Genomic and methylated biomarkers for determining risk of cardiac disease in patient and novel genomic and epigenomic drug targets for reducing said risk and / or improving outcome of patient following myocardial infarction or cardiac injury

Abstract

Disclosed herein are methods, compositions, and devices for diagnosing and treating diseases, including cardiovascular diseases as well as such diseases and dysfunctions associated with administration of cancer therapy. Methods include sequencing a set of regions in a cell-free nucleic acid molecule and detecting one or more biomarkers indicative of cardiovascular disease and dysfunction.

Description

[0001] Cross-references to other applications This application claims priority to U.S. Provisional Patent Application No. 63 / 591,380, filed October 18, 2023, and No. 63 / 591,390, filed October 18, 2023, both of which are incorporated herein by reference in their entirety for all purposes.

[0002] background Atrial fibrillation (AF) and heart failure (HF) account for up to 45% of all cardiovascular disease (CVD) deaths. Due to the heterogeneity of CVD and its associated genetics, personalized treatment is considered important. To improve the diagnosis and treatment of CVD, we need to identify known and novel genes responsible for CVD progression and repair. With advancements in sequencing technologies, genomic data has been generated to accelerate translational research. Genomic data has the potential to reveal the genetic and epigenetic basis of various health conditions. It can identify genetic and epigenetic alterations associated with AF, HF, and other CVDs, as well as the genetic and epigenetic mechanisms by which the heart attempts to repair itself after injury.

[0003] Cardiac adverse events are a recognized toxicity in breast cancer patients receiving anti-human epidermal growth factor receptor-2 (HER2) therapy. Large phase III clinical trials have shown an incidence of >10% reduction in left ventricular ejection fraction (LVEF) of 7.1%–18.6% and an incidence of overt heart failure of 1.7%–4.1% (3, 4). Retrospective analyses from large registries such as SEER (Surveillance, Epidemiology and End Results) and the Cancer Research Network reported even higher incidences of heart failure, at 41.9% and 20.1%, respectively.

[0004] Genomic and epigenetic data were compared between case and control groups to identify potentially valuable biomarkers for diagnosing patients' risk of having / developing heart disease, including atrial fibrillation (AF) and myocardial infarction (HF), as well as an increased risk of adverse cardiac remodeling and myocardial interstitial fibrosis following myocardial infarction. These biomarkers may also be novel targets for new drugs that manipulate the levels of the biomarkers reported in this paper to reduce patients' risk of heart disease and improve patient outcomes following myocardial infarction or injury.

[0005] The genomic and epigenetic biomarkers identified in this study could be used for more sensitive or convenient diagnosis of heart disease risk than currently available. For patients undergoing cancer genotyping or monitoring, or receiving cancer treatments known to increase heart disease risk, the development of heart disease could be monitored concurrently before it occurs.

[0006] While many SNV and CNV alterations are known to be associated with heart disease, myocardial infarction, and severe cardiomyopathy, little is known about how epigenomic regions and alterations affect the risk of these conditions, or how epigenomic biomarkers influence the natural process of cardiac repair after myocardial infarction. The epigenomic biomarkers described in this article could serve as novel drug targets or enhance the efficacy of existing drugs to improve patient outcomes. Invention Overview This article describes a method comprising: detecting methylation of at least one of more than one sites of nucleic acids in a cell-free DNA (cfDNA) sample from a subject; generating more than one methylation determination for each of the more than one sites; determining one or more measures from the methylation determination; and processing one or more measures to calculate the risk probability of a cardiac event in the subject. In various embodiments, at least one of more than one sites includes one or more genes and / or is associated with one or more genes selected from the group consisting of: SIX2, KKBTBD8, TAFA4, EVC, NCA, SNCA-AS, ARAP4, NT5E, XPO1, TMEM181, NRG1, DYDC2, CACUL1, 0ORF888, FAM, FAM222A, MIR9-3HG, BCKDK, MAPK7, A1G, CACNA1, MXRA7, ADCYAP1, EPOR, MAST1, DKKL1, PCSK2, CGB7, EMB, TGFBI, COX6B1, ZNF347, and DKKL1. In various embodiments, at least one of more than one locus includes one or more genes and / or is associated with one or more genes selected from the group consisting of: SIX2, KKBTBD8, TAFA4, EVC, NCA, SNCA-AS, CGB7, EMB, TGFBI, COX6B1, ZNF347, and DKKL1. In various embodiments, cardiac events include one or more of the following: stroke, transient ischemic attack (TIA), myocardial infarction (MI), angina pectoris, and acute coronary syndrome. In various embodiments, cardiac events are associated with cardiomyopathy selected from the group consisting of: dilated cardiomyopathy, hypertrophic cardiomyopathy, restrictive cardiomyopathy, and arrhythmogenic right ventricular dysplasia. In various embodiments, cardiomyopathy includes hereditary cardiomyopathy. In various embodiments, cardiomyopathy includes acquired cardiomyopathy. In various embodiments, cardiomyopathy is associated with an agent. In various embodiments, the agent is a drug. In various embodiments, the drugs are letrozole, palbociclib, capecitabine, tamoxifen, trastuzumab, T-DM1, pertuzumab, Ado-trastuzumab (Ado-trastuzumab bemtansine), neratinib, lapatinib, tucatinib, magruciximab, and destrastuzumab. In various embodiments, determining one or more metrics from methylation determination includes determining a count of methylated molecules at at least one of more than one site. In various embodiments, the count is a peak count. In various embodiments, processing one or more metrics includes selection based on tumor score and / or methylated molecule count. In various embodiments, processing one or more metrics includes applying a model.In various implementations, the model is a probabilistic model. In various implementations, processing one or more measures includes determining probabilities based on one or more measures. In various implementations, a calculation of the risk probability of a subject's cardiac event is generated based on the probabilities of one or more measures. In various implementations, the subject's risk probability is an increased risk of a cardiac event. In various implementations, cardiomyopathy is a comorbidity of another disease or condition in the subject. In various implementations, the subject's disease or condition is selected from one or more diseases or conditions consisting of the group consisting of: cancer, coronary artery disease, heart attack, hypertension, diabetes, thyroid disease, viral hepatitis, HIV-1, and viral infection.

[0008] This article describes a method comprising: detecting methylation at at least one of more than one site in the nucleic acids of a cell-free DNA (cfDNA) sample from a subject receiving treatment for cancer; generating more than one methylation determination for each of the more than one site; determining one or more measures from the methylation determination; processing the one or more measures to calculate a risk probability of a cardiac event in the subject; and determining whether to maintain or modify the treatment based on the calculated risk probability. In various embodiments, the treatment comprises administration of a pharmaceutical agent. In various embodiments, the pharmaceutical agent comprises one or more of the following: letrozole, palbociclib, capecitabine, tamoxifen, trastuzumab, T-DM1, pertuzumab, Ado-emtansine trastuzumab, neratinib, lapatinib, tucatinib, magruciximab, and detrastuzumab. In various embodiments, the method comprises obtaining a sample from a subject or having already obtained a sample from a subject. In various embodiments, at least one of more than one sites includes one or more genes and / or is associated with one or more genes selected from the group consisting of: SIX2, KKBTBD8, TAFA4, EVC, NCA, SNCA-AS, ARAP4, NT5E, XPO1, TMEM181, NRG1, DYDC2, CACUL1, 0ORF888, FAM, FAM222A, MIR9-3HG, BCKDK, MAPK7, A1G, CACNA1, MXRA7, ADCYAP1, EPOR, MAST1, DKKL1, PCSK2, CGB7, EMB, TGFBI, COX6B1, ZNF347, and DKKL1. In various embodiments, at least one of more than one locus comprises one or more genes and / or is associated with one or more genes selected from the group consisting of: SIX2, KKBTBD8, TAFA4, EVC, NCA, SNCA-AS, CGB7, EMB, TGFBI, COX6B1, ZNF347, and DKKL1. In various embodiments, the subject with cancer is one of the following: HR+ / HER2-, HR+ / HER2+, HR- / HER2+, and HR- / HER2-.

[0009] This document describes a method comprising: detecting methylation of at least one of more than one sites of nucleic acids in a cell-free DNA (cfDNA) sample from a subject; generating more than one methylation determination for each of the more than one site; determining one or more measures from the methylation determination; and processing the one or more measures to calculate the risk probability of a cardiac event in the subject; and diagnosing the patient as susceptible to cardiomyopathy and / or cardiac events and / or the likelihood of the patient having cardiomyopathy and / or cardiac events. In various embodiments, cardiac events include one or more of the following: stroke, transient ischemic attack (TIA), myocardial infarction (MI), angina pectoris, transient ischemic attack (TIA), stroke, and acute coronary syndrome. In various embodiments, cardiomyopathy is selected from the group consisting of: dilated cardiomyopathy, hypertrophic cardiomyopathy, restrictive cardiomyopathy, and arrhythmogenic right ventricular dysplasia. In various embodiments, cardiomyopathy includes hereditary cardiomyopathy. In various embodiments, cardiomyopathy includes acquired cardiomyopathy.

[0010] This document describes a method comprising: detecting one or more biomarkers in a sample comprising cell-free DNA (cfDNA); and characterizing the sample based on the detection of one or more biomarkers in the sample. In various embodiments, detecting one or more biomarkers includes measuring the methylation level of one or more at more than one site. In various embodiments, the sites include a custom panel. In various embodiments, the custom panel is configured in an in-silico panel. In various embodiments, the custom panel is configured in a physical panel. In various embodiments, the panel includes a set of oncogenes, promoter regions of a set of oncogenes, HRR genes, immuno-oncology (IO) genes, cancer pathways, methylation peaks found in cancer, or methylation peaks found in clinical samples. In various implementations, biomarkers include the presence, absence, or increased expression of one or more genes, and / or association with one or more genes selected from the group consisting of: SIX2, KKBTBD8, TAFA4, EVC, NCA, SNCA-AS, ARAP4, NT5E, XPO1, TMEM181, NRG1, DYDC2, CACUL1, 0ORF888, FAM, FAM222A, MIR9-3HG, BCKDK, MAPK7, A1G, CACNA1, MXRA7, ADCYAP1, EPOR, MAST1, DKKL1, PCSK2, CGB7, EMB, TGFBI, COX6B1, ZNF347, and DKKL1. In various embodiments, at least one of more than one sites includes one or more genes and / or is associated with one or more genes selected from the group consisting of: SIX2, KKBTBD8, TAFA4, EVC, NCA, SNCA-AS, CGB7, EMB, TGFBI, COX6B1, ZNF347, and DKKL1.

[0011] This document describes a method comprising: detecting methylation in samples obtained from a subject; normalizing the detected methylation; applying a probabilistic model; calculating a test statistic based on one or more expected values ​​and / or detected values; and generating a quantitative score of the subject's risk of cardiomyopathy and / or cardiac events. In various embodiments, the method includes permuting more than one sample and applying the test statistic. In various embodiments, the statistic is a spurious test statistic. In various embodiments, the probabilistic model includes detecting methylation from more than one sample, each obtained from more than one subject; in various embodiments, expected values ​​include negative observations. In various embodiments, detected values ​​include the sum of probabilities.

[0012] In various implementations, the probabilistic model includes more than one probability for more than one sample. In various implementations, the method includes applying logistic regression.

