Copy number allelic strand determination

Abstract

Methods, systems, and computer-readable media for determining allele-specific copy number (ASCN) and mutant allele dosage in tumor cells using Next-generation sequencing (NGS) genomic data, and broad methylation patterns (promoter regions and selected DMRs), and for integrating both modalities into a robust estimate of mutant allele dosage (the ratio of mutant allele copies to total tumor allele copies), with clinical reporting and quality control.

Description

COPY NUMBER ALLELIC STRAND DETERMINATIONCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. provisional patent application no. 63 / 750,923 filed January 29, 2025, which is incorporated by reference herein in its entirety.FIELD OF THE INVENTION

[0002] Methods, systems, and computer-readable media for determining allele-specific copy number (ASCN) and mutant allele dosage in tumor cells.BACKGROUND

[0003] For many oncogenic activating mutations (e.g., KRAS, EGFR, ERBB2, NRAS, PIK3CA), the functional impact depends not only on presence / clonality but on the allele-specific copy context — whether the mutant allele co-exists with a wild-type allele, is present on amplified copies, or is present in the context of loss-of-heterozygosity (LoH). Traditional VAF (variant allele fraction) and naive clonality estimates conflate tumor fraction (TF), local ploidy, and allele-specific copy changes, causing ambiguous biological interpretation. This is especially problematic for plasma cfDNA where tumor fraction can be low. Existing CNV callers typically estimate total copy number but do not robustly attribute which allele carries the somatic mutation (mutant on amplified or not), nor do they exploit genome-wide methylation patterns that can provide orthogonal and sensitive readout in low-TF scenarios. There is a great need in the art for computing a robust mutant dosage metric capable of integrating genomic ASCN, tumor fraction, and methylation biology.

[0004] Described herein are methods and compositions for copy number allelic strand determination. In one example, a combined model that (a) computes ASCN (tumor_total_CN and minor_allele_CN), (b) integrates methylation signatures (15K promoters + DMRs) that are sensitive to allele context and mutant dosage (notably for KRAS), and (c) computes a robust mutant allele dosage (D = M / (M+W)), addresses sensitivity at low TFs and yields a biologically meaningful metric tied to prognosis.SUMMARY OF THE INVENTION

[0005] Described herein is a method for determining allele-specific copy number and mutant allele dosage in a biological sample, including: receiving a plurality of sequencing reads, each including at least one germline heterozygous single nucleotide polymorphism (SNP)and one or more somatic variant calls for a genomic locus; obtaining a tumor fraction estimate;

[0006] generating a matrix including each candidate allele-specific configuration

[0007] and each candidate purity and ploidy setting; computing an expected SNP minor allele fraction (MAF) and coverage likelihood; computing a combined likelihood for each of a candidateconfiguration; selecting the candidate allele-specific configuration based on maximizing the combined likelihood; determining at least one tumor specific total copy number and tumor specific minor allele copy number; and estimating mutant allele dosage based on one or more of: the selected allele specific configuration, the tumor fraction estimate, SNP MAF, and coverage likelihood. In various embodiments, the expected SNP MAF and expected VAR are determined using equations 4 and 5, respectively.

[0008] Further described herein is a method for predicting allele-specific copy number and mutant allele dosage using methylation data, including: receiving tumor-adjusted methylation measurements across a plurality of regions including genomic promoters and differentially methylated regions; generating features normalized by tumor methylation fraction and / or control gene signals; applying a predictive model, optionally a trained predictive model, to the features to estimate allele-specific copy number, optionally a probability of allele-specific states; estimating mutant allele dosage by combining the estimate allele-specific copy number, and optionally observed somatic variant allele fraction; and generating outputting a dosage value.

[0009] Further described herein is a method, including determining one or more of: genomic allelic strand copy number (ASCN), methylation predictions, somatic variant allele fraction (VAF), and tumor fraction for a plurality of sequence reads; computing mutant allele dosage based on the one or more determinations for the plurality of sequence reads; assigning one or more mutations in the plurality of sequence reads as amplified, non-amplified, or indeterminate allele scenario; and generating a dosage estimate.

[0010] In other embodiments, the method includes computation of a genomic likelihood ASCN and / or methylation, optionally for to resolving ambiguity or to evaluating otherwise non-evaluable tumor (TF) cases. In other embodiments, the method includes a methylation predictor that yields candidate ASCN and dosage, used to inform genomic likelihood search (useful at very low TF). In other embodiments, the method includes determination of a full Bayesian posterior estimate, optionally based on genomic, epigenomic methylation and / or priors by tumor type. In other embodiments, the method interrogates a locus for one or more candidate genes of interest, optionally including one or more of: KRAS, EGFR, ERBB2, and PIK3CA. In other embodiments, the method includes g reporting a dosage matrix for one or more candidate genes of interest. In other embodiments, the sequence reads are derived from tissue DNA and / or plasma cfDNA. In other embodiments, the method includes use of data trained on matched tissue / plasma pairs. In other embodiments, the method includes training sequence reads derived from tissue, and / or liquid biopsy. In other embodiments, the sequence reads are generated from sequencing nucleic acid molecules derived from a sample obtained from a subject and generating testing sequence data. In other embodiments, the nucleic acid molecules comprise cell-free DNA. In other embodiments, the sample is at least one of blood, serum, plasma or tissue. In other embodiments, the method includes determination of treatment for the subject. In other embodiment, the method includes calculation ofone or more of Equation 1, 2, 3, 4, 5, 6, 7, and / or 8. In various embodiments, a mixture model of tumor and normal samples can be determined using equation 1. In various embodiments, the mutant dosage is determined using equation 2. In various embodiments, the expected SNP MAF and expected VAR are determined using equations 3, 4 and 5, respectively. In other embodiment, observed VAR for a somatic mutation with a particular number of copies (e.g., M) is determined using equations 6 and 7. In other embodiments, an approximation of cfDNA variant allele fraction (VAF) by equation 1, can establish the mutant allegle dosage and tumor total copy number at a particular locus, at a particular tumor fraction, as determined in equation 8.

[0011] Further described herein is a system configured to perform the method of any of the preceding methods.

[0012] Further described herein is computer readable medium including the method of any of the preceding methods.BRIEF DESCRIPTION OF THE FIGURES

[0013] Figure 1. Allele Specific Copy Number - Methodology

[0014] Figure 2. Allele Specific Copy Number - Methodology

[0015] Figure 3. Deletion caller models tumor purity (fraction), ploidy CN of segments and Germline SNV VAF. Germline SNV backbone was specifically designed to capture het SNV for capturing allele imbalance

[0016] Figure 4. Deletion caller models tumor purity (fraction), ploidy CN of segments and Germline SNV VAF. Depiction of a Allele Specific Copy Number Model

[0017] Figure 5. The predicted total KRAS copy number is the starting point and model expected allele fraction: + / - 1 copy (dashed lines). Grey line - Observed allele fraction, Green line - Predicted allele fraction when mutation is only on the amplified allele, Blue line - Predicted allele fraction when mutation is only on the non-amplified allele, Orange line - Predicted allele fraction when mutation is both on the amplified and non-amplified allele

[0018] Figure 6. Example of KRAS G12C on amplified allele. Somatic variants are colored green, germline grey.

[0019] Figure 7. Example of KRAS G12C on amplified allele. Based on the line intersections between green-grey and orange-grey, we can conclude that the mutation is either on amplified allele or both amplified and non-amplified allele. Leveraging tumor fraction (0.309319), we conclude mutation is on amplified allele.

[0020] Figure 8. Example of KRAS G12C on both allele. Somatic variants are colored green, germline grey

[0021] Figure 9. Example of KRAS G12C on both allele. Based on the line intersections between green-grey and orange-grey, we can conclude that the mutation is either on amplified allele or bothamplified and non-amplified allele. Leveraging tumor fraction (0.523362), the mutation is on both allele.

[0022] Figure 10. Key Definitions and computational analysis steps, including Equations 1-3.

[0023] Figure 11. Expected minor allele fraction for allele state (a / b) and tumor fraction p, including Equations 4 and 5.

[0024] Figure 12. Mutant allele dosage calculation (integration), which include computation based on Equations 2, 6, and 7 as described.

[0025] Figure 13. ASCN / mutant dosage model flow — inputs, preprocessing, feature engineering, likelihood grid, model integration, and outputs. Legend of diagram symbols / lines. Rounded box = input / output file. Rectangular box = processing module or transform. Colored tracks: Blue = genomic / coverage / CNV evidence; Orange = methylation / epigenomic evidence and TF modeling. Dashed arrow = optional or conditional data path (e.g., methylation evidence only used if region peak > threshold). Dotted box = likelihood grid / joint inference core (combines both tracks). Small document icon / table = final report / DB upload.

[0026] Figure 14. Expected cfDNA variant allele fraction (VAF) for KRAS as a function of tumor fraction (TF) under varying mutant dosage and tumor copy-number states.Curves show the modeled observed cfDNA VAF (%) (y-axis) versus tumor fraction p (%) (x-axis) for combinations of mutant dosage D and tumor total copy number T. Dosage DDD is defined as the fraction of tumor copies at the locus that carry the mutant allele (D=M / TD=M / TD=M / T, range 0-1); T=M+WT=M+WT=M+W is the tumor total copy number. Curves were generated using the relation

[0027] Figure 15. Additional key definitions and computational analysis steps, including Equations 1 and 8.

[0028] Figure 16. Promoter DMR enrichment by allelic context (amplified vs non-amplified).Scatter plot of enrichment statistics for individual methylation DMRs comparing their frequency on amplified alleles versus non-amplified alleles, x-axis: log2 odds ratio (Variant on Amplified allele Variant on NON-Amplified allele). Positive values (to the right) indicate enrichment on amplified alleles; negative values (to the left) indicate enrichment on non-amplified alleles, y-axis: -logio(FDR) where FDR is the multiple-test-corrected p-value for the enrichment test; larger values indicate stronger statistical significance. Orange: methylation DMRs significantly enriched in the amplified-allele group (right side & above the significance threshold). Green: methylation DMRs significantly enriched in the non-amplified-allele group (left side & above the significance threshold). Blue: methylation DMRs not meeting the combined effect and significance criteria (no strong, significant enrichment in either direction). The horizontal dashed line marks the FDR significance cutoff (methylation DMRs above this line meet the FDR threshold). The vertical dashed lines mark the log2-odds ratio cutoffs used to define meaningful enrichment (methylation DMRs beyond these lines have large effect sizes in favor of one allelic context). Overview — The diagram illustrates the end-to-end analytic flow used to (a) predict allele-specific copy number (ASCN) and mutant dosage fromcombined genomic and methylation data, and (b) integrate that ASCN / dosage evidence into downstream predictive models. Boxes represent processing modules, rounded boxes represent input or output fries, arrows show data flow, colored tracks separate the genomic (blue) and epigenomic / methylation (orange) paths, and dashed arrows indicate optional or secondary data paths.

[0029] Figure 17a. and Figure 17b. Kaplan-Meier survival by allele amplification status for KRAS mutations. Kaplan-Meier estimates of survival probability (y-axis) overtime in days since index (x-axis) stratified by whether the oncogenic variant was observed on a non-amplified allele (blue line; “variant in non-amplified allele”) or on an amplified allele (orange line; “variant in amplified allele”). Shaded bands show the pointwise 95% confidence intervals. Survival functions were compared with a two-sided log -rank test (log -rank), indicating a highly significant difference between groups. Curves are right-censored; censoring ticks and numbers-at-risk are not shown on this panel. Patients with the variant on an amplified allele have substantially lower survival probability compared. Figure 17a. Kaplan-Meier Curve for Time to Overall Survival (OS). Figure 17b. Kaplan-Meier Curve for Time to Treatment Discontinuation(TTD).

[0030] Figure 18a. and Figure 18b. Kaplan-Meier survival by KRAS copy-number / mutation state. Kaplan-Meier estimates of overall survival (y-axis) plotted against days since index (x-axis), stratified by KRAS copy-number and mutation status: KRAS amplification with KRAS mutation (blue; n = 685), KRAS LOH with KRAS mutation (orange; n = 137), and KRAS amp no cnv (green; n = 432). Shaded bands show pointwise 95% confidence intervals. Group survival functions were compared with a global two-sided log-rank test (global log-rank p = 8.50x I05)- indicating a statistically significant difference in survival across the three groups. A numbers-at-risk table (and counts of censored observations and events) is shown below the plot to indicate sample attrition over time. Curves are right-censored (censoring ticks shown on the lines). In this cohort, patients in the KRAS LOH + KRAS-mutation group exhibit the poorest survival, while the KRAS amp no cnv group shows the highest survival probability. Figure 18a. Kaplan-Meier Curve for Time to Overall Survival (OS). Figure 18b. Kaplan-Meier Curve for Time to Treatment Discontinuation(TTD).

[0031] Figure 19a. and Figure 19b. Kaplan-Meier overall survival by allelic context of the somatic variant. Kaplan-Meier estimates of survival probability (y-axis) plotted against time since index (days, x-axis) for three groups defined by the allelic location of a tumor somatic variant: variant on an amplified allele (blue, n = 101), variant on both amplified and non-amplified alleles (green, n = 41), and variant on a non-amplified allele (orange, n = 73). The table beneath the plot shows numbers at risk, censor counts and events at selected time points. Curves are right-censored (censor ticks shown) and groups were compared with a global two-sided log -rank test (p = 8.995 x I05)- indicating a statistically significant difference in survival across groups. In this cohort the poorest survival is observed for patients with the variant on an amplified allele, while patients with the variant on a nonamplified allele have comparatively better survival. Plot 6a is for OS and 6b is for rwTTD. Figure19a. Kaplan-Meier Curve for Time to Overall Survival (OS). Figure 19b. Kaplan-Meier Curve for Time to Treatment Discontinuation (TTD).DETAILED DESCRIPTION

[0032] While various embodiments of the disclosure have been shown and described herein, those skilled in the art will understand that such embodiments are provided by way of example only.Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the disclosure. It should be understood that various alternatives to the embodiments of the disclosure described herein may be employed.

[0033] The term “about” and its grammatical equivalents in relation to a reference numerical value can include a range of values up to plus or minus 10% from that value. For example, the amount “about 10” can include amounts from 9 to 11. The term “about” in relation to a reference numerical value can include a range of values plus or minus 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, or 1% from that value.

[0034] The term “at least” and its grammatical equivalents in relation to a reference numerical value can include the reference numerical value and greater than that value. For example, the amount “at least 10” can include the value 10 and any numerical value above 10, such as 11, 100, and 1,000.

[0035] The term “at most” and its grammatical equivalents in relation to a reference numerical value can include the reference numerical value and less than that value. For example, the amount “at most 10” can include the value 10 and any numerical value under 10, such as 9, 8, 5, 1, 0.5, and 0.1.

[0036] As used herein the singular forms “a”, “an”, and “the” can include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a cell” can include a plurality of such cells and reference to “the culture” can include reference to one or more cultures and equivalents thereof known to those skilled in the art, and so forth. All technical and scientific terms used herein can have the same meaning as commonly understood to one of ordinary skill in the art to which this disclosure belongs unless clearly indicated otherwise.

[0037] Current approaches are to omit testing both genomic and epigenomic attributes of the patient sample or to perform multiple tests separately. Omitting genomic or epigenomic information can result in prescription of cancer therapies that could be known to be ineffective or withholding cancer therapies that could be known to be effective, had both genomic and epigenomic information been available. Cancer can be indicated by epigenetic variations, such as methylation. Examples of methylation changes in cancer include local gains of DNA methylation in the 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 an aberrant loss of transcriptional capacity of involved genes and occurs at least as frequently as point mutations and deletions as a cause of altered gene expression. DNA methylation profding can be used to detectregions with different extents of methylation (“differentially methylated regions” or “DMRs”) of the genome that are altered during development or that are perturbed by disease, for example, cancer or any cancer-associated disease. The genome of cancer cells harbor imbalance in the above DNA methylation patterns, and therefore in functional packaging of the DNA. The abnormalities of chromatin organization are therefore coupled with methylation changes and may contribute to enhanced cancer profding when analyzed jointly. Combining MBD-partitioning with fragmentomic data, such as fragment mapped starts and stops positions (correlated with nucleosome positions) , fragment length and associated nucleosome occupancy, can be used for chromatin structure analysis in hypermethylation studies with the aim to improve biomarker detection rate.

[0038] In various embodiments described herein, it is readily ascertain that real-world time to treatment discontinuation (rwTTD) is in relation to the time from therapy start to end-of-treatment (end date - start date + 1), real-world time to next treatment (rwTTNT) is in relation to the time from therapy start to initiation of the subsequent systemic therapy (proxy for progression requiring next line) and overall survival (OS) is in relation to time from therapy start to death.

[0039] Described herein are methods and compositions related to copy number allelic strand determination, which is achieved in a variety of embodiments, including the technical contributions which may be used individually or in combination with each other.:Likelihood-based genomic ASCN caller — computes allele-specific copy number (a, b per tumor cell) and associated likelihoods by modeling heterozygous SNP MAFs and locus coverage across candidate purity / ploidy grid; provides confidence metrics and flags for ambiguous cases.Methylation-based ASCN and dosage predictor — supervised models trained on a ~15K promoter panel and selected DMRs to predict allele-specific states and / or numeric tumor total CN and minor allele CN, and to provide a biological readout of mutant dosage (especially for KRAS) when genomic signal is weak.Joint integration & dosage inference — combines genomic likelihoods and methylation-derived probabilities in a Bayesian or weighted ensemble to derive a posterior over ASCN and mutant copy number M, then computes mutant dosage D = M / (M+W). The method resolves scenario ambiguity (mutation on amplified vs nonamplified allele) and reports numeric dosage, categories (e.g., low / mid / high), confidence intervals, and reporting flags.Practical, clinical reporting — suppression / flagging rules for low TF, not-evaluable calls, LDT vs RUO handling, and appendage of provenance (genomic vs methylationevidence) and confidence.Mathematical / algorithmic framework — explicit mixing model for observed cfDNA VAF as a function of tumor fraction p, mutant copies M, and tumor total copy number T (M+W), and an optimization / likelihood approach to estimate M and T.Applications and clinical utility — Dosage-aware biomarkers for prognosis (rwTTD, rwTTNT, OS), therapy selection, and trials. It is readily appreciated by one of ordinary skill that the aforementioned provides a series of advantages and improvements as technical contributions over existing methods in the technical field, this includes:Allele resolution - Provides allele-specific CN calls (a,b) rather than only total CN, enabling precise dosage inference.Low TF sensitivity - Methylation models leveraging ~15K promoters and DMRs provide orthogonal sensitivity for ASCN and dosage estimation when genomic signal is weak.Biological readout of dosage - Methylation patterns (especially around promoters and pathway DMRs) capture downstream effects of mutant allele dominance (notably KRAS), enabling a biologically-informed dosage estimate.Clinical utility - Mutant dosage, as produced here, better predicts functional impact and can inform therapy selection relative to VAF alone.

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

[0041] A characteristic of nucleic acid molecules may be a modification, which may include various chemical or protein modifications (i.e. epigenetic modifications). Non-limiting examples of chemical modification may include, but are not limited to, covalent DNA modifications, including DNA methylation. In some embodiments, DNA methylation includes addition of a methyl group to a cytosine at a CpG site (a cytosine followed by a guanine in a nucleic acid sequence). In some embodiments, DNA methylation includes addition of a methyl group to adenine, such as in N6-methyladenine. In some embodiments, DNA methylation is 5 -methylation (modification of the 5th carbon of the 6 carbon ring of cytosine). In some embodiments, 5 -methylation includes addition of amethyl group to the 5C position of the cytosine to create 5 -methylcytosine (m5c). In some embodiments, methylation includes a derivative of m5c. Derivatives of m5c include, but are not limited to, 5 -hydroxymethylcytosine (5-hmC), 5 -formylcytosine (5-fC), and 5-caryboxylcytosine (5-caC). In some embodiments, DNA methylation is 3C methylation (modification of the 3rd carbon of the 6 carbon ring of cytosine). In some embodiments, 3C methylation includes addition of a methyl group to the 3C position of the cytosine to generate 3 -methylcytosine (3mC). Other examples include N6-methyladenine or glycosylation. DNA methylation includes addition of methyl groups to DNA (e.g. CpG) and can change the expression of methylated DNA region.. Methylation can also occur at non CpG sites, for example, methylation can occur at a CpA, CpT, or CpC site. DNA methylation can change the activity of methylated DNA region. For example, when DNA in a promoter region is methylated, transcription of the gene may be repressed. DNA methylation is critical for normal development and abnormality in methylation may disrupt epigenetic regulation. The disruption, e.g., repression, in epigenetic regulation may cause diseases, such as cancer. Promoter methylation in DNA may be indicative of cancer.

