washes of the nucleic acid-MBD protein solution with a salt solution to produce a number of nucleic acid fractions, an individual nucleic acid fraction having a threshold number of methylated cytosines in a region of the plurality of nucleic acids having at least a threshold cytosine-guanine content. In other embodiments, a wash of the plurality of washes is performed with a solution having a concentration of sodium chloride (NaCl), and produces a nucleic acid fraction of the number of nucleic acid fractions having a range of binding strengths to the MBD protein. In other embodiments, the method includes determining that a first nucleic acid fraction is associated with a first partition of the plurality of nucleic acid partitions, the first partition corresponding to a first range of binding strengths to the MBD protein; attaching a first molecular barcode to nucleic acids of the first nucleic acid fraction, the first molecular barcode being included in a first set of molecular barcodes associated with the first partition; determining that a second nucleic acid fraction is associated with a second partition of the plurality of nucleic acid partitions, the second partition corresponding to a second range of binding strengths to the MBD protein different from the first range of binding strengths to the MBD protein; and attaching a second molecular barcode to nucleic acids of the second nucleic acid fraction, the second molecular barcode being included in a second set of molecular barcodes associated with the second partition.

[0016] In other embodiments, the method includes combining at least a portion of the plurality of nucleic acid fractions with an amount of a restriction enzyme that cleaves molecules having one or more unmethylated cytosines to produce at least a portion of a plurality of samples for generating sequencing reads, wherein the threshold amount of methylated cytosines corresponds to a minimum frequency of methylated cytosines within regions having at least a threshold cytosine-guanine content.

[0017] Described herein is a method comprising: obtaining, by a computing system having one or more hardware processors and memory, sequencing reads derived from a sample of a subject, determining one or more categorical regions corresponding to a plurality of genes included in the sample, determining a methylation level of the one or more categorical regions by generating a quantitative measure comprising a ratio of a number of molecules overlapping a categorical region normalized by a total positive control molecule, wherein the molecules exhibit a threshold amount of methylated cytosines; and comparing the quantitative measure to a predetermined threshold to call a methylation state of the one or more categorical regions.

[0018] In various embodiments, the determination of the quantitative measure can include combining a plurality of nucleic acids derived from at least one of blood or tissue of a subject with a solution comprising an amount of a methyl binding domain (MBD) protein to produce a nucleic acid-MBD protein solution; and subjecting the nucleic acid-MBD protein solution to a plurality of washes with a salt solution to produce a plurality of nucleic acid fractions. In some cases, an individual nucleic acid fraction has a threshold number of methylated cytosines in a region of the plurality of nucleic acids having at least a threshold cytosine-guanine content. Thereafter, a wash of the plurality of washes is performed with a solution having a concentration of sodium chloride (NaCl) and produces a nucleic acid fraction of the plurality of nucleic acid fractions having a range of binding strengths to the MBD protein.

[0019] One can determine that a first nucleic acid fraction is associated with a first partition of the plurality of nucleic acid partitions, the first partition corresponding to a first range of binding strengths to the MBD protein; attach a first molecular barcode to nucleic acids of the first nucleic acid fraction, the first molecular barcode being included in a first set of molecular barcodes associated with the first partition, and subsequently determine that a second nucleic acid fraction is associated with a second partition of the plurality of nucleic acid partitions, the second partition corresponding to a second range of binding strengths to the MBD protein different from the first range of binding strengths to the MBD protein; and subsequently attach a second molecular barcode to nucleic acids of the second nucleic acid fraction, the second molecular barcode being included in a second set of molecular barcodes associated with the second partition.

[0020] In some cases, one can combine at least a portion of the plurality of nucleic acid fractions with an amount of a restriction enzyme that cleaves molecules having one or more unmethylated cytosines to produce at least a portion of a plurality of samples for generating sequencing reads, where a threshold amount of methylated cytosines corresponds to a minimum frequency of methylated cytosines within regions having at least a threshold cytosine-guanine content.

[0021] Additionally, one can combine at least a portion of the plurality of nucleic acid fractions with an amount of a restriction enzyme that cleaves molecules having one or more methylated cytosines to produce at least a portion of a plurality of samples for generating sequencing reads, where a threshold amount of unmethylated cytosines corresponds to a maximum frequency of unmethylated cytosines within regions having at least a threshold cytosine-guanine content that are not cleaved. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 . BRCA1 promoter region. 11 CpG sites with core promoter activity (circles), shown to be hypermethylated in breast cancer (pink circles), are encompassed in the BRCA1 promoter definition. Numbers refer to nucleotide positions relative to BRCA1 transcription start.

[0023] Figure 2 : 95% limit of detection (LoD) for BRCA1. BRCA1 promoter methylation LoD is 0.6%, as determined by titration of HCC-38, a well-characterized breast cancer cell line. Previously, HCC-38 was confirmed to be epigenetically silenced at the BRCA1 locus by promoter methylation by bisulfite sequencing and RT-PCR (Stefansson 2012, Xu 2010). By comparison, our method did not detect any BRCA1 promoter methylation in 80 cancer-free donors tested, demonstrating 100% specificity.

[0024] Figure 3 : Incidence of BRCA1 promoter methylation in different cancer types in selected patient cohorts. Note that differences in methylation frequency can be attributed to non-random patient subtype composition not selected in the Guardant Infinity cohort, as well as stage of cancer (where patients can lose methylation during treatment), and can not be directly comparable to patient cohorts in TCGA. Abbreviations: ovarian cancer (OVCA), breast cancer (BRCA), bladder cancer (BLCA), lung adenocarcinoma (LUAD), colorectal adenocarcinoma (COAD), lung squamous cell carcinoma (LUSC), melanoma (SKCM).

[0025] Figure 4Oncoprint analysis of epigenetic and genomic alterations in HRR genes. Pathogenicity defined as any nonsense, frameshift, rearrangement, or pathogenic ClinVar missense mutation in the aforementioned HRR genes. Somatic truncating mutations in ATM and CHEK2 were omitted from this analysis due to possible interference from clonal hematopoiesis. Promoter methylation highlighted in pink - note that these alterations are largely mutually exclusive with other pathogenic alterations in other HRR genes.

[0026] Figure 5 . Characterization of promoter coverage in sample set. Depicted here is the minimum 10 CpGs in a 200bp sliding window.

[0027] Figure 6 . Promoter methylation region definition region removal: sex chromosomes + normal noise region. 5kb upstream of TSS aggregated per gene. If multiple TSS, aggregate at gene level. Definition method: split per TSS -> report at transcript level; refine promoter region by literature, other data (e.g. MBD partition peaks, RNA / methylation associations). At least 2 probes in promoter region, covering almost all 16,000 genes in a set.

[0028] Figure 7 . Illustrative analysis validation: limit of detection.

[0029] Figure 8 . Epigenomic MLH1 association with MSI-H, MSI promoter definition.

[0030] Figure 9 . MSI-H in MLH1+ vs. MSS / MSI-L regional patterns.

[0031] Figure 10 . BRCA1 - clinical samples and cell lines.

[0032] Figure 11 . Promoter methylation: partial vs. complete methylation. In some cases, only complete methylation can lead to gene inactivation. Promoter methylation often occurs in one allele, while the other allele is inactivated by other events. (e.g. BRCA LoH / promoter co-occurrence in HRD+). Functional methylation alterations can include distinguishing between partial and complete methylation.

[0033] Figure 12EM-seq overview: panel design. In the orthogonal approach, to demonstrate the ability of the detection scheme, an EM-seq panel for pan-cancer methylation enrichment was designed. 1.54 Mb (125,080 CpG) was targeted with 13,090 probes @ 15,000x depth. 1.00 Mb and 90,949 CpG were covered by epigenomic probes (65% of sequences, 73% of CpG); 876 kb and 70,493 of these CpG overlapped with refseq promoter regions, and showed MLH1 and BRCA1.

[0034] Figure 13 EM-seq data agreement with public array data. Accuracy of orthogonal EM-seq results using neat cell lines is depicted. Variability level (left panel) and probe level (right panel) β agree between KM12 Em-seq (x-axis) and Illumina 450K array data (NCI, y-axis). Probe β is the average of all CpG β in each EM-seq region (two datasets).

[0035] Figure 14 Epigenomic detection vs. region-wise EM-Seq. Here, the accuracy of positive predictive accuracy (PPA) in samples >= epigenomic LoD (rough estimate >0.3% TF = red box on left panel). PPA for EM-Seq can be expected to be >80% as most samples have >0.1% β values (decision threshold). In positive clinical samples for all genes on the epigenomic detection and EMSeq panels, both positive and negative promoter methylation calls are mixed. In negative (no cancer) clinical samples for all genes on the epigenomic detection and EMSeq panels, promoter methylation calls are mostly negative. DETAILED DESCRIPTION

[0036] BRCA1 promoter methylation (PM) is an early initiating event in cancer, occurs in 3-65.2% of all breast tumors depending on subtype, and 30-65% of triple negative tumors. BRCA1 promoter methylation is associated with defective homologous recombination repair (HRR), early onset of breast and ovarian cancer, and improved clinical response to adjuvant chemotherapy. To date, no diagnostic assay has comprehensively assessed both BRCA1 promoter methylation and genomic alterations in cell-free circulating tumor DNA (ctDNA). Here, the inventors have established a detection method not achieved to date for interrogating both promoter methylation status, genomic alterations, and further quantifying methylation with or without epigenetic allele status. Here, this multi-modal detection of BRCA1 PM and genomic alterations in an epigenomic detection platform using methylation binding domain partitioning allows for liquid biopsy detection of 800+ genes and whole genome methylation detection. BRCA1 PM in ctDNA from 1016 advanced breast cancer patients was assessed, along with genomic sequencing of 800+ genes, and PM profiling of 398 cancer related genes by an epigenomic methylation detection assay. The predefined promoter region of each covered gene was analyzed, including a novel method of promoter definition. For each sample, a methylation score was calculated for each gene and used as a basis for making PM calls. Limits of detection (LoD) were determined by in silico simulation and experimental titration of ctDNA from clinical samples and cell lines with known gene PM into cancer-free donor plasma.

[0037] Furthermore, establishing the above detection method allows for determination of epigenetic allele status at the system level. Allele-specific methylation patterns play an important role in controlling gene expression and maintaining normal cellular function, and disruption of these patterns can contribute to pathogenesis including tumorigenesis. Imprinting is a form of allele-specific methylation pattern in which one allele of a gene is methylated and silenced depending on whether it was inherited from the father or mother. Differential methylation of imprinted genes and resulting monoallelic expression is important for normal development and physiological function, and abnormal changes in these imprinting patterns (loss or gain of methylation) can lead to developmental disorders and increased susceptibility to disease, including cancer. For example, loss of imprinting (LOI) can result in expression of both alleles of a normally imprinted gene, potentially doubling expression of a gene that promotes cell growth, a common feature in various cancers, see, e.g. Figure 10Figure A. In other cases, normally unmethylated and active tumor suppressor genes can undergo methylation on one allele. This methylation can silence gene expression in that allele, and if the other allele is lost or mutated, it can contribute to cancer progression. A well-known example is the p16 gene (CDKN2A), which can undergo hypermethylation in various cancers such as melanoma, bladder cancer, etc. See, for example... Figure 10 Figure B. Compared to complete methylation of the entire allele, partial allele-specific methylation patterns (see Figure B). Figure 10 Figure C) shows that selective methylation can have more subtle effects on gene function. This selective methylation can occur in specific regions of a gene, such as promoters, enhancers, or other regulatory elements, affecting the gene's transcriptional activity in a cell-type-specific manner. In cancer, partial methylation of the promoter region of a tumor suppressor gene can downregulate gene expression without completely silencing the gene. This partial methylation may occur only in certain CpG islands within the promoter region. Additionally, methylation of enhancer regions can modulate enhancer activity, thereby indirectly affecting the expression of genes associated with these enhancers. Partial methylation of enhancer regions can lead to alterations in the gene expression profile that contributes to tumorigenesis.

[0038] Current approaches either omit testing both the genomic and epigenomic properties of patient samples or perform multiple tests separately. If both genomic and epigenomic information are available, omitting either information may result in prescribing potentially ineffective cancer therapies or rejecting potentially effective ones. For example, a patient with the KRASG12C biomarker might be prescribed a KRAS inhibitor, but if epigenomic information shows that the KRAS promoter is methylated and therefore the gene is silenced, the KRAS inhibitor would obviously be ineffective. On the other hand, a patient without a detected BRCA1 mutation might not be prescribed a PARP inhibitor, but if epigenomic information shows that the BRCA1 promoter is methylated and therefore the gene is silenced, the patient would be a good candidate for a PARP inhibitor. Multiple testing is generally not performed for various reasons, including a lack of sufficient patient samples. Other disadvantages include lack of reimbursement, inconvenience, and a lack of commercially available products.

[0039] Cancers can be indicated by epigenetic variations such as methylation. Examples of methylation changes in cancer include localized increases in DNA methylation in CpG islands at the transcription start site (TSS) of genes involved in normal growth control, DNA repair, cell cycle regulation, and / or cell differentiation. This hypermethylation can be associated with abnormal loss of transcriptional competence of the involved genes and occurs at least as frequently as point mutations and deletions that cause changes in gene expression. DNA methylation profiling can be used to detect regions of the genome with different degrees of methylation ("differentially methylated regions" or "DMRs"), which change during development or are perturbed by disease (e.g., cancer or any cancer-related disease). The genome of a cancer cell has an imbalance in the above-mentioned DNA methylation patterns, and thus in the functional packaging of DNA. Thus, abnormalities in chromatin organization, in combination with methylation changes, can contribute to enhance cancer profiling when jointly analyzed. The combination of MBD partitioning with fragmentomic data (e.g., fragment mapping start and end positions (related to nucleosome positions), fragment length, and associated nucleosome occupancy) can be used for chromatin structure analysis in hypermethylation studies with the aim of increasing biomarker detection rates.

[0040] Methylation profiling can include determining methylation patterns across different regions of the genome. For example, after partitioning and sequencing molecules based on the degree of methylation (e.g., the relative number of methylated sites per molecule), the sequences of molecules in different partitions can be mapped to a reference genome. This can reveal regions of the genome that are more or less hypermethylated compared to other regions. In this way, regions of the genome can differ in their degree of methylation as compared to individual molecules.

[0041] A feature of a nucleic acid molecule can be a modification, which can include various chemical or protein modifications (i.e., epigenetic modifications). Non-limiting examples of chemical modifications can include, but are not limited to, covalent DNA modifications, including DNA methylation. In some embodiments, DNA methylation includes the addition of a methyl group to a cytosine (a cytosine followed by a guanine in a nucleic acid sequence) at a CpG site. In some embodiments, DNA methylation includes the addition of a methyl group to an adenine, such as N6-methyladenine. In some embodiments, DNA methylation is 5-methylation (a modification to the 5th carbon of the 6-carbon ring of a cytosine). In some embodiments, 5-methylation includes the addition of a methyl group to the 5C position of a cytosine to produce 5-methylcytosine (m5c). In some embodiments, methylation includes derivatives of m5c. Derivatives of m5c include, but are not limited to, 5-hydroxymethylcytosine (5-hmC), 5-formylcytosine (5-fC), and 5-carboxylcytosine (5-caC). In some embodiments, DNA methylation is 3C methylation (a modification to the 3rd carbon of the 6-carbon ring of a cytosine). In some embodiments, 3C methylation includes the addition of a methyl group to the 3C position of a cytosine to generate 3-methylcytosine (3mC). Other examples include N6-methyladenine or glycosylation. DNA methylation includes the addition of a methyl group to DNA (e.g., CpG), and can alter expression of a region of methylated DNA. Methylation can also occur at non-CpG sites, for example, methylation can occur at CpA, CpT, or CpC sites. DNA methylation can alter activity of a region of methylated DNA. For example, when DNA in a promoter region is methylated, transcription of a gene can be inhibited. DNA methylation is critical for normal development, and abnormalities in methylation can disrupt epigenetic regulation. Disruptions in epigenetic regulation, for example, inhibition, can cause disease, such as cancer. DNA methylation of a promoter can be indicative of cancer.

[0042] A CpG dinucleotide is a dinucleotide CpG (cytosine-phosphate-guanine, i.e., cytosine followed by guanine in the 5’-3’ direction of a nucleic acid sequence) on the sense strand of a double-stranded DNA molecule and its complementary CpG on the anti-sense strand. A CpG dinucleotide can be fully methylated or hemi-methylated (methylated on only one strand).

[0043] CpG dinucleotides are underrepresented in the normal human genome, with most CpG dinucleotide sequences being transcriptionally inert (e.g., DNA heterochromatin regions in the pericentromeric portion of chromosomes and repetitive elements) and methylated. However, many CpG islands are protected from such methylation, especially around the transcription start site (TSS).

[0044] Protein modifications include components that bind to chromatin, particularly histones (including modified versions thereof), as well as binding to other proteins, such as proteins involved in replication or transcription. The present disclosure provides methods of processing and analyzing nucleic acids with varying degrees of modification such that their original modified properties are correlated with a nucleic acid tag and can be decoded by sequencing the tag when the nucleic acid is analyzed. Genetic variations of sample nucleic acid modifications can then be correlated with the degree of modification of that nucleic acid in the original sample (epigenetic variations). Single-stranded (e.g., ssDNA or RNA) or double-stranded molecules (e.g., dsDNA) are included.

[0045] Loss of DNA can reduce the presence of one or more types of DNA, making it difficult to detect the presence of one or more types of DNA (e.g., cfDNA). In one or more additional instances, existing methods of measuring DNA methylation, such as enrichment or depletion methods, can have a relatively high level of resolution, such as about 100 base pairs (bp) to about 200 bp, which can make it difficult to precisely determine the amount of DNA methylation. The accuracy of determining DNA methylation can impact the accuracy of sample tumor fraction estimates. Since tumor fraction can be used to determine whether a sample is derived from a subject in which a tumor is present or absent, determining the accuracy of tumor fraction estimates can impact diagnostic and / or treatment decisions for individuals.