[0013] This document describes a method comprising: determining the state of a biomolecule obtained from a sample derived from a human subject. In other embodiments, the method includes combining more than one nucleic acid molecule derived from the subject with a solution containing a methyl-binding domain (MBD) protein to produce a nucleic acid-MBD protein solution; and washing the nucleic acid-MBD protein solution more than once with a salt solution to produce a plurality of nucleic acid fractions, each individual nucleic acid fraction having a threshold number of methylated cytosines in a region of more than one nucleic acid having at least a threshold cytosine-guanine content. In other embodiments, the washings in the more than one washing are performed with a solution of sodium chloride (NaCl) of a certain concentration, and produce a plurality of nucleic acid fractions having a certain range of binding strength to the MBD protein. In other embodiments, the method includes: determining that a first nucleic acid fraction is associated with a first partition among more than one nucleic acid fraction, the first partition corresponding to a first range of binding strengths to the MBD protein; attaching a first molecule barcode to the nucleic acid of the first nucleic acid fraction, the first molecule barcode being included in a first set of molecule barcodes associated with the first partition; determining that a second nucleic acid fraction is associated with a second partition among more than one nucleic acid fraction, the second partition corresponding to a second range of binding energies to the MBD protein, different from the first range of binding strengths; and attaching a second molecule barcode to the nucleic acid of the second nucleic acid fraction, the second molecule barcode being included in a second set of molecule barcodes associated with the second partition. In other embodiments, the method includes combining at least a portion of a plurality of nucleic acid fractions with a restriction endonuclease that cleaves molecules having one or more unmethylated cytosine to produce at least a portion of more than one sample for generating sequencing reads, wherein the threshold amount of methylated cytosine corresponds to the minimum frequency of methylated cytosine in a region having at least a threshold cytosine-guanine content.

[0014] In other embodiments, the method includes combining at least a portion of a plurality of nucleic acid fractions with an amount of restriction endonuclease, said restriction endonuclease cleaving molecules having one or more methylated cytosines to produce at least a portion of more than one sample for generating sequencing reads, wherein a threshold amount of unmethylated cytosine corresponds to the maximum frequency of uncleaved methylated cytosines in a region having at least a threshold cytosine-guanine content. In other embodiments, the method includes sequencing nucleic acid molecules derived from a sample obtained from a subject, analyzing sequence reads derived from the sequencing to identify one or more driver mutations in the nucleic acid molecules, and using information about the presence, absence, or amount of one or more driver mutations in the nucleic acid molecules to identify a tumor in the subject. In other embodiments, the nucleic acid molecules are cell-free DNA. In other embodiments, the sample is at least one of blood, serum, plasma, or tissue. In other embodiments, the method includes determining a treatment for the subject. In other embodiments, the detection limit of the model used to determine the tumor fraction of the sample is not greater than 0.05%. In other embodiments, one or more driver mutations include somatic variations detected at a mutational allele frequency (MAF) of no more than 0.05%. In other embodiments, one or more driver mutations include fusions detected at a mutational allele frequency (MAF) of no more than 0.1%. In other embodiments, the method includes detecting the mutational distribution of each of one or more driver mutations, wherein the mutational distribution of each of the detected one or more driver mutations has a correlation of at least 0.99 with the mutational distribution of driver mutations detected in a subject cohort by tissue genotyping. In other embodiments, the method detects a tumor in a subject with at least 85% sensitivity, at least 99% specificity, and at least 99% diagnostic accuracy. In other embodiments, the method includes identifying one or more driver mutations in cell-free DNA and cfDNA. In other embodiments, the method includes obtaining test sequence data from a subject via a computing system having one or more hardware processors and memory, the test sequence data including test sequencing reads derived from a subject sample; analyzing the test sequencing reads by the computing system to determine a first quantitative measure of the test sequencing reads derived from a genomic region corresponding to a reference genome; analyzing the test sequencing reads by the computing system to determine a second quantitative measure of the test sequencing reads derived from a genomic region corresponding to a reference genome; determining a metric based on the first and second quantitative measures; and generating an input vector including the metric by the computing system; determining an indication of cancer status in the subject by the computing system providing the input vector to a model, said model implementing one or more machine learning techniques to generate an indication of cancer status in the subject, the model including weights of individual classification regions in more than one classification region, and at least a portion of the weights of the individual classification regions being distinct from each other.In other embodiments, a single test sequencing read includes a nucleotide sequence corresponding to a nucleic acid fragment contained in the sample, and the single test sequencing read corresponds to a molecule having a threshold amount of methylated cytosine, the molecule comprising a region of a nucleotide sequence having at least a threshold cytosine-guanine content, a first quantification derived from a test sequencing read corresponding to a single taxonomic region among more than one taxonomic region, at least a portion of the single taxonomic region among more than one taxonomic region corresponding to a genomic region of a reference genome, the genomic region having a threshold amount of methylated cytosine and having at least a threshold cytosine-guanine content in subjects in which cancer is detected, and a second quantification derived from a test sequencing read corresponding to a single control region among more than one control region, the single control region among more than one control region corresponding to another genomic region of the reference genome, the other genomic region having at least a threshold cytosine-guanine content and having at least a threshold amount of methylated cytosine in subjects in which cancer is detected and in other subjects in which cancer is not detected. In other embodiments, the method includes obtaining training sequence data via a computing system having one or more hardware processors and memories, the training sequence data comprising training sequencing reads derived from more than one sample from more than one training subject, each training sequencing read comprising a nucleotide sequence corresponding to a nucleic acid fragment contained in the more than one sample, and each training sequencing read corresponding to a molecule having a threshold amount of methylated cytosine contained in a region having at least a threshold cytosine-guanine content; analyzing the training sequencing reads by the computing system to determine an additional first quantitative measure derived from the training sequencing reads corresponding to a single taxonomic region in the more than one taxonomic region; and analyzing the training sequence data by the computing system. The sequencing reads determine additional second quantitative measures corresponding to more than one control region derived from training sequencing reads; a computing system determines additional measures of a single subclass region within more than one subclass region based on an additional first quantitative measure of a single subclass region and an additional second quantitative measure of more than one control region; a computing device generates training data comprising additional measures of a single subclass region within more than one subclass region from training sequence reads from samples of more than one training subject; the computing system implements one or more machine learning algorithms using the training data to generate a model to determine an indication of the subject's cancer status based on the amount of methylated cytosine in at least a portion of more than one subclass region. In other embodiments, one or more machine learning algorithms include one or more classification algorithms. In other embodiments, one or more machine learning algorithms include one or more regression algorithms and indicate an estimate of a tumor score corresponding to a sample.In other embodiments, training sequencing reads constitute a first portion of training sequence data, and additional training sequencing reads constitute a second portion of training sequence data, wherein the additional training sequencing reads are different from the training sequencing reads, and the method includes analyzing at least one of the first portion or the second portion of training sequence data by a computing system to determine a single frequency of more than one variant present in a single sample from more than one sample; determining, by the computing system and for a single sample, the variant with the highest frequency among the more than one variant, the highest frequency corresponding to a single frequency having the highest value among the single frequencies derived from the single sample; and determining, by the computing system, a single measure of the tumor score of the single sample based on the highest value of the single frequency derived from the single sample. In other embodiments, the training data includes a single measure of the tumor score of a single sample from more than one sample, and the model is generated based on the single measure of the tumor score of a single sample from more than one sample. In other embodiments, the method includes: generating a data file by a computing system including processing circuitry and memory, the data file including a first token generated using a first hash function, a single first token corresponding to a corresponding individual in a group of individuals having data stored in a molecular data repository; sending the data file to a health insurance claims data management system by the computing system; in response to the data file, obtaining health data corresponding to the group of individuals by the computing system and from the health insurance claims data management system; generating a plurality of identifiers by the computing system using a second hash function different from the first hash function, each identifier corresponding to one or more tokens associated with each individual in the group of individuals; obtaining second data of the group of individuals by the computing system using the plurality of identifiers from the molecular data repository; determining a corresponding portion of first data corresponding to a corresponding portion of the second data of the group of individuals by the computing system; and generating an integrated data repository by the computing system storing the corresponding portions of the first data and the corresponding portions of the second data associated with the corresponding identifiers among the plurality of identifiers. In other implementations, the method includes: determining a first set of data processing instructions by a computing system, the first set of data processing instructions being executable for first data stored in an integrated data repository; executing the first set of data processing instructions by the computing system to analyze a first health insurance claim code contained in the first data to determine a first subset of individuals with biological conditions; and generating a first dataset by the computing system indicating a subset of individuals with biological conditions.In other embodiments, the method includes: a computing system determining a second set of data processing instructions executable for second data stored in an integrated data repository; the computing system executing the second set of data processing instructions to analyze a second health insurance claim code contained in the second data to determine one or more treatments to be provided to a second subset of an individual group; and the computing system generating a second dataset indicating one or more treatments to be provided to the second subset of the individual group. In other embodiments, the method includes: a computing system determining a third subset of an individual group, the third subset including a portion of a first subset of the individual group overlapping with a portion of the second subset of the individual group; the computing system receiving a request to analyze a first dataset and a second dataset associated with the third subset of the individual group; and the computing system, in response to the request, analyzing the first dataset and the second dataset associated with the third subset of the individual group to determine a saliency measure of the characteristics of the third subset of the individual group relative to biological conditions.

[0015] In other embodiments, the method includes determining, by a computational system, one or more genomic mutations present in a third subset of the individual group; determining, by the computational system, more than one treatment to be provided to the third subset of the individual group; and determining, by the computational system, the corresponding survival rate of the third subset of the individual group. In other embodiments, a significance metric corresponds to the survival rate with respect to treatments among more than one treatment and genomic mutations among one or more genomic mutations. In other embodiments, the method includes determining, by a computational system, the effectiveness of treatments for the third subset of the individual group based on a significance metric. In other embodiments, the method includes determining, by the computational system, individuals in a third subset of the individual group who have not received treatment. In other embodiments, the method includes administering one or more therapeutically effective amounts of treatment to individuals in the third subset who have not received treatment. In other embodiments, the integrated data repository is arranged according to a data repository schema that includes more than one data table and more than one logical link between the more than one data table, a single logical link in the more than one logical link indicating one or more rows of a data table in the more than one data table, the rows corresponding to one or more additional rows of another data table in the more than one data table.

[0016] In other embodiments, more than one data table includes: a first data table storing genomic data of an individual group; a second data table storing data related to one or more patient visits by an individual to one or more healthcare providers; a third data table storing information corresponding to services provided to an individual for one or more patient visits to one or more healthcare providers indicated in the second data table; a fourth data table storing personal information of the individual group; a fifth data table storing information related to health insurance companies or government entities that pay for services provided to the individual group; a sixth data table storing information corresponding to health insurance coverage information of the individual group; and a seventh data table storing information related to drug treatments received by the individual group. In other embodiments, the plurality of identifiers generated using a second hash function includes intermediate identifiers; and the method includes applying a salt function by a computational system to the intermediate identifiers to generate a final set of identifiers. In other embodiments, the method includes obtaining information by a computing system from an additional data repository comprising electronic medical records of additional individual groups; determining, by the computing system, a subset of additional individual groups corresponding to the individual groups with data stored in a genomic data repository; and modifying the integrated data repository by the computing system to store at least a portion of the information of the medical records of the subset of additional individual groups associated with multiple identifiers. In other embodiments, the method includes performing one or more optical character recognition operations on the additional information by the computing system; analyzing the additional information obtained from the additional data repository by the computing system to determine one or more portions of the additional information to be removed to generate an information corpus. In other embodiments, the method includes analyzing the information corpus by the computing system to determine a portion of the subset of additional individual groups corresponding to one or more biomarkers; and generating one or more data structures by the computing system storing identifiers of the portion of the subset of additional individual groups and storing indications that the portion of the subset of additional individual groups corresponds to one or more biomarkers.