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

[0043] The CpG dinucleotide is underrepresented in the normal human genome, with the majority of CpG dinucleotide sequences being transcriptionally inert (e.g. DNA heterochromatic regions in pericentromeric parts of the chromosome and in repeat elements) and methylated. However, many CpG islands are protected from such methylation especially around transcription start sites (TSS).

[0044] Protein modifications include binding to components of chromatin, particularly histones including modified forms thereof, and binding to other proteins, such as proteins involved in replication or transcription. The disclosure provides methods of processing and analyzing nucleic acids with different extents of modification, such that the nature of their original modification is correlated with a nucleic acid tag and can be decoded by sequencing the tag when nucleic acids are analyzed. Genetic variation of sample nucleic acid modifications can then be associated with the extent of modification (epigenetic variation) of that nucleic acid in the original sample, include single stranded (e.g., ssDNA or RNA) or double stranded molecules (e.g., dsDNA).

[0045] The loss of DNA can reduce the presence of one or more types of DNA such that the presence of the one or more types of DNA such as cfDNA, is difficult to detect. In one or more additional scenarios, existing methods to measure 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 that can make accurately determining an amount of methylation of DNA difficult. The accuracy with which DNA methylation is determined can impact the accuracy of estimates of tumor fraction for samples. Since tumor fraction can be used to determine whether a sample is derived from a subject inwhich a tumor is present or not, the accuracy of determinations of tumor fraction estimates can impact diagnosis and / or treatment decisions for individuals.

[0046] In addition to these detection schemes, more data is needed to understand the behavior of tumors and performance of treatments and guidelines outside the highly selective confines of randomized controlled trials, often designed and conducted by entities with a commercial interest in their success. Real -world evidence (RWE), specifically the use of databases featuring integrated clinical and molecular data, plays an increasingly important role in precision oncologyresearch. However, most of these databases feature genomic information from tumor limited to a single time point, generally at diagnosis, due in part to the practical challenges of genomic profiling of serial tumor specimens in real -world clinical practice. Genomic data for tumors is often limited to those naive to systemic treatment, despite evidence that treatments can significantly alter the tumor genomic landscape and lead to drug resistance. Combining data from a liquid biopsy assay with rich clinical information can overcome these challenges and help improve understanding of tumor evolution and the emergence of biomarkers that confer resistance to guide the development of novel therapeutics addressing areas of unmet need

[0047] The analysis of healthcare data using existing systems and techniques is typically performed with respect to medical records generated by healthcare providers. As used herein, a healthcare provider may refer to an entity, individual, or group of individuals involved in provided care to individuals in relation to at least one of the treatment or prevention of one or more biological conditions. In addition, as used herein, a biological condition can refer to an abnormality of function and / or structure in an individual to such a degree as to produce or threaten to produce a detectable feature of the abnormality. A biological condition can be characterized by external and / or internal characteristics, signs, and / or symptoms that indicate a deviation from a biological norm in one or more populations. A biological condition can be characterized by external and / or internal characteristics, signs, and / or symptoms that indicate a deviation from a biological norm in one or more populations. In various examples, a biological condition can include one or more molecular phenotypes. For example, a biological condition may correspond to genetic or epigenetic lesions. In one or more additional examples, a biological condition can include at least one of one or more diseases, one or more disorders, one or more injuries, one or more syndromes, one or more disabilities, one or more infections, one or more isolated symptoms, or other atypical variations of biological structure and / or function of individuals. Additionally, a treatment, as used herein, can refer to a substance, procedure, routine, device, and / or other intervention that can administered or performed with the intent of treating one or more effects of a biological condition in an individual. In one or more examples, a treatment may include a substance that is metabolized by the individual. The substance may include a composition of matter, such as a pharmaceutical composition. The substance may be delivered to the individual via a number of methods, such as ingestion, injection, absorption,or inhalation. A treatment may also include physical interventions, such as one or more surgeries. In at least some examples, the treatment can include a therapeutically meaningful intervention.

[0048] The healthcare data typically analyzed by existing systems includes unstructured data.Unstructured data can include data that is not organized according to a pre-defined or standardized format. For example, unstructured data may include notes made by a healthcare provider that is comprised of free text. That is, the manner in which the notes are captured does not include predefined inputs that are selectable by the healthcare provider, such as via a drop-down menu or via a list. Rather, the notes include text entered by a healthcare provider that may include sentences, sentence fragments, words, letters, symbols, abbreviations, one or more combinations thereof, and so forth. In some cases, unstructured data may be partially structured. For example, a provider could select an insurance billing code from a predefined list of insurance billing codes, and add unstructured notes to data associated with that billing code.

[0049] Existing systems typically devote a large amount of computing resources to analyzing unstructured data in order to extract information that may be relevant to analyses being performed by the existing systems. In some cases, existing systems may analyze unstructured data and transform the unstructured data to a structured format in order to facilitate the analysis of the previously unstructured data. The analysis of unstructured data by existing systems can be inefficient as well as inaccurate. In scenarios where the unstructured data is obtained from healthcare data, the importance of accurately analyzing the information is high because the analysis may be related to at least one of the treatment or diagnosis of a number of individuals with respect to one or more biological conditions. Thus, inaccurate analyses of healthcare data may result in suboptimal treatment of individuals.

[0050] The implementations of techniques, architectures, frameworks, systems, processes, and computer-readable instructions described herein are directed to analyzing health insurance claims data to derive information about at least one of the health or treatment of individuals. In contrast to existing systems, health insurance claims data is structured according to one or more formats and stored by a number of data tables. The data tables may include codes or other alphanumeric information indicating treatments received by individuals, dates of treatments, dosage information, diagnoses of individuals with respect to one or more biological conditions, information related to visits to healthcare providers, dates of visits to healthcare providers, billing information, and the like. The implementations described herein may be used to accurately analyze health insurance claims data for hundreds, up to thousands, up to tens of thousands of individuals or more in which one or more biological conditions are present. In various examples, tens of thousands, hundreds of thousands, up to millions of rows and / or columns of health insurance claims data may be analyzed to determine health-related information for individuals in which one or more biological conditions are present.

[0051] In various examples, the implementations described herein can integrate molecular data with health insurance claims data. The molecular data may include information derived from tissuesamples extracted from a number of individuals. The molecular data may also include information derived from blood samples extracted from a number of individuals. In one or more illustrative examples, the molecular data may include genomics data. Further, in one or more examples, the health insurance claims data may be integrated with germline genetic information for a number of individuals.

[0052] An integrated data repository may be created that combines the health insurance claims data for individuals with the molecular data of the individuals. In one or more examples, an identifier may be generated for an individual that is associated with both the health insurance claims data of the individual and the molecular data of the individual. Both the molecular data and the health insurance claims data stored by the integrated data repository may be accessible using a single identifier of the individual. In one or more illustrative examples, the identifier for an individual may include an encrypted security key. In various examples, the integrated data repository may include a number of data tables corresponding to different aspects of the data stored within the data repository. For example, a first data table may be generated that includes summary data of individuals included in the integrated data repository, such as personal information, and a second data table may be generated that includes data corresponding to visits to healthcare providers. Additionally, a third data table may be generated indicating medical procedures provided to individuals and a fourth data table may be generated indicating information related to prescriptions obtained by individuals. Further, a fifth data table may be generated that includes multiomics profiling of individuals. Multiomics profiles may include at least one of genomic profiles, transcriptomic profiles, epigenetic profiles, or proteomic profiles.

[0053] The data tables included in the integrated data repository may be linked via logical links. In this way, a query to retrieve information from one data table may cause information from one or more additional data tables to be retrieved. Information stored by the linked data tables may be accessed to generate a number of different datasets that may be used to analyze the information stored by the integrated data repository. For example, the information stored by the integrated data repository may be analyzed by one or more algorithms to generate datasets that are organized according to one or more schemas. The datasets may indicate treatment received by an individual over a period of time with respect to a biological condition. The datasets may also indicate cohorts of individuals included in the integrated data repository having a number of common characteristics. In various examples, the datasets may consolidate and arrange information from a number of different data sources, including the integrated data repository. The datasets may be analyzed with respect to a number of queries to indicate information that may be of interest to at least one of healthcare providers, patients, or providers of treatments of biological conditions. For example, one or more datasets may be analyzed to more accurately determine a survival rate of individuals in which a biological condition is present and having a specified genomic profile in response to receiving a specified treatment.

[0054] The implementations described herein may provide a platform to integrate health insurance claims data and molecular data for individuals that is not found in existing systems that typically rely on electronic medical records that include an amount of unstructured data. By generating and analyzing structured health insurance claims data that has been integrated with molecular data, the implementations described herein may provide more accurate characterizations of the integrated data in relation to existing systems that rely on relatively inaccurate, unstructured electronic medical records data. Additionally, implementations described herein generate analytics ready datasets that enable the analysis of health information about individuals in a confidential and anonymized manner.Samples

[0055] A sample can be any biological sample isolated from a subject. A sample can be a bodily sample. Samples can include body tissues, such as known or suspected solid tumors, whole blood, platelets, serum, plasma, stool, red blood cells, white blood cells or leucocytes, endothelial cells, tissue biopsies, cerebrospinal fluid synovial fluid, lymphatic fluid, ascites fluid, interstitial or extracellular fluid, the fluid in spaces between cells, including gingival crevicular fluid, bone marrow, pleural effusions, cerebrospinal fluid, saliva, mucous, sputum, semen, sweat, urine. Samples are preferably body fluids, particularly blood and fractions thereof, and urine. A sample can be in the form originally isolated from a subject or can have been subjected to further processing to remove or add components, such as cells, or enrich for one component relative to another. Thus, a preferred body fluid for analysis is plasma or serum containing cell -free nucleic acids. A sample can be isolated or obtained from a subject and transported to a site of sample analysis. The sample may be preserved and shipped at a desirable temperature, e.g., room temperature, 4°C, -20°C, and / or -80°C. A sample can be isolated or obtained from a subject at the site of the sample analysis. The subject can be a human, a mammal, an animal, a companion animal, a service animal, or a pet. The subject may have a cancer. The subject may not have cancer or a detectable cancer symptom. The subject may have been treated with one or more cancer therapy, e.g., any one or more of chemotherapies, antibodies, vaccines or biologies. The subject may be in remission. The subject may or may not be diagnosed of being susceptible to cancer or any cancer-associated genetic mutations / disorders.

[0056] The volume of plasma can depend on the desired read depth for sequenced regions.Exemplary volumes are 0.4-40 ml, 5-20 ml, 10-20 ml. For examples, the volume can be 0.5 m , 1 m , 5 mb 10 mb, 20 mb, 30 mb, or 40 mb A volume of sampled plasma may be 5 to 20 mb.

[0057] A sample can comprise various amount of nucleic acid that contains genome equivalents. For example, a sample of about 30 ng DNA can contain about 10,000 (104) haploid human genome equivalents and, in the case of cfDNA, about 200 billion (2x1011) individual polynucleotide molecules. Similarly, a sample of about 100 ng of DNA can contain about 30,000 haploid human genome equivalents and, in the case of cfDNA, about 600 billion individual molecules.

[0058] A sample can comprise nucleic acids from different sources, e.g., from cells and cell-free of the same subject, from cells and cell-free of 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 existing in germline DNA of a subject. Somatic mutations refer to mutations originating in somatic cells of a subject, e.g., cancer cells. A sample can comprise DNA carrying cancer-associated mutations (e.g., cancer-associated somatic mutations). A sample can comprise an epigenetic variant (i.e. a chemical or protein modification), wherein the epigenetic variant associated with the presence of a genetic variant such as a cancer-associated mutation. In some embodiments, the sample includes an epigenetic variant associated with the presence of a genetic variant, wherein the sample does not comprise the genetic variant.

[0059] Exemplary amounts of cell-free nucleic acids in a sample before amplification range from about 1 fg to about 1 pg, 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.

[0060] Cell-free nucleic acids are nucleic acids not contained within or otherwise bound to a cell or in other words nucleic acids remaining in a sample after removing intact cells. 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), or fragments of any of these. Cell-free nucleic acids can be double-stranded, single-stranded, or a hybrid thereof. A cell-free nucleic acid can be released into bodily fluid through secretion or cell death processes, e.g., cellular necrosis and apoptosis. Some cell -free nucleic acids are released into bodily fluid from cancer cells e.g., 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.

[0061] Cell-free nucleic acids have an exemplary size distribution of about 100-500 nucleotides, with molecules of 110 to about 230 nucleotides representing about 90% of molecules, with a mode of about 168 nucleotides and a second minor peak in a range between 240 to 440 nucleotides. Cell-free nucleic acids can be isolated from bodily fluids through a fractionation or partitioning step in which cell-free nucleic acids, as found in solution, are separated from intact cells and other non-soluble components of the bodily fluid. Partitioning may include techniques such as centrifugation orfiltration. Alternatively, cells in bodily fluids can be lysed and cell-free and cellular nucleic acids processed together. Generally, after addition of buffers and wash steps, nucleic acids can be precipitated with an alcohol. Further clean up steps may be used such as silica based columns to remove contaminants or salts. Non-specific bulk carrier nucleic acids, such as Cot-1 DNA, DNA or protein for bisulfite sequencing, hybridization, and / or ligation, may be added throughout the reaction to optimize certain aspects of the procedure such as yield.

[0062] After such processing, samples can include various forms of nucleic acid 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.Analytes

[0063] Analytes can include nucleic acid analytes, and non-nucleic acid analytes. The disclosure provides for detecting genetic variations in biological samples from a subject. Biological samples may include polynucleotides from cancer cells. Polynucleotides may be DNA (e.g., genomic DNA, cDNA), RNA (e.g., mRNA, small RNAs), or any combination thereof. Biological samples may include tumor tissue, e.g., from a biopsy. In some cases, biological samples may include blood or saliva. In particular cases, biological samples may comprise cell free DNA (“cfDNA”) or circulating tumor DNA (“ctDNA”). Cell free DNA can be present in, e.g., blood.

[0064] Examples of non-nucleic acid analytes include, but are not limited to, lipids, carbohydrates, peptides, proteins, glycoproteins (N-linked or O-linked), lipoproteins, phosphoproteins, specific phosphorylated or acetylated variants of proteins, amidation variants of proteins, hydroxylation variants of proteins, methylation variants of proteins, ubiquity lati on variants of proteins, sulfation variants of proteins, viral proteins (e.g., viral capsid, viral envelope, viral coat, viral accessory, viral glycoproteins, viral spike, etc.), extracellular and intracellular proteins, antibodies, and antigen binding fragments. This further includes receptor, an antigen, a surface protein, a transmembrane protein, a cluster of differentiation protein, a protein channel, a protein pump, a carrier protein, a phospholipid, a glycoprotein, a glycolipid, a cell-cell interaction protein complex, an antigen-presenting complex, a major histocompatibility complex, an engineered T-cell receptor, a T-cell receptor, a B-cell receptor, a chimeric antigen receptor, an extracellular matrix protein, a posttranslational modification (e.g., phosphorylation, glycosylation, ubiquitination, nitrosylation, methylation, acetylation or lipidation) state of a cell surface protein, a gap junction, and an adherens junction.

[0065] In general, the systems, apparatus, 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 that are 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 leastabout 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 the sample or within an individual feature of the substrate. Methods for performing multiplexed assays to analyze two or more different analytes will be discussed in a subsequent section of this disclosure.

[0066] One or more nucleic acid analytes and / or non-nucleic acid analytes constitute a set of molecular interactions in a biological system under study (e.g., cells), which may be regarded as “interactome” - the molecular interactions that occur between molecules belonging to different biochemical families (proteins, nucleic acids, lipids, carbohydrates, etc.) and also within a given family. In various embodiments, an interactome is a protein-DNA interactome (network formed by transcription factors (and DNA or chromatin regulatory proteins) and their target genes. In other embodiments, interactome refers to protein-protein interaction network(PPI), or protein interaction network (PIN). The methods described herein allow for study and analysis of the interactome.Techniques such as proteogenomics (whole genome sequencing, whole exome sequencing and RNA-seq, and mass spectrometry as examples) can support study of the interactome.Analysis

[0067] The present methods can be used to diagnose presence of conditions, particularly cancer, in a subject, to characterize conditions (e.g., staging cancer or determining heterogeneity of a cancer), monitor response to treatment of a condition, effect prognosis risk of developing a condition or subsequent course of a condition. The present disclosure can also be useful in determining the efficacy of a particular treatment option. Successful treatment options may increase the amount of copy number variation or rare mutations detected in subject's blood if the treatment is successful as more cancers may die and shed DNA. In other examples, this may not occur. In another example, perhaps certain treatment options may be correlated with genetic profiles of cancers over time. This correlation may be useful in selecting a therapy. Additionally, if a cancer is observed to be in remission after treatment, the present methods can be used to monitor residual disease or recurrence of disease.

[0068] The types and number of cancers that may be detected may include blood cancers, brain cancers, lung cancers, skin cancers, nose cancers, throat cancers, liver cancers, bone cancers, lymphomas, pancreatic cancers, skin cancers, bowel cancers, rectal cancers, thyroid cancers, bladder cancers, kidney cancers, mouth cancers, stomach cancers, solid state tumors, heterogeneous tumors, homogenous tumors and the like. Type and / or stage of cancer can be detected from genetic variations including mutations, rare mutations, indels, copy number variations, transversions, translocations, inversion, deletions, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structure alterations, gene fusions, chromosome fusions, gene truncations, geneamplification, gene duplications, chromosomal lesions, DNA lesions, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns, and abnormal changes in nucleic acid 5 -methylcytosine.

[0069] Genetic and other analyte data can also be used for characterizing a specific form of cancer. Cancers are often heterogeneous in both composition and staging. Genetic profile data may allow characterization of specific sub-types of cancer that may be important in the diagnosis or treatment of that specific sub-type. This information may also provide a subject or practitioner clues regarding the prognosis of a specific type of cancer and allow either a subject or practitioner to adapt treatment options in accord with the progress of the disease. Some cancers can progress to become more aggressive and genetically unstable. Other cancers may remain benign, inactive or dormant. The system and methods of this disclosure may be useful in determining disease progression.

[0070] The present analyses are also useful in determining the efficacy of a particular treatment option. Successful treatment options may increase the amount of copy number variation or rare mutations detected in subject's blood if the treatment is successful as more cancers may die and shed DNA. In other examples, this may not occur. In another example, perhaps certain treatment options may be correlated with genetic profiles of cancers over time. This correlation may be useful in selecting a therapy. Additionally, if a cancer is observed to be in remission after treatment, the present methods can be used to monitor residual disease or recurrence of disease.

[0071] The present methods can also be used for detecting genetic variations in conditions other than cancer. Immune cells, such as B cells, may undergo rapid clonal expansion upon the presence certain diseases. Clonal expansions may be monitored using copy number variation detection and certain immune states may be monitored. In this example, copy number variation analysis may be performed over time to produce a profile of how a particular disease may be progressing. Copy number variation or even rare mutation detection may be used to determine how a population of pathogens is changing during the course of infection. This may be particularly important during chronic infections, such as HIV / AIDS or Hepatitis infections, whereby viruses may change life cycle state and / or mutate into more virulent forms during the course of infection. The present methods may be used to determine or profile rejection activities of the host body, as immune cells attempt to destroy transplanted tissue to monitor the status of transplanted tissue as well as altering the course of treatment or prevention of rejection.

[0072] Further, the methods of the disclosure may be used to characterize the heterogeneity of an abnormal condition in a subject. Such methods can include, e.g., generating a genetic profile of extracellular polynucleotides derived from the subject, wherein the genetic profile includes a plurality of data resulting from copy number variation and rare mutation analyses. In some embodiments, an abnormal condition is cancer. In some embodiments, the abnormal condition may be one resulting in a heterogeneous genomic population. In the example of cancer, some tumors are known to comprise tumor cells in different stages of the cancer. In other examples, heterogeneity may comprise multiplefoci of disease. Again, in the example of cancer, there may be multiple tumor foci, perhaps where one or more foci are the result of metastases that have spread from a primary site.

[0073] The present methods can be used to generate or profde, fingerprint or set of data that is a summation of genetic information derived from different cells in a heterogeneous disease. This set of data may comprise copy number variation and mutation analyses alone or in combination.