[0046] More specifically, the technology described herein allows for quantification of promoter region methylation. Gedatolisib is an intravenously administered PI3K and mTOR inhibitor that has been shown to be safe in patients with metastatic breast cancer, both when used alone and in combination with oral therapies. Previous studies have shown that PI3K inhibitors decrease the pool of nucleotides required for DNA synthesis and progression through S phase. In addition, inhibition of PI3K / mTOR can impede the interaction of PI3K with homologous recombination complexes, increasing the dependence of DNA repair on PARP enzymes. Based on this data, the combination of a PI3K inhibitor and a PARP inhibitor can bring a new, non-chemotherapy treatment option to TNBC with wild-type BRCA and improve the modest PFS observed with PARP inhibitors as single agents in BRCA1 / 2-mutated advanced breast cancer. The hypothesis of this trial is that gedatolisib will sensitize advanced TNBC or BRCA1 / 2-mutated breast cancer to the PARP-inhibiting effects of talazoparib. It is of interest to determine the recommended Phase 2 dose of gedatolisib in combination with talazoparib and to evaluate the efficacy of the combination in advanced HER2-negative breast cancer that is triple-negative or BRCA1 / 2-positive (mutated / deficient).

[0047] Sample A sample can be any biological sample isolated from a subject. A sample can be a bodily sample. A sample can include a bodily tissue such as a known or suspected solid tumor, whole blood, platelets, serum, plasma, stool, red blood cells, white blood cells or leukocytes, endothelial cells, tissue biopsy, cerebrospinal fluid, synovial fluid, lymphatic fluid, ascites fluid, interstitial or extracellular fluid, fluid in the space between cells (including gingival crevicular fluid), bone marrow, pleural effusion, cerebrospinal fluid, saliva, mucus, sputum, semen, sweat, urine. A sample is preferably a bodily fluid, particularly blood and fractions thereof, and urine. A sample can be in the form as initially isolated from a subject, or can be further processed to remove or add components, such as cells, or to enrich for one component relative to other components. Thus, a preferred bodily fluid for analysis is plasma or serum containing cell-free nucleic acids. A sample can be isolated or obtained from a subject and shipped to a sample analysis site. A sample can be stored and shipped at a desired temperature, for example, room temperature, 4°C, -20°C, and / or -80°C. A sample can be isolated or obtained from a subject at a sample analysis site. A subject can be a human, mammal, animal, companion animal, service animal, or pet. A subject can have cancer. A subject can not have cancer or detectable symptoms of cancer. A subject can have been treated with one or more cancer therapies, for example, any one or more of chemotherapy, antibodies, vaccines, or biologies. A subject can be in remission. A subject can or can not be diagnosed as susceptible to cancer or any cancer-related genetic mutation / disorder.

[0048] The volume of plasma can depend on the desired read depth for the sequenced region. Exemplary volumes are 0.4-40 ml, 5-20 ml, 10-20 ml. For example, the volume can be 0.5 mL, 1 mL, 5 mL, 10 mL, 20 mL, 30 mL, or 40 mL. The volume of plasma sampled can be 5 ml to 20 ml.

[0049] A sample can comprise various amounts of nucleic acids comprising genomic equivalents. For example, a sample of about 30 ng DNA can comprise about 10,000 (10 4 ) haploid human genome equivalents, and in the case of cfDNA, can comprise about 200 billion (2 x 10 11 ) individual polynucleotide molecules. Similarly, a sample of about 100 ng DNA can contain about 30,000 haploid human genome equivalents, and in the case of cfDNA, contain about 600 billion individual molecules.

[0050] A sample can comprise nucleic acids from different sources, e.g., cellular and cell-free nucleic acids from the same subject, cellular and cell-free nucleic acids from different subjects. A sample can comprise nucleic acids carrying mutations. For example, a sample can comprise DNA carrying germline mutations and / or somatic mutations. Germline mutations refer to mutations present in the germline DNA of a subject. Somatic mutations refer to mutations derived from somatic cells of a subject, e.g., cancer cells. A sample can comprise DNA carrying cancer-related mutations (e.g., cancer-related somatic mutations). A sample can comprise epigenetic variations (i.e., chemical or protein modifications), where the epigenetic variations are associated with the presence of genetic variations, such as cancer-related mutations. In some embodiments, a sample comprises epigenetic variations associated with the presence of genetic variations, where the sample does not comprise the genetic variations.

[0051] An exemplary amount of cell-free nucleic acids in a sample prior to amplification ranges from about 1 fg to about 1 µg, e.g., 1 pg to 200 ng, 1 ng to 100 ng, 10 ng to 1000 ng. For example, the amount can be up to about 600 ng, up to about 500 ng, up to about 400 ng, up to about 300 ng, up to about 200 ng, up to about 100 ng, up to about 50 ng, or up to about 20 ng of cell-free nucleic acid molecules. The amount can be at least 1 fg, at least 10 fg, at least 100 fg, at least 1 pg, at least 10 pg, at least 100 pg, at least 1 ng, at least 10 ng, at least 100 ng, at least 150 ng, or at least 200 ng of cell-free nucleic acid molecules. The amount can be up to 1 femtogram (fg), 10 fg, 100 fg, 1 picogram (pg), 10 pg, 100 pg, 1 ng, 10 ng, 100 ng, 150 ng, or 200 ng of cell-free nucleic acid molecules. The method can comprise obtaining 1 femtogram (fg) to 200 ng.

[0052] Cell-free nucleic acids are nucleic acids that are not contained within or otherwise bound to a cell, or in other words, nucleic acids that remain in a sample after intact cells are removed. Cell-free nucleic acids include DNA, RNA, and hybrids thereof, including genomic DNA, mitochondrial DNA, siRNA, miRNA, circulating RNA (cRNA), tRNA, rRNA, small nucleolar RNA (snoRNA), Piwi-interacting RNA (piRNA), long non-coding RNA (long ncRNA), and / or fragments of any of these. Cell-free nucleic acids can be double-stranded, single-stranded, or hybrids thereof. Cell-free nucleic acids can be released into bodily fluids by secretion or cell death programs such as cellular necrosis and apoptosis. Some cell-free nucleic acids are released into bodily fluids from cancer cells, such as circulating tumor DNA (ctDNA). Others are released from healthy cells. In some embodiments, cfDNA is cell-free fetal DNA (cffDNA). In some embodiments, cell-free nucleic acids are produced by tumor cells. In some embodiments, cell-free nucleic acids are produced by a mixture of tumor cells and non-tumor cells.

[0053] Cell-free nucleic acids have an example size distribution of about 100-500 nucleotides, with molecules of 110 to about 230 nucleotides representing about 90% of the molecules, with a mode of about 168 nucleotides, and a second small peak in the range of 240 to 440 nucleotides. Cell-free nucleic acids can be isolated from bodily fluids by a fractionation or partitioning step in which cell-free nucleic acids present in solution are separated from intact cells and other insoluble components in the bodily fluid. Partitioning can include techniques such as centrifugation or filtration. Alternatively, cells in the bodily fluid can be lysed and cell-free and cellular nucleic acids are processed together. Typically, after the addition of buffers and washing steps, nucleic acids can be precipitated with alcohol. Further cleaning steps such as silica-based columns can be used to remove contaminants or salts. Non-specific bulk carrier nucleic acids such as Cot-1 DNA, DNA or proteins for bisulfite sequencing, hybridization, and / or ligation can be added throughout the reaction to optimize certain aspects of the procedure such as yield.

[0054] After such processing, the sample can include various forms of nucleic acids, including double-stranded DNA, single-stranded DNA, and single-stranded RNA. In some embodiments, single-stranded DNA and RNA can be converted to double-stranded forms, so they are included in subsequent processing and analysis steps.

[0055] Analyte The analytes can include nucleic acid analytes and non-nucleic acid analytes. The present disclosure provides for detecting genetic variations in a biological sample from a subject. The biological sample can include polynucleotides from cancer cells. The polynucleotides can be DNA (e.g., genomic DNA, cDNA), RNA (e.g., mRNA, small RNA), or any combination thereof. The biological sample can include, for example, tumor tissue from a biopsy. In some cases, the biological sample can include blood or saliva. In particular cases, the biological sample can comprise cell-free DNA (“cfDNA”) or circulating tumor DNA (“ctDNA”). Cell-free DNA can be present in, for example, blood.

[0056] Examples of non-nucleic acid analytes include, but are not limited to, lipids, saccharides, peptides, proteins, glycoproteins (N-linked or O-linked), lipoproteins, phosphoproteins, specific phosphorylated or acetylated variants of proteins, amidated variants of proteins, hydroxylated variants of proteins, methylated variants of proteins, ubiquitinated variants of proteins, sulfated variants of proteins, viral proteins (e.g., viral capsid, viral envelope, viral coat, viral appendage, viral glycoprotein, viral spike, etc.), extracellular and intracellular proteins, antibodies and antigen-binding fragments. This also includes receptors, antigens, surface proteins, transmembrane proteins, clusters of differentiation proteins, protein channels, protein pumps, carrier proteins, phospholipids, glycoproteins, glycolipids, cell-cell interaction protein complexes, antigen presentation complexes, major histocompatibility complexes, engineered T cell receptors, T cell receptors, B cell receptors, chimeric antigen receptors, post-translational modification (e.g., phosphorylation, glycosylation, ubiquitination, nitrosylation, methylation, acetylation, or lipidation) states of cell surface proteins, gap junctions, and adherens junctions.

[0057] Generally, the systems, devices, methods, and compositions can be used to analyze any number of analytes, further including both nucleic acid analytes and non-nucleic acid analytes. For example, the number of analytes being analyzed can be at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, at least about 8, at least about 9, at least about 10, at least about 11, at least about 12, at least about 13, at least about 14, at least about 15, at least about 20, at least about 25, at least about 30, at least about 40, at least about 50, at least about 100, at least about 1,000, at least about 10,000, at least about 100,000, or more different analytes present in a region of a sample or within a single feature of a substrate. Methods for performing multiplexed assays to analyze two or more different analytes will be discussed in subsequent sections of the present disclosure.

[0058] One or more nucleic acid analytes and / or non-nucleic acid analytes constitute a set of molecular interactions in a biological system (e.g., a cell) under study, which can be considered an "interaction set" - molecular interactions that occur between molecules belonging to different biochemical families (proteins, nucleic acids, lipids, carbohydrates, etc.) as well as within a given family. In various embodiments, the interaction set is a protein-DNA interaction set (a network formed by transcription factors (and DNA or chromatin modulating proteins) and their target genes). In other embodiments, the interaction set refers to a protein-protein interaction network (PPI) or protein interaction network (PIN). The methods described herein allow for the study and analysis of interaction sets. Technologies such as proteogenomics (whole genome sequencing, whole exome sequencing, and RNA-seq, and mass spectrometry as examples) can support the study of interaction sets.

[0059] Analysis The methods of the present invention can be used to diagnose the presence of a condition, particularly cancer, in a subject, to characterize the condition (e.g., to stage a cancer or determine the heterogeneity of a cancer), to monitor the response of the condition to a treatment, to enable prognosis of the risk of development or subsequent course of the condition. The present disclosure can also be used to determine the efficacy of a particular treatment selection. If the treatment is successful, then a successful treatment selection can increase the amount of copy number variants or rare mutations detected in the subject's blood as more cancer can die and shed DNA. In other examples, this can not occur. In another example, perhaps certain treatment selections can be correlated with the genetic profile of the cancer over time. This correlation can be used to select a therapy. Additionally, if a cancer is observed to be in remission following a treatment, the methods of the present invention can be used to monitor for residual disease or recurrence of the disease.

[0060] The types and amounts of cancers that can be detected can include blood cancer, brain cancer, lung cancer, skin cancer, nasal cancer, throat cancer, liver cancer, bone cancer, lymphoma, pancreatic cancer, skin cancer, intestinal cancer, rectal cancer, thyroid cancer, bladder cancer, kidney cancer, oral cancer, stomach cancer, solid tumors, heterogeneous tumors, homogeneous tumors, etc. The type and / or stage of the cancer can be detected from genetic variations, including mutations, rare mutations, insertions / deletions, copy number variations, transversions, translocations, inversions, deletions, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structural alterations, gene fusions, chromosomal fusions, gene truncations, gene amplifications, gene duplications, chromosomal damage, DNA damage, abnormal alterations in nucleic acid chemical modifications, abnormal alterations in epigenetic patterns, and abnormal alterations in nucleic acid 5-methylcytosine.

[0061] Genetic and other analyte data can also be used to characterize particular forms of cancer. Cancer is often heterogeneous both in terms of composition and stage. Genetic profile data can allow a specific subtype of cancer to be characterized, which can be important in the diagnosis or treatment of that specific subtype. This information can also provide a clue to the prognosis of a particular type of cancer to the subject or practitioner, and allow the subject or practitioner to adjust treatment options according to the progression of the disease. Some cancers can progress to become more aggressive and genetically unstable. Other cancers can remain benign, non-active, or dormant. The systems and methods of the present disclosure can be used to determine disease progression.

[0062] The analyses of the present disclosure can also be used to determine the efficacy of a particular treatment option. If the treatment is successful, the successful treatment option can increase the amount of copy number variants or rare mutations detected in the subject's blood as more cancer can die and shed DNA. In other instances, this can not occur. In another instance, perhaps certain treatment options can be correlated with the genetic profile of the cancer over time. This correlation can be used to select a therapy. Additionally, if the cancer is observed to be in remission after treatment, the methods of the present disclosure can be used to monitor for residual disease or recurrence of the disease.

[0063] The methods of the present disclosure can also be used to detect genetic variations in conditions other than cancer. After the onset of certain diseases, immune cells, such as B cells, can undergo rapid clonal expansion. Clonal expansion can be monitored using copy number variant detection, and certain immune states can be monitored. In this instance, copy number variant analysis can be performed over time to generate a profile of how a particular disease can progress. Copy number variant or even rare mutation detection can be used to determine how a pathogen population changes during the course of an infection. This can be particularly important during chronic infections, such as HIV / AID or hepatitis infections, through which viruses can change life cycle state and / or mutate to more virulent forms during the course of the infection. When immune cells attempt to destroy a transplanted tissue, the methods of the present disclosure can be used to determine or dissect the rejection activity of the host body to monitor the status of the transplanted tissue and change the course of rejection treatment or prevention.

[0064] Further, the methods of the present disclosure can be used to characterize the heterogeneity of an abnormal condition in a subject. Such methods can include, for example, generating a genetic profile of extracellular polynucleotides derived from a subject, wherein the genetic profile comprises more than one data from copy number variation and rare mutation analysis. In some embodiments, the abnormal condition is cancer. In some embodiments, the abnormal condition can be one that results in a heterogeneous population of genomes. In the example of cancer, some tumors are known to comprise tumor cells at different stages of cancer. In other examples, the heterogeneity can include multiple lesions of a disease. Again, in the example of cancer, there can be multiple tumor lesions, perhaps one or more of which is the result of metastasis from a primary site.

[0065] The methods of the present invention can be used to generate or profile a fingerprint or dataset that is a sum of genetic information derived from different cells in a heterogeneous disease. The dataset can comprise copy number variation and mutation analysis separately or in combination.

[0066] The methods of the present invention can be used to diagnose, prognose, monitor, or observe cancer or other diseases. In some embodiments, the methods herein do not involve the diagnosis, prognosis, or monitoring of a fetus, and thus do not involve non-invasive prenatal testing. In other embodiments, the methods can be used in a pregnant subject to diagnose, prognose, monitor, or observe cancer or other diseases in a non-born subject whose DNA and other polynucleotides can be co-circulating with maternal molecules.

[0067] Determination of 5-methylcytosine patterns of nucleic acids Bisulfite-based sequencing and variations thereof provide a means to determine methylation patterns of nucleic acids. In some embodiments, determining methylation patterns includes distinguishing 5-methylcytosine (5mC) from non-methylated cytosine. In some embodiments, determining methylation patterns includes distinguishing N6-methyladenine from non-methylated adenine. In some embodiments, determining methylation patterns includes distinguishing 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxylcytosine (5caC) from non-methylated cytosine. Examples of bisulfite sequencing include, but are not limited to, oxidative bisulfite sequencing (OX-BS-seq), Tet-assisted bisulfite sequencing (TAB-seq), and reduced bisulfite sequencing (redBS-seq).

[0068] Oxidized bisulfite sequencing (OX-BS-seq) is used to distinguish 5mC and 5hmC by first converting 5hmC to 5fC and then performing bisulfite sequencing as previously described. Tet-assisted bisulfite sequencing (TAB-seq) can also be used to distinguish 5mc and 5hmC. In TAB-seq, 5hmC is protected by glycosylation. 5mC is converted to 5caC using Tet enzyme prior to performing bisulfite sequencing as previously described. Reduced bisulfite sequencing is used to distinguish 5fC from modified cytosines.

[0069] Generally, in bisulfite sequencing, a nucleic acid sample is split into two aliquots, and one aliquot is treated with bisulfite. Bisulfite converts natural cytosines and certain modified cytosine nucleotides (e.g., 5-formylcytosine or 5-carboxylcytosine) to uracil, while other modified cytosines (e.g., 5-methylcytosine, 5-hydroxymethylcytosine) are not converted. Comparison of nucleic acid sequences of molecules from the two aliquots indicates which cytosines were converted to uracil and which were not. Thus, modified and unmodified cytosines can be determined. Initially splitting a sample into two aliquots is disadvantageous for samples containing only small amounts of nucleic acid and / or including heterogeneous cell / tissue sources such as bodily fluids containing cell-free DNA.