[0017] In other embodiments, the method includes storing one or more data structures in an intermediate data repository by a computing system, performing one or more de-identification operations on identifiers of additional subsets of individual groups by the computing system, and then modifying the integrated data repository to store at least a portion of additional information of the medical records of the additional subsets of individual groups in relation to the number of identifiers. In other embodiments, the molecular data repository stores at least one or more of genomic information, genetic information, metabolomics information, transcriptomics information, fragmentomics information, immune receptor information, methylation information, epigenomic information, or proteomics information. In other embodiments, the method includes cfDNA testing, real-world evidence (RWE), or both.

[0018] For any of the above methods, in some implementations, the method includes administering a therapy to a subject to treat cardiomyopathy and / or cardiac events (including reducing the risk of developing cardiomyopathy and / or the risk of cardiac events occurring). In some implementations, the therapy includes one or more of the following: apixaban, rivaroxaban, dabigatran, edoxaban, soralol hydrochloride, ibutilide fumarate injection, flecainide acetate, digoxin, tafamidis, tafamidis meglumine, mavacamten, spironolactone, eplerenone, isosorbide nitrate, dapagliflozin, tenecteplase, clopidogrel, abciximab, tirofiban, eptifibatide, colchicine, flupiridaben F18, ticagrelor, valsartan, hydrochlorothiazide, cangrelorol, fenelitonee, enoxaparin sodium injection, reteplase, alteplase, and ticagrelor. In some implementations, the treatment includes antiplatelet agents, anticoagulants, beta-blockers, nitrates, fibrinolytic agents, or any combination thereof. In some implementations, the therapy includes administering one or more treatments to the subject to treat both cancer and a cardiac event.

[0019] A system comprising one or more components configured to perform any of the aforementioned methods.

[0020] A computer-readable medium for performing any of the foregoing methods. Brief description of the attached diagram Figure 1 Epigenomic analysis of differentially methylated regions is included. Methylated regions can be identified by testing normal and cancer samples from large cohorts, including plasma from 3,000 cancer-free donors to establish regions in DNA without high methylation signals, and plasma from 1,700+ patients with CRC, breast, lung, and bladder cancer to detect tumor methylation mutations. A subset of all regions can be used for tumor detection and quantification; >15K regions, even if not used for tumor detection and quantification, can be used for applications such as adverse event detection. Adverse event detection distinguishes tumor signals from non-tumor signals released due to adverse events.

[0022] Figure 2 Case-control design. Criteria included: (1) positive and negative HF. (2) positive and negative sex, age, and comorbidity balance. (3) no prior diagnosis of serious cardiac conditions. It was of interest to identify hypermethylation and genomic alterations enriched / depleted in cases and controls. Which alterations predicted whether a patient would develop heart failure were determined.

[0023] Figure 3 Cancer treatment may be associated with the risk of heart failure and severe myocarditis. A case-control design was developed to predict methylation signatures. All samples were from female BC patients receiving trastuzumab. *Including trastuzumab and certain types of chemotherapy. Liquid biopsies were performed during trastuzumab treatment and less than 300 days prior to a heart failure event. The control cohort was matched to the case cohort by sex, age, and other parameters.

[0024] Figure 4 Tumor methylation score and genomic MaxVAF Figure 5 Predicting the diagnosis of heart failure from liquid biopsy Figure 6 TGFBI hypermethylation was more frequent in cases (positive) than in controls. The figure shows the raw counts (x-axis) of methylated molecules in selected genes in cases (patients who developed heart failure, orange) and controls (patients who did not develop heart failure, blue), while the y-axis shows the tumor content of the samples. Many relevant genes (such as those shown below) had signals that distinguished cases from controls and were not significantly associated with the presence of tumors (a potential confounding factor when assessing the likelihood of patients with cancer developing heart failure). Only a subset of genes are shown here.

[0025] Figure 7 CGB7 hypermethylation was detected more frequently in the cases (positive) than in the control group.

[0026] Figure 8 Methylation signal: chr1:167789527-167789647 (ADCY10) Figure 9 Methylation signal: chr19:49,866,910-49,867,251 (DKKL1) Figure 10 Methylation signal: chr19:49,866,910-49,867,251 (DKKL1) Figure 11 Methylation signal: chr2: 119611083-119611424 Figure 12 Methylation signal: chr2: 119611083-119611424 Figure 13 Regional pseudo-chi-square test (negative and positive) Figure 14 p-value Figure 15Example logistic regression (LR) model (all samples). Association between individual gene methylation and heart failure status based on logistic regression. Methylated genes are shown on the x-axis, and the corresponding p-values ​​associated with heart failure are shown on the y-axis.

[0027] Figure 16 Posterior probability heatmap Figure 17 Posterior probability heatmap Figure 18 Compared with matched heart failure-negative patients, among the methylated genes associated with patients who later developed heart failure, CGB7, EMB, TGFBI, COX6B1, ZNF347, and DKKL1 were the genes with the most significant p-value associations.

[0028] Figure 19 Validation cohort selection. Breast cancer patients were not selected except for requirements regarding previous treatment history; both HER2-targeted therapy and non-HER2-targeted subcohorts were included.

[0029] Figure 20 Patients with recorded heart failure in the validation queue.

[0030] Figure 21 Promoter methylation of NT5E and XPO1 (genes whose function in heart health has been documented) was significantly higher in the heart failure-positive group. Predictive models included NT5E, XPO1 (left panel), and XPO1 (right panel).

[0031] Figure 22 ROC curve. The model's validation performance reaches at least AUC=0.86.

[0032] Figure 23 The heatmap shows the methylation levels of the 30 genes (promoter regions) used in the model, with blue indicating higher methylation. Rows represent normalized methylation signals from selected DMRs. Columns represent patients.

[0033] Detailed description While various embodiments of this disclosure have been shown and described herein, those skilled in the art will understand that such embodiments are provided by way of example only. Many variations, modifications, and substitutions will occur to those skilled in the art without departing from this disclosure. It should be understood that various alternatives may be adopted to the embodiments of this disclosure described herein.

[0034] The term “about” and its grammatical equivalents associated with a reference value can include a range of values ​​that are plus or minus 10% of that value. For example, the quantity “about 10” can include quantities from 9 to 11. The term “about” associated with a reference value can include a range of values ​​that are plus or minus 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, or 1%.

[0035] The term "at least" and its grammatical equivalents associated with a reference value can include the reference value and be greater than that value. For example, a quantity "at least 10" can include the value 10 and any value higher than 10, such as 11, 100, and 1,000.

[0036] The term “at most” and its syntactic equivalents associated with a reference value can include the reference value and be less than that value. For example, a quantity “at most 10” can include any value of 10 and any value less than 10, such as 9, 8, 5, 1, 0.5, and 0.1.

[0037] Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “the” as used herein can include plural referents. Thus, for example, reference to “cell” can include more than one such cell, and reference to “culture” can include reference to one or more cultures and their equivalents known to those skilled in the art, and so on. Unless otherwise clearly indicated, all technical and scientific terms used herein may have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0038] Current approaches omit both genomic and epigenomic attributes of patient samples, or perform multiple tests separately. If both genomic and epigenomic information are available, omitting either genomic or epigenomic information may lead to the prescribing of potentially ineffective cancer therapies or the withholding of potentially effective ones. Cancer can be indicated by epigenetic variations such as methylation. Examples of methylation changes in cancer include localized increases in DNA methylation in CpG islands at transcription start sites (TSS) of genes involved in normal growth control, DNA repair, cell cycle regulation, and / or cell differentiation. This hypermethylation may be associated with an aberrant loss of transcriptional capacity of the genes involved and occurs at least as frequently as point mutations and deletions that cause altered gene expression. DNA methylation profiling can be used to detect regions in the genome with different levels of methylation (“differentially methylated regions” or “DMRs”) that have changed during development or been perturbed by disease (e.g., cancer or any cancer-related disease). The genomes of cancer cells exhibit imbalances in the aforementioned DNA methylation patterns, and therefore imbalances in the functional packaging of DNA. Therefore, the combination of chromatin organization abnormalities and methylation changes, when analyzed together, may help enhance cancer profiling. Combining MBD partitioning with fragmentomics data (such as the start and end positions of fragment mappings (correlated with nucleosome location), fragment length, and associated nucleosome occupancy) can be used for chromatin structure analysis in hypermethylation studies, with the aim of improving biomarker detection rates.

[0039] Methylation profiling can include identifying methylation patterns across different regions of the genome. For example, after partitioning and sequencing molecules based on their methylation levels (e.g., the relative number of methylation sites per molecule), the sequences of molecules in different partitions can be mapped to a reference genome. This can reveal regions of the genome that are more or less methylated compared to other regions. In this way, genomic regions can differ in their methylation levels in contrast to individual molecules.

[0040] Nucleic acid molecules can be characterized by modifications, which can include various chemical or protein modifications (i.e., epigenetic modifications). Non-limiting examples of chemical modifications may include, but are not limited to, covalent DNA modifications, including DNA methylation. In some embodiments, DNA methylation includes adding a methyl group to cytosine (the cytosine followed by guanine in a nucleic acid sequence) at a CpG site. In some embodiments, DNA methylation includes adding a methyl group to adenine, such as N6-methyladenine. In some embodiments, DNA methylation is 5-methylation (modification of the fifth carbon of the 6-carbon ring of cytosine). In some embodiments, 5-methylation includes adding a methyl group to the 5C position of cytosine to produce 5-methylcytosine (m5c). In some embodiments, methylation includes derivatives of m5c. Derivatives of m5c include, but are not limited to, 5-hydroxymethylcytosine (5-hmC), 5-formylcytosine (5-fC), and 5-carboxycytosine (5-caC). In some embodiments, DNA methylation is 3C methylation (modification of the third carbon of the 6-carbon ring of cytosine). In some embodiments, 3C methylation involves adding a methyl group to the 3C position of cytosine to generate 3-methylcytosine (3mC). Other examples include N6-methyladenine or glycosylation. DNA methylation involves adding a methyl group to DNA (e.g., CpG) and can alter the expression of methylated DNA regions. Methylation can also occur at non-CpG sites; for example, methylation can occur at CpA, CpT, or CpC sites. DNA methylation can alter the activity of methylated DNA regions. For example, when DNA in a promoter region is methylated, gene transcription can be repressed. DNA methylation is essential for normal development, and abnormalities in methylation can disrupt epigenetic regulation. Disruptions in epigenetic regulation, such as repression, can lead to diseases such as cancer. DNA methylation of the promoter may indicate cancer.

[0041] The CpG binary is a dinucleotide CpG (cytosine-phosphate-guanine, i.e., at the 5' end of the nucleic acid sequence) on the sense strand of a double-stranded DNA molecule. In the 3' direction, cytosine is followed by guanine and its complementary CpG on the antisense chain. The CpG binary can be fully methylated or hemimethylated (methylated only on one chain).

[0042] CpG dinucleotides are underrepresented in the normal human genome, where most CpG dinucleotide sequences are transcriptionally inert (e.g., near-centromere regions of chromosomes and heterochromatin regions of DNA in repetitive elements) and are methylated. However, many CpG islands are protected from such methylation, especially around transcription start sites (TSS).