[0074] The present methods can be used to diagnose, prognose, monitor or observe cancers, or other diseases. In some embodiments, the methods herein do not involve the diagnosing, prognosing or monitoring a fetus and as such are not directed to non-invasive prenatal testing. In other embodiments, these methodologies may be employed in a pregnant subject to diagnose, prognose, monitor or observe cancers or other diseases in an unborn subject whose DNA and other polynucleotides may co-circulate with maternal molecules.Determination of 5-methylcvtosine pattern of nucleic acids

[0075] Bisulfite-based sequencing and variants thereof provides a means of determining the methylation pattern of a nucleic acid. In some embodiments, determining the methylation pattern includes distinguishing 5 -methylcytosine (5mC) from non-methylated cytosine. In some embodiments, determining methylation pattern includes distinguishing N6-methyladenine from nonmethylated adenine. In some embodiments, determining the methylation pattern 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).

[0076] Oxidative bisulfite sequencing (OX-BS-seq) is used to distinguish between 5mC and 5hmC, by first converting the 5hmC to 5fC, and then proceeding with 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 glucosylation. A Tet enzyme is then used to convert 5mC to 5caC before proceeding with bisulfite sequencing, as previously described. Reduced bisulfite sequencing is used to distinguish 5fC from modified cytosines.

[0077] Generally, in bisulfite sequencing, a nucleic acid sample is divided into two aliquots and one aliquot is treated with bisulfite. The bisulfite converts native cytosine and certain modified cytosine nucleotides (e.g. 5 -formylcytosine or 5 -carboxylcytosine) to uracil whereas other modified cytosines (e.g., 5- methylcytosine, 5-hydroxylmethylcystosine) are not converted. Comparison of nucleic acid sequences of molecules from the two aliquots indicates which cytosines were and were not converted to uracils. Consequently, cytosines which were and were not modified can be determined. The initial splitting of the sample into two aliquots is disadvantageous for samples containing only small amounts of nucleic acids, and / or composed of heterogeneous cell / tissue origins such as bodily fluids containing cell-free DNA.

[0078] The present disclosure provides methods allowing bisulfite sequencing and variants thereof. These methods work by linking nucleic acids in a population to a capture moiety, i.e., a label that can be captured or immobilized. Capture moieties include, without limitation, biotin, avidin, streptavidin, a nucleic acid including a particular nucleotide sequence, a hapten recognized by an antibody, 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, a capture moiety that is attached to an analyte is captured by its binding pair which is attached to an isolatable moiety, such as a magnetically attractable particle or a large particle that can be sedimented through centrifugation. The capture moiety can be any type of molecule that allows affinity separation of nucleic acids bearing the capture moiety from nucleic acids lacking the capture moiety. Exemplary capture moieties are biotin which allows affinity separation by binding to streptavidin linked or linkable to a solid phase or an oligonucleotide, which allows affinity separation through binding to a complementary oligonucleotide linked or linkable to a solid phase. Following linking of capture moieties to sample nucleic acids, the sample nucleic acids serve as templates for amplification. Following amplification, the original templates remain linked to the capture moieties but amplicons are not linked to capture moieties.

[0079] The capture moiety can be linked to sample nucleic acids as a component of an adapter, which may also provide amplification and / or sequencing primer binding sites. In some methods, sample nucleic acids are linked to adapters at both ends, with both adapters bearing a capture moiety. Preferably any cytosine residues in the adapters are modified, such as by 5methylcytosine, to protect against the action of bisulfite. In some instances, the capture moieties are linked to the original templates by a cleavable linkage (e.g., photocleavable desthiobiotin-TEG or uracil residues cleavable with USER™ enzyme, Chem. Commun. (Camb). 2015 Feb 21; 51(15): 3266-3269), in which case the capture moieties can, if desired, be removed.

[0080] The amplicons are denatured and contacted with an affinity reagent for the capture tag.Original templates bind to the affinity reagent whereas nucleic acid molecules resulting from amplification do not. Thus, the original templates can be separated from nucleic acid molecules resulting from amplification.

[0081] Following separation or partition, the respective populations of nucleic acids (i.e., original templates and amplification products) can be subjected to bisulfite treatment with the original template population receiving bisulfite treatment and the amplification products not. Alternatively, the amplification products can be subjected to bisulfite treatment and the original template population not. Following such treatment, the respective populations can be amplified (which in the case of the original template population converts uracils to thymines). The populations can also be subjected to biotin probe hybridization for enrichment. The respective populations are then analyzed and sequences compared to determine which cytosines were 5-methylated (or 5 -hydroxylmethylated) in the original. Detection of a T nucleotide in the template population (corresponding to an unmethylated cytosine converted to uracil) and a C nucleotide at the corresponding position of theamplified population indicates an unmodified C. The presence of C's at corresponding positions of the original template and amplified populations indicates a modified C in the original sample.

[0082] In some embodiments, a method uses sequential DNA-seq and bisulfite-seq (BlS-seq) NGS library preparation of molecular tagged DNA libraries. This process is performed by labeling of adapters (e.g., biotin), DNA-seq amplification of whole library, parent molecule recovery (e.g. streptavidin bead pull down), bisulfite conversion and BlS-seq. In some embodiments, the method identifies 5 -methylcytosine with single-base resolution, through sequential NGS-preparative amplification of parent library molecules with and without bisulfite treatment. This can be achieved by modifying the 5 -methyl -ated NGS-adapters (directional adapters; Y-shaped / forked with 5-methylcytosine replacing) used in BlS-seq with a label (e.g., biotin) on one of the two adapter strands. Sample DNA molecules are adapter ligated, and amplified (e.g., by PCR). As only the parent molecules will have a labeled adapter end, they can be selectively recovered from their amplified progeny by label-specific capture methods (e.g., streptavidin-magnetic beads). As the parent molecules retain 5 -methylation marks, bisulfite conversion on the captured library will yield singlebase resolution 5-methylation status upon BlS-seq, retaining molecular information to corresponding DNA-seq. In some embodiments, the bisulfite treated library can be combined with a non-treated library prior to enrichment / NGS by addition of a sample tag DNA sequence in standard multiplexed NGS workflow. As with BlS-seq workflows, bioinformatics analysis can be carried out for genomic alignment and 5 -methylated base identification. In sum, this method provides the ability to selectively recover the parent, ligated molecules, carrying 5 -methylcytosine marks, after library amplification, thereby allowing for parallel processing for bisulfite converted DNA. This overcomes the destructive nature of bisulfite treatment on the quality / sensitivity of the DNA-seq information extracted from a workflow. With this method, the recovered ligated, parent DNA molecules (via labeled adapters) allow amplification of the complete DNA library and parallel application of treatments that elicit epigenetic DNA modifications. The present disclosure discusses the use of BIS-seq methods to identify cytosine5 -methylation (5 -methylcytosine), but this should is not limiting. Variants of BlS-seq have been developed to identify hydroxymethylated cytosines (5hmC; OX- BS-seq, TAB-seq), formylcytosine (5fC; redBS-seq) and carboxylcytosines. These methodologies can be implemented with the sequential / parallel library preparation described herein.Alternative Methods of Modified Nucleic Acid Analysis

[0083] The disclosure provides alternative methods for analyzing modified nucleic acids (e.g., methylated, linked to histones and other modifications discussed above). In some such methods, a population of nucleic acids bearing the modification to different extents (e.g., 0, 1, 2, 3, 4, 5 or more methyl groups per nucleic acid molecule) is contacted with adapters before fractionation of the population depending on the extent of the modification. Adapters attach to either one end or both ends of nucleic acid molecules in the population. Preferably, the adapters include different tags ofsufficient numbers that the number of combinations of tags results in a low probability e.g., 95, 99 or 99.9% of two nucleic acids with the same start and stop points receiving the same combination of tags. Following attachment of adapters, the nucleic acids are amplified from primers binding to the primer binding sites within the adapters. Adapters, whether bearing the same or different tags, can include the same or different primer binding sites, but preferably adapters include the same primer binding site. Following amplification, the nucleic acids are contacted with an agent that preferably binds to nucleic acids bearing the modification (such as the previously described such agents). The nucleic acids are separated into at least two partitions differing in the extent to which the nucleic acids bear the modification from binding to the agents. For example, if the agent has affinity for nucleic acids bearing the modification, nucleic acids overrepresented in the modification (compared with median representation in the population) preferentially bind to the agent, whereas nucleic acids underrepresented for the modification do not bind or are more easily eluted from the agent. Following separation, the different partitions can then be subject to further processing steps, which typically include further amplification, and sequence analysis, in parallel but separately. Sequence data from the different partitions can then be compared.

[0084] Nucleic acids can be linked at both ends to Y -shaped adapters including primer binding sites and tags. The molecules are amplified. The amplified molecules are then fractionated by contact with an antibody preferentially binding to 5 -methylcytosine to produce two partitions. One partition includes original molecules lacking methylation and amplification copies having lost methylation. The other partition includes original DNA molecules with methylation. The two partitions are then processed and sequenced separately with further amplification of the methylated partition. The sequence data of the two partitions can then be compared. In this example, tags are not used to distinguish between methylated and unmethylated DNA but rather to distinguish between different molecules within these partitions so that one can determine whether reads with the same start and stop points are based on the same or different molecules.

[0085] The disclosure provides further methods for analyzing a population of nucleic acid in which at least some of the nucleic acids include 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 adapters including one or more cytosine residues modified at the 5C position, such as 5 -methylcytosine. Preferably all cytosine residues in such adapters are also modified, or all such cytosines in a primer binding region of the adapters are modified. Adapters attach to both ends of nucleic acid molecules in the population. Preferably, the adapters include different tags of sufficient numbers that the number of combinations of tags results in a low probability e.g., 95, 99 or 99.9% of two nucleic acids with the same start and stop points receiving the same combination of tags. The primer binding sites in such adapters can be the same or different, but are preferably the same. After attachment of adapters, the nucleic acids are amplified from primers binding to the primer binding sites of the adapters. The amplified nucleic acids are splitinto first and second aliquots. The first aliquot is assayed for sequence data with or without further processing. The sequence data on molecules in the first aliquot is thus determined irrespective 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 primed by primers to the original primer binding sites of the adapters linked to nucleic acid. Only the nucleic acid molecules originally linked to adapters (as distinct from amplification products thereof) are now amplifiable because these nucleic acids retain cytosines in the primer binding sites of the adapters, whereas amplification products have lost the methylation of these cytosine residues, which have undergone conversion to uracils in the bisulfite treatment. Thus, only original molecules in the populations, at least some of which are methylated, undergo amplification. After amplification, these nucleic acids are subject to sequence analysis. Comparison of sequences determined from the first and second aliquots can indicate among other things, which cytosines in the nucleic acid population were subject to methylation.Partitioning the Sample into a Plurality of Subsamples; Aspects of Samples; Analysis of Epigenetic Characteristics

[0086] In certain embodiments described herein, a population of different forms of nucleic acids (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 characteristics of the nucleic acids prior to further analysis, e.g., differentially modifying or isolating a nucleobase, tagging, and / or sequencing. This approach can be used to determine, for example, whether certain sequences are hypermethylated or hypomethylated. In some embodiments, hypermethylation variable epigenetic target regions are analyzed to determine whether they show hypermethylation characteristic of tumor cells and / or hypomethylation variable epigenetic target regions are analyzed to determine whether they show hypomethylation characteristic of tumor cells. Additionally, by partitioning a heterogeneous nucleic acid population, one may increase rare signals, e.g., by enriching rare nucleic acid molecules that are more prevalent in one fraction (or partition) of the population. For example, a genetic variation present in hyper-methylated DNA but less (or not) in hypomethylated DNA can be more easily detected by partitioning a sample into hyper-methylated and hypo-methylated nucleic acid molecules. By analyzing multiple fractions of a sample, a multi-dimensional analysis of a single locus of a genome or species of nucleic acid can be performed and hence, greater sensitivity can be achieved.

[0087] In some instances, 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. Tagged partitions can then be pooled together for collective sample prep and / or sequencing. The partitioning-tagging-pooling steps can occur more than once, with each round of partitioningoccurring based on a different characteristics (examples provided herein), and tagged using differential tags that are distinguished from other partitions and partitioning means.

[0088] Examples of characteristics that can be used for partitioning include sequence length, methylation level, nucleosome binding, sequence mismatch, immunoprecipitation, and / or proteins that bind to DNA. 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 a cytosine modification (e.g., cytosine methylation) or methylation generally is performed and is optionally combined with at least one additional partitioning step, which may be based on any of the foregoing characteristics or forms of DNA. In some embodiments, a heterogeneous population of nucleic acids is partitioned into nucleic acids with one or more epigenetic modifications and without the one or more epigenetic modifications. Examples of epigenetic modifications include presence or absence of methylation; level of methylation; type of methylation (e.g., 5 -methylcytosine versus other types of methylation, such as adenine methylation and / or cytosine hydroxymethylation); and association 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 devoid of nucleosomes. Alternatively or additionally, a heterogeneous population of nucleic acids may be partitioned into single-stranded DNA (ssDNA) and double-stranded DNA (dsDNA). Alternatively, or additionally, a heterogeneous population of nucleic acids may be partitioned based on nucleic acid length (e.g., molecules of up to 160 bp and molecules having a length of greater than 160 bp).

[0089] In some instances, each partition (representative of a different nucleic acid form) is differentially labelled, and the partitions are pooled together prior to sequencing. In other instances, the different forms are separately sequenced. In some embodiments, a population of different nucleic acids is partitioned into two or more different partitions. Each partition is representative of a different nucleic acid form, and a first partition (also referred to as a subsample) includes DNA with a cytosine modification in a greater proportion than a second subsample. Each partition is distinctly tagged. The first subsample is subjected to a procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample, 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. The tagged nucleic acids are pooled together prior to sequencing. Sequence reads are obtained and analyzed, including to distinguish the first nucleobase from the second nucleobase in the DNA of the first subsample, in silico. Tags are used to sort reads from different partitions. Analysis to detect genetic variants can be performed on a partition-by-partition level, as well as whole nucleic acid population level. For example, analysis can include in silico analysis to determine genetic variants, such as CNV, SNV, indel, fusion in nucleic acids in each partition. In some instances, insilico analysis can include determining chromatin structure. For example, coverage of sequence reads can be used to determine nucleosome positioning in chromatin. Higher coverage can correlate with higher nucleosome occupancy in genomic region while lower coverage can correlate with lower nucleosome occupancy or nucleosome depleted region (NDR).

[0090] Samples can include nucleic acids varying in modifications including post-replication modifications to nucleotides and binding, usually noncovalently, to one or more proteins.

[0091] In an embodiment, the population of nucleic acids is one obtained from a serum, plasma or blood sample from a subject suspected of having neoplasia, a tumor, or cancer or previously diagnosed with neoplasia, a tumor, or cancer. The population of nucleic acids includes nucleic acids having varying levels of methylation. Methylation can occur from any one or more post-replication or transcriptional modifications. Post-replication modifications include modifications of the nucleotide cytosine, particularly at the 5-position of the nucleobase, e.g., 5 -methylcytosine, 5-hydroxymethylcytosine, 5 -formylcytosine and 5 -carboxylcytosine. The affinity agents can be antibodies with the desired specificity, natural binding partners or variants thereof (Bock et al., Nat Biotech 28: 1106-1114 (2010); Song et al., Nat Biotech 29: 68-72 (2011)), or artificial peptides selected e.g., by phage display to have specificity to a given target.

[0092] Examples of capture moieties contemplated herein include methyl binding domain (MBDs) and methyl binding proteins (MBPs) as described herein, including proteins such as MeCP2 and antibodies preferentially binding to 5 -methylcytosine. Likewise, partitioning of different forms of nucleic acids can be performed using histone binding proteins which 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. Although for some affinity agents and modifications, binding to the agent may occur in an essentially all or none manner depending on whether a nucleic acid bears a modification, the separation may be one of degree. In such instances, nucleic acids overrepresented in a modification bind to the agent at a greater extent that nucleic acids underrepresented in the modification. Alternatively, nucleic acids having modifications may bind in an all or nothing manner. But then, various levels of modifications may be sequentially eluted from the binding agent.

[0093] For example, in some embodiments, partitioning can be binary or based on degree / level of modifications. For example, all methylated fragments can be partitioned from unmethylated fragments using methyl-binding domain proteins (e.g., MethylMiner Methylated DNA Enrichment Kit (ThermoFisher Scientific)). Subsequently, additional partitioning may involve eluting fragments having different levels of methylation by adjusting the salt concentration in a solution with the methyl-binding domain and bound fragments. As salt concentration increases, fragments having greater methylation levels are eluted. In some instances, the final partitions are representative of nucleic acids having different extents of modifications (overrepresentative or underrepresentative of modifications). Overrepresentation and underrepresentation can be defined by the number ofmodifications bom by a nucleic acid relative to the median number of modifications per strand in a population. For example, if the median number of 5 -methylcytosine residues in nucleic acid in a sample is 2, a nucleic acid including more than two 5 -methylcytosine residues is overrepresented in this modification and a nucleic acid with 1 or zero 5 -methylcytosine residues is underrepresented. The effect of the affinity separation is to enrich for nucleic acids overrepresented in a modification in a bound phase and for nucleic acids underrepresented in a modification in an unbound phase (i.e. in solution). The nucleic acids in the bound phase can be eluted before subsequent processing.

[0094] When using MethylMiner Methylated DNA Enrichment Kit (ThermoFisher Scientific) various levels of methylation can be partitioned using sequential elutions. For example, a hypomethylated partition (e.g., no methylation) can be separated from a methylated partition by contacting the nucleic acid population with the MBD from the kit, which is attached to magnetic beads. The beads are used to separate out the methylated nucleic acids from the non- methylated nucleic acids. Subsequently, one or more elution steps are performed sequentially to elute nucleic acids having different levels of methylation. For example, a first set of methylated nucleic acids can be eluted at a salt concentration of 160 mM or higher, 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 once again used to separate higher level of methylated nucleic acids from those with lower level of methylation. The elution and magnetic separation steps can repeat themselves to create various partitions such as a hypomethylated partition (representative of no methylation), a methylated partition (representative of low level of methylation), and a hyper methylated partition (representative of high level of methylation).

[0095] In some methods, nucleic acids bound to an agent used for affinity separation are subjected to a wash step. The wash step washes off nucleic acids weakly bound to the affinity agent. Such nucleic acids can be enriched in nucleic acids having the modification to an extent close to the mean or median (i.e., intermediate between nucleic acids remaining bound to the solid phase and nucleic acids not binding to the solid phase on initial contacting of the sample with the agent). The affinity separation results in at least two, and sometimes three or more partitions of nucleic acids with different extents of a modification. While the partitions are still separate, the nucleic acids of at least one partition, and usually two or three (or more) partitions are linked to nucleic acid tags, usually provided as components of adapters, with the nucleic acids in different partitions receiving different tags that distinguish members of one partition from another. The tags linked to nucleic acid molecules of the same partition can be the same or different from one another. But if different from one another, the tags may have part of their code in common so as to identify the molecules to which they are attached as being of a particular partition. For further details regarding portioning nucleic acid samples based on characteristics such as methylation, see WO2018 / 119452, which is incorporated herein by reference. In some embodiments, the nucleic acid molecules can be fractionated intodifferent partitions based on the nucleic acid molecules that are bound to a specific protein or a fragment thereof and those that are not bound to that specific protein or fragment thereof.

[0096] Nucleic acid molecules can be fractionated based on DNA-protein binding. Protein-DNA complexes can be fractionated based on a specific property of a protein. Examples of such properties include various epitopes, modifications (e.g., histone methylation or acetylation) or enzymatic activity. Examples of proteins which may bind to DNA and serve as a basis for fractionation may include, but are not limited to, protein A and protein G. Any suitable method can be used to fractionate the nucleic acid molecules based on protein bound regions. Examples of methods used to fractionate nucleic acid molecules based on protein bound regions include, but are not limited to, SDS-PAGE, chromatin-immuno-precipitation (ChIP), heparin chromatography, and asymmetrical field flow fractionation (AF4).

[0097] In some embodiments, partitioning of the nucleic acids is performed by contacting the nucleic acids with a methylation binding domain (“MBD”) of a methylation binding protein (“MBP”). MBD binds to 5 -methylcytosine (5mC). MBD is coupled to paramagnetic beads, such as Dynabeads® M-280 Streptavidin via a biotin linker. Partitioning into fractions with different extents of methylation can be performed by eluting fractions by increasing the NaCl concentration.

[0098] An exemplary method for molecular tag identification of MBD-bead partitioned libraries through NGS is as follows:

[0099] Physical partitioning of an extracted DNA sample (e.g., extracted blood plasma DNA from a human sample) using a methyl-binding domain protein-bead purification kit, saving all elutions from process for downstream processing.