[0070] The present disclosure provides methods that allow for bisulfite sequencing and variations thereof. These methods work by linking nucleic acids in a population to a capture moiety (i.e., a tag that can be captured or immobilized). Capture moieties include, but are not limited to, biotin, avidin, streptavidin, nucleic acids comprising a particular nucleotide sequence, haptens recognized by antibodies, and magnetically attractable particles. The extraction moiety can be a member of a binding pair, such as biotin / streptavidin or hapten / antibody. In some embodiments, the capture moiety attached to the analyte is captured by its binding partner, which is attached to a separable moiety, such as a magnetically attractable particle or a large particle that can be pelleted by centrifugation. The capture moiety can be any type of molecule that allows for the affinity separation of nucleic acids bearing the capture moiety from nucleic acids lacking the capture moiety. An example capture moiety is biotin, which allows for affinity separation by binding to streptavidin attached or attachable to a solid phase, or an oligonucleotide, which allows for affinity separation by binding to a complementary oligonucleotide attached or attachable to a solid phase. After the capture moiety is linked to the sample nucleic acid, the sample nucleic acid is used as a template for amplification. After amplification, the original template remains linked to the capture moiety, but the amplicon is not linked to the capture moiety.

[0071] The capture moiety can be attached to the sample nucleic acid as a component of an adaptor, which can also provide amplification and / or sequencing primer binding sites. In some methods, the sample nucleic acid is attached to an adaptor at both ends, with two adaptors bearing capture moieties. Preferably, any cytosine residues in the adaptor are modified, such as by 5-methylcytosine modification, to protect against bisulfite action. In some cases, the capture moiety is removed, if desired, by a cleavable linker (e.g., photolyzable desthiobiotin-TEG or uracil residues cleavable by USER™ enzyme, Chem. Commun. (Camb). 2015 Feb 21; 51(15): 3266-3269).

[0072] The amplicons are denatured and contacted with an affinity reagent for the capture tag. The original template binds the affinity reagent, while the amplification- generated nucleic acid molecules do not. Thus, the original template can be separated from the amplification-generated nucleic acid molecules.

[0073] After separation or partitioning, the respective populations of nucleic acids (i.e., original template and amplification product) can be subjected to bisulfite treatment, with the original template population receiving bisulfite treatment and the amplification product not. Alternatively, the amplification product can be subjected to bisulfite treatment and the original template population not. After such treatment, the respective populations can be amplified (in the case of the original template population, this converts uracils to thymines). The populations can also be subjected to biotin probe hybridization for enrichment. The respective populations are then analyzed and the sequences compared to determine which cytosines were 5-methylated (or 5-hydroxymethylated) in the original sample. Detection of T nucleotides in the template population (corresponding to unmethylated cytosines converted to uracils) and C nucleotides at the corresponding positions of the amplification population indicates unmodified C. Presence of C at the corresponding positions of the original template and amplification populations indicates a modified C in the original sample.

[0074] In some embodiments, the method uses sequential DNA-seq and bisulfite-seq (BIS-seq) NGS library preparation of molecule tagged DNA libraries. The process proceeds through labeling of adaptors (e.g. biotin), DNA-seq amplification of the entire library, parental molecule recovery (e.g. streptavidin bead pull down), bisulfite conversion and BIS-seq. In some embodiments, the method identifies 5-methylcytosine at single base resolution through sequential NGS preparative amplification of parental library molecules with and without bisulfite treatment. This can be achieved through modification of 5-methylated NGS adaptors (directional adaptors; Y-shaped / forked, with 5-methylcytosine substitutions) used in BIS-seq with a label (e.g. biotin) on one of the two adaptor strands. Sample DNA molecules are ligated adaptors and amplified (e.g. by PCR). As only parental molecules will have labeled adaptor ends, they can be selectively recovered from their amplified progeny by a label specific capture method (e.g. streptavidin magnetic beads). As the parental molecules retain the 5-methylation label, bisulfite conversion on the captured library will yield single base resolution 5-methylation status at BIS-seq, preserving the molecular information to the corresponding DNA-seq. In some embodiments, the bisulfite treated library can be combined with the untreated library prior to enrichment / NGS by adding a sample tag DNA sequence in the standard multiplex NGS workflow. As with the BIS-seq workflow, bioinformatics analysis can be performed for genomic alignment and 5-methylation base identification. In summary, this method provides the ability to selectively recover parental, ligated molecules carrying the 5-methylcytosine marker after library amplification, allowing for parallel processing of bisulfite converted DNA. This overcomes the destructive nature of bisulfite treatment on the quality / sensitivity of DNA-seq information extracted from the workflow. With this method, the recovered ligated, parental DNA molecules (via labeled adaptors) allow for amplification of the full DNA library and parallel application of treatments that elicit epigenetic DNA modifications. The present disclosure discusses the use of the BIS-seq method to identify cytosine 5-methylation (5-methylcytosine), but this should not be limiting. Variations of BIS-seq have been developed to identify hydroxymethylated cytosine (5hmC; OX-BS-seq, TAB-seq), formylcytosine (5fC; redBS-seq) and carboxylcytosine. These methods can be implemented with the sequential / parallel library preparation described herein.

[0075] Alternative methods of analysing modified nucleic acids The present disclosure provides alternative methods for analyzing modified nucleic acids (e.g., methylated, histone-associated, and other modifications discussed above). In some such methods, a population of nucleic acids with varying degrees of modification (e.g., 0, 1, 2, 3, 4, 5, or more methyl groups per nucleic acid molecule) is contacted with adaptors, and then the population is fractionated according to the degree of modification. The adaptors are attached to one or both ends of the nucleic acid molecules in the population. Preferably, the adaptors comprise a sufficient number of different tags such that the number of tag combinations results in a high probability, e.g., 95%, 99%, or 99.9%, that two nucleic acids with the same starting and ending points receive different tag combinations. After the adaptors are attached, the nucleic acids are amplified from primers bound to primer binding sites within the bound adaptors. The adaptors, whether with the same or different tags, can comprise the same or different primer binding sites, but preferably the adaptors comprise the same primer binding sites. After amplification, the nucleic acids are contacted with an agent that preferably binds to nucleic acids with modifications, such as such an agent described previously. The nucleic acids are separated into at least two partitions that differ in the degree to which the nucleic acids with modifications bind to the agent. For example, if the agent has affinity for nucleic acids with modifications, then nucleic acids with modifications that are over-represented (compared to the median representation in the population) preferentially bind to the agent, while nucleic acids with modifications that are under-represented do not bind to the agent or are more easily eluted from the agent. After separation, the different partitions can then undergo additional processing steps, which typically include additional amplification and sequence analysis in parallel but separately. Sequence data from the different partitions can then be compared.

[0076] The nucleic acids can be attached to Y-shaped adaptors comprising primer binding sites and tags at both ends. The molecules are amplified. The amplified molecules are then partitioned by contact with an antibody that preferentially binds 5-methylcytosine to generate two partitions. One partition comprises original molecules that lack methylation and amplified copies that lost methylation. The other partition comprises original DNA molecules that have methylation. Both partitions are then processed and sequenced separately, and the methylation partition is further amplified. Sequence data from both partitions can then be compared. In this example, the tags are not used to distinguish between methylated and unmethylated DNA, but rather to distinguish between different molecules in these partitions, so that one can determine whether reads with the same starting and ending points are based on the same or different molecules.

[0077] The present disclosure also provides methods for analyzing a population of nucleic acids, wherein at least some of the nucleic acids comprise one or more modified cytosine residues, such as 5-methylcytosine and any of the other modifications described previously. In these methods, the population of nucleic acids is contacted with an adaptor comprising one or more modified cytosine residues at the 5C position, such as 5-methylcytosine. Preferably, all of the cytosine residues in such an adaptor are also modified, or all such cytosines in the primer binding region of the adaptor are modified. The adaptor is attached to both ends of the nucleic acid molecules in the population. Preferably, the adaptor comprises a sufficient number of different tags such that the number of tag combinations results in a high probability, e.g., 95%, 99%, or 99.9%, that two nucleic acids having the same starting and ending points receive different tag combinations. The primer binding sites in such an adaptor can be the same or different, but are preferably the same. After the adaptor is attached, the nucleic acids are amplified by primers that bind to the primer binding sites of the adaptor. The amplified nucleic acids are divided into a first aliquot and a second aliquot. The first aliquot is subjected to sequence data determination, with or without further processing. The sequence data for the molecules in the first aliquot is thereby determined regardless of the initial methylation state of the nucleic acid molecules. The nucleic acid molecules in the second aliquot are treated with bisulfite. This treatment converts unmodified cytosines to uracils. The bisulfite treated nucleic acids are then subjected to amplification, which is primed by primers directed to the original primer binding sites of the adaptor attached to the nucleic acids. Only the nucleic acid molecules that were originally attached to the adaptor (as distinguished from their amplification products) are now amplified, because these nucleic acids retain cytosines at the primer binding sites of the adaptor, whereas the amplification products have lost the methylation of these cytosine residues, which have undergone conversion to uracils in the bisulfite treatment. Thus, only the original molecules in the population (at least some of which were methylated) are subjected to amplification. After amplification, these nucleic acids are subjected to sequence analysis. Comparison of the sequences determined from the first aliquot and the second aliquot can be particularly indicative of which cytosines in the population of nucleic acids were methylated.

[0078] Partitioning of a sample into more than one subsample; aspects of the sample; analysis of epigenetic features In certain embodiments described herein, a population of nucleic acids of different forms (e.g., hypermethylated and hypomethylated DNA in a sample, such as a captured set of cfDNA as described herein) can be physically partitioned based on one or more features of the nucleic acids, and then further analyzed, e.g., differential modification or separation of nucleobases, tagging, and / or sequencing. Such an approach can be used to determine, for example, whether certain sequences are hypermethylated or hypomethylated. In some embodiments, hypermethylated variable epigenetic target regions are analyzed to determine whether they exhibit a hypermethylation signature of tumor cells, and / or hypomethylated variable epigenetic target regions are analyzed to determine whether they exhibit a hypomethylation signature of tumor cells. Additionally, by partitioning a heterogeneous population of nucleic acids, one can increase rare signals, e.g., by enriching for rare nucleic acid molecules that are more prevalent in one fraction (or one partition) of the population. For example, by partitioning a sample into hypermethylated and hypomethylated nucleic acid molecules, genetic variations that are present in hypermethylated DNA but less prevalent (or absent) in hypomethylated DNA can be more easily detected. By analyzing more than one fraction of a sample, a single locus or nucleic acid species of the genome can be analyzed in multiple dimensions, and thus greater sensitivity can be achieved.

[0079] In some cases, a heterogeneous nucleic acid sample is partitioned into two or more partitions (e.g., at least 3, 4, 5, 6, or 7 partitions). In some embodiments, each partition is differentially tagged. The tagged partitions can then be pooled together for collective sample preparation and / or sequencing. The partition-tag-pooling steps can occur more than once, with each round of partitioning occurring based on a different feature (examples provided herein), and tagging with a differential tag that is distinct from other partitions and partitioning means.

[0080] Examples of features that can be used for partitioning include sequence length, methylation level, nucleosome binding, sequence mismatch, immunoprecipitation, and / or proteins bound to DNA. The resulting partitions can include one or more of the following nucleic acid forms: single-stranded DNA (ssDNA), double-stranded DNA (dsDNA), shorter DNA fragments, and longer DNA fragments. In some embodiments, partitioning based on cytosine modification (e.g., cytosine methylation) or methylation is typically performed, and is optionally combined with at least one additional partitioning step, which can be based on any of the foregoing features or forms of DNA. In some embodiments, a heterogeneous population of nucleic acids is partitioned into nucleic acids having one or more epigenetic modifications and nucleic acids not having the one or more epigenetic modifications. Examples of epigenetic modifications include the presence or absence of methylation, methylation level, methylation type (e.g., 5-methylcytosine versus other types of methylation, such as adenine methylation and / or cytosine hydroxymethylation), and association with and level of association with one or more proteins, such as histones. Alternatively or additionally, a heterogeneous population of nucleic acids can be partitioned into nucleic acid molecules associated with nucleosomes and nucleic acid molecules free of nucleosomes. Alternatively or additionally, a heterogeneous population of nucleic acids can be partitioned into single-stranded DNA (ssDNA) and double-stranded DNA (dsDNA). Alternatively or additionally, a heterogeneous population of nucleic acids can be partitioned based on nucleic acid length (e.g., molecules of a maximum of 160 bp and molecules having a length greater than 160 bp).

[0081] In some cases, each partition (representing a different nucleic acid form) is differentially labeled and the partitions are pooled together and sequenced. In other cases, the different forms are sequenced separately. In some embodiments, the different nucleic acid population is partitioned into two or more different partitions. Each partition represents a different nucleic acid form, and a first partition (also referred to as a subsample) includes a greater proportion of cytosine-modified DNA than a second subsample. Each partition is differentially tagged. The first subsample is subjected to a procedure that differentially affects a first nucleobase in the DNA of the first subsample and a second nucleobase in the DNA, where the first nucleobase is a modified or unmodified nucleobase, the second nucleobase is a modified or unmodified nucleobase different from the first nucleobase, and the first nucleobase and the second nucleobase have the same base-pairing specificity. The tagged nucleic acids are pooled together and sequenced. Sequence reads are obtained and analyzed, including in silico differentiation of the first nucleobase and the second nucleobase in the DNA of the first subsample. The tags are used to sort reads from different partitions. Analysis can be performed at the level of individual partitions as well as at the level of the entire nucleic acid population to detect genetic variations. For example, analysis can include in silico analysis to determine genetic variations, such as CNVs, SNVs, insertions / deletions, fusions in the nucleic acids of each partition. In some cases, the in silico analysis can include determination of chromatin structure. For example, coverage of sequence reads can be used to determine positioning of nucleosomes in chromatin. Higher coverage can be associated with higher nucleosome occupancy in a genomic region, while lower coverage can be associated with lower nucleosome occupancy or nucleosome depleted regions (NDRs).

[0082] The sample can include nucleic acids of different modifications, including post-replication modifications to nucleotides and binding (typically non-covalent) to one or more proteins.

[0083] In embodiments, the population of nucleic acids is a population of nucleic acids obtained from a serum, plasma, or blood sample of a subject suspected of having a neoplasm, tumor, or cancer or previously diagnosed with a neoplasm, tumor, or cancer. The population of nucleic acids includes nucleic acids having different levels of methylation. Methylation can occur from any one or more post-replication or post-transcriptional modifications. Post-replication modifications include modifications to the nucleotide cytosine, particularly at the 5-position of the nucleobase, such as 5-methylcytosine, 5-hydroxymethylcytosine, 5-formylcytosine, and 5-carboxylcytosine. The affinity agent can be an antibody, natural binding partner, or variant thereof having the desired specificity (Bock et al., Nat Biotech 28: 1106-1114 (2010); Song et al., Nat Biotech 29: 68-72 (2011)), or an artificial peptide specific for a given target, for example, selected by phage display.

[0084] Examples of capture moieties contemplated herein include methyl binding domains (MBDs) and methyl binding proteins (MBPs) as described herein, including proteins such as MeCP2 and antibodies that preferentially bind to 5-methylcytosine. Likewise, partitioning of different forms of nucleic acids can be performed using histone binding proteins that can separate nucleic acids bound to histones from free or unbound nucleic acids. Examples of histone binding proteins that can be used in the methods disclosed herein include RBBP4, RbAp48, and SANT domain peptides. For some affinity agents and modifications, although binding to the agent can occur in a substantially all-or-none fashion depending on whether the nucleic acid bears the modification, the separation can be to some degree. In such cases, nucleic acids overrepresented in a modification bind to the agent to a greater degree than nucleic acids underrepresented in the modification. Alternatively, nucleic acids with the modification can bind in an all-or-none fashion. Then, however, various levels of modification can be sequentially eluted from the binding agent.

[0085] For example, in some embodiments, partitions can be binary or based on degree / level of modification. For example, all methylated fragments can be partitioned from unmethylated fragments using a methyl binding domain protein (e.g., MethylMiner Methylated DNA Enrichment Kit (ThermoFisher Scientific)). Subsequently, additional partitions can include eluting fragments with different levels of methylation by adjusting the salt concentration of the solution containing the methyl binding domain and bound fragments. As the salt concentration is increased, fragments with greater levels of methylation are eluted. In some cases, the final partitions represent nucleic acids with different degrees of modification (over representative or under representative modification). Over representative and under representative can be defined by the number of modifications a nucleic acid bears relative to the median number of modifications per strand in the population. For example, if the median number of 5-methylcytosine residues in nucleic acids in a sample is two, then nucleic acids containing more than two 5-methylcytosine residues are over representative of modification, while nucleic acids with one or zero 5-methylcytosine residues are under representative. The effect of the affinity separation is to enrich for nucleic acids that are over represented in the bound phase for modification and under represented in the non-bound phase (i.e., in solution) for modification. Nucleic acids of the bound phase can be eluted prior to subsequent processing.

[0086] When using the MethylMiner Methylated DNA Enrichment Kit (ThermoFisher Scientific), different levels of methylation can be partitioned using sequential elutions. For example, low methylation (e.g., no methylation) can be partitioned from methylation by contacting the population of nucleic acids with MBDs attached to magnetic beads from the kit. The beads are used to separate methylated nucleic acids from unmethylated nucleic acids. Subsequently, one or more elution steps are performed sequentially to elute nucleic acids with different levels of methylation. For example, a first set of methylated nucleic acids can be eluted at a salt concentration of 160 mM or greater, e.g., at least 150 mM, at least 200 mM, at least 300 mM, at least 400 mM, at least 500 mM, at least 600 mM, at least 700 mM, at least 800 mM, at least 900 mM, at least 1000 mM, or at least 2000 mM. After such methylated nucleic acids are eluted, magnetic separation is again used to separate higher levels of methylated nucleic acids from nucleic acids with lower levels of methylation. The elution and magnetic separation steps can themselves be repeated to produce various partitions, such as a low methylation partition (representative of no methylation), a methylation partition (representative of low levels of methylation), and a high methylation partition (representative of high levels of methylation).