[0043] Protein modifications include components that bind chromatin, particularly histones (including their modified forms), as well as components that bind other proteins, such as those involved in replication or transcription. This disclosure provides methods for processing and analyzing nucleic acids with varying degrees of modification, such that the nature of their original modifications is correlated with nucleic acid tags, which can be decoded during nucleic acid analysis by sequencing. Genetic variations in the nucleic acid modifications of a sample can then be correlated with the degree of modification (epigenetic variation) of that nucleic acid in the original sample, said nucleic acids including single-stranded (e.g., ssDNA or RNA) or double-stranded molecules (e.g., dsDNA).

[0044] DNA loss can reduce the presence of one or more types of DNA, making them difficult to detect (e.g., cfDNA). In one or more other cases, existing methods for measuring DNA methylation, such as enrichment or depletion methods, can have relatively high levels of resolution, such as from about 100 base pairs (bp) to about 200 bp, which can make it difficult to accurately determine the amount of DNA methylation. The accuracy of DNA methylation determination can affect the accuracy of tumor score estimation in a sample. Since tumor scores are used to determine whether a sample is derived from a subject with or without a tumor, the accuracy of tumor score estimation can influence individual diagnostic and / or treatment decisions.

[0045] The genomic and epigenetic biomarkers identified here can be used to predict whether a patient is at high risk of cardiovascular disease prior to a heart attack or severe myocarditis. They can then be given existing medications to reduce their risk of heart attack or severe myocarditis. For example, adverse cardiac events are a recognized toxicity in breast cancer patients receiving anti-human epidermal growth factor receptor-2 (HER2) therapy. 1 Large-scale phase III clinical trials have shown that the incidence of a decrease in left ventricular ejection fraction (LVEF) of >10% is 7.1%–18.6%, and the incidence of overt heart failure is 1.7%–4.1%. 2,3 Retrospective analyses from large registries such as SEER (Surveillance, Epidemiology and End Results) and the Cancer Research Network reported even higher rates of heart failure, at 41.9% and 20.1%, respectively.

[0046] Drugs targeting the genes and epigenetic loci identified in this paper could be used to reduce the risk of heart disease and / or improve patient outcomes after myocardial infarction by influencing cardiac remodeling and myocardial interstitial fibrosis. For example, the widely prescribed drug valsartan has been shown to significantly attenuate the increase in periosteal protein expression, and this may be part of its mechanism of action. Inhibition of periosteal protein by valsartan may contribute to its beneficial effects on cardiac remodeling after myocardial infarction. In our dataset, we identified increased TGFBI hypermethylation in patients (case group) who ultimately developed heart failure and / or severe myocarditis relative to controls. Given that the role of TGFBI overexpression is similar to that of periosteal protein overexpression, as it is characterized by supporting a stronger cardiac growth response (1), drugs targeting TGFBI expression could be used to reduce the risk of heart failure or improve cardiac remodeling after myocardial infarction.

[0047] The genomic and epigenetic biomarkers identified in this study could be used for more sensitive or convenient diagnosis of heart disease risk than currently available. For patients undergoing cancer genotyping or surveillance, and those receiving cancer treatments known to increase heart disease risk, the development of heart disease could be monitored concurrently before it occurs.

[0048] While many SNV and CNV alterations are known to be associated with heart disease, myocardial infarction, and severe cardiomyopathy, little is known about how epigenomic regions and alterations affect the risk of these conditions, or how epigenomic biomarkers influence the natural process of cardiac repair after myocardial infarction. The epigenomic biomarkers described in this article could serve as novel drug targets or enhance the efficacy of existing drugs to improve patient outcomes.

[0049] sample The sample can be any biological sample isolated from the subject. The sample can be a bodily sample. Samples can include body tissues such as known or suspected solid tumors, whole blood, platelets, serum, plasma, feces, red blood cells, white blood cells or leukocytes, endothelial cells, tissue biopsies, cerebrospinal fluid, synovial fluid, lymph, ascites, interstitial fluid or extracellular fluid, fluids in the intercellular spaces (including gingival crevicular fluid), bone marrow, pleural effusion, cerebrospinal fluid, saliva, mucus, sputum, semen, sweat, and urine. Samples are preferably bodily fluids, particularly blood and its fractions, and urine. Samples can be in the form initially isolated from the subject, or can be further processed to remove or add components, such as cells, or to enrich one component relative to other components. Therefore, preferred bodily fluids for analysis are plasma or serum containing cell-free nucleic acids. Samples can be isolated or obtained from the subject and transported to the sample analysis site. Samples can be stored and transported at desirable temperatures, such as room temperature, 4°C, -20°C, and / or -80°C. Samples may be isolated from or obtained from the subject at the sample analysis site. Subjects may be humans, mammals, animals, companion animals, service animals, or pets. Subjects may have cancer. Subjects may not have cancer or may have detectable symptoms of cancer. Subjects may have been treated with one or more cancer therapies, such as chemotherapy, antibodies, vaccines, or biologics. Subjects may be in remission. Subjects may be diagnosed or may not be diagnosed with a predisposition to cardiovascular disease, such as heart failure, cancer, or any cancer-related gene mutations / disorders.

[0050] The volume of plasma can depend on the desired read depth for the sequenced region. Exemplary volumes are 0.4–40 ml, 5–20 ml, and 10–20 ml. For example, the volume can be 0.5 mL, 1 mL, 5 mL, 10 mL, 20 mL, 30 mL, or 40 mL. The volume of plasma sampled can be from 5 mL to 20 mL.

[0051] Samples can contain varying amounts of nucleic acids containing genomic equivalents. For example, a sample of approximately 30 ng DNA can contain approximately 10,000 (10^3) ​​nucleotides. 4 The genome equivalent of 200 billion haploid human genomes, and in the case of cfDNA, it can contain approximately 200 billion (2 × 10⁻⁶) haploid human genomes. 11 ) individual polynucleotide molecules. Similarly, a sample of about 100 ng DNA can contain about 30,000 haploid human genome equivalents, and in the case of cfDNA, about 600 billion individual molecules.

[0052] The sample may contain nucleic acids from different sources, such as nucleic acids from cells and cell-free samples from the same subject, or from cells and cell-free samples from different subjects. The sample may contain nucleic acids carrying mutations. For example, the sample may contain DNA carrying germline mutations and / or somatic mutations. Germline mutations refer to mutations present in the germline DNA of the subject. Somatic mutations refer to mutations originating from somatic cells of the subject, such as cancer cells. The sample may contain DNA carrying cancer-related mutations (e.g., cancer-related somatic mutations). The sample may contain epigenetic variations (i.e., chemical or protein modifications) that are associated with the presence of genetic variations (such as cancer-related mutations). In some embodiments, the sample contains epigenetic variations associated with the presence of genetic variations, wherein the sample does not contain said genetic variations.

[0053] Exemplary amounts of cell-free nucleic acid in the pre-amplification sample range from about 1 fg to about 1 µg, such as 1 pg to 200 ng, 1 ng to 100 ng, 10 ng to 1000 ng. For example, amounts 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. Amounts 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 quantity 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 may include obtaining 1 femtogram (fg) to 200 ng.

[0054] Cell-free nucleic acids are nucleic acids that are not contained within cells or otherwise bound to cells, or in other words, nucleic acids that remain in a sample after the removal of intact cells. Cell-free nucleic acids include DNA, RNA, and their hybrids, including genomic DNA, mitochondrial DNA, siRNA, miRNA, circulating RNA (cRNA), tRNA, rRNA, small nucleolar RNA (snoRNA), Piwi-interacting RNA (piRNA), long non-coding RNA (long ncRNA), and / or fragments of any of these. Cell-free nucleic acids can be double-stranded, single-stranded, or hybrids thereof. Cell-free nucleic acids can be released into body fluids through secretion or cell death procedures such as cell necrosis and apoptosis. Some cell-free nucleic acids are released into body fluids from cancer cells, such as circulating tumor DNA (ctDNA). Others are released from healthy cells. In some embodiments, cfDNA is cell-free fetal DNA (cffDNA). In some embodiments, cell-free nucleic acids are produced by tumor cells. In some embodiments, cell-free nucleic acids are produced by a mixture of tumor cells and non-tumor cells.

[0055] Cell-free nucleic acids have an example size distribution of approximately 100-500 nucleotides, with molecules of 110 to 230 nucleotides representing approximately 90% of the molecules, a mode of approximately 168 nucleotides, and a second small peak in the range of 240 to 440 nucleotides. Cell-free nucleic acids can be separated from body fluids via a fractionation or partitioning step, in which the cell-free nucleic acids present in solution are separated from intact cells and other insoluble components in the body fluid. Partitioning can include techniques such as centrifugation or filtration. Optionally, cells in the body fluid can be lysed, and cell-free and cellular nucleic acids are processed together. Typically, nucleic acids can be precipitated with alcohol after adding buffer and washing steps. Further cleaning steps, such as silica-based columns, can be used to remove contaminants or salts. Nonspecific bulk carrier nucleic acids, such as Cot-1 DNA, DNA or proteins for bisulfite sequencing, hybridization, and / or ligation, can be added throughout the reaction to optimize certain aspects of the procedure, such as yield.

[0056] Following such processing, the sample can include various forms of nucleic acids, including double-stranded DNA, single-stranded DNA, and single-stranded RNA. In some embodiments, single-stranded DNA and RNA can be converted into double-stranded forms, and therefore included in subsequent processing and analytical steps.

[0057] Analytes Analytes may include nucleic acid analytes and non-nucleic acid analytes. This disclosure provides a method 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 RNA), or any combination thereof. Biological samples may include, for example, tumor tissue from a biopsy. In some cases, biological samples may include blood or saliva. In specific cases, biological samples may contain cell-free DNA (“cfDNA”) or circulating tumor DNA (“ctDNA”). Cell-free DNA may be present, for example, in blood.

[0058] 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, amidated variants of proteins, hydroxylated variants of proteins, methylated variants of proteins, ubiquitinated variants of proteins, sulfated variants of proteins, viral proteins (e.g., viral capsid, viral envelope, viral outer shell, viral appendages, viral glycoproteins, viral spikes, etc.), extracellular and intracellular proteins, antibodies, and antigen-binding fragments. This also includes receptors, antigens, surface proteins, transmembrane proteins, differentiation protein clusters, protein channels, protein pumps, carrier proteins, phospholipids, glycoproteins, glycolipids, cell-cell interaction protein complexes, antigen-presenting complexes, major histocompatibility complexes, engineered T-cell receptors, T-cell receptors, B-cell receptors, chimeric antigen receptors, extracellular matrix proteins, post-translational modifications (e.g., phosphorylation, glycosylation, ubiquitination, nitrosation, methylation, acetylation, or lipidation) of cell surface proteins, gap junctions, and adhesion junctions.

[0059] Typically, 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 being analyzed can be at least about 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 20, at least 25, at least 30, at least 40, at least 50, at least 100, at least 1,000, at least 10,000, at least 100,000, or more different analytes present in a region of the sample or a single feature of the substrate.

[0060] One or more nucleic acid analytes and / or non-nucleic acid analytes constitute a set of molecular interactions in the biological system under study (e.g., a cell), which can be considered as an “interaction set”—molecular interactions occurring between molecules belonging to different biochemical families (proteins, nucleic acids, lipids, carbohydrates, etc.) and also within a given family. In various embodiments, the interaction set is a protein-DNA interaction set (a network formed by transcription factors (and DNA or chromatin regulatory proteins) and their target genes). In other embodiments, the interaction set refers to a protein-protein interaction network (PPI) or a protein-protein interaction network (PIN). The methods described herein allow for the study and analysis of interaction sets. Techniques such as proteogenomics (whole genome sequencing, whole exome sequencing, and RNA-seq, as well as mass spectrometry, as examples) can support the study of interaction sets.