[0100] Parallel application of differential molecular tags and NGS-enabling adapter sequences to each partition. For example, the hypermethylated, residual methylation ('wash'), and hypomethylated partitions are ligated with NGS-adapters with molecular tags.

[0101] Re-combining all molecular tagged partitions, and subsequent amplification using adapterspecific DNA primer sequences.

[0102] Enrichment / hybridization of re-combined and amplified total library, targeting genomic regions of interest (e.g., cancer-specific genetic variants and differentially methylated regions).

[0103] Re-amplification of the enriched total DNA library, appending a sample tag. Different samples are pooled, and assayed in multiplex on an NGS instrument.

[0104] Bioinformatics analysis of NGS data, with the molecular tags being used to identify unique molecules, as well deconvolution of the sample into molecules that were differentially MBD-partitioned. This analysis can yield information on relative 5 -methylcytosine for genomic regions, concurrent with standard genetic sequencing / variant detection.

[0105] Examples of MBPs contemplated herein include, but are not limited to:

[0106] (a) MeCP2 is a protein preferentially binding to 5 -methyl -cytosine over unmodified cytosine.

[0107] (b) RPL26, PRP8 and the DNA mismatch repair protein MHS6 preferentially bind to 5-hydroxymethyl-cytosine over unmodified cytosine.

[0108] (c) FOXK1, FOXK2, FOXP1, FOXP4 and FOXI3 preferably bind to 5 -formyl -cytosine over unmodified cytosine (lurlaro et al., Genome Biol. 14: R119 (2013)).

[0109] (d) Antibodies specific to one or more methylated nucleotide bases.

[0110] In general, elution is a function of number of methylated sites per molecule, with molecules having more methylation eluting under increased salt concentrations. To elute the DNA into distinct populations based on the extent of methylation, one can use a series of elution buffers of increasing NaCl concentration. 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 at a first salt concentration and including a molecule including a methyl binding domain, which molecule can be attached to a capture moiety, such as streptavidin. At the first salt concentration a population of molecules will bind to the MBD and a population will remain unbound. The unbound population can be separated as a “hypomethylated” population. For example, a first partition representative of the hypomethylated form of DNA is that which remains unbound at a low salt concentration, e.g., 100 mM or 160 mM. A second partition representative of intermediate methylated DNA is eluted using an intermediate salt concentration, e.g., between 100 mM and 2000 mM concentration. This is also separated from the sample. A third partition representative of hypermethylated form of DNA is eluted using a high salt concentration, e.g., at least about 2000 mM.[oni] The disclosure provides further methods for analyzing a population of nucleic acids in which at least some of the nucleic acids include one or more modified cytosine residues, such as 5-methylcytosine and any of the other modifications described previously. In these methods, after partitioning, the subsamples of nucleic acids are contacted with adapters including one or more cytosine residues modified at the 5C position, such as 5 -methylcytosine. Preferably all cytosine residues in such adapters are also modified, or all such cytosines in a primer binding region of the adapters are modified. Adapters attach to both ends of nucleic acid molecules in the population. Preferably, the adapters include different tags of sufficient numbers that the number of combinations of tags results in a low probability e.g., 95, 99 or 99.9% of two nucleic acids with the same start and stop points receiving the same combination of tags. The primer binding sites in such adapters can be the same or different, but are preferably the same. After attachment of adapters, the nucleic acids are amplified from primers binding to the primer binding sites of the adapters. The amplified nucleic acids are split into first and second aliquots. The first aliquot is assayed for sequence data with or without further processing. The sequence data on molecules in the first aliquot is thus determined irrespective of the initial methylation state of the nucleic acid molecules. The nucleic acid molecules in the second aliquot are subjected to a procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA, wherein the first nucleobase includes a cytosine modified at the 5 position, and the second nucleobase includes unmodified cytosine. This proceduremay be bisulfite treatment or another procedure that converts unmodified cytosines to uracils. The nucleic acids subjected to the procedure are then amplified with primers to the original primer binding sites of the adapters linked to nucleic acid. Only the nucleic acid molecules originally linked to adapters (as distinct from amplification products thereof) are now amplifiable because these nucleic acids retain cytosines in the primer binding sites of the adapters, whereas amplification products have lost the methylation of these cytosine residues, which have undergone conversion to uracils in the bisulfite treatment. Thus, only original molecules in the populations, at least some of which are methylated, undergo amplification. After amplification, these nucleic acids are subject to sequence analysis. Comparison of sequences determined from the first and second aliquots can indicate among other things, which cytosines in the nucleic acid population were subject to methylation.

[0112] Such an analysis can be performed using the following exemplary procedure. After partitioning, methylated DNA is linked to Y -shaped adapters at both ends including primer binding sites and tags. The cytosines in the adapters are modified at the 5 position (e.g., 5 -methylated). The modification of the adapters serves to protect the primer binding sites in a subsequent conversion step (e.g., bisulfite treatment, TAP conversion, or any other conversion that does not affect the modified cytosine but affects unmodified cytosine). After attachment of adapters, the DNA molecules are amplified. The amplification product is split into two aliquots for sequencing with and without conversion. The aliquot not subjected to conversion can be subjected to sequence analysis with or without further processing. The other aliquot is subjected to a procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA, wherein the first nucleobase includes a cytosine modified at the 5 position, and the second nucleobase includes unmodified cytosine. This procedure may be bisulfite treatment or another procedure that converts unmodified cytosines to uracils. Only primer binding sites protected by modification of cytosines can support amplification when contacted with primers specific for original primer binding sites. Thus, only original molecules and not copies from the first amplification are subjected to further amplification. The further amplified molecules are then subjected to sequence analysis. Sequences can then be compared from the two aliquots. As in the separation scheme discussed above, nucleic acid tags in adapters are not used to distinguish between methylated and unmethylated DNA but to distinguish nucleic acid molecules within the same partition.Subjecting the First Subsample to a Procedure that Affects a First Nucleobase in the DNA Differently from a Second Nucleobase in the DNA of the First Subsample

[0113] Methods disclosed herein comprise a step of subjecting the first subsample to a procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample, 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. In some embodiments,if the first nucleobase is a modified or unmodified adenine, then the second nucleobase is a modified or unmodified adenine; if the first nucleobase is a modified or unmodified cytosine, then the second nucleobase is a modified or unmodified cytosine; if the first nucleobase is a modified or unmodified guanine, then the second nucleobase is a modified or unmodified guanine; and if the first nucleobase is a modified or unmodified thymine, then the second nucleobase is a modified or unmodified thymine (where modified and unmodified uracil are encompassed within modified thymine for the purpose of this step).

[0114] In some embodiments, the first nucleobase is a modified or unmodified cytosine, then the second nucleobase is a modified or unmodified cytosine. For example, first nucleobase may comprise unmodified cytosine (C) and the second nucleobase may comprise one or more of 5 -methylcytosine (mC) and 5 -hydroxymethylcytosine (hmC). Alternatively, the second nucleobase may comprise C and the first nucleobase may comprise one or more of mC and hmC. Other combinations are also possible, as indicated, e.g., in the Summary above and the following discussion, such as where one of the first and second nucleobases includes mC and the other includes hmC.

[0115] In some embodiments, the procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample includes bisulfite conversion. Treatment with bisulfite converts unmodified cytosine and certain modified cytosine nucleotides (e.g. 5 -formyl cytosine (fC) or 5 -carboxylcytosine (caC)) to uracil whereas other modified cytosines (e.g., 5-methylcytosine, 5-hydroxylmethylcystosine) are not converted. Thus, where bisulfite conversion is used, the first nucleobase includes one or more of unmodified cytosine, 5 -formyl cytosine, 5-carboxylcytosine, or other cytosine forms affected by bisulfite, and the second nucleobase may comprise one or more of mC and hmC, such as mC and optionally hmC. Sequencing of bisulfite-treated DNA identifies positions that are read as cytosine as being mC or hmC positions. Meanwhile, positions that are read as T are identified as being T or a bisulfite-susceptible form of C, such as unmodified cytosine, 5-formyl cytosine, or 5 -carboxylcytosine. Performing bisulfite conversion on a first subsample as described herein thus facilitates identifying positions containing mC or hmC using the sequence reads obtained from the first subsample. For an exemplary description of bisulfite conversion, see, e.g., Moss et al., Nat Commun. 2018; 9: 5068..

[0116] In some embodiments, the procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample includes oxidative bisulfite (Ox-BS) conversion. In some embodiments, the procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample includes Tet-assisted bisulfite (TAB) conversion. In some embodiments, the procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample includes Tet-assisted conversion with a substituted borane reducing agent, optionally wherein the substituted borane reducing agent is 2-picoline borane, borane pyridine, tert-butylamine borane, or ammonia borane. In some embodiments, the procedure that affects a first nucleobase in the DNA differently from a secondnucleobase in the DNA of the first subsample includes chemical-assisted conversion with a substituted borane reducing agent, optionally wherein the substituted borane reducing agent is 2-picoline borane, borane pyridine, tert-butylamine borane, or ammonia borane. In some embodiments, the procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample includes APOBEC-coupled epigenetic (ACE) conversion.

[0117] In some embodiments, procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample includes 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 / 2019.12.20.884692vl. For example, TET2 and T4-J3GT can be used to convert 5mC and 5hmC into substrates 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.

[0118] In some embodiments, the procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample includes separating DNA originally including the first nucleobase from DNA not originally including the first nucleobase.

[0119] 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).

[0120] Techniques including methylated DNA immunoprecipitation (MeDIP) can be used to separate 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. An antibody specific for mA is described in Sun et al., Bioessays 2015; 37: 1155-62. Antibodies for 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 alterations in their base-pairing specificity. For example, hypoxanthine is a modified form of adenine that can result from deamination and is read in sequencing as a G. See, e.g., US Patent 8,486,630; Brown, Genomes, 2nd Ed., John Wiley & Sons, Inc., New York, N.Y., 2002, chapter 14, “Mutation, Repair, and Recombination.”Enriching / Capturing Step, Amplification, Adaptors, Barcodes

[0121] In some embodiments, methods disclosed herein comprise a step of capturing one or more sets of target regions of DNA, such as cfDNA. Capture may be performed using any suitable approach known in the art. In some embodiments, capturing includes contacting the DNA to be captured with a set of target-specific probes. The set of target-specific probes may have any of the features described herein for sets of target-specific probes, including but not limited to in the embodiments set forth above and the sections relating to probes below. Capturing may be performedon one or more subsamples prepared during 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 undergoes a separation step (e.g., separating DNA originally including the first nucleobase (e.g., hmC) from DNA not originally including the first nucleobase, such as hmC-seal), capturing may be performed on any, any two, or all of the DNA originally including the first nucleobase (e.g., hmC), the DNA not originally including the first nucleobase, and the second subsample. In some embodiments, the subsamples are differentially tagged (e.g., as described herein) and then pooled before undergoing capture.

[0122] The capturing step may be performed using conditions suitable for specific nucleic acid hybridization, which generally depend to some extent on features of the probes such as length, base composition, etc. Those skilled in the art will be familiar with appropriate conditions given general knowledge in the art regarding nucleic acid hybridization. In some embodiments, complexes of targetspecific probes and DNA are formed.

[0123] In some embodiments, a method described herein includes capturing cfDNA obtained from a test subject for a plurality of sets of target regions. The target regions comprise epigenetic target regions, which may show differences in methylation levels and / or fragmentation patterns depending on whether they originated from a tumor or from healthy cells. The target regions also comprise sequence-variable target regions, which may show differences in sequence depending on whether they originated from a tumor or from healthy cells. The capturing step produces a captured set of cfDNA molecules, and the cfDNA molecules corresponding to the sequence -variable target region set are captured at a greater capture yield in the captured set of cfDNA molecules than cfDNA molecules corresponding to the epigenetic target region set. For additional discussion of capturing steps, capture yields, and related aspects, see W02020 / 160414, which is incorporated herein by reference for all purposes.

[0124] In some embodiments, a method described herein includes contacting cfDNA obtained from a test subject with a set of target-specific probes, wherein the set of target-specific probes 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.

[0125] It can be beneficial 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 because a greater depth of sequencing may be necessary to analyze the sequence -variable target regions with sufficient confidence or accuracy than may be necessary to analyze the epigenetic target regions. The volume of data needed to determine fragmentation patterns (e.g., to test fsor perturbation of transcription start sites or CTCF binding sites) or fragment abundance (e.g., in hypermethylated and hypomethylated partitions) is generally less than the volume of data needed to determine the presence or absence of cancer-related sequence mutations. Capturing the target region sets at different yieldscan facilitate sequencing the target regions to different depths of sequencing in the same sequencing run (e.g., using a pooled mixture and / or in the same sequencing cell).

[0126] In various embodiments, the methods further comprise sequencing the captured cfDNA, e.g., to different degrees of sequencing depth for the epigenetic and sequence-variable target region sets, consistent with the discussion herein. In some embodiments, complexes of target-specific probes and DNA are separated from DNA not bound to target-specific probes. For example, where target-specific probes are bound covalently or noncovalently to a solid support, a washing or aspiration step can be used to separate unbound material. Alternatively, where the complexes have chromatographic properties distinct from unbound material (e.g., where the probes comprise a ligand that binds a chromatographic resin), chromatography can be used.

[0127] As discussed in detail elsewhere herein, the set of target-specific probes may comprise a plurality of sets such as probes for a sequence-variable target region set and probes for an epigenetic target region set. In some such embodiments, the capturing step is performed with the probes for the sequence-variable target region set and the probes for the epigenetic target region set in the same vessel at the same time, e.g., the probes for the sequence-variable and epigenetic target region sets are in the same composition. This approach provides a relatively streamlined workflow. In some embodiments, the concentration of the probes for the sequence-variable target region set is greater that the concentration of the probes for the epigenetic target region set.

[0128] Alternatively, the capturing step is performed with the sequence -variable target region probe set in a first vessel and with the epigenetic target region probe set in a second vessel, or the contacting step is performed with the sequence-variable target region probe set at a first time and a first vessel and the epigenetic target region probe set at a second time before or after the first time. This approach allows for preparation of separate first and second compositions including captured DNA corresponding to the sequence-variable target region set and captured DNA corresponding to the epigenetic target region set. The compositions can be processed separately as desired (e.g., to fractionate based on methylation as described elsewhere herein) and recombined in appropriate proportions to provide material for further processing and analysis such as sequencing.

[0129] In some embodiments, the DNA is amplified. In some embodiments, amplification is performed before the capturing step. In some embodiments, amplification is performed after the capturing step.

[0130] In some embodiments, adapters are included in the DNA. This may be done concurrently with an amplification procedure, e.g., by providing the adapters in a 5’ portion of a primer, e.g., as described above. Alternatively, adapters can be added by other approaches, such as ligation.

[0131] In some embodiments, tags, which may be or include barcodes, are included in the DNA. Tags can facilitate identification of the origin of a nucleic acid. For example, barcodes can be used to allow the origin (e.g., subject) whence the DNA came to be identified following pooling of a plurality of samples for parallel sequencing. This may be done concurrently with an amplification procedure,e.g., by providing the barcodes in a 5’ portion of a primer, e.g., as described above. In some embodiments, adapters and tags / barcodes are provided by the same primer or primer set. For example, the barcode may be located 3 ’ of the adapter and 5 ’ of the target-hybridizing portion of the primer. Alternatively, barcodes can be added by other approaches, such as ligation, optionally together with adapters in the same ligation substrate.

[0132] Additional details regarding amplification, tags, and barcodes are discussed in the “General Features of the Methods” section below, which can be combined to the extent practicable with any of the foregoing embodiments and the embodiments set forth in the introduction and summary section.Captured Set

[0133] In some embodiments, a captured set of DNA (e.g., cfDNA) is provided. With respect to the disclosed methods, the captured set of DNA may be provided, e.g., by performing a capturing step after a partitioning step as described herein. The captured set may comprise DNA corresponding to a sequence-variable target region set, an epigenetic target region set, or a combination thereof. In some embodiments the quantity of captured sequence-variable target region DNA is greater than the quantity of the captured epigenetic target region DNA, when normalized for the difference in the size of the targeted regions (footprint size).

[0134] Alternatively, first and second captured sets may be provided, including, respectively, DNA corresponding to a sequence-variable target region set and DNA corresponding to an epigenetic target region set. The first and second captured sets may be combined to provide a combined captured set.

[0135] In some embodiments in which a captured set including DNA corresponding to the sequence -variable target region set and the epigenetic target region set includes a combined captured set as discussed above, the DNA corresponding to the sequence-variable target region set may be present at a greater concentration than the DNA corresponding to the epigenetic target region set, e.g., a 1.1 to 1.2-fold greater concentration, a 1.2- to 1.4-fold greater concentration, a 1.4- to 1.6-fold greater concentration, a 1.6- to 1.8-fold greater concentration, a 1.8- to 2.0-fold greater concentration, a 2.0-to 2.2-fold greater concentration, a 2.2- to 2.4-fold greater concentration a 2.4- to 2.6-fold greater concentration, a 2.6- to 2.8-fold greater concentration, a 2.8- to 3.0-fold greater concentration, a 3.0-to 3.5-fold greater concentration, a 3.5- to 4.0, a 4.0- to 4.5-fold greater concentration, a 4.5- to 5.0-fold greater concentration, a 5.0- to 5.5 -fold greater concentration, a 5.5- to 6.0-fold greater concentration, a 6.0- to 6.5-fold greater concentration, a 6.5- to 7.0-fold greater, a 7.0- to 7.5-fold greater concentration, a 7.5- to 8.0-fold greater concentration, an 8.0- to 8.5-fold greater concentration, an 8.5- to 9.0-fold greater concentration, a 9.0- to 9.5-fold greater concentration, 9.5- to 10.0-fold greater concentration, a 10- to 11-fold greater concentration, an 11- to 12-fold greater concentration a 12- to 13 -fold greater concentration, a 13- to 14-fold greater concentration, a 14- to 15 -fold greater concentration, a 15 - to 16-fold greater concentration, a 16- to 17-fold greater concentration, a 17- to 18-fold greater concentration, an 18- to 19-fold greater concentration, a 19- to20-fold greater concentration, a 20- to 30-fold greater concentration, a 30- to 40-fold greater concentration, a 40- to 50-fold greater concentration, a 50- to 60-fold greater concentration, a 60- to 70-fold greater concentration, a 70- to 80-fold greater concentration, a 80- to 90-fold greater concentration, a 90- to 100-fold greater concentration, a 10- to 20-fold greater concentration, a 10- to 40-fold greater concentration, a 10- to 50-fold greater concentration, a 10- to 70-fold greater concentration, or a 10- to 100-fold greater concentration. The degree of difference in concentrations accounts for normalization for the footprint sizes of the target regions, as discussed in the definition section.Epigenetic Target Region Set

[0136] The epigenetic target region set may comprise one or more types of target regions likely to differentiate DNA from neoplastic (e.g., tumor or cancer) cells and from healthy cells, e.g., non-neoplastic circulating cells. Exemplary types of such regions are discussed in detail herein. The epigenetic target region set may also comprise one or more control regions, e.g., as described herein. In some embodiments, the epigenetic target region set 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 set has a footprint in the range of 100-1000 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.Hypermethylation Variable Target Regions

[0137] In some embodiments, the epigenetic target region set includes one or more hypermethylation variable target regions. In general, hypermethylation variable target regions refer to regions where an increase in the level of observed methylation, e.g., in a cfDNA sample, indicates an increased likelihood that a sample (e.g., of cfDNA) contains DNA produced by neoplastic cells, such as tumor or cancer cells. For example, hypermethylation of promoters of tumor suppressor genes has been observed repeatedly. See, e.g., Kang et al., Genome Biol. 18:53 (2017) and references cited therein. In an example, hypermethylation variable target regions can include regions that do not necessarily differ in methylation in cancerous tissue relative to DNA from healthy tissue of the same type, but do differ in methylation (e.g., have more methylation) relative to cfDNA that is typical in healthy subjects. Where, for example, the presence of a cancer results in increased cell death such as apoptosis of cells of the tissue type corresponding to the cancer, such a cancer can be detected at least in part using such hypermethylation variable target regions. In some embodiments, hypermethylation variable target regions include one or more genomic regions, where the cfDNA molecules in those regions do not differ in methylation state in cancer subjects relative to cfDNA from healthy subjects, but the presence / increased quantity of hypermethylated cfDNA in those regions is indicative of a particular tissue type (e.g., cancer origin) and is presented as cfDNA with increased apoptosis (e.g. tumor shedding) into circulation.