[0087] In some methods, the nucleic acids bound to the agent used for affinity separation are subjected to a washing step. The washing step washes away nucleic acids that weakly bound to the affinity agent. Such nucleic acids can be enriched for nucleic acids having a degree of modification that is near the mean or median (i.e., an intermediate value between nucleic acids that remain bound to the solid phase and nucleic acids that do not bind to the solid phase at the time the sample is initially contacted with the agent). Affinity separation results in at least two and sometimes three or more partitions of nucleic acids having different degrees of modification. When the partitions are still separate, the nucleic acids of at least one and often two or three (or more) partitions are attached to nucleic acid tags, which are often provided as a component of an adapter, and the nucleic acids in different partitions receive different tags that distinguish members of one partition from members of another partition. The tags attached to nucleic acid molecules of the same partition can be the same as or different from one another. But if different from one another, the tags can have a portion in common that encodes to identify the molecules to which they are attached as belonging to a particular partition. For more details on partitioning a nucleic acid sample based on features such as methylation, see WO 2018 / 119452, which is incorporated by reference herein. In some embodiments, nucleic acid molecules can be fractionated into different partitions based on nucleic acid molecules that bind to a particular protein or fragment thereof and nucleic acid molecules that do not bind to the particular protein or fragment thereof.

[0088] Nucleic acid molecules can be fractionated based on DNA-protein binding. Protein-DNA complexes can be fractionated based on specific properties of the protein. Examples of such properties include various epitopes, modifications (e.g., histone methylation or acetylation), or enzymatic activity. Examples of proteins that can bind DNA and serve as a basis for fractionation can include, but are not limited to, protein A and protein G. Any suitable method can be used to fractionate nucleic acid molecules based on protein binding regions. Examples of methods for fractionating nucleic acid molecules based on protein binding regions include, but are not limited to, SDS-PAGE, chromatin immunoprecipitation (ChIP), heparin chromatography, and asymmetric field flow fractionation (AF4).

[0089] In some embodiments, partitioning of nucleic acids is performed by contacting the nucleic acids with a methylation binding domain (“MBD”) of a methylation binding protein (“MBP”). The MBD binds to 5-methylcytosine (5mC). The MBD is coupled to paramagnetic beads (such as Dynabeads® M-280 streptavidin) via a biotin linker. Partitioning into fractions having different degrees of methylation can be performed by eluting fractions with increasing concentrations of NaCl.

[0090] An exemplary method for molecular tag identification of MBD bead partitioned libraries by NGS is as follows: Extracted DNA samples (e.g., plasma DNA extracted from a human sample) are physically partitioned using a methyl binding domain protein-bead purification kit, retaining all eluate from the process for downstream processing.

[0091] Differential molecular tags and NGS feasibility adaptor sequences are applied to each partition in parallel. For example, high methylation partitions, residual methylation ('wash') partitions, and low methylation partitions are ligated to NGS adaptors with molecular tags.

[0092] All molecular tag-added partitions are recombined and subjected to subsequent amplification using adaptor-specific DNA primer sequences.

[0093] The recombined and amplified total library is subjected to enrichment / hybridization targeting genomic regions of interest (e.g., cancer-specific genetic variants and differentially methylated regions).

[0094] The enriched total DNA library is re-amplified, with additional sample tags. Different samples are pooled and subjected to multiplexed assays on NGS instruments.

[0095] Bioinformatic analysis of NGS data, where molecular tags are used to identify unique molecules, and samples are deconvoluted to molecules of differential MBD partitions. This analysis can produce information on relative 5-methylcytosine of genomic regions simultaneously with standard gene sequencing / variant detection.

[0096] Examples of MBPs contemplated herein include, but are not limited to: (a) the protein MeCP2, which preferentially binds 5-methyl-cytosine compared to unmodified cytosine; (b) RPL26, PRP8, and DNA mismatch repair protein MHS6, which preferentially bind 5-hydroxymethyl-cytosine compared to unmodified cytosine; (c) FOXK1, FOXK2, FOXP1, FOXP4, and FOXI3 (Iurlaro et al., Genome Biol. 14: R119 (2013)), which preferentially bind 5-formyl-cytosine compared to unmodified cytosine.

[0097] (d) antibodies specific for one or more methylated nucleotide bases.

[0098] Generally, elution varies with the number of methylation sites per molecule, with more methylated molecules eluting at increasing salt concentrations. To elute DNA into different populations based on degree of methylation, one can use a series of elution buffers with increasing concentrations of NaCl. The salt concentration can range from about 100 nM to about 2500 mM NaCl. In one embodiment, the process results in three (3) partitions. Molecules are contacted with a solution of a first salt concentration, and the solution contains molecules with a methyl binding domain that can be attached to a capture moiety such as streptavidin. At the first salt concentration, one population of molecules will bind to the MBD, and one population will remain unbound. The unbound population can be separated as a "low methylation" population. For example, a first partition representing low methylated forms of DNA is the partition that remains unbound at low salt concentrations (e.g., 100 mM or 160 mM). A second partition representing moderately methylated DNA is eluted using a moderate salt concentration (e.g., a concentration between 100 mM and 2000 mM). This is also separated from the sample. A third partition representing highly methylated forms of DNA is eluted using a high salt concentration (e.g., at least about 2000 mM).

[0099] The present disclosure also provides methods for analyzing a population of nucleic acids, wherein at least some of the nucleic acids comprise one or more modified cytosine residues, such as 5-methylcytosine and any of the other modifications described previously. In these methods, after partitioning, the nucleic acid subsamples are contacted with adaptors comprising one or more modified cytosine residues at the 5C position, such as 5-methylcytosine. Preferably, all of the cytosine residues in such adaptors are also modified, or all such cytosines in the primer binding region of the adaptor are modified. The adaptors are attached to both ends of the nucleic acid molecules in the population. Preferably, the adaptors comprise a sufficient number of different tags such that the number of tag combinations results in a high probability, e.g., 95%, 99%, or 99.9%, that two nucleic acids having the same starting and ending points receive different tag combinations. The primer binding sites in such adaptors can be the same or different, but are preferably the same. After attachment of the adaptors, the nucleic acids are amplified by primers that bind to the primer binding sites of the adaptors. The amplified nucleic acids are divided into a first aliquot and a second aliquot. The first aliquot is subjected to sequence data determination, with or without further treatment. The sequence data of the molecules in the first aliquot is thereby determined regardless of the initial methylation state of the nucleic acid molecules. The nucleic acid molecules in the second aliquot are subjected to a procedure that differentially affects a first nucleobase in the DNA that comprises a modified cytosine at position 5 and a second nucleobase in the DNA that comprises an unmodified cytosine. The procedure can be bisulfite treatment or another procedure that converts unmodified cytosines to uracils. The nucleic acids that are subjected to the procedure are then amplified with primers directed to the original primer binding sites of the adaptors attached to the nucleic acids. Only the nucleic acid molecules that were originally attached to the adaptors (as opposed to their amplification products) are now amplified, because these nucleic acids retain cytosines at the primer binding sites of the adaptors, whereas the amplification products have lost the methylation of these cytosine residues, which have undergone conversion to uracils in the bisulfite treatment. Thus, only the original molecules in the population (at least some of which were methylated) are subjected to amplification. After amplification, these nucleic acids are subjected to sequence analysis. Comparison of the sequences determined from the first aliquot and the second aliquot can be particularly indicative of which cytosines in the population of nucleic acids were methylated.

[0100] Such an analysis can be performed using the following exemplary procedure. After partitioning, both ends of the methylated DNA are ligated to Y-shaped adapters containing a primer binding site and a tag. The cytosine in the adapter is modified (e.g., 5-methylated) at position 5. The modification of the adapter serves to protect the primer binding site in subsequent conversion steps (e.g., bisulfite treatment, TAP conversion, or any other conversion that does not affect the modified cytosine but does affect unmodified cytosines). After adapter attachment, the DNA molecules are amplified. The amplification products are divided into two aliquots for sequencing with and without conversion. The aliquot that did not undergo conversion can be subjected to sequence analysis with or without further treatment. The other aliquot is subjected to a procedure that differentially affects a first nucleobase in the DNA and a second nucleobase in the DNA, where the first nucleobase comprises a modified cytosine at position 5 and the second nucleobase comprises an unmodified cytosine. The procedure can be bisulfite treatment or another procedure that converts unmodified cytosines to uracils. When contacted with a primer specific for the original primer binding site, only the primer binding sites protected by the cytosine modification can support amplification. Thus, only the original molecules, and not copies from the first amplification, are subjected to further amplification. The further amplified molecules are then subjected to sequence analysis. The sequences from the two aliquots can then be compared. As in the separation schemes discussed above, the nucleic acid tag in the adapter is not used to distinguish between methylated and unmethylated DNA, but rather to distinguish between nucleic acid molecules within the same partition.

[0101] Subjecting a first subsample to a procedure that differentially affects a first nucleobase in DNA and a second nucleobase in DNA of the first subsample The methods disclosed herein include a step of subjecting a first subsample to a procedure that differentially affects a first nucleobase in the DNA of the first subsample and a second nucleobase in the DNA, where the first nucleobase is a modified or unmodified nucleobase, the second nucleobase is a modified or unmodified nucleobase that is different from the first nucleobase, and the first nucleobase and the second nucleobase have the same base-pairing specificity. In some embodiments, if the first nucleobase is a modified or unmodified adenine, the second nucleobase is a modified or unmodified adenine; if the first nucleobase is a modified or unmodified cytosine, the second nucleobase is a modified or unmodified cytosine; if the first nucleobase is a modified or unmodified guanine, the second nucleobase is a modified or unmodified guanine; if the first nucleobase is a modified or unmodified thymine, the second nucleobase is a modified or unmodified thymine (where, for the purposes of this step, modified and unmodified uracils are included in modified thymine).

[0102] In some embodiments, the first nucleobase is a modified or unmodified cytosine, and then the second nucleobase is a modified or unmodified cytosine. For example, the first nucleobase can comprise an unmodified cytosine (C), and the second nucleobase can comprise one or more of 5-methylcytosine (mC) and 5-hydroxymethylcytosine (hmC). Alternatively, the second nucleobase can comprise C, and the first nucleobase can comprise one or more of mC and hmC. Other combinations are also possible, as indicated, for example, in the above summary and discussion below, such as where one of the first nucleobase and the second nucleobase comprises mC and the other comprises hmC.

[0103] In some embodiments, the procedure that differentially affects the first nucleobase in the DNA of the first subsample and the second nucleobase in the DNA comprises bisulfite conversion. Bisulfite treatment converts unmodified cytosine and certain modified cytosine nucleotides (e.g., 5-formylcytosine (fC) or 5-carboxylcytosine (caC)) to uracil, while other modified cytosines (e.g., 5-methylcytosine and 5-hydroxymethylcytosine) are not converted. Thus, in the case of using bisulfite conversion, the first nucleobase comprises one or more of unmodified cytosine, 5-formylcytosine, 5-carboxylcytosine, or other bisulfite-affected forms of cytosine, and the second nucleobase can comprise one or more of mC and hmC, such as mC and optionally hmC. Sequencing of the bisulfite-treated DNA identifies positions that read as cytosine as mC positions or hmC positions. At the same time, positions that read as T are identified as T or a bisulfite-susceptible form of C, such as unmodified cytosine, 5-formylcytosine, or 5-carboxylcytosine. Thus, performing bisulfite conversion on the first subsample as described herein facilitates the identification of positions containing mC or hmC using sequence reads obtained from the first subsample. See, e.g., Moss et al., Nat Commun. 2018; 9: 5068, for an exemplary description of bisulfite conversion.

[0104] In some embodiments, the procedure that differentially affects a first nucleobase in the DNA of the first subsample and a second nucleobase in the DNA comprises oxidative bisulfite (Ox-BS) conversion. In some embodiments, the procedure that differentially affects a first nucleobase in the DNA of the first subsample and a second nucleobase in the DNA comprises TET-assisted bisulfite (TAB) conversion. In some embodiments, the procedure that differentially affects a first nucleobase in the DNA of the first subsample and a second nucleobase in the DNA comprises Tet-assisted substituted-borane reductant conversion, optionally wherein the substituted-borane reductant is 2-methylpyridine borane, pyridine borane, t-butyl amine borane, or ammonia borane. In some embodiments, the procedure that differentially affects a first nucleobase in the DNA of the first subsample and a second nucleobase in the DNA comprises chemical-assisted substituted-borane reductant conversion, optionally wherein the substituted-borane reductant is 2-methylpyridine borane, pyridine borane, t-butyl amine borane, or ammonia borane. In some embodiments, the procedure that differentially affects a first nucleobase in the DNA of the first subsample and a second nucleobase in the DNA comprises APOBEC-coupled epigenetic (ACE) conversion.

[0105] In some embodiments, the procedure that differentially affects a first nucleobase in the DNA of the first subsample and a second nucleobase in the DNA comprises enzymatic conversion of the first nucleobase, e.g., as in EM-Seq. See, e.g., Vaisvila R et al. (2019) EM-seq: Detection of DNA methylation at single base resolution from picograms of DNA. bioRxiv; DOI: 10.1101 / 2019.12.20.884692, available at www.biorxiv.org / content / 10.1101 / 2019.12.20.884692v1. For example, TET2 and T4-pGT can be used to convert 5mC and 5hmC to a substrate that cannot be deaminated by a deaminase (e.g., APOBEC3A), and then a deaminase (e.g., APOBEC3A) can be used to deaminate unmodified cytosines, converting them to uracils.

[0106] In some embodiments, the procedure that differentially affects a first nucleobase in the DNA of the first subsample and a second nucleobase in the DNA comprises separating DNA that initially comprises the first nucleobase from DNA that initially does not comprise the first nucleobase.

[0107] In some embodiments, the first nucleobase is a modified or unmodified adenine, and the second nucleobase is a modified or unmodified adenine. In some embodiments, the modified adenine is N6-methyladenine (mA). In some embodiments, the modified adenine is one or more of N6-methyladenine (mA), N6-hydroxymethyladenine (hmA), or N6-formyladenine (fA).

[0108] Techniques including methylated DNA immunoprecipitation (MeDIP) can be used to isolate DNA containing modified bases, such as mA, from other DNA. See, e.g., Kumar et al., Frontiers Genet. 2018; 9: 640; Greer et al., Cell 2015; 161: 868-878. Antibodies specific for mA are described in Sun et al., Bioessays 2015; 37: 1155-62. Antibodies against various modified nucleobases, such as forms of thymine / uracil, including halogenated forms such as 5-bromouracil, are commercially available. Various modified bases can also be detected based on their altered base-pairing specificity. For example, hypoxanthine is a modified form of adenine, which can arise from deamination, and is read as G in sequencing. See, e.g., U.S. Patent 8,486,630; Brown, Genomes, 2nd Ed., John Wiley & Sons, Inc., New York, N.Y., 2002, Chapter 14, “Mutation, Repair, and Recombination.”

[0109] Enrichment / capture steps; amplification; adaptors; barcodes In some embodiments, the methods disclosed herein include a step of capturing one or more target sets of DNA, such as cfDNA. Capture can be performed using any suitable method known in the art. In some embodiments, capture includes contacting the DNA to be captured with a target-specific probe set. The target-specific probe set can have any of the features of the target-specific probe sets described herein, including but not limited to the embodiments set forth above and in the section below relating to probes. Capture can be performed on one or more subsamples prepared during the course of the methods disclosed herein. In some embodiments, DNA is captured from at least the first subsample or the second subsample, e.g., at least the first subsample and the second subsample. Where the first subsample is subjected to a separation step (e.g., separating DNA that initially includes the first nucleobase (e.g., hmC) from DNA that does not initially include the first nucleobase, such as hmC-seal), capture can be performed on any one of, any two of, or all of the DNA that initially includes the first nucleobase (e.g., hmC), the DNA that does not initially include the first nucleobase, and the second subsample. In some embodiments, the subsamples are differentially tagged (e.g., as described herein) and then pooled prior to being subjected to capture.

[0110] The capture step can be performed using conditions appropriate for hybridization of the particular nucleic acids, which conditions generally depend to some extent on the characteristics of the probes, such as length, base composition, etc. One of skill in the art would be familiar with appropriate conditions in light of the general knowledge in the art relating to hybridization of nucleic acids. In some embodiments, complexes of the target-specific probes and the DNA are formed.

[0111] In some embodiments, the methods described herein include capturing more than one target set of cfDNA obtained from a test subject. The target sets include epigenetic target sets, which can show differences in methylation levels and / or fragmentation patterns depending on whether they are derived from tumor cells or healthy cells. The target sets also include sequence-variable target sets, which can show sequence differences depending on whether they are derived from tumor cells or healthy cells. The capture step results in a captured set of cfDNA molecules, and in the captured set of cfDNA molecules, cfDNA molecules corresponding to the sequence-variable target set are captured in greater capture yield than cfDNA molecules corresponding to the epigenetic target set. For additional discussion of capture steps, capture yield, and related aspects, see WO 2020 / 160414, which is incorporated herein by reference for all purposes.

[0112] In some embodiments, the methods described herein include contacting cfDNA obtained from a test subject with a target-specific probe set, wherein the target-specific probe set is configured to capture cfDNA corresponding to the sequence-variable target region set at a greater capture yield than cfDNA corresponding to the epigenetic target region set.

[0113] Capturing cfDNA corresponding to the sequence-variable target region set at a greater capture yield than cfDNA corresponding to the epigenetic target region set is beneficial because the sequencing depth that can be required to analyze the sequence-variable target regions with sufficient confidence or accuracy can be greater than the sequencing depth that can be required to analyze the epigenetic target regions. The amount of data required to determine fragmentation patterns (e.g., perturbations of transcriptional start sites or CTCF binding sites) or fragment abundances (e.g., in hypermethylated partitions and hypomethylated partitions) is typically less than the amount of data required to determine the presence or absence of cancer-related sequence mutations. Capturing the target region sets at different yields can facilitate sequencing the target regions to different sequencing depths in the same sequencing run (e.g., using a pooled mixture and / or in the same sequencing pool).