[0061] analyze The methods of this invention can be used to diagnose the presence of a condition, particularly cancer, in a subject, to characterize the condition (e.g., to stage the cancer or determine its heterogeneity), monitor the condition's response to treatment, and achieve prognostic assessment of the risk of condition progression or subsequent disease development. This disclosure can also be used to determine the efficacy of a particular treatment option. If treatment is successful, a successful treatment option may increase the amount of copy number variations or rare mutations detected in the subject's blood as more cancer cells may die and shed DNA. In other instances, this may not occur. In yet another instance, some treatment options may be correlated with the genetic profile of the cancer over time. This correlation can be used to select a therapy. Additionally, if cancer is observed to be in remission after treatment, the methods of this invention can be used to monitor residual disease or disease recurrence.

[0062] The types and numbers of cancers that can be detected include leukemia, brain cancer, lung cancer, skin cancer, nasal cancer, laryngeal cancer, liver cancer, bone cancer, lymphoma, pancreatic cancer, colorectal cancer, rectal cancer, thyroid cancer, bladder cancer, kidney cancer, oral cancer, stomach cancer, solid tumors, heterogeneous tumors, and homogeneous tumors. Cancer type and / or stage can be detected based on genetic variations, including mutations, rare mutations, insertions / deletions, copy number variations, transversions, translocations, inversions, deletions, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structural changes, gene fusions, gene truncation, gene amplification, gene duplication, chromosomal damage, DNA damage, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns, and abnormal changes in the 5-methylcytosine content of nucleic acids.

[0063] Genetic and other analyte data can also be used to characterize specific forms of cancer. Cancers are often heterogeneous in both composition and stage. Genetic profiling data can allow for the characterization of specific subtypes of cancer, which may be important in the diagnosis or treatment of that specific subtype. This information can also provide subjects or practitioners with clues about the prognosis of a specific type of cancer and allow them to adjust treatment options based on disease progression. Some cancers can progress and become more aggressive and genetically unstable. Other cancers may remain benign, inactive, or dormant. The systems and methods disclosed herein can be used to determine disease progression.

[0064] The analyses of this invention can also be used to determine the efficacy of a particular treatment option. If the treatment is successful, a successful treatment option may increase the amount of copy number variations or rare mutations detected in the subject's blood as more cancer cells may die and shed DNA. In other instances, this may not occur. In yet another instance, some treatment options may be correlated with the genetic profile of the cancer over time. This correlation can be used to select a therapy. Additionally, if cancer is observed to be in remission after treatment, the method of this invention can be used to monitor residual disease or recurrence of disease.

[0065] The methods of this invention can also be used to detect genetic variations in conditions other than cancer. Following the onset of certain diseases, immune cells, such as B cells, can undergo rapid clonal expansion. Copy number variation detection can be used to monitor clonal expansion and certain immune states. In this instance, copy number variation analysis can be performed over time to generate a spectrum of how a particular disease may progress. Copy number variation or even rare mutation detection can be used to determine how a pathogen population changes during the course of infection. This can be particularly important during chronic infections (such as HIV / AID or hepatitis infections), where the virus can alter its life cycle state and / or mutate into a more virulent form during the course of infection. When immune cells attempt to destroy transplanted tissue, the methods of this invention can be used to determine or analyze the host body's rejection activity to monitor the state of the transplanted tissue and to modify the process of rejection treatment or prevention.

[0066] For example, many types of dysfunction and abnormalities commonly occur in the cardiovascular system, gradually reducing the body's ability to supply enough oxygen to meet the oxygen demands of the coronary arteries when an individual experiences stress. This progressive decline in the cardiovascular system's ability to supply oxygen under stress will eventually lead to a heart attack, i.e., a myocardial infarction caused by oxygen starvation in the myocardial tissue (i.e., the heart muscle) due to an interruption of blood flow through the heart. In many cases, permanent damage will occur to the cells that make up the heart muscle, which will subsequently make the individual more susceptible to further myocardial infarction events.

[0067] The methods disclosed herein can characterize dysfunctions and abnormalities (e.g., hypertrophy) associated with myocardial and valvular tissue, and reductions in blood flow and oxygen supply to the heart are often secondary symptoms of weakening and / or worsening of the blood flow and supply systems caused by physical and biochemical stress. Examples of cardiovascular diseases directly affected by these types of stress include atherosclerosis, coronary artery disease, peripheral vascular disease, and peripheral artery disease, as well as a variety of heart diseases and arrhythmias that may represent other forms of disease and functional abnormalities.

[0068] Furthermore, the methods of this disclosure can be used to characterize the heterogeneity of anomalies in a subject. Such methods may include, for example, generating a genetic profile of extracellular polynucleotides derived from the subject, wherein the genetic profile includes more than one set of data obtained from copy number variation and rare mutation analysis. In some embodiments, the anomaly is cancer. In some embodiments, the anomaly may be a condition leading to a heterogeneous genomic population. In the example of cancer, some tumors are known to contain tumor cells at different stages of cancer. In other examples, heterogeneity may include multiple lesions of the disease. Again, in the example of cancer, multiple tumor lesions may be present, perhaps one or more of which are the result of metastases that have spread from the primary site.

[0069] The method of this invention can be used to generate or analyze a fingerprint or dataset that represents the sum of genetic information derived from different cells in heterogeneous diseases. This dataset can include individual or combined copy number variation and mutation analyses.

[0070] The methods of this invention can be used for the diagnosis, prognosis, monitoring, or observation of cancer or other diseases. In some embodiments, the methods herein do not involve the diagnosis, prognosis, or monitoring of the fetus, and therefore do not involve noninvasive prenatal testing. In other embodiments, these methods can be used in pregnant subjects to diagnose, prognose, monitor, or observe cancer or other diseases in unborn subjects whose DNA and other polynucleotides may co-circulate with maternal molecules.

[0071] Determination of the 5-methylcytosine pattern of nucleic acids Bisulfite-based sequencing and its variations provide a means of determining the methylation patterns of nucleic acids. In some embodiments, determining the methylation pattern includes distinguishing between 5-methylcytosine (5mC) and unmethylated cytosine. In some embodiments, determining the methylation pattern includes distinguishing between N6-methyladenine and unmethylated adenine. In some embodiments, determining the methylation pattern includes distinguishing between 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxycytosine (5caC) and unmethylated cytosine. Examples of bisulfite sequencing include, but are not limited to, oxidized bisulfite sequencing (OX-BS-seq), Tet-assisted bisulfite sequencing (TAB-seq), and reduced bisulfite sequencing (redBS-seq).

[0072] Oxidized bisulfite sequencing (OX-BS-seq) is used to distinguish between 5mC and 5hmC by first converting 5hmC to 5fC, followed by bisulfite sequencing as described above. Tet-assisted bisulfite sequencing (TAB-seq) can also be used to distinguish between 5mC and 5hmC. In TAB-seq, 5hmC is protected by glycosylation. As mentioned earlier, 5mC is converted to 5caC using the Tet enzyme before bisulfite sequencing. Reduced bisulfite sequencing is used to distinguish 5fC from modified cytosine.

[0073] Typically, in bisulfite sequencing, nucleic acid samples are split into two aliquots, and one aliquot is treated with bisulfite. Bisulfite converts native cytosine and certain modified cytosine nucleotides (e.g., 5-formylcytosine or 5-carboxycytosine) to uracil, while other modified cytosines (e.g., 5-methylcytosine, 5-hydroxymethylcytosine) are not converted. Comparison of the nucleic acid sequences of molecules from the two aliquots indicates which cytosines were converted to uracil and which were not. Therefore, modified and unmodified cytosines can be identified. Initially splitting the sample into two aliquots is disadvantageous for samples containing only small amounts of nucleic acids and / or including heterogeneous cellular / tissue-derived samples such as body fluids containing cell-free DNA.

[0074] This disclosure provides methods for enabling bisulfite sequencing and its variations. These methods function by linking nucleic acids in a population to a capture motif (i.e., a tag that can be captured or immobilized). Capture motifs include, but are not limited to, biotin, avidin, streptavidin, nucleic acids containing a specific nucleotide sequence, haptens recognized by antibodies, and magnetically attractive particles. Extraction motifs can be members of binding pairs such as biotin / streptavidin or haptens / antibodies. In some embodiments, the capture motif attached to the analyte is captured by its binding pair, which is attached to a separable motif, such as magnetically attractive particles or large particles that can be precipitated by centrifugation. The capture motif can be any type of molecule that allows affinity separation of nucleic acids with the capture motif from nucleic acids lacking the capture motif. Example capture motifs are biotin or oligonucleotides, where biotin allows affinity separation by binding to streptavidin linked to or capable of being linked to a solid phase, and oligonucleotides allow affinity separation by binding to complementary oligonucleotides linked to or capable of being linked to a solid phase. After the capture motif is linked to the sample nucleic acid, the sample nucleic acid is used as an amplification template. After amplification, the original template remains connected to the captured portion, but the amplicon does not connect to the captured portion.

[0075] The capture portion can be ligated to the sample nucleic acid as a component of an adaptor, which can also provide binding sites for amplification and / or sequencing primers. In some methods, the sample nucleic acid is ligated to the adaptor at both ends, with both adaptors containing the capture portion. Preferably, any cytosine residues in the adaptor are modified, such as by 5-methylcytosine, to protect against bisulfite. In some cases, the capture portion is ligated to the original template via a cleavable linker (e.g., photocleavable dethiobiotin-TEG or uracil residues cleavable by the USER™ enzyme, Chem. Commun. (Camb). 21 Feb 2015; 51(15): 3266-3269), in which case the capture portion can be removed if necessary.

[0076] The amplicon is denatured and then contacted with an affinity reagent used to capture the tag. The original template binds to the affinity reagent, while the amplified nucleic acid molecules do not. Therefore, the original template can be separated from the amplified nucleic acid molecules.

[0077] After isolation or partitioning, the corresponding populations of nucleic acids (i.e., the original template and amplification products) can be subjected to bisulfite treatment, with the original template population receiving bisulfite treatment while the amplification products do not. Optionally, the amplification products can undergo bisulfite treatment while the original template population does not. After such treatment, the corresponding populations can be amplified (in the case of the original template population, this converts uracil to thymine). The populations can also undergo biotinylated probe hybridization for enrichment. The corresponding populations are then analyzed and sequences are compared to determine which cytosines are 5-methylated (or 5-hydroxymethylated) in the original sample. Detection of T nucleotides (corresponding to unmethylated cytosine converted to uracil) in the template population and C nucleotides at corresponding positions in the amplification population indicates unmodified C. The presence of C at corresponding positions in the original template and amplification population indicates the presence of modified C in the original sample.