[0138] Hypermethylation target regions may be obtained, e.g., from the Cancer Genome Atlas. Kang et al., Genome Biology 18:53 (2017), describe construction of a probabilistic method called CancerLocator using hypermethylation target regions from breast, colon, kidney, liver, and lung. In some embodiments, the hypermethylation target regions can be specific to one or more types of cancer. Accordingly, in some embodiments, the hypermethylation target regions include one, two, three, four, or five subsets of hypermethylation target regions that collectively show hypermethylation in one, two, three, four, or five of breast, colon, kidney, liver, and lung cancers.

[0139] In some embodiments, the probes for the epigenetic target region set comprise probes specific for one or more hypermethylation variable target regions. The hypermethylation variable target regions may be any of those set forth above. For example, in some embodiments, the probes specific for hypermethylation variable target regions comprise probes specific for a plurality of loci 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 for hypermethylation variable target regions comprise probes specific for a plurality of loci 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 for hypermethylation variable target regions comprise probes specific for a plurality of loci 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 may be one or more probes with a hybridization site that binds between the transcription start site and the stop codon (the last stop codon for genes that are alternatively spliced) of the gene. In some embodiments, the one or more probes bind within 300 bp of the listed position, e.g., within 200 or 100 bp. In some embodiments, a probe has a hybridization site overlapping the position listed above. In some embodiments, the probes specific for the hypermethylation target regions include probes specific for one, two, three, four, or five subsets of hypermethylation target regions that collectively show hypermethylation in one, two, three, four, or five of breast, colon, kidney, liver, and lung cancers.Hypomethylation Variable Target Regions

[0140] Global hypomethylation is a commonly observed phenomenon in various 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, ovarian, prostate, leukemia, hepatocellular, and cervical cancers). For example, regions such as repeated elements, e.g., LINE1 elements, Alu elements, centromeric tandem repeats, pericentromeric tandem repeats, and satellite DNA, and intergenic regions that are ordinarily methylated in healthy cells may 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 level of observed methylation indicates an increased likelihood that a sample (e.g., of cfDNA) contains DNA producedby neoplastic cells, such as tumor or cancer cells. In an example, hypomethylation variable target regions can include regions that do not necessarily differ in methylation state in cancerous tissue relative to DNA from healthy tissue of the same type, but do differ in methylation (e.g., are less methylated) relative to cfDNA that is typical in healthy subjects. Where, for example, the presence of a cancer results in increased cell death such as apoptosis of cells of the tissue type corresponding to the cancer, such a cancer can be detected at least in part using such hypomethylation variable target regions. In some embodiments, hypomethylation variable target regions include one or more genomic regions, where the cfDNA molecules in those regions do not differ in methylation state in cancer subjects relative to cfDNA from healthy subjects, but the presence / increased quantity of hypomethylated cfDNA in those regions is indicative of a particular tissue type (e.g., cancer origin) and is presented as cfDNA with increased apoptosis (e.g. tumor shedding) into circulation.

[0141] In some embodiments, hypomethylation variable target regions include repeated elements and / or intergenic regions. In some embodiments, repeated elements include one, two, three, four, or five of LINE 1 elements, Alu elements, centromeric tandem repeats, pericentromeric tandem repeats, and / or satellite DNA.

[0142] Exemplary specific genomic regions that show cancer-associated hypomethylation include nucleotides 8403565-8953708 and 151104701-151106035 ofhuman chromosome 1. In some embodiments, the hypomethylation variable target regions overlap or comprise one or both of these regions.

[0143] In some embodiments, the probes for the epigenetic target region set comprise probes specific for one or more hypomethylation variable target regions. The hypomethylation variable target regions may be any of those set forth above. For example, the probes specific for one or more hypomethylation variable target regions may include probes for regions such as repeated elements, e.g., LINE1 elements, Alu elements, centromeric tandem repeats, pericentromeric tandem repeats, and satellite DNA, and intergenic regions that are ordinarily methylated in healthy cells may show reduced methylation in tumor cells.

[0144] In some embodiments, probes specific for hypomethylation variable target regions include probes specific for repeated elements and / or intergenic regions. In some embodiments, probes specific for repeated elements include probes specific for one, two, three, four, or five of LINE 1 elements, Alu elements, centromeric tandem repeats, pericentromeric tandem repeats, and / or satellite DNA.

[0145] Exemplary probes specific for genomic regions that show cancer-associated hypomethylation include probes specific for nucleotides 8403565-8953708 and / or 151104701-151106035 ofhuman chromosome 1. In some embodiments, the probes specific for hypomethylation variable target regions include probes specific for regions overlapping or including nucleotides 8403565-8953708 and / or 151104701-151106035 ofhuman chromosome

[0146] Probes for detecting the panel of regions can include those for detecting genomic regions of interest (hotspot regions) as well as nucleosome-aware probes (e.g., KRAS codons 12 and 13) andmay be designed to optimize capture based on analysis of cfDNA coverage and fragment size variation impacted by nucleosome binding patterns and GC sequence composition. Regions used herein can also include non-hotspot regions optimized based on nucleosome positions and GC models. Subjects

[0147] 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 neoplasia. In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject suspected of having neoplasia. In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject in remission from a tumor, cancer, or neoplasia (e.g., following chemotherapy, surgical resection, radiation, or a combination thereof). In any of the foregoing embodiments, the cancer, tumor, or neoplasia or suspected cancer, tumor, or neoplasia may be of the lung, colon, rectum, kidney, breast, prostate, or liver. In some embodiments, the cancer, tumor, or neoplasia or suspected cancer, tumor, or neoplasia is of the lung. In some embodiments, the cancer, tumor, or neoplasia or suspected cancer, tumor, or neoplasia is of the colon or rectum. In some embodiments, the cancer, tumor, or neoplasia or suspected cancer, tumor, or neoplasia is of the breast. In some embodiments, the cancer, tumor, or neoplasia or suspected cancer, tumor, or neoplasia is of the prostate. In any of the foregoing embodiments, the subject may be a human subject.

[0148] 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 in the range of 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.

[0149] In some embodiments, the probes specific for the sequence-variable target region set comprise 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, ESRI, FGFR1, FGFR2, FGFR3, FOXL2, GATA3, GNA11, GNAQ, GNAS, HRAS, IDH1, IDH2, KIT, KRAS, MED 12, MET, MYC, NFE2L2, NRAS, PDGFRA, PIK3CA, PPP2R1A, PTEN, RET, STK11, TP53, and U2AF1.Compositions Including Captured DNA

[0150] Provided herein is a combination including first and second populations of captured DNA. The first population may comprise or be derived from DNA with a cytosine modification in a greater proportion than the second population. The first population may comprise a form of a first nucleobase originally present in the DNA with altered base pairing specificity and a second nucleobase without altered base pairing specificity, wherein the form of the first nucleobase originally present in the DNAprior to alteration of base pairing specificity is a modified or unmodified nucleobase, the second nucleobase is a modified or unmodified nucleobase different from the first nucleobase, and the form of the first nucleobase originally present in the DNA prior to alteration of 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 with 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 nucleobase may be any of those discussed herein in the Summary or with respect to subjecting the first subsample to a procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample.

[0151] In some embodiments, the first population includes a sequence tag selected from a first set of one or more sequence tags and the second population includes 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 tags may comprise barcodes.

[0152] In some embodiments, the first population includes protected hmC, such as glucosylated hmC. In some embodiments, the first population was subjected to any of the conversion procedures discussed herein, such as bisulfite conversion, Ox-BS conversion, TAB conversion, ACE conversion, TAP conversion, TAPSJ3 conversion, or CAP conversion. In some embodiments, the first population was subjected to protection of hmC followed by deamination of mC and / or C. In some embodiments of the combination, the first population includes or was derived from DNA with a cytosine modification in a greater proportion than the second population and the first population includes 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.

[0153] In some embodiments, the first nucleobase (e.g., a modified cytosine) is biotinylated. In some embodiments, the first nucleobase (e.g., a modified cytosine) is a product of a Huisgen cycloaddition to P-6-azide-glucosyl-5-hydroxymethylcytosine that includes an affinity label (e.g., biotin).

[0154] In any of the combinations described herein, the captured DNA may comprise cfDNA. The captured DNA may have any of the features described herein concerning captured sets, including, e.g., a greater concentration of the DNA corresponding to the sequence-variable target region set (normalized for footprint size as discussed above) than of the DNA corresponding to the epigenetic target region set. In some embodiments, the DNA of the captured set includes sequence tags, whichmay be added to the DNA as described herein. In general, the inclusion of sequence tags results in the DNA molecules differing from their naturally occurring, untagged form.

[0155] The combination may further comprise a probe set described herein or sequencing primers, each of which may differ from naturally occurring nucleic acid molecules. For example, a probe set described herein may comprise a capture moiety, and sequencing primers may comprise a non-naturally occurring label.Computer Systems, Processing of Real World Evidence (RWE)

[0156] Methods of the present disclosure can be implemented using, or with the aid of, computer systems. For example, such methods may comprise: partitioning the sample into a plurality of subsamples, including a first subsample and a second subsample, wherein the first subsample includes DNA with a cytosine modification in a greater proportion than the second subsample; subjecting the first subsample to a procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample, 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 DNA in the first subsample and DNA in the second subsample in a manner that distinguishes the first nucleobase from the second nucleobase in the DNA of the first subsample.

[0157] In an aspect, the present disclosure provides a non-transitory computer-readable medium including computer-executable instructions which, when executed by at least one electronic processor, perform at least a portion of a method including: collecting cfDNA from a test subject; capturing a plurality of sets of target regions from the cfDNA, wherein the plurality of target region sets includes a sequence -variable target region set and an epigenetic target region set, whereby a captured set of cfDNA molecules is produced; sequencing the captured cfDNA molecules, wherein the captured cfDNA molecules of the sequence-variable target region set are sequenced to a greater depth of sequencing than the captured cfDNA molecules of the epigenetic target region set; obtaining a plurality of sequence reads generated by a nucleic acid sequencer from sequencing the captured cfDNA molecules; mapping the plurality of sequence reads to one or more reference sequences to generate mapped sequence reads; and processing the mapped sequence reads corresponding to the sequence-variable target region set and to the epigenetic target region set to determine the likelihood that the subject has cancer.

[0158] The code can be pre-compiled and configured for use with a machine have a processer adapted to execute the code or can be compiled during runtime. The code can be supplied in a programming language that can be selected to enable the code to execute in a pre-compiled or as-compiled fashion.

[0159] Additional details relating to computer systems and networks, databases, and computer program products are also provided in, for example, Peterson, Computer Networks: A Systems Approach, Morgan Kaufmann, 5th Ed. (2011), Kurose, Computer Networking: A Top-Down Approach, Pearson, 7th Ed. (2016), Elmasri, Fundamentals of Database Systems, Addison Wesley, 6th Ed. (2010), Coronel, Database Systems: Design, Implementation, & Management, Cengage Learning, 11th Ed. (2014), Tucker, Programming Languages, McGraw-Hill Science / Engineering / Math, 2nd Ed. (2006), and Rhoton, Cloud Computing Architected: Solution Design Handbook, Recursive Press (2011), each of which is hereby incorporated by reference in its entirety.Cancer and Other Diseases

[0160] The present methods can be used to diagnose presence of conditions, particularly cancer, in a subject, to characterize conditions (e.g., staging cancer or determining heterogeneity of a cancer), monitor response to treatment of a condition, effect prognosis risk of developing a condition or subsequent course of a condition. The present disclosure can also be useful in determining the efficacy of a particular treatment option. Successful treatment options may increase the amount of copy number variation or rare mutations detected in subject's blood if the treatment is successful as more cancers may die and shed DNA. In other examples, this may not occur. In another example, perhaps certain treatment options may be correlated with genetic profiles of cancers over time. This correlation may be useful in selecting a therapy.

[0161] Additionally, if a cancer is observed to be in remission after treatment, the present methods can be used to monitor residual disease or recurrence of disease.

[0162] In some embodiments, the methods and systems disclosed herein may be used to identify customized or targeted therapies to treat a given disease or condition in patients based on the classification of a nucleic acid variant as being of somatic or germline origin. Typically, the disease under consideration is a type of cancer. Non-limiting examples of such cancers include biliary tract cancer, bladder cancer, transitional cell carcinoma, urothelial carcinoma, brain cancer, gliomas, astrocytomas, breast carcinoma, metaplastic carcinoma, cervical cancer, cervical squamous cell carcinoma, rectal cancer, colorectal carcinoma, colon cancer, hereditary nonpolyposis colorectal cancer, colorectal adenocarcinomas, gastrointestinal stromal tumors (GISTs), endometrial carcinoma, endometrial stromal sarcomas, esophageal cancer, esophageal squamous cell carcinoma, esophageal adenocarcinoma, ocular melanoma, uveal melanoma, gallbladder carcinomas, gallbladder adenocarcinoma, renal cell carcinoma, clear cell renal cell carcinoma, transitional cell carcinoma, urothelial carcinomas, Wilms tumor, leukemia, acute lymphocytic 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 lymphomas, non-Hodgkin lymphoma, diffuse large B-cell lymphoma, Mantle cell lymphoma, T cell lymphomas, non-Hodgkin lymphoma, precursor T-lymphoblastic lymphoma / leukemia, peripheral T cell lymphomas, multiple myeloma, nasopharyngeal carcinoma (NPC), neuroblastoma, oropharyngeal cancer, oral cavity squamous cell carcinomas, osteosarcoma, ovarian carcinoma, pancreatic cancer, pancreatic ductal adenocarcinoma, pseudopapillary neoplasms, acinar cell carcinomas. Prostate cancer, prostate adenocarcinoma, skin cancer, melanoma, malignant melanoma, cutaneous melanoma, small intestine carcinomas, stomach cancer, gastric carcinoma, gastrointestinal stromal tumor (GIST), uterine cancer, or uterine sarcoma. Type and / or stage of cancer can be detected from genetic variations including mutations, rare mutations, indels, copy number variations, transversions, translocations, inversion, deletions, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structure alterations, gene fusions, chromosome fusions, gene truncations, gene amplification, gene duplications, chromosomal lesions, DNA lesions, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns, and abnormal changes in nucleic acid 5 -methylcytosine.

[0163] Genetic data can also be used for characterizing a specific form of cancer. Cancers are often heterogeneous in both composition and staging. Genetic profile data may allow characterization of specific sub-types of cancer that may be important in the diagnosis or treatment of that specific subtype. This information may also provide a subject or practitioner clues regarding the prognosis of a specific type of cancer and allow either a subject or practitioner to adapt treatment options in accord with the progress of the disease. Some cancers can progress to become more aggressive and genetically unstable. Other cancers may remain benign, inactive or dormant. The system and methods of this disclosure may be useful in determining disease progression.

[0164] Further, the methods of the disclosure may be used to characterize the heterogeneity of an abnormal condition in a subject. Such methods can include, e.g., generating a genetic profile of extracellular polynucleotides derived from the subject, wherein the genetic profile includes a plurality of data resulting from copy number variation and rare mutation analyses. In some embodiments, an abnormal condition is cancer. In some embodiments, the abnormal condition may be one resulting in a heterogeneous genomic population. In the example of cancer, some tumors are known to comprise tumor cells in different stages of the cancer. In other examples, heterogeneity may comprise multiple foci of disease. Again, in the example of cancer, there may be multiple tumor foci, perhaps where one or more foci are the result of metastases that have spread from a primary site.

[0165] The present methods can be used to generate or profile, fingerprint or set of data that is a summation of genetic information derived from different cells in a heterogeneous disease. This set of data may comprise copy number variation, epigenetic variation, and mutation analyses alone or in combination.

[0166] The present methods can be used to diagnose, prognose, monitor or observe cancers, or other diseases. In some embodiments, the methods herein do not involve the diagnosing, prognosing ormonitoring a fetus and as such are not directed to non-invasive prenatal testing. In other embodiments, these methodologies may be employed in a pregnant subject to diagnose, prognose, monitor or observe cancers or other diseases in an unborn subject whose DNA and other polynucleotides may cocirculate with maternal molecules.

[0167] Non-limiting examples of other genetic-based diseases, disorders, or conditions that are 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, 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, velocardiofacial syndrome, WAGR syndrome, Wilson disease, or the like.

[0168] In some embodiments, a method described herein includes detecting a presence or absence of DNA originating or derived from a tumor cell at a preselected timepoint following a previous cancer treatment of a subject previously diagnosed with cancer using a set of sequence information obtained as described herein. The method may further comprise determining a cancer recurrence score that is indicative of the presence or absence of the DNA originating or derived from the tumor cell for the test subject. Where a cancer recurrence score is determined, it may further be used to determine a cancer recurrence status. The cancer recurrence status may be at risk for cancer recurrence, e.g., when the cancer recurrence score is above a predetermined threshold. The cancer recurrence status may be at low or lower risk for cancer recurrence, e.g., when the cancer recurrence score is above a predetermined threshold. In particular embodiments, a cancer recurrence score equal to the predetermined threshold may result in a cancer recurrence status of either at risk for cancer recurrence or at low or lower risk for cancer recurrence.

[0169] In some embodiments, a cancer recurrence score is compared with 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 above the cancer recurrence threshold or not a candidate for therapy when the cancer recurrence score is below the cancer recurrence threshold. In particular embodiments, a cancer recurrence score equal to the cancer recurrence threshold may result in classification as either a candidate for a subsequent cancer treatment or not a candidate for therapy.

[0170] The methods discussed above may further comprise any compatible feature or features set forth elsewhere herein, including in the section regarding methods of determining a risk of cancerrecurrence in a test subject and / or classifying a test subject as being a candidate for a subsequent cancer treatment.Methods of Determining a Risk of Cancer Recurrence in a Test Subject and / or Classifying a Test Subject as Being a Candidate for a Subsequent Cancer Treatment.

[0171] In some embodiments, a method provided herein is a method of determining a risk of cancer recurrence in a test subject. In some embodiments, a method provided herein is a method of classifying a test subject as being a candidate for a subsequent cancer treatment.

[0172] Any of such methods may comprise collecting DNA (e.g., originating or derived from a tumor cell) from the test subject diagnosed with the cancer at one or more preselected timepoints following one or more previous cancer treatments to the test subject. The subject may be any of the subjects described herein. The DNA may be cfDNA. The DNA may be obtained from a tissue sample.

[0173] Any of such methods may comprise capturing a plurality of sets of target regions from DNA from the subject, wherein the plurality of target region sets includes a sequence-variable target region set and an epigenetic target region set, whereby a captured set of DNA molecules is produced. The capturing step may be performed according to any of the embodiments described elsewhere herein. In any of such methods, the previous cancer treatment may comprise surgery, administration of a therapeutic composition, and / or chemotherapy.

[0174] Any of such methods may comprise sequencing the captured DNA molecules, whereby a set of sequence information is produced. The captured DNA molecules of the sequence -variable target region set may be sequenced to a greater depth of sequencing than the captured DNA molecules of the epigenetic target region set.

[0175] Any of such methods may comprise detecting a presence or absence of DNA originating or derived from a tumor cell at a preselected timepoint using the set of sequence information. The detection of the presence or absence of DNA originating or derived from a tumor cell may be performed according to any of the embodiments thereof described elsewhere herein.

[0176] Methods of determining a risk of cancer recurrence in a test subject may comprise determining a cancer recurrence score that is indicative of the presence or absence, or amount, of the DNA originating or derived from the tumor cell for the test subject. The cancer recurrence score may further be used to determine a cancer recurrence status. The cancer recurrence status may be at risk for cancer recurrence, e.g., when the cancer recurrence score is above a predetermined threshold. The cancer recurrence status may be at low or lower risk for cancer recurrence, e.g., when the cancer recurrence score is above a predetermined threshold. In particular embodiments, a cancer recurrence score equal to the predetermined threshold may result in a cancer recurrence status of either at risk for cancer recurrence or at low or lower risk for cancer recurrence.

[0177] Methods of classifying a test subject as being a candidate for a subsequent cancer treatment may comprise comparing the cancer recurrence score of the test subject with a predetermined cancerrecurrence threshold, thereby classifying the test subject as a candidate for the subsequent cancer treatment when the cancer recurrence score is above the cancer recurrence threshold or not a candidate for therapy when the cancer recurrence score is below the cancer recurrence threshold. In particular embodiments, a cancer recurrence score equal to the cancer recurrence threshold may result in classification as either a candidate for a subsequent cancer treatment or not a candidate for therapy. In some embodiments, the subsequent cancer treatment includes chemotherapy or administration of a therapeutic composition.

[0178] Any of such methods may comprise determining a disease-free survival (DFS) period for the test subject based on the cancer recurrence score; for example, the DFS period may be 1 year, 2 years, 3, years, 4 years, 5 years, or 10 years.