[0114] In various embodiments, the method further includes sequencing the captured cfDNA to, e.g., different extents of sequencing depth for the epigenetic target region set and the sequence-variable target region set, consistent with that discussed herein. In some embodiments, the complex of target-specific probes and DNA is separated from DNA that is not bound to the target-specific probes. For example, where the target-specific probes are covalently or non-covalently bound to a solid support, a washing or aspiration step can be used to separate unbound material. Alternatively, where the complex has chromatographic properties that are different from unbound material (e.g., where the probes comprise a ligand that binds to a chromatographic resin), chromatography can be used.

[0115] As discussed in detail elsewhere herein, the target-specific probe set can include more than one set, such as probes to the sequence-variable target region set and probes to the epigenetic target region set. In some such embodiments, the capture step is performed simultaneously in the same vessel using both probes to the sequence-variable target regions and probes to the epigenetic target regions, e.g., the probes to the sequence-variable target region set and the epigenetic target region set are in the same composition. This approach provides a relatively more efficient workflow. In some embodiments, the concentration of probes to the sequence-variable target region set is greater than the concentration of probes to the epigenetic target region set.

[0116] Optionally, the capture step is performed in a first vessel with the sequence variable target region probe set and in a second vessel with the epigenetic target region probe set, or the contacting step is performed at a first time and in a first vessel with the sequence variable target region probe set and at a second time before or after the first time with the epigenetic target region probe set. This method allows for the preparation of separate first and second compositions comprising captured DNA corresponding to the sequence variable target region set and captured DNA corresponding to the epigenetic target region set. The compositions can be separately processed as desired (e.g., fractionated based on methylation, as described elsewhere herein), and recombined in appropriate proportions to provide material for further processing and analysis such as sequencing.

[0117] In some embodiments, the DNA is amplified. In some embodiments, amplification is performed prior to the capture step. In some embodiments, amplification is performed after the capture step.

[0118] In some embodiments, an adaptor is included in the DNA. This can be performed simultaneously with the amplification procedure, e.g., by providing the adaptor in the 5' portion of the primer, e.g., as described above. Alternatively, the adaptor can be added by other methods such as ligation.

[0119] In some embodiments, a tag is included in the DNA, which can be or comprise a barcode. The tag can aid in identifying the source of the nucleic acid. For example, the barcode can be used to allow identification of the source from which the DNA came, e.g., the subject, after pooling more than one sample for parallel sequencing. This can be performed simultaneously with the amplification procedure, e.g., by providing the barcode in the 5' portion of the primer, e.g., as described above. In some embodiments, the adaptor and tag / barcode are provided by the same primer or set of primers. For example, the barcode can be located 3' of the adaptor and 5' of the target-hybridizing portion of the primer. Alternatively, the barcode can be added by other methods such as ligation, optionally together with the adaptor in the same ligation substrate.

[0120] Additional details regarding amplification, tags, and barcodes are discussed in the "General Features of the Methods" section below, which can be combined with any of the foregoing embodiments and the embodiments set forth in the "Introduction and Summary" section to the extent practicable.

[0121] Capture set In some embodiments, a capture panel of DNA (e.g., cfDNA) is provided. For the disclosed methods, a capture panel of DNA can be provided, for example, by performing a capture step after a partitioning step as described herein. The capture panel can include DNA corresponding to a sequence variable target region panel, DNA corresponding to an epigenetic target region panel, or a combination thereof. In some embodiments, the amount of captured sequence variable target region DNA is greater than the amount of captured epigenetic target region DNA when normalized for differences in size of the target regions (footprint size).

[0122] Alternatively, a first capture panel and a second capture panel including DNA corresponding to a sequence variable target region panel and DNA corresponding to an epigenetic target region panel, respectively, can be provided. The first capture panel and the second capture panel can be combined to provide a combined capture panel.

[0123] In some embodiments wherein the capture panel includes DNA corresponding to both the sequence variable target region set and the epigenetic target region set (including a combined capture panel as discussed above), the DNA corresponding to the sequence variable target region set can be present in a greater concentration than the DNA corresponding to the epigenetic target region set, e.g., 1.1-fold to 1.2-fold greater concentration, 1.2-fold to 1.4-fold greater concentration, 1.4-fold to 1.6-fold greater concentration, 1.6-fold to 1.8-fold greater concentration, 1.8-fold to 2.0-fold greater concentration, 2.0-fold to 2.2-fold greater concentration, 2.2-fold to 2.4-fold greater concentration, 2.4-fold to 2.6-fold greater concentration, 2.6-fold to 2.8-fold greater concentration, 2.8-fold to 3.0-fold greater concentration, 3.0-fold to 3.5-fold greater concentration, 3.5-fold to 4.0-fold greater concentration, 4.0-fold to 4.5-fold greater concentration, 4.5-fold to 5.0-fold greater concentration, 5.0-fold to 5.5-fold greater concentration, 5.5-fold to 6.0-fold greater concentration, 6.0-fold to 6.5-fold greater concentration, 6.5-fold to 7.0-fold greater concentration, 7.0-fold to 7.5-fold greater concentration, 7.5-fold to 8.0-fold greater concentration, 8.0-fold to 8.5-fold greater concentration, 8.5-fold to 9.0-fold greater concentration, 9.0-fold to 9.5-fold greater concentration, 9.5-fold to 10.0-fold greater concentration, 10-fold to 11-fold greater concentration, 11-fold to 12-fold greater concentration, 12-fold to 13-fold greater concentration, 13-fold to 14-fold greater concentration, 14-fold to 15-fold greater concentration, 15-fold to 16-fold greater concentration, 16-fold to 17-fold greater concentration, 17-fold to 18-fold greater concentration, 18-fold to 19-fold greater concentration, 19-fold to 20-fold greater concentration, 20-fold to 30-fold greater concentration, 30-fold to 40-fold greater concentration, 40-fold to 50-fold greater concentration, 50-fold to 60-fold greater concentration, 60-fold to 70-fold greater concentration, 70-fold to 80-fold greater concentration, 80-fold to 90-fold greater concentration, 90-fold to 100-fold greater concentration, 10-fold to 20-fold greater concentration, 10-fold to 40-fold greater concentration, 10-fold to 50-fold greater concentration, 10-fold to 70-fold greater concentration, or 10-fold to 100-fold greater concentration. The degree of concentration difference is calculated normalized to target region footprint size as discussed in the Definitions section.

[0124] Epigenetic target region set The epigenetic target region panel can include one or more types of target regions that can distinguish DNA from a neoplastic (e.g., tumor or cancer) cell from DNA from a healthy cell (e.g., a non-neoplastic circulating cell). Example types of such regions are discussed in detail herein. The epigenetic target region panel can also include one or more control regions, e.g., as described herein. In some embodiments, the epigenetic target region panel has a footprint of at least 100 kb, e.g., at least 200 kb, at least 300 kb, or at least 400 kb. In some embodiments, the epigenetic target region panel has a footprint in the range of 100-1,000 kb, e.g., 100-200 kb, 200-300 kb, 300-400 kb, 400-500 kb, 500-600 kb, 600-700 kb, 700-800 kb, 800-900 kb, and 900-1,000 kb.

[0125] Hypomethylated variable target region In some embodiments, the epigenetic target region panel includes one or more hypermethylated variable target regions. Generally, a hypermethylated variable target region refers to a region in which, e.g., in a cfDNA sample, an increase in the observed level of methylation is indicative of an increased likelihood that the sample (e.g., cfDNA) contains DNA produced by a neoplastic cell, such as a tumor cell or a cancer cell. For example, hypermethylation of tumor suppressor gene promoters has been repeatedly observed. See, e.g., Kang et al., Genome Biol. 18:53 (2017) and references cited therein. In examples, a hypermethylated variable target region can include a region whose methylation in a cancerous tissue is not necessarily different relative to DNA from a healthy tissue of the same type, but whose methylation is different (e.g., has more methylation) relative to typical cfDNA in a healthy subject. For example, such a hypermethylated variable target region can be used, at least in part, to detect a cancer when the presence of the cancer results in increased cell death, such as apoptosis of cells corresponding to the tissue type of the cancer. In some embodiments, a hypermethylated variable target region includes one or more genomic regions in which the methylation state of cfDNA molecules in these regions in a cancer subject is not different relative to cfDNA from a healthy subject, but the presence / increased amount of hypermethylated cfDNA in these regions is indicative of a particular tissue type (e.g., cancer origin) and is due to increased apoptosis (e.g., tumor shedding) into circulation as cfDNA.

[0126] The hypermethylated target regions can be obtained, for example, from the Cancer Genome Atlas. Kang et al., Genome Biology 18:53 (2017) describes constructing a probabilistic method called CancerLocator using hypermethylated target regions from breast, colon, kidney, liver, and lung. In some embodiments, the hypermethylated target regions can be specific to one or more types of cancer. Thus, in some embodiments, the hypermethylated target regions include one, two, three, four, or five subsets of hypermethylated target regions that collectively show hypermethylation in one, two, three, four, or five of breast cancer, colon cancer, kidney cancer, liver cancer, and lung cancer.

[0127] In some embodiments, the probes for the epigenetic target region panel include probes specific to one or more hypermethylated variable target regions. The hypermethylated variable target regions can be any of the hypermethylated variable target regions listed above. For example, the probes specific to hypermethylated variable target regions include probes specific to more than one locus listed in Table 1 (e.g., at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% of the loci listed in Table 1). In some embodiments, the probes specific to hypermethylated variable target regions include probes specific to more than one locus listed in Table 2 (e.g., at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% of the loci listed in Table 2). In some embodiments, the probes specific to hypermethylated variable target regions include probes specific to more than one locus listed in Table 1 or Table 2 (e.g., at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% of the loci listed in Table 1 or Table 2). In some embodiments, for each locus included as a target region, there can be one or more probes with a hybridization site that binds between the transcription start site and the stop codon (for alternatively spliced genes, the last stop codon) of the gene. In some embodiments, the one or more probes bind within 300 bp (e.g., within 200 bp or 100 bp) of the listed location. In some embodiments, the probes have a hybridization site that overlaps with the locations listed above. In some embodiments, the probes specific to hypermethylated target regions include probes specific to one, two, three, four, or five subsets of hypermethylated target regions that collectively show hypermethylation in one, two, three, four, or five of breast cancer, colon cancer, kidney cancer, liver cancer, and lung cancer.

[0128] Hypomethylated variable target region Global hypomethylation is a phenomenon that is commonly observed in a variety of cancers. See, e.g., Hon et al., Genome Res. 22:246-258 (2012) (breast cancer); Ehrlich, Epigenomics 1 :239-259 (2009) (review article noting observations of hypomethylation in colon cancer, ovarian cancer, prostate cancer, leukemia, hepatocellular carcinoma, and cervical cancer). For example, regions that are normally methylated in healthy cells, such as repetitive elements (e.g., LINE1 elements, Alu elements, centromeric tandem repeat sequences, pericentromeric tandem repeat sequences, and satellite DNA) and intergenic regions, can show reduced methylation in tumor cells. Accordingly, in some embodiments, the epigenetic target region set includes hypomethylation variable target regions, where a decrease in the observed level of methylation indicates an increased likelihood that the sample (e.g., cfDNA) contains DNA produced by a neoplastic cell, such as a tumor cell or a cancer cell. In examples, a hypomethylation variable target region can include a region whose methylation status in a cancerous tissue is not necessarily different relative to DNA from a healthy tissue of the same type, but is different (e.g., less methylated) relative to cfDNA that is typical in a healthy subject. For example, such a hypomethylation variable target region can be used, at least in part, to detect a cancer when the presence of the cancer results in an increase in cell death, such as apoptosis of cells corresponding to the tissue type of the cancer. In some embodiments, a hypomethylation variable target region includes one or more genomic regions where the methylation status of cfDNA molecules in these regions is not different in a cancer subject relative to cfDNA from a healthy subject, but the presence / increased amount of hypomethylated cfDNA in these regions is indicative of a particular tissue type (e.g., cancer origin) and is presented with an accompanying increase in apoptosis (e.g., tumor shedding) into the circulating cfDNA.

[0129] In some embodiments, the hypomethylation variable target region includes a repetitive element and / or an intergenic region. In some embodiments, the repetitive element includes one, two, three, four, or five of a LINE1 element, an Alu element, a centromeric tandem repeat sequence, a pericentromeric tandem repeat sequence, and / or a satellite DNA.

[0130] Example specific genomic regions that show cancer-related hypomethylation include nucleotides 8403565-8953708 and 151104701-151106035 of human chromosome 1. In some embodiments, the hypomethylation variable target region overlaps with or includes one or both of these regions.

[0131] In some embodiments, the probes directed to the epigenetic target panel include probes specific to one or more hypomethylated variable target regions. The hypomethylated variable target regions can be any of the hypomethylated target regions listed above. For example, the probes specific to one or more hypomethylated variable target regions can include probes directed to regions such as repetitive elements (e.g., LINE1 elements, Alu elements, centromeric tandem repeat sequences, pericentromeric tandem repeat sequences, and satellite DNA) and intergenic regions that are typically methylated in healthy cells and can show reduced methylation in tumor cells.

[0132] In some embodiments, the probes specific to hypomethylated variable target regions include probes specific to repetitive elements and / or intergenic regions. In some embodiments, the probes specific to repetitive elements include probes specific to one, two, three, four, or five of LINE1 elements, Alu elements, centromeric tandem repeat sequences, pericentromeric tandem repeat sequences, and / or satellite DNA.

[0133] Exemplary probes specific to genomic regions that show cancer-related hypomethylation include probes specific to nucleotides 8403565-8953708 and / or 151104701-151106035 of human chromosome 1. In some embodiments, the probes specific to hypomethylated variable target regions include probes specific to regions that overlap or contain nucleotides 8403565-8953708 and / or 151104701-151106035 of human chromosome 1.

[0134] Probes for detecting this panel of regions can include probes for detecting genomic regions of interest (hotspot regions) as well as nucleosome-aware probes (e.g., KRAS codons 12 and 13) and can be designed to optimize capture based on analyzing cfDNA coverage, fragment size variation, and GC sequence composition affected by nucleosome binding patterns. Regions used herein can also include non-hotspot regions optimized based on nucleosome position and GC models.

[0135] In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject having a cancer. In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject suspected of having a cancer. In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject having a tumor. In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject suspected of having a tumor. In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject having a neoplasm. In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject suspected of having a neoplasm. In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject in remission from a tumor, cancer, or neoplasm (e.g., after chemotherapy, surgical resection, radiation, or a combination thereof). In any of the foregoing embodiments, the cancer, tumor, or neoplasm or suspected cancer, tumor, or neoplasm can be of the lung, colon, rectum, kidney, breast, prostate, or liver. In some embodiments, the cancer, tumor, or neoplasm or suspected cancer, tumor, or neoplasm is of the lung. In some embodiments, the cancer, tumor, or neoplasm or suspected cancer, tumor, or neoplasm is of the colon or rectum. In some embodiments, the cancer, tumor, or neoplasm or suspected cancer, tumor, or neoplasm is of the breast. In some embodiments, the cancer, tumor, or neoplasm or suspected cancer, tumor, or neoplasm is of the prostate. In any of the foregoing embodiments, the subject can be a human subject.

[0136] In some embodiments, the sequence-variable target region probe set has a footprint of at least 0.5 kb (e.g., at least 1 kb, at least 2 kb, at least 5 kb, at least 10 kb, at least 20 kb, at least 30 kb, or at least 40 kb). In some embodiments, the epigenetic target region probe set has a footprint ranging from 0.5-100 kb (e.g., 0.5-2 kb, 2-10 kb, 10-20 kb, 20-30 kb, 30-40 kb, 40-50 kb, 50-60 kb, 60-70 kb, 70-80 kb, 80-90 kb, and 90-100 kb).

[0137] In some embodiments, the probes specific for the set of sequence-variable target regions include probes specific for target regions from at least 10, 20, 30, or 35 cancer- related genes, such as AKT1, ALK, BRAF, CCND1, CDK2A, CTNNB1, EGFR, ERBB2, ESR1, FGFR1, FGFR2, FGFR3, FOXL2, GATA3, GNA11, GNAQ, GNAS, HRAS, IDH1, IDH2, KIT, KRAS, MED12, MET, MYC, NFE2L2, NRAS, PDGFRA, PIK3CA, PPP2R1A, PTEN, RET, STK11, TP53, and U2AF1.

[0138] Composition comprising captured DNA Provided herein is a combination of a first population and a second population comprising captured DNA. The first population can comprise or be derived from DNA having a greater proportion of cytosine modifications than the second population. The first population can comprise a form of a first nucleobase originally present in the DNA having an altered base-pairing specificity and a second nucleobase that does not have an altered base-pairing specificity, wherein the form of the first nucleobase originally present in the DNA prior to the alteration of the base-pairing specificity is a modified or unmodified nucleobase, the second nucleobase is a modified or unmodified nucleobase that is different from the first nucleobase, and the form of the first nucleobase originally present in the DNA prior to the alteration of the base-pairing specificity and the second nucleobase have the same base-pairing specificity. The second population does not comprise the form of the first nucleobase originally present in the DNA having an altered base-pairing specificity. In some embodiments, the cytosine modification is cytosine methylation. In some embodiments, the first nucleobase is a modified or unmodified cytosine and the second nucleobase is a modified or unmodified cytosine. The first and second nucleobases can be any of the nucleobases discussed herein in the SUMMARY or with respect to subjecting the first subsample to a procedure that differentially affects the first nucleobase in the DNA of the first subsample and the second nucleobase in the DNA.

[0139] In some embodiments, the first population comprises a sequence tag selected from a first set of one or more sequence tags and the second population comprises a sequence tag selected from a second set of one or more sequence tags, and the second set of sequence tags is different from the first set of sequence tags. The sequence tag can comprise a barcode.