[0078] In some embodiments, the method uses sequential DNA-seq and bisulfite-seq (BIS-seq) NGS library preparation with molecularly tagged DNA libraries. The process involves tagging of an adaptor (e.g., biotin), DNA-seq amplification of the entire library, parental molecule recovery (e.g., streptavidin bead pull-down), bisulfite conversion, and BIS-seq. In some embodiments, the method identifies 5-methylcytosine at single-base resolution through sequential NGS preparative amplification of parental library molecules with and without bisulfite treatment. This can be achieved by modifying one of the two adaptor strands with a label (e.g., biotin) the 5-methylated NGS adaptor used in BIS-seq (oriented adaptor; Y-shaped / forked, replaced with 5-methylcytosine). Sample DNA molecules are the linked adaptors and are amplified (e.g., by PCR). Since only parental molecules will have the tagged adaptor ends, they can be selectively recovered from their amplified progeny using label-specific capture methods (e.g., streptavidin magnetic beads). Because the parental molecules retain the 5-methylation marker, bisulfite conversion on the captured library will produce a 5-methylation state at single-base resolution during BIS-seq, thus preserving molecular information in the corresponding DNA-seq. In some embodiments, the bisulfite-treated library can be combined with the untreated library prior to enrichment / NGS by adding a sample-tagged DNA sequence in a standard multiplex NGS workflow. As with the BIS-seq workflow, bioinformatics analysis can be performed for genome alignment and 5-methylation base recognition. In summary, this method provides the ability to selectively recover parental, ligated molecules carrying the 5-methylcytosine marker after library amplification, allowing for parallel processing of bisulfite-converted DNA. This overcomes the detrimental nature of bisulfite treatment to the quality / sensitivity of DNA-seq information extracted from the workflow. With this method, the recovered ligated, parental DNA molecules (via tagged adaptors) allow for the amplification of a complete DNA library and the parallel application of treatments that induce epigenetic DNA modifications. This disclosure discusses the use of BIS-seq methods to identify 5-methylated cytosine (5-methylcytosine), but this is not limiting. Variations of BIS-seq have been developed to identify hydroxymethylated cytosine (5hmC; OX-BS-seq, TAB-seq), formylcytosine (5fC; redBS-seq), and carboxycytosine. These methods can be implemented using sequential / parallel library preparation as described herein.

[0079] Alternative methods for analyzing modified nucleic acids This disclosure provides alternative methods for analyzing modified nucleic acids (e.g., methylated, histone-linked, and other modifications discussed above). In some such methods, a population of nucleic acids with varying degrees of modification (e.g., each nucleic acid molecule has 0, 1, 2, 3, 4, 5, or more methyl groups) is contacted with an adaptor, and the population is then stratified according to the degree of modification. The adaptor is attached to one or both ends of the nucleic acid molecules in the population. Preferably, the adaptor contains a sufficient number of different tags such that the number of tag combinations results in a high probability, e.g., 95%, 99%, or 99.9%, that two nucleic acids with the same start and end points receive different tag combinations. After attaching the adaptor, the nucleic acid is amplified from a primer that binds to a primer binding site within the adaptor. Adaptors with the same or different tags may contain the same or different primer binding sites, but preferably the adaptor contains the same primer binding site. After amplification, the nucleic acid is contacted with an agent that preferably binds the modified nucleic acid (such as those previously described). The nucleic acid is divided into at least two partitions, the difference between the at least two partitions being the degree to which the modified nucleic acid binds to the agent. For example, if the reagent has an affinity for the modified nucleic acid, the overrepresented modified nucleic acid (compared to the median representation in the population) preferentially binds to the reagent, while the underrepresented modified nucleic acid does not bind to the reagent or is more easily eluted from it. After separation, the different partitions can then undergo additional processing steps, which typically include parallel but separate additional amplification and sequence analysis. The sequence data from the different partitions can then be compared.

[0080] Nucleic acid molecules can be coupled to Y-adaptors containing primer binding sites and tags. The molecule is then amplified. The amplified molecule is then partitioned by contacting an antibody that preferentially binds to 5-methylcytosine to produce two partitions. One partition contains the unmethylated original molecule and the amplified copy that has lost methylation. The other partition contains the original DNA molecule with methylation. The two partitions are then processed and sequenced separately, and the methylated partition is further amplified. The sequence data of the two partitions can then be compared. In this example, the tag is not used to distinguish between methylated and unmethylated DNA, but rather to distinguish the different molecules within these partitions, allowing one to determine whether reads with the same start and end points are based on the same or different molecules.

[0081] This disclosure also provides methods for analyzing nucleic acid populations, wherein at least some nucleic acids contain one or more modified cytosine residues, such as 5-methylcytosine and any other modifications previously described. In these methods, the nucleic acid population is contacted with an adaptor comprising one or more cytosine residues, such as 5-methylcytosine, modified at the 5C position. Preferably, all cytosine residues in such an adaptor are also modified, or all such cytosine residues in the primer-binding region of the adaptor are modified. The adaptor is attached to both ends of the nucleic acid molecules in the population. Preferably, the adaptor contains a sufficient number of different tags such that the number of tag combinations results in a high probability, for example, 95%, 99%, or 99.9%, that two nucleic acids with the same start and end points receive different tag combinations. The primer-binding sites in such an adaptor may be the same or different, but are preferably the same. After the adaptor is attached, the nucleic acid is amplified by primers that bind to the primer-binding sites of the adaptor. The amplified nucleic acid is divided into a first aliquot and a second aliquot. The first aliquot is sequenced, with or without further processing. This determines the sequence data of molecules in the first aliquot without considering the initial methylation state of the nucleic acid molecules. Nucleic acid molecules in the second aliquot are treated with bisulfite. This treatment converts unmodified cytosine to uracil. The bisulfite-treated nucleic acids then undergo amplification, initiated by primers targeting the original primer binding sites of the adaptor attached to the nucleic acid. Now only the nucleic acid molecules initially attached to the adaptor (unlike their amplification products) are amplified because these nucleic acids retain cytosine at the primer binding sites of the adaptor, while the amplification products have lost the methylation of these cytosine residues, which were converted to uracil during the bisulfite treatment. Therefore, only the original molecules in the population (at least some of which are methylated) undergo amplification. After amplification, these nucleic acids are sequenced. Comparison of the sequences determined from the first and second aliquots can particularly indicate which cytosine residues in the nucleic acid population have undergone methylation.

[0082] Dividing the sample into more than one subsample; aspects of the sample; analysis of epigenetic characteristics. In some embodiments described herein, different forms of nucleic acid populations (e.g., hypermethylated and hypomethylated DNA in a sample, such as capture groups of cfDNA as described herein) can be physically partitioned based on one or more characteristics of the nucleic acids, followed by further analysis, such as differential modification or isolation of nucleotides, tagging, and / or sequencing. This approach can be used to determine, for example, whether certain sequences are hypermethylated or hypomethylated. In some embodiments, analysis is performed on hypermethylated variable epigenetic target regions to determine whether they exhibit the hypermethylation signature of tumor cells, and / or analysis is performed on hypomethylated variable epigenetic target regions to determine whether they exhibit the hypomethylation signature of tumor cells. Additionally, by partitioning heterogeneous nucleic acid populations, one can increase rare signals, for example, by enriching rare nucleic acid molecules that are more prevalent in one fraction (or partition) of the population. For example, by partitioning a sample into hypermethylated and hypomethylated nucleic acid molecules, it is easier to detect genetic variations that are present in hypermethylated DNA but less so (or absent) in hypomethylated DNA. By analyzing more than one fraction of a sample, multidimensional analysis of individual loci or nucleic acid species in the genome can be performed, thus enabling greater sensitivity.

[0083] In some cases, heterogeneous nucleic acid samples are partitioned into two or more partitions (e.g., at least three, four, five, six, or seven partitions). In some implementations, each partition is differentially tagged. The tagged partitions can then be pooled together for collective sample preparation and / or sequencing. The partitioning-tag-pooling step can occur more than once, with each round of partitioning occurring based on different characteristics (in the examples provided herein) and tagged using differential tags that distinguish them from other partitioning and partitioning methods.

[0084] Examples of features that can be used for partitioning include sequence length, methylation level, nucleosome binding, sequence mismatch, immunoprecipitation, and / or proteins that bind to DNA. The resulting partitions can include one or more of the following nucleic acid forms: single-stranded DNA (ssDNA), double-stranded DNA (dsDNA), shorter DNA fragments, and longer DNA fragments. In some embodiments, partitioning is typically performed based on cytosine modification (e.g., cytosine methylation) or methylation, and optionally in combination with at least one additional partitioning step, which can be based on any of the aforementioned features or forms of DNA. In some embodiments, a heterogeneous population of nucleic acids is partitioned into nucleic acids having one or more epigenetic modifications and those not having said one or more epigenetic modifications. Examples of epigenetic modifications include the presence or absence of methylation, methylation level, methylation type (e.g., 5-methylcytosine with other types of methylation, such as adenine methylation and / or cytosine hydroxymethylation), and association with one or more proteins (such as histones) and the level of association. Optionally or additionally, the heterogeneous nucleic acid population can be partitioned into nucleic acid molecules associated with nucleosomes and nucleic acid molecules that do not contain nucleosomes. Optionally or additionally, the heterogeneous nucleic acid population can be partitioned into single-stranded DNA (ssDNA) and double-stranded DNA (dsDNA). Optionally or additionally, the heterogeneous nucleic acid population can be partitioned based on nucleic acid length (e.g., molecules with a maximum length of 160 bp and molecules with a length greater than 160 bp).

[0085] In some cases, each partition (representing a different nucleic acid form) is differentially labeled, and the partitions are pooled together and then sequenced. In other cases, the different forms are sequenced separately. In some implementations, different nucleic acid populations are partitioned into two or more distinct partitions. Each partition represents a different nucleic acid form, and the first partition (also called a subsample) contains DNA with a larger proportion of cytosine modifications than the second subsample. Each partition is tagged differently. The first subsample undergoes a procedure that differently affects the first and second nucleotides in the DNA of the first subsample, where the first nucleotide is modified or unmodified, and the second nucleotide is modified or unmodified, different from the first nucleotide, and the first and second nucleotides have the same base-pairing specificity. The tagged nucleic acids are pooled together and then sequenced. Sequence reads are obtained and analyzed, including computer simulation (in silico) to distinguish the first and second nucleotides in the DNA of the first subsample. Tags are used to sort reads from different partitions. Analysis can be performed at the level of individual partitions and at the level of the entire nucleic acid population to detect genetic variation. For example, the analysis may include computer simulation analysis to determine genetic variations, such as CNVs, SNVs, insertions / deletions, and fusions in the nucleic acids of each partition. In some cases, computer simulation analysis may include determining chromatin structure. For example, the coverage of sequence reads can be used to determine the location of nucleosomes in chromatin. Higher coverage may be associated with higher nucleosome occupancy in a genomic region, while lower coverage may be associated with lower nucleosome occupancy or nucleosome depleted regions (NDRs).

[0086] Samples may include nucleic acids with various modifications, including post-replication modifications of nucleotides and binding to one or more proteins (typically non-covalent).

[0087] In the implementation scheme, the nucleic acid population is a population of nucleic acids obtained from serum, plasma, or blood samples of subjects suspected of having vegetations, tumors, or cancer, or previously diagnosed with vegetations, tumors, or cancer. The nucleic acid population includes nucleic acids with different levels of methylation. Methylation can occur by any one or more post-replication or post-transcriptional modifications. Post-replication modifications include modifications to nucleotide cytosine, particularly at the 5-position of the nucleobase, such as 5-methylcytosine, 5-hydroxymethylcytosine, 5-formylcytosine, and 5-carboxycytosine. The affinity agent can be an antibody with desired specificity, a natural binding partner or a variant thereof (Bock et al., Nat Biotech 28: 1106-1114 (2010); Song et al., Nat Biotech 29: 68-72 (2011)), or, for example, an artificial peptide selected for specificity to a given target via phage display.