[0179] In some embodiments, the set of sequence information includes sequence -variable target region sequences, and determining the cancer recurrence score may comprise determining at least a first subscore indicative of the amount of SNVs, insertions / deletions, CNVs and / or fusions present in sequence-variable target region sequences.

[0180] In some embodiments, a number of mutations in the sequence-variable target regions chosen from 1, 2, 3, 4, or 5 is sufficient for the first subscore to result in a cancer recurrence score classified as positive for cancer recurrence. In some embodiments, the number of mutations is chosen from 1, 2, or 3.

[0181] 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 the 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 of the same type of tissue as was obtained from the test subject). These abnormal molecules (i.e., molecules with an epigenetic state different from DNA found in a corresponding sample from a healthy subject) may be consistent with epigenetic changes associated with cancer, e.g., methylation of hypermethylation variable target regions and / or perturbed fragmentation of fragmentation variable target regions, where “perturbed” means different from DNA found in a corresponding sample from a healthy subject.

[0182] In some embodiments, a proportion of molecules corresponding to the hypermethylation variable target region set and / or fragmentation variable target region set that indicate hypermethylation in the hypermethylation variable target region set and / or abnormal fragmentation in the fragmentation variable target region set greater than or equal to a value in the range of 0.001%-10% is sufficient for the second subscore to be classified as positive for cancer recurrence. The range may be 0.001%-l%, 0.005%-l%, 0.01%-5%, 0.01%-2%, or 0.01%-1%.

[0183] In some embodiments, any of such methods may comprise determining a fraction of tumor DNA from the fraction of molecules in the set of sequence information that indicate one or morefeatures indicative of origination from a tumor cell. This may be done for molecules corresponding to some or all of the epigenetic target regions, e.g., including one or both of hypermethylation variable target regions and fragmentation variable target regions (hypermethylation of a hypermethylation variable target region and / or abnormal fragmentation of a fragmentation variable target region may be considered indicative of origination from a tumor cell). This may be done for molecules corresponding to sequence variable target regions, e.g., molecules including alterations consistent with cancer, such as SNVs, indels, CNVs, and / or fusions. The fraction of tumor DNA may be determined based on a combination of molecules corresponding to epigenetic target regions and molecules corresponding to sequence variable target regions.

[0184] Determination of a cancer recurrence score may be based at least in part on the fraction of tumor DNA, wherein a fraction of tumor DNA greater than a threshold in the range of 10-11 to 1 or 10-10 to 1 is sufficient for the cancer recurrence score to be classified as positive for cancer recurrence. In some embodiments, a fraction of tumor DNA greater than or equal to a threshold in the range of 10-10 to 10-9, 10-9 to 10-8, 10-8 to 10-7, 10-7 to 10-6, 10-6 to 10-5, 10-5 to 10-4, 10-4 to 10-3, 10-3 to 10-2, or 10-2 to 10-1 is sufficient for the cancer recurrence score to be classified as positive for cancer recurrence. In some embodiments, the fraction of tumor DNA greater than a threshold of at least 10-7 is sufficient for the cancer recurrence score to be classified as positive for cancer recurrence. A determination that a fraction of tumor DNA is greater than a threshold, such as a threshold corresponding to any of the foregoing embodiments, may be made based on a cumulative probability. For example, the sample was considered positive if the cumulative probability that the tumor fraction was greater than a threshold in any of the foregoing ranges 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.

[0185] In some embodiments, the set of sequence information includes sequence -variable target region sequences and epigenetic target region sequences, and determining the cancer recurrence score includes determining a first subscore indicative of the amount of SNVs, insertions / deletions, CNVs and / or fusions present in sequence-variable target region sequences and a second subscore indicative of the amount of abnormal molecules in epigenetic target region sequences, and combining the first and second subscores to provide the cancer recurrence score. Where the first and second subscores are combined, they may be combined by applying a threshold to each subscore independently (e.g., greater than a predetermined number of mutations (e.g., > 1) in sequence-variable target regions, and greater than a predetermined fraction of abnormal molecules (i.e., molecules with an epigenetic state different from the DNA found in a corresponding sample from a healthy subject; e.g., tumor) in epigenetic target regions), or training a machine learning classifier to determine status based on a plurality of positive and negative training samples.

[0186] In some embodiments, a value for the combined score in the range of -4 to 2 or -3 to 1 is sufficient for the cancer recurrence score to be classified as positive for cancer recurrence.

[0187] In any embodiment where a cancer recurrence score is classified as positive for cancer recurrence, the cancer recurrence status of the subject may be at risk for cancer recurrence and / or the subject may be classified as a candidate for a subsequent cancer treatment.

[0188] In some embodiments, the cancer is any one of the types of cancer described elsewhere herein, e.g., colorectal cancer.

[0189] For example, as shown in Figure 10, comprehensive evaluation, diagnostic testing, molecular and genetic profiling and / or risk assessment, can be utilized in combination for an assessment. As shown in Figure 11, patient consultation, treatment strategy and / or tailored treatment can be utilized in combination for treatment planning. For Figure 12, treatment implementation can include pretreatment and / or treatment execution. For Figure 13, regular follow-ups and / or response assessment can constitute mechanisms for determining monitoring and adjustment. For Figure 14, post-treatment surveillance and / or recurrence management can support long term management and / or survivorship.Therapies and Related Administration

[0190] In certain embodiments, the methods disclosed herein relate to identifying and administering customized therapies to patients given the status of a nucleic acid variant as being of somatic or germline origin. In some embodiments, essentially any cancer therapy (e.g., surgical therapy, radiation therapy, chemotherapy, and / or the like) may be included as part of these methods.Typically, customized therapies include at least one immunotherapy (or an immunotherapeutic agent). Immunotherapy refers generally to methods of enhancing an immune response against a given cancer type. In certain embodiments, immunotherapy refers to methods of enhancing a T cell response against a tumor or cancer.

[0191] In certain embodiments, the status of a nucleic acid variant from a sample from a subject as being of somatic or germline origin may be compared with a database of comparator results from a reference population to identify customized or targeted therapies for that subject. Typically, the reference population includes patients with the same cancer or disease type as the test subject and / or patients who are receiving, or who have received, the same therapy as the test subject. A customized or targeted therapy (or therapies) may be identified when the nucleic variant and the comparator results satisfy certain classification criteria (e.g., are a substantial or an approximate match).

[0192] In certain embodiments, the customized therapies described herein are typically administered parenterally (e.g., intravenously or subcutaneously). Pharmaceutical compositions containing an immunotherapeutic agent are typically administered intravenously. Certain therapeutic agents are administered orally. However, customized therapies (e.g., immunotherapeutic agents, etc.) may also be administered by methods such as, for example, buccal, sublingual, rectal, vaginal, intraurethral, topical, intraocular, intranasal, and / or intraauricular, which administration may include tablets, capsules, granules, aqueous suspensions, gels, sprays, suppositories, salves, ointments, or the like.

[0193] While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the invention be limited by the specific examples provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the embodiments herein are not meant to be construed in a limiting sense. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the disclosure described herein may be employed in practicing the invention. It is therefore contemplated that the disclosure shall also cover any such alternatives, modifications, variations or equivalents. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

[0194] While the foregoing disclosure has been described in some detail by way of illustration and example for purposes of clarity and understanding, it will be clear to one of ordinary skill in the art from a reading of this disclosure that various changes in form and detail can be made without departing from the true scope of the disclosure and may be practiced within the scope of the appended claims. For example, all the methods, systems, computer readable media, and / or component features, steps, elements, or other aspects thereof can be used in various combinations.Biomarkers

[0195] The disclosure provides methods of using biomarkers for the diagnosis, prognosis, and therapy selection of a subject suffering from e.g., cancer. A biomarker may be any gene or variant of a gene whose presence, mutation, deletion, substitution, copy number, or translation (i.e., to a protein) is an indicator of a disease state. Biomarkers of the present disclosure may include the presence, mutation, deletion, substitution, copy number, or translation in any one or more of EGFR, KRAS, MET, BRAF, MYC, NRAS, ERBB2, ALK, Notch, PIK3CA, APC, and SMO.

[0196] A biomarker is a genetic variant associated with one or more cancers. Biomarkers may be determined using any of several resources or methods. A biomarker may have been previously discovered or may be discovered de novo using experimental or epidemiological techniques.Detection of a biomarker may be indicative of cancer when the biomarker is highly correlated a cancer. Detection of a biomarker may be indicative of cancer when a biomarker in a region or gene occur with a frequency that is greater than a frequency for a given background population or dataset.

[0197] Publicly available resources such as scientific literature and databases may describe in detail genetic variants found to be associated with cancer. Scientific literature may describe experiments or genome-wide association studies (GWAS) associating one or more genetic variants with cancer.Databases may aggregate information gleaned from sources such as scientific literature to provide a more comprehensive resource for determining one or more biomarkers. Non-limiting examples of databases include FANTOM, GTex, GEO, Body Atlas, INSiGHT, OMIM (Online Mendelian Inheritance in Man, omim.org), cBioPortal (cbioportal.org), CIViC (Clinical Interpretations of Variants in Cancer, civic.genome.wustl.edu), DOCM (Database of Curated Mutations, docm.genome.wustl.edu), and ICGC Data Portal (dcc.icgc.org). In a further example, the COSMIC (Catalogue of Somatic Mutations in Cancer) database allows for searching of biomarkers by cancer, gene, or mutation type. Biomarkers may also be determined de novo by conducting experiments such as case control or association (e.g, genome-wide association studies) studies.

[0198] One or more biomarkers may be detected in the sequencing panel. A biomarker may be one or more genetic variants associated with cancer. Biomarkers can be selected from single nucleotide variants (SNVs), copy number variants (CNVs), insertions or deletions (e.g., indels), gene fusions and inversions. Biomarkers may affect the level of a protein. Biomarkers may be in a promoter or enhancer, and may alter the transcription of a gene. The biomarkers may affect the transcription and / or translation efficacy of a gene. The biomarkers may affect the stability of a transcribed mRNA. The biomarker may result in a change to the amino acid sequence of a translated protein. The biomarker may affect splicing, may change the amino acid coded by a particular codon, may result in a frameshift, or may result in a premature stop codon. The biomarker may result in a conservative substitution of an amino acid. One or more biomarkers may result in a conservative substitution of an amino acid. One or more biomarkers may result in a nonconservative substitution of an amino acid.

[0199] One or more of the biomarkers may be a driver mutation. A driver mutation is a mutation that gives a selective advantage to a tumor cell in its microenvironment, through either increasing its survival or reproduction. None of the biomarkers may be a driver mutation. One or more of the biomarkers may be a passenger mutation. A passenger mutation is a mutation that has no effect on the fitness of a tumor cell but may be associated with a clonal expansion because it occurs in the same genome with a driver mutation.

[0200] The frequency of a biomarker may be as low as 0.001%. The frequency of a biomarker may be as low as 0.005%. The frequency of a biomarker may be as low as 0.01%. The frequency of a biomarker may be as low as 0.02%. The frequency of a biomarker may be as low as 0.03%. The frequency of a biomarker may be as low as 0.05%. The frequency of a biomarker may be as low as 0.1%. The frequency of a biomarker may be as low as 1%.

[0201] No single biomarker may be present in more than 50%, of subjects having the cancer. No single biomarker may be present in more than 40%, of subjects having the cancer. No single biomarker may be present in more than 30%, of subjects having the cancer. No single biomarker may be present in more than 20%, of subjects having the cancer. No single biomarker may be present in more than 10%, of subjects having the cancer. No single biomarker may be present in more than 5%, of subjects having the cancer. A single biomarker may be present in 0.001% to 50% of subjectshaving cancer. A single biomarker may be present in 0.01% to 50% of subjects having cancer. A single biomarker may be present in 0.01% to 30% of subjects having cancer. A single biomarker may be present in 0.01% to 20% of subjects having cancer. A single biomarker may be present in 0.01% to 10% of subjects having cancer. A single biomarker may be present in 0.1% to 10% of subjects having cancer. A single biomarker may be present in 0.1% to 5% of subjects having cancer.

[0202] Detection of a biomarker may indicate the presence of one or more cancers. Detection may indicate presence of a cancer selected from the group including ovarian cancer, pancreatic cancer, breast cancer, colorectal cancer, non-small cell lung carcinoma (e.g., squamous cell carcinoma, or adenocarcinoma) or any other cancer. Detection may indicate the presence of any cancer selected from the group including ovarian cancer, pancreatic cancer, breast cancer, colorectal cancer, non-small cell lung carcinoma (squamous cell or adenocarcinoma) or any other cancer. Detection may indicate the presence of any of a plurality of cancers selected from the group including ovarian cancer, pancreatic cancer, breast cancer, colorectal cancer and non-small cell lung carcinoma (squamous cell or adenocarcinoma), or any other cancer. Detection may indicate presence of one or more of any of the cancers mentioned in this application.

[0203] One or more cancers may exhibit a biomarker in at least one exon in the panel. One or more cancers selected from the group including ovarian cancer, pancreatic cancer, breast cancer, colorectal cancer, non-small cell lung carcinoma (squamous cell or adenocarcinoma), or any other cancer, each exhibit a biomarker in at least one exon in the panel. Each of at least 3 of the cancers may exhibit a biomarker in at least one exon in the panel. Each of at least 4 of the cancers may exhibit a biomarker in at least one exon in the panel. Each of at least 5 of the cancers may exhibit a biomarker in at least one exon in the panel. Each of at least 8 of the cancers may exhibit a biomarker in at least one exon in the panel. Each of at least 10 of the cancers may exhibit a biomarker in at least one exon in the panel. All of the cancers may exhibit a biomarker in at least one exon in the panel.

[0204] If a subject has a cancer, the subject may exhibit a biomarker in at least one exon or gene in the panel. At least 85% of subjects having a cancer may exhibit a biomarker in at least one exon or gene in the panel. At least 90%, of subjects having a cancer may exhibit a biomarker in at least one exon or gene in the panel. At least 92% of subjects having a cancer may exhibit a biomarker in at least one exon or gene in the panel. At least 95% of subjects having a cancer may exhibit a biomarker in at least one exon or gene in the panel. At least 96% of subjects having a cancer may exhibit a biomarker in at least one exon or gene in the panel. At least 97% of subjects having a cancer may exhibit a biomarker in at least one exon or gene in the panel. At least 98% of subjects having a cancer may exhibit a biomarker in at least one exon or gene in the panel. At least 99% of subjects having a cancer may exhibit a biomarker in at least one exon or gene in the panel. At least 99.5% of subjects having a cancer may exhibit a biomarker in at least one exon or gene in the panel.

[0205] If a subject has a cancer, the subject may exhibit a biomarker in at least one region in the panel. At least 85% of subjects having a cancer may exhibit a biomarker in at least one region in thepanel. At least 90%, of subjects having a cancer may exhibit a biomarker in at least one region in the panel. At least 92% of subjects having a cancer may exhibit a biomarker in at least one region in the panel. At least 95% of subjects having a cancer may exhibit a biomarker in at least one region in the panel. At least 96% of subjects having a cancer may exhibit a biomarker in at least one region in the panel. At least 97% of subjects having a cancer may exhibit a biomarker in at least one region in the panel. At least 98% of subjects having a cancer may exhibit a biomarker in at least one region in the panel. At least 99% of subjects having a cancer may exhibit a biomarker in at least one region in the panel. At least 99.5% of subjects having a cancer may exhibit a biomarker in at least one region in the panel.

[0206] Detection may be performed with a high sensitivity and / or a high specificity. Sensitivity can refer to a measure of the proportion of positives that are correctly identified as such. In some cases, sensitivity refers to the percentage of all existing biomarkers that are detected. In some cases, sensitivity refers to the percentage of sick people who are correctly identified as having certain disease. Specificity can refer to a measure of the proportion of negatives that are correctly identified as such. In some cases, specificity refers to the proportion of unaltered bases which are correctly identified. In some cases, specificity refers to the percentage of healthy people who are correctly identified as not having certain disease. The non-unique tagging method described previously significantly increases specificity of detection by reducing noise generated by amplification and sequencing errors, which reduces frequency of false positives. Detection may be performed with a sensitivity of at least 95%, 97%, 98%, 99%, 99.5%, or 99.9% and / or a specificity of at least 80%, 90%, 95%, 97%, 98% or 99%. Detection may be performed with a sensitivity of at least 90%, 95%, 97%, 98%, 99%, 99.5%, 99.6%, 99.98%, 99.9% or 99.95%. Detection may be performed with a specificity of at least 90%, 95%, 97%, 98%, 99%, 99.5%, 99.6%, 99.98%, 99.9% or 99.95%.Detection may be performed with a specificity of at least 70% and a sensitivity of at least 70%, a specificity of at least 75% and a sensitivity of at least 75%, a specificity of at least 80% and a sensitivity of at least 80%, a specificity of at least 85% and a sensitivity of at least 85%, a specificity of at least 90% and a sensitivity of at least 90%, a specificity of at least 95% and a sensitivity of at least 95%, a specificity of at least 96% and a sensitivity of at least 96%, a specificity of at least 97% and a sensitivity of at least 97%, a specificity of at least 98% and a sensitivity of at least 98%, a specificity of at least 99% and a sensitivity of at least 99%, or a specificity of 100% a sensitivity of 100%. In some cases, the methods can detect a biomarker at a sensitivity of sensitivity of about 80% or greater. In some cases, the methods can detect a biomarker at a sensitivity of sensitivity of about 95% or greater. In some cases, the methods can detect a biomarker at a sensitivity of sensitivity of about 80% or greater, and a sensitivity of sensitivity of about 95% or greater.

[0207] Detection may be highly accurate. Accuracy may apply to the identification of biomarkers in cell free DNA, and / or to the diagnosis of cancer. Statistical tools, such as co-variate analysis described above, may be used to increase and / or measure accuracy. The methods can detect abiomarker at an accuracy of at least 80%, 90%, 95%, 97%, 98% or 99%, 99.5%, 99.6%, 99.98%, 99.9%, or 99.95%. In some cases, the methods can detect a biomarker at an accuracy of at least 95% or greater.Cancer Treatments, Therapies

[0208] In some cases, the cancer treatment includes, without limitation, imatinib, gefatinib, afatinib, dacomitinib, sunitinib, sorafenib, vandetanib, brivanib, cabozantib, neratinib, tivantinib, bevacizumab, cixutumumab, dalotuzumab, figitumumab, rilotumumab, onartuzumab, ganitumab, ramucirumab, ridaforolimus, tensirolimus, 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. In some cases, the cancer treatment includes EGFR tyrosine kinase inhibitors such as gefitinib (Trade Name: Iressa), erlotinib (Trade Name: Tarceva), lapatinib, canertinib, and cetuximab.

[0209] In some instances, therapties may be used in combination, such as an anti -EGFR therapy and an anti-EGFR therapy. Anti -EGFR therapy may be used in combination with any combination of chemotherapeutic agents or chemotherapeutic regimens, for example, FOLFOX (fluorouracil [5-FU] / leucovorin / oxaliplatin), FOLFIRI (5-FU / leucovorin / irinotecan), and the like.

[0210] In some aspects, a cancer treatment is administered to a subject. In some cases, the cancer treatment is administered in combination another therapy, such as a non-anti-EGFR therapy with anti-EGFR therapy.Sequencing panel

[0211] To improve the likelihood of detecting tumor indicating mutations, the region of DNA sequenced may comprise a panel of genes or genomic regions. Selection of a limited region for sequencing (e.g., a limited panel) can reduce the total sequencing needed (e.g., a total amount of nucleotides sequenced. A sequencing panel can target a plurality of different genes or regions to detect a single cancer, a set of cancers, or all cancers.

[0212] In some aspects, a panel targets a plurality of different genes or genomic regions is selected such that a determined proportion of subjects having a cancer exhibits a genetic variant or biomarker in one or more different genes or genomic regions in the panel. The panel may be selected to limit a region for sequencing to a fixed number of base pairs. The panel may be selected to sequence a desired amount of DNA. The panel may be further selected to achieve a desired sequence read depth. The panel may be selected to achieve a desired sequence read depth or sequence read coverage for an amount of sequenced base pairs. The panel may be selected to achieve a theoretical sensitivity, a theoretical specificity and / or a theoretical accuracy for detecting one or more genetic variants in a sample.