[0140] In some embodiments, the first population comprises protected hmC, such as glucosylated hmC. In some embodiments, the first population is subjected to any of the conversion procedures discussed herein, such as bisulfite conversion, Ox-BS conversion, TAB conversion, ACE conversion, TAP conversion, TAPSbeta conversion, or CAP conversion. In some embodiments, the first population is subjected to protection of hmC followed by deamination of mC and / or C. In some embodiments of the combination, the first population comprises or is derived from DNA having a greater proportion of cytosine modifications than the second population, and the first population comprises first and second subpopulations, and the first nucleobase is a modified or unmodified nucleobase, the second nucleobase is a modified or unmodified nucleobase different from the first nucleobase, and the first nucleobase and the second nucleobase have the same base-pairing specificity. In some embodiments, the second population does not comprise the first nucleobase. In some embodiments, the first nucleobase is a modified or unmodified cytosine, and the second nucleobase is a modified or unmodified cytosine, optionally wherein the modified cytosine is mC or hmC. In some embodiments, the first nucleobase is a modified or unmodified adenine, and the second nucleobase is a modified or unmodified adenine, optionally wherein the modified adenine is mA.

[0141] In some embodiments, the first nucleobase (e.g., modified cytosine) is biotinylated. In some embodiments, the first nucleobase (e.g., modified cytosine) is the product of Huisgen cycloaddition on beta-6-azido-glucosyl-5-hydroxymethylcytosine, which comprises an affinity tag (e.g., biotin).

[0142] In any of the combinations described herein, the captured DNA can comprise cfDNA. The captured DNA can have any of the features described herein with respect to the capture panel, including, for example, a greater concentration of DNA corresponding to the sequence-variable target set than the concentration of DNA corresponding to the epigenetic target set (as normalized for footprint size as discussed above). In some embodiments, the DNA of the capture panel comprises a sequence tag, which can be added to the DNA as described herein. Generally, the inclusion of a sequence tag results in DNA molecules that are different from their naturally-occurring, untagged form.

[0143] The combination can also comprise a probe set or sequencing primer described herein, each of which can be different from a naturally-occurring nucleic acid molecule. For example, the probe set described herein can comprise a capture moiety, and the sequencing primer can comprise a non-naturally occurring label.

[0144] Computer system Methods of the present disclosure can be implemented using or with the aid of a computer system. For example, such methods can comprise partitioning a sample into a plurality of subsamples, the plurality of subsamples comprising a first subsample and a second subsample, wherein the first subsample comprises a greater proportion of cytosine-modified DNA than the second subsample; subjecting the first subsample to a procedure that differentially affects a first nucleobase in the DNA of the first subsample and a second nucleobase in the DNA, wherein the first nucleobase is a modified or unmodified nucleobase, the second nucleobase is a modified or unmodified nucleobase different from the first nucleobase, and the first nucleobase and the second nucleobase have the same base-pairing specificity; and sequencing the DNA in the first subsample and the DNA in the second subsample in a manner that distinguishes the first nucleobase from the second nucleobase in the DNA of the first subsample.

[0145] In one aspect, the present disclosure provides a non-transitory computer-readable medium comprising computer-executable instructions that, when executed by at least one electronic processor, perform at least a portion of a method comprising: collecting cfDNA from a test subject; capturing a plurality of target panels from the cfDNA, wherein the plurality of target panels comprises a sequence-variable target panel and an epigenetic target panel, thereby producing a set of captured cfDNA molecules; sequencing the captured cfDNA molecules, wherein the captured cfDNA molecules of the sequence-variable target panel are sequenced to a deeper sequencing depth than the captured cfDNA molecules of the epigenetic target panel; obtaining a plurality of sequence reads produced by a nucleic acid sequencer by sequencing the captured cfDNA molecules; mapping the plurality of sequence reads to one or more reference sequences to produce mapped sequence reads; and processing the mapped sequence reads corresponding to the sequence-variable target panel and the epigenetic target panel to determine a likelihood that the subject has a cancer.

[0146] The code can be pre-compiled and configured for use with a machine having a processor suitable for executing the code, or can be compiled during run-time. The code can be provided in a programming language that can be selected so that the code can be executed in pre-compiled or as-compiled fashion.

[0147] Additional details related to computer systems and networks, databases, and computer program products are also provided in, for example, Peterson, Computer Networks: A Systems Approach, Morgan Kaufmann, 5thEd. (2011), Kurose, Computer Networking: A Top-Down Approach, Pearson, 7thEd. (2016), Elmasri, Fundamentals of Database Systems, Addison Wesley, 6thEd. (2010), Coronel, Database Systems: Design, Implementation, & Management, Cengage Learning, 11thEd. (2014), Tucker, Programming Languages, McGraw-Hill Science / Engineering / Math, 2ndEd. (2006), and Rhoton, Cloud Computing Architected: Solution Design Handbook, Recursive Press (2011), which are all incorporated by reference in their entirety.

[0148] Cancer and other diseases The methods of the present application can be used to diagnose the presence of a condition, particularly cancer, in a subject, to characterize the condition (e.g., to stage a cancer or determine the heterogeneity of a cancer), to monitor the response of the condition to a treatment, to enable prognosis of the risk of development of the condition or subsequent course of the condition. The present disclosure can also be used to determine the efficacy of a particular treatment selection. If the treatment is successful, then a successful treatment selection can increase the amount of copy number variants or rare mutations detected in the blood of the subject as more cancer can die and shed DNA. In other instances, this can not occur. In another instance, perhaps certain treatment selections can be correlated with the genetic profile of the cancer over time. This correlation can be used to select a therapy.

[0149] Additionally, if it is observed that the cancer is in remission after treatment, the methods of the present application can be used to monitor for residual disease or recurrence of the disease.

[0150] In some embodiments, the methods and systems disclosed herein can be used to identify a tailored or targeted therapy to treat a particular disease or condition of a patient based on classifying nucleic acid variants as somatically or germline-derived. Typically, the disease under consideration is a cancer. Non-limiting examples of such cancers include biliary tract cancer, bladder cancer, transitional cell carcinoma, urothelial carcinoma, brain cancer, glioma, astrocytoma, breast cancer, metaplastic carcinoma, cervical cancer, cervical squamous cell carcinoma, rectal cancer, colorectal cancer, colon cancer, hereditary nonpolyposis colorectal cancer, colon adenocarcinoma, gastrointestinal stromal tumor (GIST), endometrial carcinoma, endometrial stromal sarcoma, esophageal cancer, esophageal squamous cell carcinoma, esophageal adenocarcinoma, ocular melanoma, uveal melanoma, gallbladder cancer, gallbladder adenocarcinoma, renal cell carcinoma, clear cell renal cell carcinoma, transitional cell carcinoma, urothelial carcinoma, hepatoblastoma, leukemia, acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myeloid leukemia (CML), chronic myelomonocytic leukemia (CMML), liver cancer, liver carcinoma, hepatoma, hepatocellular carcinoma, cholangiocarcinoma, hepatoblastoma, lung cancer, non-small cell lung cancer (NSCLC), mesothelioma, B-cell lymphoma, non-Hodgkin lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, T-cell lymphoma, non-Hodgkin lymphoma, precursor T-lymphoblastic lymphoma / leukemia, peripheral T-cell lymphoma, multiple myeloma, nasopharyngeal carcinoma (NPC), neuroblastoma, oropharyngeal cancer, oral squamous cell carcinoma, osteosarcoma, ovarian cancer, pancreatic cancer, pancreatic ductal adenocarcinoma, pseudopapillary tumor, alveolar cell carcinoma. prostate cancer, prostate adenocarcinoma, skin cancer, melanoma, malignant melanoma, cutaneous melanoma, small intestine cancer, stomach cancer, gastric carcinoma, gastrointestinal stromal tumor (GIST), uterine cancer, or uterine sarcoma. The type and / or stage of the cancer can be determined from genetic variant detection, including mutations, rare mutations, insertions / deletions, copy number variants, transversions, translocations, inversions, deletions, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structural alterations, gene fusions, chromosomal fusions, gene truncations, gene amplifications, gene duplications, chromosomal damage, DNA damage, aberrant alterations in nucleic acid chemical modifications, aberrant alterations in epigenetic patterns, and aberrant alterations in nucleic acid 5-methylcytosine.

[0151] Genetic data can also be used to characterize particular forms of cancer. Cancer is often heterogeneous in both composition and stage. Genetic profile data can allow characterization of a specific subtype of cancer that can be important in the diagnosis or treatment of that specific subtype. This information can also provide a clue to the prognosis of a particular type of cancer to the subject or practitioner and allow the subject or practitioner to adjust treatment options according to the progression of the disease. Some cancers can progress to become more aggressive and genetically unstable. Other cancers can remain benign, non-aggressive, or dormant. The systems and methods of the present disclosure can be used to determine disease progression.

[0152] Further, the methods of the present disclosure can be used to characterize the heterogeneity of an abnormal condition in a subject. Such methods can include, for example, generating a genetic profile of extracellular polynucleotides derived from a subject, wherein the genetic profile comprises a plurality of data from copy number variation and rare mutation analysis. In some embodiments, the abnormal condition is cancer. In some embodiments, the abnormal condition can be a condition that results in a heterogeneous population of genomes. In the example of cancer, some tumors are known to comprise tumor cells at different stages of cancer. In other examples, heterogeneity can include multiple foci of disease. Again, in the example of cancer, there can be multiple tumor foci, perhaps one or more of which is the result of metastasis from a primary site.

[0153] The methods of the present disclosure can be used to generate or profile a fingerprint or data set that is a sum of genetic information derived from different cells in a heterogeneous disease. The data set can include copy number variation, epigenetic variation, and mutation analysis, alone or in combination.

[0154] The methods of the present disclosure can be used to diagnose, prognose, monitor, or observe cancer or other diseases. In some embodiments, the methods herein do not involve the diagnosis, prognosis, or monitoring of a fetus, and thus do not involve non-invasive prenatal testing. In other embodiments, the methods can be used in a pregnant subject to diagnose, prognose, monitor, or observe cancer or other diseases in an unborn subject whose DNA and other polynucleotides can be co-circulating with maternal molecules.

[0155] Non-limiting examples of other genetic-based diseases, disorders, or conditions optionally evaluated using the methods and systems disclosed herein include achondroplasia, alpha- 1 antitrypsin deficiency, antiphospholipid syndrome, autism, autosomal dominant polycystic kidney disease, Charcot-Marie-Tooth (CMT), Cri du chat syndrome, Crohn's disease, cystic fibrosis, Dercum disease, Down syndrome, Duane syndrome, Duchenne muscular dystrophy, Factor V Leiden thrombophilia, familial hypercholesterolemia, familial Mediterranean fever, fragile X syndrome, Gaucher disease, hemochromatosis, hemophilia, holoprosencephaly, Huntington's disease, Klinefelter syndrome, Marfan syndrome, myotonic dystrophy, neurofibromatosis, Noonan syndrome, osteogenesis imperfecta, Parkinson's disease, phenylketonuria, Poland anomaly, porphyria, progeria, retinitis pigmentosa, severe combined immunodeficiency (SCID), sickle cell disease, spinal muscular atrophy, Tay-Sachs, thalassemia, trimethylaminuria, Turner syndrome, velo-cardiofacial syndrome, WAGR syndrome, Wilson's disease, and the like.

[0156] In some embodiments, the methods described herein include detecting the presence or absence of DNA derived or originating from tumor cells using a set of sequence information obtained as described herein at a preselected time point following a prior cancer treatment of a subject previously diagnosed with a cancer. The method can further include determining a cancer recurrence score indicative of the presence or absence of DNA derived or originating from tumor cells of the test subject. Where a cancer recurrence score is determined, it can be further used to determine a cancer recurrence status. For example, when the cancer recurrence score is higher than a predetermined threshold, the cancer recurrence status can be at risk of cancer recurrence. For example, when the cancer recurrence score is lower than a predetermined threshold, the cancer recurrence status can be at a low or lower risk of cancer recurrence. In particular embodiments, a cancer recurrence score equal to a predetermined threshold can result in a cancer recurrence status at risk of cancer recurrence or at a low or lower risk of cancer recurrence.

[0157] In some embodiments, the cancer recurrence score is compared to a predetermined cancer recurrence threshold, and the test subject is classified as a candidate for a subsequent cancer treatment when the cancer recurrence score is higher than the cancer recurrence threshold, or as a non-candidate for treatment when the cancer recurrence score is lower than the cancer recurrence threshold. In particular embodiments, a cancer recurrence score equal to a cancer recurrence threshold can result in a classification as a candidate for a subsequent cancer treatment or as a non-candidate for treatment.

[0158] The methods discussed above can also include any compatible features or features set forth elsewhere herein, including in the sections regarding methods of determining a test subject's risk of cancer recurrence and / or classifying a test subject as a candidate for a subsequent cancer treatment.

[0159] Methods of determining the risk of cancer recurrence in a test subject and / or classifying a test subject as a candidate for a subsequent cancer treatment. Therapeutic and related administration

[0160] In some embodiments, the methods provided herein are methods of determining a test subject's risk of cancer recurrence. In some embodiments, the methods provided herein are methods of classifying a test subject as a candidate for a subsequent cancer treatment.

[0161] Any of such methods can include collecting DNA (e.g., derived or originating from tumor cells) from a test subject diagnosed with cancer at one or more preselected time points after one or more prior cancer treatments of the test subject. The subject can be any of the subjects described herein. The DNA can be cfDNA. The DNA can be obtained from a tissue sample.

[0162] Any of such methods can include capturing a plurality of target region sets from DNA from the subject, wherein the plurality of target region sets comprises a sequence-variable target region set and an epigenetic target region set, thereby producing a captured set of DNA molecules. The capturing step can be performed according to any of the embodiments described elsewhere herein. In any of such methods, the prior cancer treatment can comprise surgery, administration of a therapeutic composition, and / or chemotherapy.

[0163] Any of such methods can include sequencing the captured DNA molecules, thereby producing a set of sequence information. The captured DNA molecules of the sequence-variable target region set can be sequenced to a greater sequencing depth than the captured DNA molecules of the epigenetic target region set.

[0164] Any of such methods can include detecting, at the preselected time points, the presence or absence of DNA derived or originating from tumor cells using the set of sequence information. The detection of the presence or absence of DNA derived or originating from tumor cells can be performed according to any of the embodiments thereof described elsewhere herein.

[0165] Methods of determining a cancer recurrence risk for a test subject can include determining a cancer recurrence score indicative of the presence or absence or amount of DNA of tumor cells derived or originating from the test subject. The cancer recurrence score can further be used to determine a cancer recurrence status. For example, when the cancer recurrence score is above a predetermined threshold, the cancer recurrence status can be at a cancer recurrence risk. For example, when the cancer recurrence score is below a predetermined threshold, the cancer recurrence status can be at a low or lower cancer recurrence risk. In particular embodiments, a cancer recurrence score equal to a predetermined threshold can result in a cancer recurrence status that is at a cancer recurrence risk or at a low or lower cancer recurrence risk.

[0166] Methods of classifying a test subject as a candidate for a subsequent cancer treatment can include comparing a cancer recurrence score for the test subject to a predetermined cancer recurrence threshold, whereby the test subject is classified as a candidate for a subsequent cancer treatment when the cancer recurrence score is above the cancer recurrence threshold, or as a non-candidate for treatment when the cancer recurrence score is below the cancer recurrence threshold. In particular embodiments, a cancer recurrence score equal to a cancer recurrence threshold can result in a classification as a candidate for a subsequent cancer treatment or as a non-candidate for treatment. In some embodiments, the subsequent cancer treatment comprises chemotherapy or administration of a therapeutic composition.

[0167] Any of such methods can include determining a disease-free survival (DFS) period for the test subject based on the cancer recurrence score; for example, the DFS period can be 1 year, 2 years, 3 years, 4 years, 5 years, or 10 years.

[0168] In some embodiments, the set of sequence information comprises sequence variable target region sequences, and determining the cancer recurrence score can comprise determining at least a first subscore indicative of an amount of SNVs, indels, CNVs, and / or fusions present in the sequence variable target region sequences.

[0169] In some embodiments, a number of mutations selected from 1, 2, 3, 4, or 5 in the sequence variable target region is sufficient for the first subscore to result in the cancer recurrence score being classified as cancer recurrence positive. In some embodiments, the number of mutations is selected from 1, 2, or 3.

[0170] In some embodiments, the set of sequence information includes epigenetic target region sequences, and determining the cancer recurrence score includes determining a second subscore indicative of an amount of molecules (obtained from the epigenetic target region sequences) that represent an epigenetic state different from DNA found in a corresponding sample from a healthy subject (e.g., cfDNA found in a blood sample from a healthy subject, or DNA found in a tissue sample from a healthy subject, where the tissue sample is the same type of tissue as the tissue obtained from the test subject). These aberrant molecules (i.e., molecules with an epigenetic state different from DNA found in a corresponding sample from a healthy subject) can be consistent with epigenetic alterations associated with cancer, e.g., methylation of hypermethylated variable target regions and / or perturbed fragmentation of fragmented variable target regions, where “perturbed” means different from DNA found in a corresponding sample from a healthy subject.

[0171] In some embodiments, a proportion of molecules corresponding to the set of hypermethylated variable target regions and / or the set of fragmented variable target regions that are indicative of hypermethylation in the set of hypermethylated variable target regions and / or aberrant fragmentation in the set of fragmented variable target regions that is greater than or equal to a value in the range of 0.001-10% is sufficient for the second subscore to be classified as cancer recurrence positive. The range can be 0.001-1%, 0.005-1%, 0.01-5%, 0.01-2%, or 0.01-1%.

[0172] In some embodiments, any of such methods can include determining a tumor DNA fraction from a proportion of molecules in the set of sequence information indicative of one or more features from a source of tumor cells. This can be for molecules corresponding to some or all of the epigenetic target regions, e.g., one or both of the hypermethylated variable target regions and the fragmented variable target regions (hypermethylation of the hypermethylated variable target regions and / or aberrant fragmentation of the fragmented variable target regions can be considered indicative of a source of tumor cells). This can be for molecules corresponding to the sequence variable target regions, e.g., molecules containing alterations consistent with cancer such as SNVs, indels, CNVs, and / or fusions. The tumor DNA fraction can be determined based on a combination of molecules corresponding to the epigenetic target regions and molecules corresponding to the sequence variable target regions.