[0088] Examples of capture fractions envisioned herein include methyl-binding domains (MBDs) and methyl-binding proteins (MBPs) as described herein, including proteins such as MeCP2 and antibodies that preferentially bind to 5-methylcytosine. Similarly, partitioning of different forms of nucleic acids can be performed using histone-binding proteins that can separate histone-bound nucleic acids from free or unbound nucleic acids. Examples of histone-binding proteins that can be used in the methods disclosed herein include RBBP4, RbAp48, and SANT domain peptides. For some affinity agents and modifications, although binding to the agent may occur substantially all-or-nothing depending on whether the nucleic acid is modified, separation may be to a certain extent. In such cases, nucleic acids overrepresented in a modification bind to the agent to a greater extent than nucleic acids underrepresented in the modification. Optionally, modified nucleic acids may bind in an all-or-nothing manner. However, various levels of modification can then be eluted sequentially from the binding agent.

[0089] For example, in some implementations, partitioning can be binary or based on the degree / level of modification. For instance, a methyl-binding domain protein (e.g., the MethylMiner methylated DNA enrichment kit (ThermoFisherScientific)) can be used to partition all methylated fragments with unmethylated fragments. Subsequently, additional partitioning can include eluting fragments with different methylation levels by adjusting the salt concentration of the solution containing the methyl-binding domain and the binding fragment. As the salt concentration increases, fragments with higher methylation levels are eluted. In some cases, the final partitioning represents nucleic acids with different degrees of modification (overrepresentation or underrepresentation). Overrepresentation and underrepresentation can be defined by the number of modifications a nucleic acid carries relative to the median number of modifications per strand in the population. For example, if the median number of 5-methylcytosine residues in the nucleic acids in a sample is 2, then nucleic acids containing more than two 5-methylcytosine residues are overrepresented, while nucleic acids with one or zero 5-methylcytosine residues are underrepresented. The purpose of affinity separation is to enrich over-represented nucleic acids in the binding phase and under-represented nucleic acids in the non-binding phase (i.e., in solution). The nucleic acids in the binding phase can be eluted before subsequent processing.

[0090] When using the MethylMiner Methylated DNA Enrichment Kit (ThermoFisher Scientific), sequential elution can be used to separate methylated regions with different levels of methylation. For example, low-methylated regions (e.g., unmethylated) can be separated from methylated regions by contacting a group of nucleic acids with MBDs attached to magnetic beads from the kit. The beads are used to separate methylated nucleic acids from unmethylated nucleic acids. Subsequently, one or more sequential elution steps are performed to elute nucleic acids with different methylation levels. For example, the first group of methylated nucleic acids can be eluted at salt concentrations of 160 mM or higher, such as 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 have been eluted, magnetic separation is again used to separate nucleic acids with higher levels of methylation from those with lower levels of methylation. The elution and magnetic separation steps can be repeated to produce various partitions, such as hypomethylated partitions (representing no methylation), methylated partitions (representing low methylation levels), and hypermethylated partitions (representing high methylation levels).

[0091] In some methods, nucleic acids bound to an affinity separator undergo a washing step. This washing step removes nucleic acids that are weakly bound to the affinity agent. Such nucleic acids can be enriched with a degree of modification close to the mean or median (i.e., an intermediate value between nucleic acids that remain bound to the solid and those that do not upon initial contact with the agent). Affinity separation results in at least two, and sometimes three or more, partitions of nucleic acids with different degrees of modification. While the partitions remain separate, nucleic acids from at least one partition, and usually two or three (or more) partitions, are attached to nucleic acid tags, which are typically provided as part of an adaptor, and the nucleic acids in different partitions receive different tags that distinguish members of one partition from members of another. Tags attached to nucleic acid molecules in the same partition can be the same or different from each other. However, if they are different, the tags can have a partially shared encoding to identify the molecules to which they are attached as belonging to a particular partition. For more details on partitioning nucleic acid samples based on characteristics such as methylation, see WO2018 / 119452, which is incorporated herein by reference. In some implementations, nucleic acid molecules can be hierarchically separated into different partitions based on whether they bind to a specific protein or a fragment thereof and whether they do not bind to that specific protein or a fragment thereof.

[0092] Nucleic acid molecules can be fractionated based on DNA-protein binding. Protein-DNA complexes can be fractionated based on specific properties of the protein. Examples of such properties include various epitopes, modifications (e.g., histone methylation or acetylation), or enzymatic activities. Examples of proteins that can bind DNA and be used as the basis for fractionation can include, but are not limited to, protein A and protein G. Any suitable method can be used for fractionation of nucleic acid molecules based on protein-binding regions. Examples of methods for fractionation of nucleic acid molecules based on protein-binding regions include, but are not limited to, SDS-PAGE, chromatin immunoprecipitation (ChIP), heparin chromatography, and asymmetric field flow fractionation (AF4).

[0093] In some implementations, nucleic acid partitioning is performed by contacting the nucleic acid with the methylation-binding domain (“MBD”) of a methylation-binding protein (“MBP”). Partitioning into fractions with different degrees of methylation can be achieved by eluting the fractions with increasing NaCl concentrations. The MBD binds to 5-methylcytosine (5mC). The MBD is coupled to paramagnetic beads (such as Dynabeads® M-280 streptavidin) via a biotin linker.

[0094] An exemplary method for molecular tag identification of MBD bead partition libraries using NGS is as follows: Extracted DNA samples (e.g., plasma DNA extracted from human samples) are physically partitioned using a methyl-binding domain protein-bead purification kit, retaining all eluents from the process for downstream processing.

[0095] Differential molecular tags and NGS-feasible linker sequences were applied in parallel to each partition. For example, hypermethylated, residual methylated ('washed'), and hypomethylated partitions were linked to NGS linkers with molecular tags.

[0096] All molecularly tagged partitions were reassembled and subsequently amplified using adaptor-specific DNA primer sequences.

[0097] Enrich / hybridize the recombined and amplified total library to target genomic regions of interest (e.g., cancer-specific genetic variations and differentially methylated regions).

[0098] The enriched total DNA library was re-amplified and tagged with samples. Different samples were pooled and subjected to multiplex assays on an NGS instrument.

[0099] Bioinformatics analysis of NGS data was performed, using molecular tags to identify unique molecules and deconvolving samples into molecules with differentially expressed molecular dividers (MBDs). This analysis can simultaneously produce information on the relative 5-methylcytosine content of genomic regions with standard gene sequencing / variation detection.

[0100] Examples of MBPs envisioned in this article include, but are not limited to: (a) MeCP2, a protein that preferentially binds to 5-methyl-cytosine compared to binding to unmodified cytosine.

[0101] (b) RPL26, PRP8 and DNA mismatch repair protein MHS6 preferentially bind to 5-hydroxymethyl-cytosine compared to binding to unmodified cytosine.

[0102] (c) FOXK1, FOXK2, FOXP1, FOXP4 and FOXI3 preferentially bind 5-formyl-cytosine compared to binding unmodified cytosine (Iurlaro et al., Genome Biol. 14: R119 (2013)).

[0103] (d) Antibodies specific to one or more methylated nucleotide bases.

[0104] Typically, elution varies with the number of methylation sites per molecule, with molecules having more methylation eluting at increasing salt concentrations. To elute DNA into different populations based on the degree of methylation, a series of elution buffers with increasing NaCl concentrations can be used. Salt concentrations can range from about 100 nM to about 2500 mM NaCl. In one embodiment, the process produces three (3) partitions. Molecules are contacted with a solution of a first salt concentration, and this solution contains molecules containing methyl-binding domains that can attach to capture moieties such as streptavidin. At the first salt concentration, one population of molecules will bind to MBD, and one population will remain unbound. The unbound population can be separated into a “hypomethylated” population. For example, the first partition representing hypomethylated DNA is the partition that remains unbound at low salt concentrations (e.g., 100 mM or 160 mM). The second partition representing moderately methylated DNA is eluted using a moderate salt concentration (e.g., between 100 mM and 2000 mM). This is also separated from the sample. The third partition, representing the highly methylated form of DNA, was eluted with a high salt concentration (e.g., at least about 2000 mM).

[0105] This disclosure also provides methods for analyzing nucleic acid populations, wherein at least some nucleic acids contain one or more modified cytosine residues, such as 5-methylcytosine and any other modifications previously described. In these methods, after partitioning, a sample of nucleic acid subsamples is contacted with an adaptor containing one or more cytosine residues modified at the 5C position (such as 5-methylcytosine). Preferably, all cytosine residues in such an adaptor are also modified, or all such cytosine residues in the primer-binding region of the adaptor are modified. The adaptor is attached to both ends of nucleic acid molecules in the population. Preferably, the adaptor contains a sufficient number of different tags such that the number of tag combinations results in a high probability, for example, 95%, 99%, or 99.9%, that two nucleic acids with the same start and end points receive different tag combinations. The primer-binding sites in such an adaptor can be the same or different, but are preferably the same. After the adaptor is attached, the nucleic acid is amplified by primers that bind to the primer-binding sites of the adaptor. The amplified nucleic acid is divided into a first aliquot and a second aliquot. With or without further processing, the first aliquot is sequenced. This determines the sequence data of molecules in the first aliquot without considering the initial methylation state of the nucleic acid molecules. Nucleic acid molecules in the second aliquot undergo a procedure that differently affects the first and second nucleobases in the DNA, where the first nucleobase includes cytosine modified at position 5, and the second nucleobase includes unmodified cytosine. This procedure can be bisulfite treatment or another procedure that converts unmodified cytosine to uracil. The nucleic acids that have undergone this procedure are then amplified using primers targeting the original primer binding sites of the adaptor attached to the nucleic acid. Now only the nucleic acid molecules initially attached to the adaptor (unlike their amplification products) are amplified because these nucleic acids retain cytosine at the primer binding sites of the adaptor, while the amplification products have lost the methylation of these cytosine residues, which have been converted to uracil during bisulfite treatment. Therefore, only the original molecules in the population (at least some of which are methylated) undergo amplification. After amplification, these nucleic acids are sequenced. Comparison of sequences determined from the first and second aliquots can particularly indicate which cytosines in the nucleic acid population have undergone methylation.

[0106] Such analysis can be performed using the following exemplary procedure. After partitioning, the two ends of the methylated DNA are ligated to a Y-shaped adaptor containing primer binding sites and a tag. The cytosine in the adaptor is modified at position 5 (e.g., 5-methylated). This modification of the adaptor serves to protect the primer binding sites in subsequent transformation steps (e.g., bisulfite treatment, TAP transformation, or any other transformation that does not affect the modified cytosine but affects the unmodified cytosine). After adaptor attachment, the DNA molecule is amplified. The amplified product is divided into two aliquots for sequencing with and without transformation. The untransformed aliquot may undergo sequence analysis with or without further treatment. The other aliquot undergoes a procedure that differently affects the first and second nucleotides in the DNA, where the first nucleotide includes the cytosine modified at position 5, and the second nucleotide includes the unmodified cytosine. This procedure may be bisulfite treatment or another procedure to convert the unmodified cytosine to uracil. When contacted with primers specific to the original primer binding site, only primer binding sites protected by cytosine modification can support amplification. Therefore, only the original molecule, not copies from the first amplification, undergoes further amplification. The further amplified molecules then undergo sequence analysis. The sequences from the two aliquots can then be compared. In the isolation scheme discussed above, the nucleic acid tag in the adaptor is not used to distinguish between methylated and unmethylated DNA, but rather to distinguish nucleic acid molecules within the same partition.