[0213] Probes for detecting the panel of regions can include those for detecting hotspots regions as well as nucleosome-aware probes (e.g., KRAS codons 12 and 13) and may be designed to optimize capture based on analysis of cfDNA coverage and fragment size variation impacted by nucleosome binding patterns and GC sequence composition. Regions used herein can also include non-hotspot regions optimized based on nucleosome positions and GC models. The panel can comprise a plurality of subpanels, including subpanels for identifying tissue of origin (e.g., use of published literature to define 50-100 baits representing genes with most diverse transcription profile across tissues (not necessarily promoters)), whole genome scaffold (e.g., for identifying ultra-conservative genomic content and tiling sparsely across chromosomes with handful of probes for copy number base lining purposes), transcription start site (TSS) / CpG islands (e.g., for capturing differential methylated regions (e.g., Differentially Methylated Regions (DMRs)) in for example in promoters of tumor suppressor genes (e.g., SEPT9 / VIM in colorectal cancer)). In some embodiments, markers for a tissue of origin are tissue-specific epigenetic markers.

[0214] The one or more regions in the panel can comprise one or more loci from one or a plurality of genes. The plurality of genes may be selected for sequencing and biomarker detection. Genes included in the region to be sequenced may be selected from genes known to be involved in cancer, or from genes not involved in cancer. For example the plurality of genes in the panel may be oncogenes, tumor suppressors, growth factors, DNA repair genes, signaling genes, transcription factors, receptors or metabolic genes. Examples of genes that may be in the panel include, but are not limited to: APC, AR, ARID 1 A, BRAF, BRCA1, BRCA2, CCND1, CCND2, CCNE1, CDK4, CDK6, CDKN2A, CDKN2B, EGFR, ERBB2, FGFR1, FGFR2, HRAS, KIT, KRAS, MET, MYC, NF1, NRAS, PDGFRA, PIK3CA, PTEN, RAFI, TP53, AKT1, ALK, ARAF, ATM, CDH1, CTNNB1,ESR1, EZH2, FBXW7, FGFR3, GATA3, GNA11, GNAQ, GNAS, HNF1A, IDH1, IDH2, JAK2, JAK3, MAP2K1, MAP2K2, MLH1, MPL, NFE2L2, NOTCH 1, NPM1, NTRK1, PTPN11, RET, RHEB, RHOA, RIT1, ROS1, SMAD4, SMO, SRC, STK11, TERT, VHL.

[0215] In some cases, the one or more regions in the panel can comprise one or more loci from one or a plurality of genes, including one or more of AKT1, ALK, APC, ATM, BRAF, CTNNB 1, EGFR, ERBB2, ESRI, FGFR2, GATA3, GNAS, IDH1, IDH2, KIT, KRAS, MET, NRAS, PDGFRA, PIK3CA, PTEN, RBI, SMAD4, STK11, and TP53.

[0216] In some cases, the one or more regions in a panel for colorectal cancer can comprise one or more loci from a plurality of genes, including one of, two of, three of, four of, five of, or six of TP53, BRAF, KRAS, APC, TGFBR, and PIK3CA. Many of these genes may be clinically actionable, such that an observed anomaly in MAF (e.g., significantly higher or lower than in normal control subjects) may be indicative of a clinical state relevant to colorectal cancer, such as diagnosis, prognosis, risk stratification, treatment selection, tumor resistance to treatment, tumor burden, etc. Such a colorectal cancer targeted panel may comprise a relatively small number of these colorectal cancer associated genes.

[0217] In some embodiments, the one or more regions in the panel comprise one or more loci from one or a plurality of genes for detecting residual cancer after surgery. This detection can be earlier than is possible for existing methods of cancer detection. In some embodiments, the one or more regions in the panel comprise one or more loci from one or a plurality of genes for detecting cancer in a high-risk patient population. For example, smokers have much higher rates of lung cancer than the general population. Moreover, smokers can develop other lung conditions that make cancer detection more difficult, such as the development of irregular nodules in the lungs. In some embodiments, the methods described herein detect cancer in high risk patients earlier than is possible for existing methods of cancer detection.

[0218] A region may be selected for inclusion in a sequencing panel based on a number of subjects with a cancer that have a biomarker in that gene or region. A region may be selected for inclusion in a sequencing panel based on prevalence of subjects with a cancer and a biomarker present in that gene. Presence of a biomarker in a region may be indicative of a subject having cancer.

[0219] In some instances, the panel may be selected using information from one or more databases. The information regarding a cancer may be derived from cancer tumor biopsies or cfDNA assays. A database may comprise information describing a population of sequenced tumor samples. A database may comprise information about mRNA expression in tumor samples. A databased may comprise information about regulatory elements in tumor samples. The information relating to the sequenced tumor samples may include the frequency various genetic variants and describe the genes or regions in which the genetic variants occur. The genetic variants may be biomarkers. A non-limiting example of such a database is COSMIC. COSMIC is a catalogue of somatic mutations found in various cancers. For a particular cancer, COSMIC ranks genes based on frequency of mutation. A gene may be selected for inclusion in a panel by having a high frequency of mutation within a given gene. For instance, COSMIC indicates that 33% of a population of sequenced breast cancer samples have a mutation in TP53 and 22% of a population of sampled breast cancers have a mutation in KRAS. Other ranked genes, including APC, have mutations found only in about 4% of a population of sequenced breast cancer samples. TP53 and KRAS may be included in a sequencing panel based on having relatively high frequency among sampled breast cancers (compared to APC, for example, which occurs at a frequency of about 4%). COSMIC is provided as a non-limiting example, however, any database or set of information may be used that associates a cancer with biomarker located in a gene or genetic region. In another example, as provided by COSMIC, of 1156 biliary tract cancer samples, 380 samples (33%) carried mutations in TP53. Several other genes, such as APC, have mutations in 4-8% of all samples. Thus, TP53 may be selected for inclusion in the panel based on a relatively high frequency in a population of biliary tract cancer samples.

[0220] A gene or region may be selected for a panel where the frequency of a biomarker is significantly greater in sampled tumor tissue or circulating tumor DNA than found in a given background population. A combination of regions may be selected for inclusion of a panel such that atleast a majority of subjects having a cancer will have a biomarker present in at least one of the regions or genes in the panel. The combination of regions may be selected based on data indicating that, for a particular cancer or set of cancers, a majority of subjects have one or more biomarkers in one or more of the selected regions. For example, to detect cancer 1, a panel including regions A, B, C, and / or D may be selected based on data indicating that 90% of subjects with cancer 1 have a biomarker in regions A, B, C, and / or D of the panel. Alternately, biomarkers may be shown to occur independently in two or more regions in subjects having a cancer such that, combined, a biomarker in the two or more regions is present in a majority of a population of subjects having a cancer. For example, to detect cancer 2, a panel including regions X, Y, and Z may be selected based on data indicating that 90% of subjects have a biomarker in one or more regions, and in 30% of such subjects a biomarker is detected only in region X, while biomarkers are detected only in regions Y and / or Z for the remainder of the subjects for whom a biomarker was detected. Biomarkers present in one or more regions previously shown to be associated with one or more cancers may be indicative of or predictive of a subject having cancer if a biomarker is detected in one or more of those regions 50% or more of the time. Computational approaches such as models employing conditional probabilities of detecting cancer given a known cancer frequency for a set of biomarkers within one or more regions may be used to predict which regions, alone or in combination, may be predictive of cancer. Other approaches for panel selection involve the use of databases describing information from studies employing comprehensive genomic profding of tumors with large panels and / or whole genome sequencing (WGS, RNA-seq, Chip-seq, bisulfate sequencing, ATAC-seq, and others). Information gleaned from literature may also describe pathways commonly affected and mutated in certain cancers. Panel selection may be further informed by the use of ontologies describing genetic information.

[0221] Genes included in the panel for sequencing can include the fully transcribed region, the promoter region, enhancer regions, regulatory elements, and / or downstream sequence. To further increase the likelihood of detecting tumor indicating mutations only exons may be included in the panel. The panel can comprise all exons of a selected gene, or only one or more of the exons of a selected gene. The panel may comprise of exons from each of a plurality of different genes. The panel may comprise at least one exon from each of the plurality of different genes.

[0222] In some aspects, a panel of exons from each of a plurality of different genes is selected such that a determined proportion of subjects having a cancer exhibit a genetic variant in at least one exon in the panel of exons.

[0223] At least one full exon from each different gene in a panel of genes may be sequenced. The sequenced panel may comprise exons from a plurality of genes. The panel may comprise exons from 2 to 100 different genes, from 2 to 70 genes, from 2 to 50 genes, from 2 to 30 genes, from 2 to 15 genes, or from 2 to 10 genes.

[0224] A selected panel may comprise a varying number of exons. The panel may comprise from 2 to 3000 exons. The panel may comprise from 2 to 1000 exons. The panel may comprise from 2 to 500exons. The panel may comprise from 2 to 100 exons. The panel may comprise from 2 to 50 exons. The panel may comprise no more than 300 exons. The panel may comprise no more than 200 exons. The panel may comprise no more than 100 exons. The panel may comprise no more than 50 exons. The panel may comprise no more than 40 exons. The panel may comprise no more than 30 exons. The panel may comprise no more than 25 exons. The panel may comprise no more than 20 exons. The panel may comprise no more than 15 exons. The panel may comprise no more than 10 exons. The panel may comprise no more than 9 exons. The panel may comprise no more than 8 exons. The panel may comprise no more than 7 exons.

[0225] The panel may comprise one or more exons from a plurality of different genes. The panel may comprise one or more exons from each of a proportion of the plurality of different genes. The panel may comprise at least two exons from each of at least 25%, 50%, 75% or 90% of the different genes. The panel may comprise at least three exons from each of at least 25%, 50%, 75% or 90% of the different genes. The panel may comprise at least four exons from each of at least 25%, 50%, 75% or 90% of the different genes.

[0226] The sizes of the sequencing panel may vary. A sequencing panel may be made larger or smaller (in terms of nucleotide size) depending on several factors including, for example, the total amount of nucleotides sequenced or a number of unique molecules sequenced for a particular region in the panel. The sequencing panel can be sized 5 kb to 50 kb. The sequencing panel can be 10 kb to 30 kb in size. The sequencing panel can be 12 kb to 20 kb in size. The sequencing panel can be 12 kb to 60 kb in size. The sequencing panel can be at least lOkb, 12 kb, 15 kb, 20 kb, 25 kb, 30 kb, 35 kb, 40 kb, 45 kb, 50 kb, 60 kb, 70 kb, 80 kb, 90 kb, 100 kb , 110 kb, 120 kb, 130 kb, 140 kb, or 150 kb in size. The sequencing panel may be less than 100 kb, 90 kb, 80 kb, 70 kb, 60 kb, or 50 kb in size.

[0227] The panel selected for sequencing can comprise at least 1, 5, 10, 15, 20, 25, 30, 40, 50, 60, 80, or 100 regions. In some cases, the regions in the panel are selected that the size of the regions are relatively small. In some cases, the regions in the panel have a size of about 10 kb or less, about 8 kb or less, about 6 kb or less, about 5 kb or less, about 4 kb or less, about 3 kb or less, about 2.5 kb or less, about 2 kb or less, about 1.5 kb or less, or about 1 kb or less or less. In some cases, the regions in the panel have a size from about 0.5 kb to about 10 kb, from about 0.5 kb to about 6 kb, from about 1 kb to about 11 kb, from about 1 kb to about 15 kb, from about 1 kb to about 20 kb, from about 0.1 kb to about 10 kb, or from about 0.2 kb to about 1 kb. For example, the regions in the panel can have a size from about 0.1 kb to about 5 kb.

[0228] The panel selected herein can allow for deep sequencing that is sufficient to detect low-frequency genetic variants (e.g., in cell-free nucleic acid molecules obtained from a sample). An amount of genetic variants in a sample may be referred to in terms of the minor allele frequency for a given genetic variant. The minor allele frequency may refer to the frequency at which minor alleles (e.g., not the most common allele) occurs in a given population of nucleic acids, such as a sample. Genetic variants at a low minor allele frequency may have a relatively low frequency of presence in asample. In some cases, the panel allows for detection of genetic variants at a minor allele frequency of at least 0.0001%, 0.001%, 0.005%, 0.01%, 0.05%, 0.1%, or 0.5%. The panel can allow for detection of genetic variants at a minor allele frequency of 0.001% or greater. The panel can allow for detection of genetic variants at a minor allele frequency of 0.01% or greater. The panel can allow for detection of genetic variant present in a sample at a frequency of as low as 0.0001%, 0.001%, 0.005%, 0.01%, 0.025%, 0.05%, 0.075%, 0.1%, 0.25%, 0.5%, 0.75%, or 1.0%. The panel can allow for detection of biomarkers present in a sample at a frequency of at least 0.0001%, 0.001%, 0.005%, 0.01%, 0.025%, 0.05%, 0.075%, 0.1%, 0.25%, 0.5%, 0.75%, or 1.0%. The panel can allow for detection of biomarkers at a frequency in a sample as low as 1.0%. The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.75%. The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.5%. The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.25%. The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.1%. The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.075%. The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.05%. The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.025%. The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.01%. The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.005%. The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.001%. The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.0001%. The panel can allow for detection of biomarkers in sequenced cfDNA at a frequency in a sample as low as 1.0% to 0.0001%. The panel can allow for detection of biomarkers in sequenced cfDNA at a frequency in a sample as low as 0.01% to 0.0001%.

[0229] A genetic variant can be exhibited in a percentage of a population of subjects who have a disease (e.g., cancer). In some cases, at least 1%, 2%, 3%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, or 99% of a population having the cancer exhibit one or more genetic variants in at least one of the regions in the panel. For example, at least 80% of a population having the cancer may exhibit one or more genetic variants in at least one of the regions in the panel.

[0230] The panel can comprise one or more regions from each of one or more genes. In some cases, the panel can comprise one or more regions from each of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, or 80 genes. In some cases, the panel can comprise one or more regions from each of at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, or 80 genes. In some cases, the panel can comprise one or more regions from each of from about 1 to about 80, from 1 to about 50, from about 3 to about 40, from 5 to about 30, from 10 to about 20 different genes.

[0231] The regions in the panel can be selected so that one or more epigenetically modified regions are detected. The one or more epigenetically modified regions can be acetylated, methylated, ubiquitylated, phosphorylated, sumoylated, ribosylated, and / or citrullinated. For example, the regions in the panel can be selected so that one or more methylated regions are detected.

[0232] The regions in the panel can be selected so that they comprise sequences differentially transcribed across one or more tissues. In some cases, the regions can comprise sequences transcribed in certain tissues at a higher level compared to other tissues. For example, the regions can comprise sequences transcribed in certain tissues but not in other tissues.

[0233] The regions in the panel can comprise coding and / or non-coding sequences. For example, the regions in the panel can comprise one or more sequences in exons, introns, promoters, 3 ’ untranslated regions, 5’ untranslated regions, regulatory elements, transcription start sites, and / or splice sites. In some cases, the regions in the panel can comprise other non-coding sequences, including pseudogenes, repeat sequences, transposons, viral elements, and telomeres. In some cases, the regions in the panel can comprise sequences in non-coding RNA, e.g., ribosomal RNA, transfer RNA, Piwi-interacting RNA, and microRNA.

[0234] The regions in the panel can be selected to detect (diagnose) a cancer with a desired level of sensitivity (e.g., through the detection of one or more genetic variants). For example, the regions in the panel can be selected to detect the cancer (e.g., through the detection of one or more genetic variants) with a sensitivity of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. The regions in the panel can be selected to detect the cancer with a sensitivity of 100%.

[0235] The regions in the panel can be selected to detect (diagnose) a cancer with a desired level of specificity (e.g., through the detection of one or more genetic variants). For example, the regions in the panel can be selected to detect cancer (e.g., through the detection of one or more genetic variants) with a specificity of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. The regions in the panel can be selected to detect the one or more genetic variant with a specificity of 100%.

[0236] The regions in the panel can be selected to detect (diagnose) a cancer with a desired positive predictive value. Positive predictive value can be increased by increasing sensitivity (e.g., chance of an actual positive being detected) and / or specificity (e.g., chance of not mistaking an actual negative for a positive). As a non-limiting example, regions in the panel can be selected to detect the one or more genetic variant with a positive predictive value of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. The regions in the panel can be selected to detect the one or more genetic variant with a positive predictive value of 100%.

[0237] The regions in the panel can be selected to detect (diagnose) a cancer with a desired accuracy. As used herein, the term “accuracy” may refer to the ability of a test to discriminate between a disease condition (e.g., cancer) and health. Accuracy may be can be quantified using measures such as sensitivity and specificity, predictive values, likelihood ratios, the area under the ROC curve, Youden’s index and / or diagnostic odds ratio.

[0238] Accuracy may presented as a percentage, which refers to a ratio between the number of tests giving a correct result and the total number of tests performed. The regions in the panel can beselected to detect cancer with an accuracy of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. The regions in the panel can be selected to detect cancer with an accuracy of 100%.

[0239] A panel may be selected such that when one or more regions or genes in the panel are removed, specificity is appreciably decreased. Removal of one region from the panel may result in a decrease in specificity of at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, or more.

[0240] A panel may be selected such that the addition of one or more regions or genes to the panel does not appreciably increase the specificity of the panel, e.g., does not increase the specificity by more than 1%, 2%, 5%, 10%, 15%, or 20%.

[0241] A panel may be of a size such that when one or more regions or genes in the panel are removed, this appreciably decreases sensitivity, e.g., sensitivity is decreased by at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, or more.

[0242] A panel may be selected such that the addition of one or more regions or genes to the panel does not appreciably increase the sensitivity of the panel, e.g., does not increase the sensitivity by more than 1%, 2%, 5%, 10%, 15%, or 20%.

[0243] A panel may be of a size such that when one or more regions or genes in the panel are removed, accuracy is appreciably decreased, e.g., accuracy is decreased by at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, or more.

[0244] A panel may be selected such that the addition of one or more regions or genes to the panel does not appreciably increase the accuracy of the panel, e.g., does not increase the accuracy by more than 1%, 2%, 5%, 10%, 15%, or 20%.

[0245] A panel may be of a size such that when one or more regions or genes the panel are removed, positive predictive value is appreciably decreased, e.g., positive predictive value is decreased by at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, or more.

[0246] A panel may be selected such that the addition of one or more regions or genes to the panel does not appreciably increase the positive predictive value of the panel, e.g., does not increase the positive predictive value by more than 1%, 2%, 5%, 10%, 15%, or 20%

[0247] A panel may be selected to be highly sensitive and detect low frequency genetic variants. For instance, a panel may be selected such that a genetic variant or biomarker present in a sample at a frequency as low as 0.01%, 0.05%, or 0.001% may be detected at a sensitivity of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. Regions in a panel may be selected to detect a biomarker present at a frequency of 1% or less in a sample with a sensitivity of 70% or greater. A panel may be selected to detect a biomarker at a frequency in a sample as low as 0.1% with a sensitivity of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. A panel may be selected to detect a biomarker at a frequency in a sample as low as 0.01% with a sensitivity of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. A panel may be selected todetect a biomarker at a frequency in a sample as low as 0.001% with a sensitivity of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%

[0248] A panel may be selected to be highly specific and detect low frequency genetic variants. For instance, a panel may be selected such that a genetic variant or biomarker present in a sample at a frequency as low as 0.01%, 0.05%, or 0.001% may be detected at a specificity of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. Regions in a panel may be selected to detect a biomarker present at a frequency of 1% or less in a sample with a specificity of 70% or greater. A panel may be selected to detect a biomarker at a frequency in a sample as low as 0.1% with a specificity of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. A panel may be selected to detect a biomarker at a frequency in a sample as low as 0.01% with a specificity of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. A panel may be selected to detect a biomarker at a frequency in a sample as low as 0.001% with a specificity of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%.

[0249] A panel may be selected to be highly accurate and detect low frequency genetic variants. A panel may be selected such that a genetic variant or biomarker present in a sample at a frequency as low as 0.01%, 0.05%, or 0.001% may be detected at an accuracy of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. Regions in a panel may be selected to detect a biomarker present at a frequency of 1% or less in a sample with an accuracy of 70% or greater. A panel may be selected to detect a biomarker at a frequency in a sample as low as 0.1% with an accuracy of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. A panel may be selected to detect a biomarker at a frequency in a sample as low as 0.01% with an accuracy of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. A panel may be selected to detect a biomarker at a frequency in a sample as low as 0.001% with an accuracy of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%.

[0250] A panel may be selected to be highly predictive and detect low frequency genetic variants. A panel may be selected such that a genetic variant or biomarker present in a sample at a frequency as low as 0.01%, 0.05%, or 0.001% may have a positive predictive value of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%.