[0173] The determination of the cancer recurrence score can be based at least in part on the tumor DNA fraction, where a tumor DNA fraction greater than 10 -11 to 1 or 10 -10 to 1 is sufficient for the cancer recurrence score to be classified as cancer recurrence positive. In some embodiments, a tumor DNA fraction greater than or equal to 10 -10 to 10 -9 , 10 -9 to 10 -8 , 10-8 Up to 10 -7 10 -7 Up to 10 -6 10 -6 Up to 10 -5 10 -5 Up to 10 -4 10 -4 Up to 10 -3 10 -3 Up to 10 -2 Or 10 -2 Up to 10 -1 A tumor DNA score within a certain range is sufficient to classify a cancer recurrence score as positive for cancer recurrence. In some implementations, a score greater than at least 10... -7 A tumor DNA score of a threshold is sufficient to classify a cancer recurrence score as positive for cancer recurrence. The determination of a tumor DNA score greater than a threshold can be based on a cumulative probability, such as the threshold corresponding to any of the foregoing embodiments. For example, a sample is considered positive if the cumulative probability of a tumor score greater than any of the foregoing thresholds exceeds a probability threshold of at least 0.5, 0.75, 0.9, 0.95, 0.98, 0.99, 0.995, or 0.999. In some embodiments, the probability threshold is at least 0.95, such as 0.99.

[0174] In some implementations, the sequence information set includes a sequence-variable target region sequence and an epigenetic target region sequence, and determining a cancer recurrence score includes determining a first sub-score indicating the amount of SNVs, insertions / deletions, CNVs, and / or fusions present in the sequence-variable target region sequence and a second sub-score indicating the amount of aberrant molecules in the epigenetic target region sequence, and combining the first and second sub-scores to provide a cancer recurrence score. In the case of combining the first and second sub-scores, they can be combined in such a way that thresholds are applied independently to each sub-score (e.g., a score for a predetermined number of mutations (e.g., >1) in the sequence-variable target region and a score for a predetermined number of aberrant molecules (i.e., molecules having an epigenetic state different from that found in corresponding samples from healthy subjects; e.g., tumors) in the epigenetic target region), or by training a machine learning classifier to determine the state based on more than one positive training sample and one negative training sample.

[0175] In some implementations, a combined score in the range of -4 to 2 or -3 to 1 is sufficient to classify a cancer recurrence score as positive for cancer recurrence.

[0176] In any implementation in which the cancer recurrence score is classified as positive for cancer recurrence, the subject's cancer recurrence status may be at risk of cancer recurrence and / or may classify the subject as a candidate for subsequent cancer treatment.

[0177] In some embodiments, the cancer is any of the cancer types described elsewhere herein, e.g., colorectal cancer.

[0178] Cancer treatment, therapy In certain embodiments, the methods disclosed herein involve identifying a customized therapy and administering the customized therapy to a patient in view of the status of the nucleic acid variant as somatically or germline-derived. In some embodiments, substantially any cancer therapy (e.g., surgical therapy, radiation therapy, chemotherapy therapy, and / or the like) can be included as part of these methods. Typically, the customized therapy includes at least one immunotherapy (or immunotherapeutic agent). Immunotherapy generally refers to a method of enhancing an immune response against a given cancer type. In certain embodiments, immunotherapy refers to a method of enhancing a T cell response against a tumor or cancer.

[0179] In certain embodiments, the status of the nucleic acid variant from a sample from a subject as somatically or germline-derived can be compared to a database of comparator results from a reference population to identify a customized or targeted therapy for the subject. Typically, the reference population includes patients with the same cancer or disease type as the subject being tested and / or patients who are receiving or have received the same therapy as the subject being tested. When the nucleic acid variant and the comparator results meet certain classification criteria (e.g., a substantial or approximate match), a customized or targeted therapy (or more than one therapy) can be identified.

[0180] In certain embodiments, the customized therapies described herein are typically administered parenterally (e.g., intravenously or subcutaneously). The pharmaceutical compositions containing the immunotherapeutic agents are typically administered intravenously. Certain therapeutic agents are administered orally. However, the customized therapies (e.g., immunotherapeutic agents, etc.) can also be administered by methods such as, for example, buccal, sublingual, rectal, vaginal, intraurethral, topical, intraocular, intranasal, and / or intraaural, which can include tablets, capsules, granules, aqueous suspensions, gels, sprays, suppositories, salves, ointments, and the like.

[0181] While preferred embodiments of the application have been shown and described herein, it will be apparent to those skilled in the art that many changes, modifications, and substitutions can be made thereto without departing from the application. In fact, it is the intention of the inventors that all such changes, modifications, and substitutions be embraced by the scope of the application. It is the intent of the inventors that the following claims be interpreted to encompass all such changes, modifications, and substitutions. The following claims are intended to be construed to encompass all such changes, modifications, and substitutions.

[0182] While the foregoing disclosure has been set forth in some detail with respect to the preferred embodiments, it will be apparent to those skilled in the art that many changes, modifications, and substitutions can be made without departing from the true scope of the disclosure. For example, all methods, system, computer readable media and / or components features, steps, elements or other aspects can be used in various combinations. It is the intention of the inventors that all such changes, modifications, and substitutions be embraced by the scope of the following claims.

[0183] Genetic analysis In some cases, the cancer treatment includes, but is not limited to, imatinib, gefitinib, afatinib, dacomitinib, sunitinib, sorafenib, vandetanib, brivanib, cabozantinib, neratinib, tivantinib, bevacizumab, cixutumumab, dalotuzumab, figitumumab, rilotumumab, onartuzumab, ganitumab, ramucirumab, ridaforolimus, temsirolimus, everolimus, BMS-690514, BMS-754807, EMD 525797, GDC-0973, GDC-0941, MK-2206, AZD6244, GSK1120212, PX-866, XL821, IMC-A12, MM-121, PF-02341066, RG7160, and Sym004. Antibodies suitable for use as anti-EGFR therapy include cetuximab (trade name: Erbitux) and panitumumab (trade name: Vectibex). In some cases, the cancer treatment includes an EGFR tyrosine kinase inhibitor, such as gefitinib (trade name: Iressa), erlotinib (trade name: Tarceva), lapatinib, canertinib, and cetuximab.

[0184] In some cases, the therapies can be used in combination, for example, an anti-EGFR therapy and an anti-EGFR therapy. The anti-EGFR therapy can be used in combination with any combination of chemotherapeutic agents or chemotherapeutic regimens, such as FOLFOX (fluorouracil [5-FU] / leucovorin / oxaliplatin), FOLFIRI (5-FU / leucovorin / irinotecan), and the like.

[0185] In some aspects, the subject is administered a cancer treatment. In some cases, the cancer treatment is administered in combination with another therapy, such as a non-anti-EGFR therapy in combination with an anti-EGFR therapy.

[0186] Examples Genetic analysis includes detection of nucleotide sequence variants and copy number variations. Genetic variations can be determined by sequencing. Sequencing methods can be massively parallel sequencing, i.e., sequencing at least any of 100,000, 1 million, 100 million, 100 million, or 1 billion polynucleotide molecules simultaneously (or rapidly in succession). Sequencing methods can include, but are not limited to, high-throughput sequencing, pyrosequencing, sequencing by synthesis, single molecule sequencing, nanopore sequencing, semiconductor sequencing, ligation sequencing, hybridization sequencing, RNA-Seq (Illumina), digital gene expression (Helicos), next generation sequencing, single molecule sequencing by synthesis (SMSS) (Helicos), massively parallel sequencing, clonal single molecule array (Solexa), shotgun sequencing, Maxam-Gilbert or Sanger sequencing, primer walking, sequencing using PacBio, SOLiD, Ion Torrent, or nanopore platforms, and any other sequencing method known in the art.

[0187] Sequencing can be made more efficient by performing sequence capture, i.e., enriching for target sequences of interest in the sample, e.g., sequences including KRAS and / or EGFR genes or portions thereof containing sequence variant biomarkers. Sequence capture can be performed using immobilized probes that hybridize to the target of interest.

[0188] Cell-free DNA can include small amounts of tumor DNA mixed with germline DNA. Sequencing methods that increase sensitivity and specificity of detection of tumor DNA, and in particular genetic sequence variants and copy number variations, can be used in the methods of the application. Such methods are described, e.g., in WO 2014 / 039556. These methods can not only detect molecules with a sensitivity of up to or greater than 0.1%, but also distinguish these signals from noise typical in current sequencing methods. Various methods can be used to achieve the increase in sensitivity and specificity from blood-based cfDNA samples. One method includes efficiently tagging DNA molecules in the sample, e.g., at least 50%, 75%, or 90% of any of the polynucleotides in the sample. This increases the likelihood that low-abundance target molecules in the sample will be tagged and subsequently sequenced, and significantly increases the sensitivity of target molecule detection.

[0189] Another method involves molecular tracking, which identifies sequence reads that have been redundantly generated from an original parent molecule, and assigns the most likely identity of the base at each locus or position in the parent molecule. This significantly increases the specificity of detection by reducing noise arising from amplification and sequencing errors, which reduces the frequency of false positives.

[0190] Methods of the present disclosure can be used to detect genetic variations in initially starting genetic material that is non- uniquely tagged (e.g., rare DNA) with at least 99%, 99.9%, 99.99%, 99.999%, 99.9999%, or 99.99999% specificity at a concentration of less than 5%, 1%, 0.5%, 0.1%, 0.05%, or 0.01%. Sequence reads of the tagged polynucleotides can be subsequently tracked to produce a consensus sequence of the polynucleotides with an error rate of no more than 2%, 1%, 0.1%, or 0.01%.

[0191] Copy number variation determination can involve determining a quantitative measure of polynucleotides mapping to a genetic locus (e.g., EGFR gene or KRAS gene) in a sample. The quantitative measure can be a number. After the total number of polynucleotides mapping to the locus is determined, the number can be used in standard methods of determining copy number variation at the locus. The quantitative measure can be normalized relative to a standard. In one method, the quantitative measure at the test locus can be normalized relative to a quantitative measure of polynucleotides mapping to a control locus in the genome (e.g., a gene with a known copy number). In another method, the quantitative measure can be compared to the amount of nucleic acid in the original sample. For example, the quantitative measure can be compared to the expected measure for a diploid. In another method, the quantitative measure can be normalized relative to a measure from a control sample, and the normalized measures at different loci can be compared. In another method, quantification involves quantifying parent or original molecules in the sample that map to the locus, rather than the number of sequence reads. The copy number variation can be amplification or deletion or truncation of a gene. The amplification can be 3, 4, 5, 6, 7, 8, 9, 10, or 10 or more copies of the gene. The deletion or truncation can be 0 or 1 copy of the gene.

[0192] Examples of methods for detecting copy number changes can include an array. The array can comprise more than one capture probe. The capture probe can be an oligonucleotide bound to the surface of the array. The capture probe can bind to at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 genes listed in Table 1. DNA derived from the subject can be labeled (e.g., with a fluorophore) prior to hybridization for detection.

[0193] In other examples, a gene of interest can be amplified using primers that recognize the gene of interest. The primers can hybridize to the gene upstream and / or downstream of the particular region of interest (e.g., upstream of the mutation site). A detection probe can hybridize to the amplification product. The detection probe can specifically hybridize to either the wild-type sequence or to the mutant / variant sequence. The detection probe can be labeled with a detectable label (e.g., with a fluorophore). Detection of the wild-type or mutant sequence can be performed by detecting the detectable label (e.g., fluorescence imaging). In examples of copy number variation, the gene of interest can be compared to a reference gene. Differences in copy number between the gene of interest and the reference gene can indicate amplification or deletion / truncation of the gene. Examples of platforms suitable for performing the methods described herein include digital PCR platforms such as, for example, Fluidigm digital array.

[0194] Example 1 The following examples are given to illustrate various embodiments of the application and are not meant to limit the application in any way. The examples of the application and the methods described herein are presently representative of preferred embodiments, are exemplary, and are not intended as limitations on the scope of the application. Changes therein and other uses will suggest themselves to those skilled in the art that fall within the spirit of the application defined by the scope of the claims.

[0195] Therapeutic efficacy involving promoter methylation: Example 2 In one example, it was of interest to understand whether or not talazoparib would sensitize advanced TNBC or BRCA1 / 2 mutant breast cancer to PARP inhibition with talazoparib. In the phase I study, 3 patients were classified as partial remission (PR) and 5 patients were classified as stable disease (SD). Tissue genomics was used and was unable to explain the confounding of progressing patients versus non-progressing patients. Importantly, there are alternative mechanisms of gene inactivation that can explain this difference through promoter methylation.

[0196] PI3K inhibitors are thought to reduce the nuclear pool (potentially leading to replication errors / replication fork stalling / DNA repair increases). This would increase the repair mechanism and dependence on PARP. PI3K inhibitors are also thought to hinder the interaction of PI3K with the HR complex. This would increase the dependence of DNA repair on PARP.

[0197] Here, the BRCA promoter methylation status of these patients was evaluated by analyzing TNBC samples and clinical outcome data, including the addition of promoter methylation data to calculate the HRD score.

[0198] MLH1 promoter methylation test: Example 3In another example, patients at risk for colorectal cancer or a Lynch syndrome associated tumor type in a genetic / familial form can be identified. MLH1 promoter hypermethylation (and often BRAF V600E positive) is associated with the sporadic form of CRC.

[0199] BRCA1 promoter methylation test: Example 4 In another example, variant types that are associated with homologous recombination repair deficiency related tumor types (brca, ovca, panc, prca) can be included. Many HRD related tumors exhibit single copy loss or rearrangement of BRCA1 without a second hit. A fraction of these cases can have promoter hypermethylation of the remaining allele, leading to biallelic loss of BRCA1.

[0200] MGMT promoter methylation test: Example 5 In another example, promoter hypermethylation associated with benefit when treated with certain types of chemotherapy can be incorporated.

[0201] Example 6 Allelic expression and promoter methylation: The techniques described above for quantifying the level of promoter methylation allow for determination of the methylation level in an allele specific manner.

[0202] Using the methods and compositions described herein, a sample is characterized by the methylation level of one or more taxons, including determination of a quantitative measure. Determination of the quantitative measure can include combining a plurality of nucleic acids derived from at least one of blood or tissue of a subject with a solution comprising an amount of a methyl binding domain (MBD) protein to produce a nucleic acid-MBD protein solution; and subjecting the nucleic acid-MBD protein solution to a plurality of washes with a salt solution to produce a number of nucleic acid fractions. In some cases, an individual nucleic acid fraction has a threshold number of methylated cytosines in a region of the plurality of nucleic acids having at least a threshold cytosine-guanine content. Thereafter, a wash of the plurality of washes is performed with a solution having a concentration of sodium chloride (NaCl) and produces a nucleic acid fraction of the number of nucleic acid fractions having a range of binding strengths to the MBD protein.

[0203] One can determine that a first nucleic acid fraction is associated with a first partition of a plurality of nucleic acid partitions, the first partition corresponding to a first range of binding strength to the MBD protein; attach a first molecular barcode to a nucleic acid of the first nucleic acid fraction, the first molecular barcode being included in a first set of molecular barcodes associated with the first partition, and subsequently determine that a second nucleic acid fraction is associated with a second partition of the plurality of nucleic acid partitions, the second partition corresponding to a second range of binding energy to the MBD protein different from the first range of binding strength to the MBD protein; and subsequently attach a second molecular barcode to a nucleic acid of the second nucleic acid fraction, the second molecular barcode being included in a second set of molecular barcodes associated with the second partition.

[0204] Using the above methods and compositions, one can determine a ratio of the number of molecules that overlap with a classification region normalized by the total positive control molecules, wherein the molecules exhibit a threshold amount of methylated cytosine. In some cases, this quantitative measure is compared to a predetermined threshold to determine the methylation status of one or more classification regions. Further, in some cases, determining the ratio includes filtering molecules based on at least the threshold amount of methylated cytosine and / or determining a methylation level of one or more classification regions based on the number of methylated CpGs.

[0205] Typically, the importance of specific DNA methylation patterns for proper developmental gene expression is most clearly demonstrated in imprinted loci. While genes are generally expressed from both the maternal and paternal alleles, at imprinted loci, only the maternal or paternal allele is expressed. In some cases, this restriction can be limited to a particular tissue or time during development.

[0206] The methylation status of DNA around imprinted loci also displays patterns that are specific to each allele. The location of the differentially methylated domain or region (DMD or DMR) is variable, and the expressed allele can display both low and / or high methylation domains. Parental allele-specific methylation patterns can direct allele-specific expression. In the context of cancer, one example is the H19 / Igf2 and Rasgrf1 loci and their DMRs have enhancer-blocking activity and bind CTCF in a methylation-sensitive manner. CTCF binding to the unmethylated DMRs inhibits the enhancer-promoter interactions required for Igf2 and Rasgrf1 expression, and this block is relieved to allow expression when the DMRs are methylated and CTCF binding is prevented.

[0207] Example 7 Promoter methylation silencing, imprinting: The conventional understanding is that imprinted genes are "silent," and this form of monoallelic expression is derived from either the maternal or paternal allele. In cancer, copies of some silent imprinted genes can be reactivated, leading to expression of both alleles. Loss of monoallelic regulation is referred to as loss of imprinting (LOI), and, in addition, amplification of the active copy of imprinted genes is also observed in cancer cell lines without affecting the methylation of the silent copy

[19] . In such cases, imprinted genes can be expressed in two or more transcriptional sites instead of one. Thus, an increase in the number of transcriptional sites detected for an imprinted gene in a cell nucleus can be used as a potential cancer biomarker. Here, existing reports detecting nascent RNA or pre-mRNA in situ hybridization (ISH) methods targeting introns can be used to visualize and mark these transcriptional sites and applied to study transcriptional regulation of two imprinted genes.