[0251] The concentration of probes or baits used in the panel may be increased (2 to 6 ng / pL) to capture more nucleic acid molecule within a sample. The concentration of probes or baits used in the panel may be at least 2 ng / pL, 3 ng / pL, 4 ng / pL, 5 ng / pL, 6 ng / pL, or greater. The concentration of probes may be about 2 ng / pL to about 3 ng / pL, about 2 ng / pL to about 4 ng / pL, about 2 ng / pL to about 5 ng / pL, about 2 ng / pL to about 6 ng / pL. The concentration of probes or baits used in the panel may be 2 ng / pL or more to 6 ng / pL or less. In some instances this may allow for more molecules within a biological to be analyzed thereby enabling lower frequency alleles to be detected.Genetic Analysis

[0252] Genetic analysis includes detection of nucleotide sequence variants and copy number variations. Genetic variants can be determined by sequencing. The sequencing method can be massively parallel sequencing, that is, simultaneously (or in rapid succession) sequencing any of at least 100,000, 1 million, 10 million, 100 million, or 1 billion polynucleotide molecules. Sequencing methods may include, but are not limited to: high-throughput sequencing, pyrosequencing, sequencing-by-synthesis, single-molecule sequencing, nanopore sequencing, semiconductor sequencing, sequencing-by-ligation, sequencing-by-hybridization, 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 methods known in the art.

[0253] Sequencing can be made more efficient by performing sequence capture, that is, the enrichment of a sample for target sequences of interest, e.g., sequences including the KRAS and / or EGFR genes or portions of them containing sequence variant biomarkers. Sequence capture can be performed using immobilized probes that hybridize to the targets of interest.

[0254] Cell free DNA can include small amounts of tumor DNA mixed with germline DNA.Sequencing methods that increase sensitivity and specificity of detecting tumor DNA, and, in particular, genetic sequence variants and copy number variation, can be useful in the methods of this invention. Such methods are described in, for example, in WO 2014 / 039556. These methods not only can detect molecules with a sensitivity of up to or greater than 0.1%, but also can distinguish these signals from noise typical in current sequencing methods. Increases in sensitivity and specificity from blood-based samples of cfDNA can be achieved using various methods. One method includes high efficiency tagging of DNA molecules in the sample, e.g., tagging at least any of 50%, 75% or 90% of the polynucleotides in a sample. This increases the likelihood that a low-abundance target molecule in a sample will be tagged and subsequently sequenced, and significantly increases sensitivity of detection of target molecules.

[0255] 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 a base at each locus or position in the parent molecule. This significantly increases specificity of detection by reducing noise generated by amplification and sequencing errors, which reduces frequency of false positives.

[0256] Methods of the present disclosure can be used to detect genetic variation in non-uniquely tagged initial starting genetic material (e.g., rare DNA) at a concentration that is less than 5%, 1%, 0.5%, 0.1%, 0.05%, or 0.01%, at a specificity of at least 99%, 99.9%, 99.99%, 99.999%, 99.9999%, or 99.99999%. Sequence reads of tagged polynucleotides can be subsequently tracked to generate consensus sequences for polynucleotides with an error rate of no more than 2%, 1%, 0.1%, or 0.01%.

[0257] Copy number variation determination can involve determining a quantitative measure of polynucleotides in a sample mapping to a genetic locus, such as the EGFR gene or KRAS gene. The quantitative measure can be a number. Once the total number of polynucleotides mapping to a locus is determined, this number can be used in standard methods of determining Copy Number Variation at the locus. A quantitative measure can be normalized against a standard. In one method, a quantitative measure at a test locus can be standardized against a quantitative measure of polynucleotides mapping to a control locus in the genome, such as gene of known copy number. In another method, the quantitative measure can be compared against the amount of nucleic acid in the original sample. For example, the quantitative measure can be compared against an expected measure for diploidy. In another method, the quantitative measure can be normalized against a measure from a control sample, and normalized measures at different loci can be compared. In another method, quantifying involves quantifying parent or original molecules in a sample mapping to a locus, rather than number of sequence reads. A copy number variation may be an amplification or a deletion or truncation of a gene. An amplification may be 3, 4, 5, 6, 7, 8, 9, 10, or 10 or more copies of a gene. A deletion or truncation may be 0 or 1 copies of a gene.

[0258] An example of a method for detecting copy number variation may include an array. The array may comprise a plurality of capture probes. The capture probes can be oligonucleotides that are bound to the surface of the array. The capture probes may hybridize to at least one of the genes as set forth in Table 1. The capture probes may bind to at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 genes as set forth in Table 1. DNA derived from the subject may be labeled (e.g., with a fluorophore) prior to hybidization for detection.

[0259] In other examples, a gene of interest may be amplified using primers that recognize the gene of interest. The primers may hybridize to a gene upstream and / or downstream of a particular region of interest (e.g., upstream of a mutation site). A detection probe may be hybridized to the amplification product. Detection probes may specifically hybridize to a wild-type sequence or to a mutated / variant sequence. Detection probes may be labeled with a detectable label (e.g., with a fluorophore). Detection of a wild-type or mutant sequence may be performed by detecting the detectable label (e.g., fluorescence imaging). In examples of copy number variation, a gene of interest may be compared with a reference gene. Differences in copy number between the gene of interest and the reference gene may indicate amplification or deletion / truncation of a gene. Examples of platforms suitable to perform the methods described herein include digital PCR platforms such as e.g., Fluidigm Digital Array. More information is found in PCT App. No. PCT / US2023 / 079340, PCT / US2024 / 052026, PCT / US2025 / 028133, PCT / US2024 / 050063, PCT / US2024 / 054088, PCT / US2024 / 056153, PCT / US2025 / 043397 and PCT / US2025 / 057024, each of which is folly incorporated by reference herein.EXAMPLESExample 1Likelihood-based ASCN caller (genomic)Described here are methods and compositions related to fitting observed data to a variety of different potential allele-specific copy states, which can include forms such as:• Heterozygous germline SNPs with observed allele counts (per locus / segment)• Coverage-derived relative copy number (normalized coverage) over the locus• Sample tumor fraction p (MSAF or methylation TF)• Candidate ploidy grid and possible allele states (a / b)Example 2Likelihood computationThereafter, for each candidate SNP of interest, one can thereafter compute likelihood of observed alternate / reference counts under a binomial or beta-binomial model centered on the expected SNP MAF. More information is found in PCT Pub. No. W02019060640A1, which is fully incorporated by reference herein. For example, likelihood computation can be by combining across SNPs in the segment and include a coverage / total-CN likelihood term to form a total score for the candidate (a / b, p, pl). Afterwards, searching over a grid of (p, pl) (or optimize jointly) supports identification of the maximum likelihood allele-specific configuration. One can then include tumor specific total copy number as a quantity a+b, with specific minor allel copy number as b. Classify CNV types (homdel if a+b~0; LoH if b~0; amp if a+b > threshold). A probabilistic distribution function such as use of a beta-binomial to accommodate overdispersion; require a minimum number of heterozygous SNPs; provide confidence metrics and a not evaluable flag if TF too low or insufficient SNPs, finally one can further opt to provide the allele-assignment ambiguity flag when both major / minor assignments are plausible.Example 3Methylation-based ASCN and dosage predictors (15K DMR / promoter panel) It is generally recognized that broad promoter methylation patterns and DMRs can be highly informative of gene-level allele status and of mutant dosage effects (e.g., KRAS-driven epigenomic changes). A large promoter panel (~15K promoters) provides genome-wide context and sensitivity, particularly at low tumor fractions. Yet, this gross observation has not been exploited to interrogate particular sites of interest, including quantification on precise nucleic acid strands.To achieve the described interrogation as a specific site of interest, one can nomalize methylation region statistics for a large promoter panel (~15K promoters) and DMRs near the gene of interest (e.g., KRAS locus), followed by tumor-specific methylation fraction estimate (epi tf), derived from methylation-based tumor fraction models and genomic coverage over the gene (if available) and per-sample control gene measures for normalization.With the aforementioned input parameters, a model is developed by training supervised predictors that map the tumor-adjusted methylation features (promoter / DMR signal normalized by epi tf and control genes) to a target allele-specific label (e.g., homdel vs LoH vs amp vs neutral). To take into account mutant dosage prediction, one can include features capturing systemic methylation signatures that correlate with KRAS mutant dosage (biological readout): promoter methylation changes in MAPK / EMT / immune pathways, distal DMRs showing dosage-dependent shifts, and patterns of immune-related DMRs. For KRAS in particular, validated methylation signatures correlate with KRAS oncogenic dominance (figures below / internal analyses). Optionally, one can further use model families such as gradient-boosted trees, regularized regression, or ensemble learners. Include cross-validation stratified by TF and cancer type.As a result of taking into account the above parameters, one can generate a range of probability scores for allele-specific states and / or numeric ASCN predictions (a+b and b) with potential dosage readout derived from methylation patterns and provided as an orthogonal estimate to genomic dosage.Taken together, the aforementioned methods provide a series of advantages in the technical context over existing technologies, such as improving sensitivity at low TF relative to genomic-only methods and providing a biological correlate of mutant dosage (not just copy count), especially useful for KRAS where methylation profiles reflect downstream signaling strength.Example 4Mutant allele dosage calculation (integration)To further detail this computational approach, described herein are methods where p is tumor fraction (TF) and 2 denotes diploid contribution from normal DNA. This is followed by solving for M with:With M+W known or estimated (from ASCN), evaluate candidate integer or fractional M values (consistent with M+W) and compute v_pred for each candidate. Compare v_pred with observed v and select M minimizing error. This yields D =M / (M+W).When M+W uncertain, use a joint likelihood over (M, M+W) combining genomic ASCN likelihood and methylation ASCN probability to select the most probable pair. Numerical root-finding or grid search.To account for scenario discrimination, one can evaluate a range of alternative scenarios: mutation on amplified allele, mutation on non-amplified allele, mutation on multiple copies. For each scenario, compute expected VAF curve as a function of TF and CN. Intersect observed v and p with these curves to assign scenario and dosage.Example 5Integration strategyAs an example of an integration strategy, one can combine genomic ASCN (likelihood output) and methylation ASCN (probability output) via Bayesian or weighted ensemble approach to produce a consensus ASCN with confidence. Here, one would compute mutant dosage percentage and categorize into clinically meaningful bins (e.g., dosage low < 20%, dosage mid 20-80%, dosage high > 80%). One may opt to provide not evaluable and ambiguous flags for low TF or inconsistent data. Report both numeric dosage and categorical interpretation, and a provenance field indicating whether dosage inference relied on genomic, methylation, or integrated evidence.Example 6Training datasets and featuresIn a variety of embodiments, genomic training can be performed by matching tumor DNA or orthogonal tissue ASCN calls to train / validate the genomic likelihood caller and calibrate purityxploidy grids, and methylation training can include training based on a large cohort with matched genomic ASCN labels; 15K promoter panel and selected DMRs near genes of interest (KRAS, EGFR, ERBB2, PIK3CA). Features are tumor-normalized promoter / DMR signals and control gene summaries.Example 7KRAS modelFor disease specific modeling, one uses the integrated approach: genomic ASCN + methylation dosage signature — infer mutant dosage for KRAS. For disease-specific models, such as KRAS-specific models, one can include methylation signatures correlated with KRAS oncogenic activity; validate that methylation-derived dosage correlates with genomic dosage and with biological readouts.Amplification with mutation on amplified allele (high percentage approaching 100%), amplification not on amplified allele (lower dosage than naive VAF), and mixed clonal states. Present AF vs TF curves for the three scenarios and show how the integrative method selects correct scenario.Example 8Allele-specific copy number (ASCN) and mutant-allele dosage performance evaluation Here, the Inventors evaluated whether allele-specific copy number (ASCN) and mutant-allele dosage — quantitative measures of mutant allele burden that account for copy number and loss-of-heterozygosity — are associated with real-world clinical outcomes (real-world time-to-treatment-discontinuation [rwTTD], real-world time-to-next-treatment [rwTTNT], and overall survival [OS]) in patients with advanced non-small cell lung cancer (aNSCLC).Features were trained and validated on our large Infinity Al Data Library and tested in an independent cohort using baseline plasma sequencing.Example 9ASCN / mutant dosage model flow — inputs, preprocessing, feature engineering, likelihood grid, model integration, and outputsAs shown in Figure 13, depicted is the end-to-end analytic flow used to (a) predict allelespecific copy number (ASCN) and mutant dosage from combined genomic and methylation data, and (b) integrate that ASCN / dosage evidence into downstream predictive models.Boxes represent processing modules, rounded boxes represent input or output files, arrows show data flow, colored tracks separate the genomic (blue) and epigenomic / methylation (orange) paths, and dashed arrows indicate optional or secondary data paths.Briefly, the process involves preprocessing & normalization. This includes TF validation & capping — epi-TF is checked against thresholds and as a filtering method used to decide evaluability. This is then followed by normalization which includes MSRE normalization where region peak counts are normalized by sample TF to produce adjusted scores per region (adjusts for tumor fraction and sequencing depth) and single-region normalization — genepromoter counts scaled to control genes and normalized for sample TF.Analysis then includes a genomic path and methylation / epigenomic path. For the genomic path, this includes coverage / CNV estimation — compute locus copy number and tumor total copy number from genomic coverage / CNV caller; derive candidate ASCN hypotheses (e.g., LOH, amplification states T = 1,2, 3, 4...). An additional input parameter for the genomic path includes genotype copy inference, where for each candidate T, compute expected mutant copy number M under hypotheses (mutation on amplified allele vs not) and generate the genomic likelihood of observed locus coverage and VAF.For the methylation / epigenomic path, generation of promoter and distal DMR features includes extracting methylation patterns across ~15k promoters and other tumor-specific DMRs associated with gene expression and mutant dosage (e.g., KRAS-associated methylation signatures). Additional parameter then includes region TF feature models applying trained per-region TF models to produce TF-adjusted methylation features; these encode tumor-specific signal changes associated with copy number or mutant dosage.Finally, one can apply methylation copy evidence — translate loss / attenuation of methylation signal at locus and coordinated promoter patterns into likelihoods for gene deletion / amplification and for dosage (biological readout of mutant dosage).Together, this provides input for generation of the described likelihood grid & joint inference. For each state, one computes per-hypothesis likelihoods — for each joint hypothesis (tumor TF p, tumor total CN T, mutant copies M / dosage D), each likelihood computation includeGenomic likelihood of observed VAF and coverage using the VAF mixing model v~p-Mp-(M+W)+(l-p)-2v \approx \dfrac{p\cdot M}{p\cdot (M+W) + (l-p)\cdot 2}v~p-(M+W)+(l-p)-2p-MMethylation likelihood of observed promoter / DMR patterns conditioned on the same hypothesis (loss / attenuation in homdel, increased signal for amplification, DMR signatures that scale with dosage).Combine evidence — multiply (or add log) genomic and methylation likelihoods across the grid to form a joint posterior over ASCN and dosage; optionally incorporate priors (e.g., expected copy-number frequencies).Maximum a posteriori (MAP) / posterior summaries — select MAP ASCN (T) and estimated mutant dosage D=M / TD=M / TD=M / T, and compute posterior uncertainty / credible intervals.Finally, one can engage in downstream modeling & outputs generation. This can include derived numeric outputs: a ASCN, a predicted M (mutant copies), a predicted W (wildtype copies), a predicted D (mutant dosage), and a associated ASCN state (e.g., HomDel, LOH, neutral, amp), posterior probability / confidence score. This information can be integrated into a variety of predictive models — ASCN and dosage are added as features in clinical outcome models with score, percentile, and flags. This can further includes measures for quality control (QC) & suppression rules — status flags (pass / Not evaluable) and suppression rules (e.g., skip suppression, or force non-reportable when epi-TF = 0) are applied before final reporting.Example 10Expected cfDNA variant allele fraction (VAF) for KRAS as a function of tumor fraction (TF) under varying mutant dosage and tumor copy-number states As shown in Figure 14, the curves show the modeled observed cfDNA VAF (%) (y-axis) versus tumor fraction p (%) (x-axis) for combinations of mutant dosage D and tumor total copy number T. Dosage DDD is defined as the fraction of tumor copies at the locus that carry the mutant allele (D=M / TD=M / TD=M / T, range 0-1); T=M+WT=M+WT=M+W is the tumor total copy number. Curves were generated using the relationwhich models a two-copy normal cfDNA background and assumes the mutation is present on M=D-TM=D\cdot TM=D-T tumor copies and that cfDNA is a mixture of tumor and normal DNA.This may be due to observations that at any fixed TF, higher mutant dosage DDD and / or larger tumor copy number T produce higher expected observed VAF. Conversely, for a given measured VAF, low TF requires higher D or T to explain the observation. Curves therefore illustrate how combinations of TF, dosage and copy -number state can be disambiguated when TF (for example from methylation) and locus copy number are available.Example 11Promoter DMR enrichment by allelic context (amplified vs non-amplified)As shown in Figure 16, is a scatter plot of enrichment statistics for individual methylation DMRs comparing their frequency on amplified alleles versus non-amplified alleles. Below is a table listing genes whose promoter regions show significant differential enrichment between KRAS variants occurring on amplified alleles versus non-amplified alleles .Enrichment was assessed using a region-level presence / absence analysis with Fisher’s exacttest, followed by Benjamini-Hochberg false discovery rate (FDR) correction. Genes were assigned based on overlapping promoter regions. These genes represent loci associated with differential genomic and regulatory contexts of KRAS amplification rather than directregulators of KRAS signaling.Table 1 : Genes with promoter regions significantly enriched in KRAS variants on amplified versus KRAS variants on non-amplified alleles

Claims

THE CLAIMS1. A method for determining allele-specific copy number and mutant allele dosage in a biological sample, comprising:receiving a plurality of sequencing reads, each comprising at least one germlineheterozygous single nucleotide polymorphism (SNP)and one or more somatic variant calls for a genomic locus;obtaining a tumor fraction estimate;generating a matrix comprising each candidate allele-specific configuration and each candidate purity and ploidy setting;computing an expected SNP minor allele fraction (MAF) and coverage likelihood;computing a combined likelihood for each of a candidate configuration; selecting the candidate allele-specific configuration based on maximizing the combined likelihood;determining at least one tumor specific total copy number and tumor specific minor allele copy number; andestimating mutant allele dosage based on one or more of: the selected allele specific configuration, the tumor fraction estimate, SNP MAF, and coverage likelihood.

2. A method for predicting allele-specific copy number and mutant allele dosage using methylation data, comprising:receiving tumor-adjusted methylation measurements across a plurality of regions comprising genomic promoters and differentially methylated regions;generating features normalized by tumor methylation fraction and / or control gene signals;applying a predictive model, optionally a trained predictive model, to the features to estimate allele-specific copy number, optionally a probability of allelespecific states;estimating mutant allele dosage by combining the estimate allele-specific copy number, and optionally observed somatic variant allele fraction; andgenerating outputting a dosage value.

3. A method, comprising:determining one or more of: genomic allelic strand copy number (ASCN), methylation predictions, somatic variant allele fraction (VAF), and tumor fraction for a plurality of sequence reads;computing mutant allele dosage based on the one or more determinations for the plurality of sequence reads;assigning one or more mutations in the plurality of sequence reads as amplified, nonamplified, or indeterminate allele scenario; andgenerating a dosage estimate.

4. The method of any preceding claim, comprising: computation of a genomic likelihood ASCN and / or methylation, optionally for to resolving ambiguity or to evaluating otherwse non-evaluable tumor (TF) cases.

5. The method of any preceding claim, comprising a methylation predictor to yield candidate ASCN and dosage, optionally used to inform genomic likelihood search.

6. The method of any preceding claim, comprising determination of a full Bayesian posterior estimate, optionally based on genomic, epigenomic methylation and / or priors by tumor type.

7. The method of any preceding claim, comprising): wherein the method interrogates a locus for one or more candidate genes of interest, optionally including one or more of: KRAS, EGFR, ERBB2, and PIK3CA.

8. The method of any preceding claim, comprising reporting a dosage matrix for one or more candidate genes of interest.

9. The method of any preceding claim, wherein the sequence reads are derived from tissue DNA and / or plasma cfDNA.

10. The method of any preceding claim, wherein the method comprises use of data trained on matched tissue / plasma pairs.

11. The method of any preceding claim, wherein the method comprises training sequence reads derived from tissue, and / or liquid biopsy.

12. The method of any preceding claim, wherein the sequence reads are generated from sequencing nucleic acid molecules derived from a sample obtained from a subject and generating testing sequence data.

13. The method of any preceding claim, wherein the nucleic acid molecules comprise cell-free DNA.

14. The method of any preceding claim, wherein the sample is at least one of blood, serum, plasma or tissue.

15. The method of any preceding claim, comprising determination of treatment for the subject.

16. A system configured to perform the method of any of the preceding claims.

17. A computer readable medium comprising the method of any of the preceding claims.

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