[0208] Using the methods and compositions described herein, a methylation level of one or more classification regions is characterized for a sample, including determining a quantitative measure including determining a ratio of the number of molecules overlapping the classification region normalized by the total positive control molecules, wherein the molecules exhibit a threshold amount of methylated cytosines. In some cases, the quantitative measure is compared to a predetermined threshold to determine a methylation status of the one or more classification regions. Further, in some cases, determining the ratio includes filtering molecules based on at least the threshold amount of methylated cytosines and / or determining a methylation level of the one or more classification regions based on the number of methylated CpGs.

[0209] Example 8 Other forms of expression resulting from epigenetic allelic state, imprinting: As described, the conventional understanding is that imprinted genes that lead to LOI make cells susceptible to cell transformation and tumorigenesis through aberrant biallelic expression (e.g., the imprinted IGF2 locus is thought to promote tumorigenesis in colorectal cancer by suppressing apoptosis and lead to defects in hyperproliferation in lung, colon, and ovarian cancers, as well as LOI of other imprinted genes in different cancers such as H19, PEG3, MEST, and PLAGL1).

[0210] However, while LOI is associated with silencing of the normally active allele through downregulation of imprinted genes reported in cancer, it is unclear. For example, in esophageal cancer, LOI of IGF2 is specifically associated with downregulation of expression and improved survival. Also in prostate cancer, no increase in expression of IGF2 was found despite LOI. While LOI has a primary association in cancer, this piecemeal evidence suggests that the current paradigm of the role of LOI in cancer (i.e., growth and tumor promoting expression) needs additional evaluation.

[0211] The above methods allow for the determination of imprinting gene networks, where these genes are co-regulated. In parallel, copy number variations (CNVs) can be an important cause of imprinting dysregulation in cancer, and the foregoing methods and techniques provide a multi-modal detection of genomic and epigenomic features.

[0212] The above methods and techniques support systematic analysis of LOI or other forms of allelic expression that remain poorly characterized. While monoallelic expression is better understood, only a few regions in humans are well characterized, and due to the necessity of genotyping and the existing methods for detecting abnormal monoallelic expression of cancer at individual imprinting loci, the tissue-specific imprinting patterns evaluated are not understood. Moreover, the necessity of genotyping greatly hinders the practical applicability of existing high-throughput methods. The foregoing techniques allow for systematic profiling of (i) allelic expression including monoallelic expression / imprinting loci and (ii) their dysregulation and deregulation (e.g., LOI) in cancer.

[0213] Example 9 Epigenetic regulatory imbalance can also increase the plasticity of tumor cells, and epigenetic allelic expression is used to determine tumor heterogeneity: Given the apparent importance of epigenetic regulation, including allelic expression and its role in cancer pathogenesis, it is of interest to apply the above methods and compositions in the context of determining tumor heterogeneity. Various cancers (e.g., breast cancer) are highly complex heterogeneous diseases at the molecular level, forming tumor subpopulations with different phenotypic characteristics. Differences in DNA methylation patterns between different cell subpopulations can drive phenotypic changes, which are valuable for providing new insights into intra-tumor epigenetic heterogeneity in breast cancer. In some cases, manual observation of epigenetic allelic expression has been used to identify differential epialleles between tumor core and tumor periphery, and to characterize tumor subpopulations with different methylation patterns. Methods for calculating epiallelic imbalance can be based on Jensen-Shannon divergence, although as will be readily appreciated by those skilled in the art, various other methods for calculating variance can be used. The technique can identify contiguous CpGs (e.g., four contiguous CpGs covered by the same read as an epiallele). Given that the methylation state of a CpG is either methylated or unmethylated, an epiallele contains 16 possible methylation patterns. Divergence (e.g., entropy divergence) can be used to quantify the dissimilarity between methylation patterns of one or more samples.

[0214] Here, due to the high epigenetic heterogeneity, the methylation pattern of a tumor (e.g., core tumor) can be more disordered than the tumor periphery, and thus genes with higher epigenetic heterogeneity also have higher transcriptional heterogeneity. Using the methods and techniques described above, this can be systematically analyzed to assess the full picture of the epigenetic state within a tumor. We define four consecutive CpGs covered by the same read as an epiallele.

[0215] Example 10 Measuring epigenetic promoter methylation divergence, epiallelic diversity of drift, and epigenetic burden: In other cases, the compositional entropy equation (e.g., Methyclone) can be used to calculate loci with epigenetic allelic variation. Here, the epigenetic state of each locus involves cytosine methylation at four consecutive CpG dinucleotides, supporting the possible 16 CpG methylation patterns at these loci as epialleles. When comparing one or more samples, epigenetic drift in a locus can be considered significant when the proportion of epialleles at these sites experiences a statistically significant shift in their composition (calculated by delta Boltzmann entropy AS < Δ90).

[0216] The determination of epigenetic states per million loci can be used to normalize the variable coverage depth and measured number of loci per sample to determine the overall magnitude of epiallelic drift across the genome as a form of calculating epiallelic burden, similar to tumor mutational burden. Epigenetic drift can include the gain and / or loss of epialleles between two samples. Epiallelic and systematic epigenetic locus measurements can be determined using methlyome data from the foregoing methods and compositions.

[0217] In various embodiments, (e.g., em-SEQ, ERRBS) can be used to validate a subset of samples using orthogonal methlyome sequencing methods. This systematic analysis allows for the determination of tumor genetic and epigenetic heterogeneity to characterize independent, biologically distinct phenomena, each of which can have unique functional implications. The extent of epigenetic allelic burden can or can not include other factors such as age and other clinical parameters, somatic mutations affecting epigenetic modifying genes (e.g., DNMT3A, TET2, and IDH1 / 2), dominant epigenetic allelic behavior in a similar or different manner than genetic alleles during clonal evolution, and differences between the dynamics and patterns of genetic and epigenetic alleles during progression when longitudinally monitored at a series of time points.

[0218] In various cases, these measurements can be used for classification, including by using machine learning algorithms (e.g., support vector machines) and / or various databases to identify one or more of epiallele pattern dynamics and somatic mutation burden during disease progression. For example, diagnostic criteria can be divided into diseases with major epiallele diversity and low somatic mutation (e.g., epigenetically driven) and other diseases with lower epiallele diversity and higher mutation burden (e.g., genetically driven). The latter develops more and more epigenetic diversity as it progresses. Here, in both cases, the genetic clonal composition remains essentially stable, although cases of genetic clonal stability can also be identified. In cases where there is a lack of correlation between epigenetic instability and genetic instability or specific somatic mutations, alternative patterns of manifest heterogeneity in the diagnosis of patients can be evaluated as involving: one genetic and one epigenetic form of manifest.

[0219] ​ Measurement of epigenetic drift, allelic expression: Here, the ability to determine full-systematic biological gene activation / deactivation dependent on epigenetic allele status and associated clinical cancer risk, including methylation status of epialleles, quantification of methylation events across several CpGs, can be considered a form of haplotype definition (e.g., considering methylation status of individual CpGs within a sequence read and average methylation level of the sequence read itself).

[0220] Using next-generation sequencing (NGS)-based data with exemplary default values (minimum of 2 CpG sites, minimum average methylation beta value of CpGs of 0.5), the optional thresholding measurement defines a subset of epialleles of interest and is based on a minimum number and average methylation level of cytosines in various sequence contexts (e.g., CpG, CHG, or CHH). The thresholding parameters can be fully adjustable to target a desired epiallele population; site, maximum average methylation beta value of non-CpG sites of 0.1. Optional thresholding of sequence reads without thresholding includes calculation of methylation beta value at each genomic position as the ratio of number of methylated cytosines to total number of methylated and unmethylated cytosines: b = C / (C+T). In contrast, when read thresholding is performed (default mode of action), the methylation level at each genomic position, i.e., variant epiallele frequency (VEF), can be calculated as the ratio of number of methylated cytosines in read pairs passing the threshold (Ca) to total number of methylated and unmethylated cytosines in all read pairs: VEF = Ca / (C+T).

[0221] To adjust the range to include the level of extended genomic regions rather than individual bases, the VEF can equal the ratio of the number of read pairs passing the threshold (Na) to the total number of read pairs overlapping the region of interest (N): VEF = Na / N. This allows for the definition of a set of epialleles with similar methylation properties (i.e., a single methylation pattern) by thresholding, where the VEF effectively represents the frequency of the set of epialleles passing the threshold at the level of individual cytosines or extended genomic regions.

[0222] In either case, if the experimental setup allows for the determination of methylation at the level of each base, methylation beta values from the default reporting mode with read thresholding and VEF values can be generated from any number of BAM files without prior assumptions. Both values effectively represent the methylation level at each genomic position and can therefore be further used directly as input for other bioinformatics tools, including but not limited to differential methylation analysis tools.

Claims

1. A method comprising: detecting methylation in one or more promoter regions of at least one of a plurality of genes; and generating a plurality of methylation calls to quantify methylation of the one or more promoter regions.

2. The method of claim 1, comprising obtaining a sample.

3. The method of claim 1, comprising having a sample obtained.

4. The method of claim 1, comprising processing the quantity of methylation of the one or more promoter regions to characterize a sample.

5. The method of claim 1, wherein characterizing the sample comprises HRD, cancer- derived promoter methylation, familial form of colorectal cancer, or Lynch syndrome tumor type.

6. The method of claim 1, wherein the promoter comprises a region 5 kb upstream of a transcription start site (TSS), wherein the 5 kb region is further refined using one or more of the following: custom set regions, methylation peaks found in clinical samples, and exclusion of peaks found in normal samples.

7. The method of claim 6, wherein the TSS is defined at the transcript level.

8. The method of claim 6, wherein the TSS is defined at the gene level.

9. The method of claim 1, comprising determining a ratio of the number of molecules overlapping a target region normalized by the total positive control molecules.

10. The method of claim 9, wherein determining the ratio comprises filtering molecules based on at least the number of overlapping CpGs.

11. The method of claim 1, wherein the quantification of methylation of the one or more promoter regions is based on the number of methylated CpGs.

12. The method of claim 1, comprising refining the one or more promoter regions based on at least literature annotation, co-methylation peak position, and / or public datasets.

13. The method of claim 1, wherein the genes comprise tumor suppressor genes, HRR genes, and IO genes.

14. The method of claim 13, wherein the HRR genes comprise at least BRCA1 and BRCA2.

15. The method of claim 1, comprising comparing to a minimum methylation threshold derived from a training sample population.

16. The method of claim 15, wherein the training samples comprise cancer- free samples.

17. The method of claim 15, wherein the minimum methylation threshold for calling comprises at least one of: a minimum molecule count of 1-100, and a minimum methylation score per gene is a maximum of: 95 normal quantile + 8X105 or median + 5*median absolute deviation.

18. The method of claim 1, wherein quantifying methylation of the one or more promoter regions predicts therapy response.

19. The method of claim 18, wherein quantifying methylation of the one or more promoter regions is combined with MSI-H status. ​ 20. The method of claim 18, wherein the therapy comprises one or more of an immune checkpoint inhibitor, a poly (ADP-ribose) polymerase (PARP) inhibitor, a kinase inhibitor or an aromatase inhibitor, or a PI3K and mTOR inhibitor.

21. The method of claim 20, wherein the immune checkpoint inhibitor is pembrolizumab.

22. The method of claim 20, wherein the poly (ADP-ribose) polymerase (PARP) inhibitor is olaparib or talazoparib.

23. The method of claim 20, wherein the therapy is a combination of a PI3K and mTOR inhibitor and a poly (ADP-ribose) polymerase (PARP) inhibitor.

24. The method of claim 23, wherein the PI3K and mTOR inhibitor is gedatolisib and the poly (ADP-ribose) polymerase (PARP) inhibitor is talazoparib.

25. A method comprising: determining a promoter region of at least one of a plurality of genes, each gene obtained from a plurality of samples; determining a methylation score for the promoter region to produce a plurality of methylation calls and / or a quantification of promoter methylation; processing the plurality of methylation calls to produce a prediction that a test sample exhibits a genomic state.

26. A method comprising: obtaining sequencing reads derived from a sample of a subject by a computing system having one or more hardware processors and memory, determining one or more classification regions corresponding to a plurality of genes included in the sample; and determining a methylation level of the one or more classification regions by producing a quantitative measure of the sequencing reads derived from the sample of the subject.

27. The method of claim 26, comprising obtaining a sample.

28. The method of claim 26, comprising having a sample obtained.

29. The method of claim 26, comprising processing the methylation level of the one or more classification regions to characterize the sample.

30. The method of claim 29, wherein characterizing the sample comprises determining a HRD status, a promoter methylation associated with cancer.

31. The method of any of the preceding claims, wherein the quantitative measure comprises determining a ratio of a number of molecules overlapping a classification region normalized by a total positive control molecule, wherein the molecules exhibit a threshold amount of methylated cytosine.

32. The method of any of the preceding claims, wherein the quantitative measure is compared to a predetermined threshold to determine a methylation state of the one or more classification regions.

33. The method of any of the preceding claims, wherein determining the ratio comprises filtering molecules based on at least a threshold amount of methylated cytosine.

34. The method of any of the preceding claims, wherein the determining the methylation level of the one or more classification regions is based on a number of methylated CpGs.

35. The method of any of the preceding claims, wherein the classification region comprises a promoter region.

36. The method of any of the preceding claims, wherein the one or more classification zones individually correspond to a genomic region in which a cytosine methylation rate in a nucleic acid genomic region derived from cells obtained from a subject in which cancer is present is different from a cytosine methylation rate in a nucleic acid genomic region derived from cells obtained from a subject in which cancer is not present.

37. The method of any of the preceding claims, wherein the plurality of samples and the additional sample comprise cell-free nucleic acids.

38. The method of any of the preceding claims, comprising: training, by the computing system, a training process using training data to generate a model, wherein the training process comprises: determining, by the computing system, one or more additional weights for a single sample included in the training data based on an indication of cancer for the single sample being within a threshold confidence level.

39. The method of any of the preceding claims, wherein the indication of cancer for a single sample is outside the threshold confidence level, and the method comprises: applying, by the computing system, a penalty to a weight for the single sample during the training process.

40. The method of any of the preceding claims, comprising: performing, by the computing system and using one or more machine learning algorithms, one or more first iterations of the training process for the model using a portion of the training data; and generating, by the computing system, first output data for the model based on the one or more first iterations of the training process, the first output data corresponding to one or more first additional indications of presence of cancer in a first single subject of the plurality of subjects, the first single subject corresponding to the portion of the training data.

41. The method of any of the preceding claims, comprising: combining, by the computing system, the first output data and the training data to produce additional training data; performing, by the computing system, one or more second iterations of the training process for the model using a portion of the additional training data; and generating, by the computing system, second output data for the model based on the one or more second iterations of the training process, the second output data indicating one or more second additional indications of presence of cancer in a second single subject of the plurality of subjects, the second single subject corresponding to the portion of the additional training data.

42. The method of any of the preceding claims, wherein the weight for an individual classification zone of the plurality of classification zones is determined based on the first output data and the second output data.

43. The method of any of the preceding claims, comprising: determining, by the computing system, that a number of indications of presence of cancer determined during one or more iterations of the training process is at least a threshold for one or more samples included in the training data; and ​ ​ ​ determining, by the computing system, that the modification of the one or more weights of the model is not modified or is modified by a minimum amount.

44. The method of any of the preceding claims, comprising: determining, by the computing system, that a further number of indications of the presence of cancer determined during the one or more iterations of the training process is less than the threshold for one or more further samples included in the training data; and determining, by the computing system, modification of one or more further weights of the model to be modified by an amount that exceeds the minimum amount.

45. The method of any of the preceding claims, comprising: combining a plurality of nucleic acids derived from at least one of blood or tissue of a subject with a solution comprising an amount of a methyl binding domain (MBD) protein to produce a nucleic acid-MBD protein solution; and washing the nucleic acid-MBD protein solution with a salt solution a plurality of times to produce a number of nucleic acid fractions, individual nucleic acid fractions having a threshold number of methylated cytosines in regions of the plurality of nucleic acids having at least the threshold cytosine-guanine content.

46. The method of claim 20, wherein a wash of the plurality of washes is performed with a solution having a concentration of sodium chloride (NaCl) and produces nucleic acid fractions of the number of nucleic acid fractions having a range of binding strengths to MBD protein.

47. The method of any of the preceding claims, comprising: determining a first nucleic acid fraction is associated with a first partition of a plurality of nucleic acid partitions, the first partition corresponding to a first range of binding strengths to MBD protein; attaching a first molecular barcode to nucleic acids of the first nucleic acid fraction, the first molecular barcode being included in a first set of molecular barcodes associated with the first partition; determining a second nucleic acid fraction is associated with a second partition of the plurality of nucleic acid partitions, the second partition corresponding to a second range of binding strengths to MBD protein different from the first range of binding strengths to MBD protein; and attaching a second molecular barcode to nucleic acids of the second nucleic acid fraction, the second molecular barcode being included in a second set of molecular barcodes associated with the second partition.

48. The method of any of the preceding claims, comprising: combining at least a portion of the number of nucleic acid fractions with an amount of a restriction enzyme that cleaves molecules having one or more unmethylated cytosines to produce at least a portion of the plurality of samples for use in producing the sequencing reads, wherein the threshold amount of methylated cytosines corresponds to a minimum frequency of methylated cytosines within regions having at least the threshold cytosine-guanine content.

49. A method, comprising: obtaining, by a computing system having one or more hardware processors and memory, sequencing reads of a sample derived from a subject, determining one or more taxons corresponding to a plurality of genes included in the sample, determining a methylation level of the one or more classification bins by generating a quantitative measure comprising a ratio of a number of molecules overlapping a classification bin that exhibit a threshold amount of methylated cytosines normalized by a total positive control molecules, wherein the molecules are normalized by a total positive control molecules; and comparing the quantitative measure to a predetermined threshold to call a methylation state of the one or more classification bins.

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