[0107] This causes the first subsample to undergo different processes, affecting the first nucleotide and the second nucleotide in the DNA of the first subsample. The program of nucleobases The method disclosed herein includes steps of subjecting a first subsample to procedures that differently affect a first nucleotide and a second nucleotide in the DNA of the first subsample, wherein the first nucleotide is a modified or unmodified nucleotide, the second nucleotide is a modified or unmodified nucleotide different from the first nucleotide, and the first and second nucleotides have the same base-pairing specificity. In some embodiments, if the first nucleotide is modified or unmodified adenine, then the second nucleotide is modified or unmodified adenine; if the first nucleotide is modified or unmodified cytosine, then the second nucleotide is modified or unmodified cytosine; if the first nucleotide is modified or unmodified guanine, then the second nucleotide is modified or unmodified guanine; if the first nucleotide is modified or unmodified thymine, then the second nucleotide is modified or unmodified thymine (wherein, for the purposes of this step, modified and unmodified uracil are included in modified thymine).

[0108] In some embodiments, the first nucleobase is a modified or unmodified cytosine, and then the second nucleobase is a modified or unmodified cytosine. For example, the first nucleobase 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, such as those indicated, for example, in the overview above and the discussion below, such as where one of the first and second nucleobases comprises mC and the other comprises hmC.

[0109] In some implementations, the procedure that differently affects the first and second nucleobases in the DNA of the first subsample includes bisulfite conversion. Bisulfite treatment converts unmodified cytosine and certain modified cytosine nucleotides (e.g., 5-formylcytosine (fC) or 5-carboxycytosine (caC)) into uracil, while other modified cytosines (e.g., 5-methylcytosine and 5-hydroxymethylcytosine) are not converted. Therefore, in the case of bisulfite conversion, the first nucleobase comprises one or more of unmodified cytosine, 5-formylcytosine, 5-carboxycytosine, or other bisulfite-affected cytosine forms, and the second nucleobase may comprise one or more of mC and hmC, such as mC and optionally hmC. Sequencing of the bisulfite-treated DNA identifies the position read as a cytosine as the mC position or the hmC position. Simultaneously, positions read as T are identified as T or bisulfite-susceptible forms of C, such as unmodified cytosine, 5-formylcytosine, or 5-carboxycytosine. Therefore, bisulfite conversion of the first subsample as described herein facilitates the identification of positions containing mC or hmC using sequence reads obtained from the first subsample. For an exemplary description of bisulfite conversion, see, for example, Moss et al., Nat Commun. 2018; 9: 5068.

[0110] In some embodiments, the procedures that differently affect the first and second nucleotides of the DNA in the first subsample include oxidized bisulfite (Ox-BS) conversion. In some embodiments, the procedures that differently affect the first and second nucleotides of the DNA in the first subsample include Tet-assisted substituted borane reducing agent conversion, optionally wherein the substituted borane reducing agent is 2-methylpyridineborane, pyridineborane, tert-butylamineborane, or aminoborane. In some embodiments, the procedures that differently affect the first and second nucleotides of the DNA in the first subsample include chemically assisted substituted borane reducing agent conversion, optionally wherein the substituted borane reducing agent is 2-methylpyridineborane, pyridineborane, tert-butylamineborane, or aminoborane. In some implementations, procedures that differently affect the first and second nucleobases in the DNA of the first subsample include APOBEC-coupled epigenetic (ACE) transformation.

[0111] In some implementations, procedures that differently affect the first and second nucleotides in the DNA of the first subsample include enzymatic conversion of the first nucleotide, for example, as in EM-Seq. See, for example, Vaisvila R, et al. (2019) EM-seq: Detection of DNA methylation at single base resolution from picograms of DNA. bioRxiv; DOI: 10.1101 / 2019.12.20.884692, available at www.biorxiv.org / content / 10.1101 / 2019.12.20.884692v1. For example, TET2 and T4-βGT can be used to convert 5mC and 5hmC into substrates that cannot be deaminated by deaminases (e.g., APOBEC3A), and then the deaminase (e.g., APOBEC3A) can be used to deaminate unmodified cytosine, converting it to uracil.

[0112] In some implementations, the procedure that differently affects the first nucleobase in the DNA of the first subsample and the second nucleobase in the DNA includes separating the DNA that initially contains the first nucleobase from the DNA that initially does not contain the first nucleobase.

[0113] 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).

[0114] Techniques including methylated DNA immunoprecipitation (MeDIP) can be used to separate DNA containing modified bases (such as mA) from other DNA. See, for example, Kumar et al., Frontiers Genet. 2018; 9: 640; Greer et al., Cell 2015; 161: 868-878. Antibodies specific to mA are described in Sun et al., Bioessays 2015; 37:1155-62. Antibodies against various modified nucleobases (such as thymine / uracil forms, including halogenated forms such as 5-bromouracil) are commercially available. Various modified bases can also be detected based on changes in their base pairing specificity. For example, hypoxanthine is a modified form of adenine that can be produced by deamination and is read as G in sequencing. See, for example, U.S. Patent 8,486,630; Brown, Genomes, 2nd Edition, John Wiley & Sons, Inc., New York, NY, 2002, Chapter 14, “Mutation, Repair, and Recombination”.

[0115] Enrichment / capture steps; amplification; adaptor; barcode In some embodiments, the methods disclosed herein include the step of capturing one or more target regions of DNA, such as cfDNA. Capture can be performed using any suitable method known in the art. In some embodiments, capture includes contacting the DNA to be captured with a set of target-specific probes. The target-specific probe set may have any of the characteristics of the target-specific probe set described herein, including but not limited to the embodiments set forth above and the characteristics described below in the probe-related section. One or more subsamples prepared during the methods disclosed herein may be captured. In some embodiments, DNA is captured from at least a first subsample or a second subsample, e.g., at least a first subsample and a second subsample. If the first subsample undergoes a separation step (e.g., separating DNA initially containing a first nucleobase (e.g., hmC) from DNA initially not containing a first nucleobase, such as hmC-seal), capture can be performed on any one, any two, or all of the DNA initially containing a first nucleobase (e.g., hmC), the DNA initially not containing a first nucleobase, and the second subsample. In some embodiments, the subsamples are differentially tagged (e.g., as described herein) and then pooled prior to capture.

[0116] The capture step can be performed using conditions suitable for a specific nucleic acid hybridization, which are typically dependent to some extent on the characteristics of the probe, such as length, base composition, etc. Given the general knowledge of nucleic acid hybridization in the art, those skilled in the art will be familiar with appropriate conditions. In some embodiments, a complex of the target-specific probe and DNA is formed.

[0117] In some embodiments, the method described herein includes capturing more than one set of target regions of cfDNA obtained from a test subject. Target regions include epigenetic target regions that may exhibit differences in methylation levels and / or fragmentation patterns, depending on whether they originate from tumor cells or healthy cells. Target regions also include sequence-variable target regions that may exhibit sequence differences, depending on whether they originate from tumor cells or healthy cells. The capture step produces a capture set of cfDNA molecules, and within the capture set of cfDNA molecules, cfDNA molecules corresponding to the sequence-variable target region set are captured with a greater capture yield than cfDNA molecules corresponding to the epigenetic target region set. For further discussion of the capture steps, capture yield, and related aspects, see WO2020 / 160414, which is incorporated herein by reference for all purposes.

[0118] In some implementations, the method described herein includes contacting cfDNA obtained from a test subject with a target-specific probe set, wherein the target-specific probe set is configured to capture cfDNA corresponding to a sequence-variable target region set with a greater capture yield than cfDNA corresponding to an epigenetic target region set.

[0119] Capturing cfDNA corresponding to sequence-variable target regions at a higher capture yield than that corresponding to epigenetic target regions is beneficial because analyzing sequence-variable target regions with sufficient confidence or accuracy may require a greater sequencing depth than analyzing epigenetic target regions. The amount of data required to determine fragmentation patterns (e.g., testing for perturbations of transcription start sites or CTCF binding sites) or fragment abundance (e.g., in hypermethylated and hypomethylated regions) is generally less than the amount of data required to determine the presence or absence of cancer-related sequence mutations. Capturing target regions at different yields can facilitate sequencing target regions to different sequencing depths within the same sequencing run (e.g., using pooled mixtures and / or within the same sequencing pool).

[0120] In various embodiments, the method further includes sequencing the captured cfDNA to, for example, different sequencing depths for epigenetic target groups and sequence-variable target groups, consistent with those discussed herein. In some embodiments, the complex of the target-specific probe and DNA is separated from DNA not bound to the target-specific probe. For example, in cases where the target-specific probe is covalently or nonvalently bound to a solid support, washing or aspiration steps can be used to separate the unbound material. Alternatively, chromatography can be used where the complex has different chromatographic properties than the unbound material (e.g., where the probe contains ligands that bind to chromatographic resins).

[0121] As discussed in detail elsewhere herein, a target-specific probe set may include more than one set, such as probes for a sequence-variable target set and probes for an epigenetic target set. In some such embodiments, the capture step is performed simultaneously using probes for sequence-variable targets and probes for epigenetic targets in the same container; for example, probes for both sequence-variable and epigenetic target sets are in the same composition. This method provides a relatively more efficient workflow. In some embodiments, the concentration of probes for sequence-variable targets is greater than the concentration of probes for epigenetic target sets.

[0122] Optionally, a capture step is performed in a first container with a sequence-variable target region probe set and in a second container with an epigenetic target region probe set, or a contact step is performed at a first time and in the first container with a sequence-variable target region probe set and at a second time before or after the first time with an epigenetic target region probe set. This method allows for the preparation of separate first and second compositions comprising captured DNA corresponding to the sequence-variable target region set and captured DNA corresponding to the epigenetic target region set. The compositions can be processed individually as desired (e.g., graded based on methylation, as described elsewhere herein) and recombine in appropriate proportions to provide material for further processing and analysis, such as sequencing.

[0123] In some implementations, DNA is amplified. In some implementations, amplification occurs before the capture step. In some implementations, amplification occurs after the capture step.

[0124] In some implementations, the DNA contains an adaptor. This can be performed simultaneously with the amplification process, for example, by providing the adaptor in the 5' portion of the primer, as described above. Alternatively, the adaptor can be added by other methods such as ligation.

[0125] In some implementations, the DNA contains a tag, which may be a barcode or contain a barcode. The tag can aid in identifying the origin of the nucleic acid. For example, a barcode can be used to allow identification of the DNA's origin, such as a subject, after pooling more than one sample for parallel sequencing. This can be performed concurrently with the amplification procedure, for example, by providing a barcode in the 5' portion of the primer, as described above. In some implementations, the adaptor and the tag / barcode are provided by the same primer or primer set. For example, the barcode may be located at the 3' of the adaptor and the 5' of the target hybridization portion of the primer. Alternatively, the barcode can be added by other methods, such as ligation, optionally along with the adaptor in the same ligation substrate.

[0126] Further details regarding amplification, labeling, and barcodes are discussed in the following “General Characteristics of the Method” section, and these details can be combined, to a degree of feasibility, with any of the aforementioned implementation schemes and the implementation schemes described in the “Introduction and Overview” section.

[0127] Capture Group In some embodiments, a capture set of DNA (e.g., cfDNA) is provided. For the disclosed methods, the capture set of DNA may be provided, for example, by performing a capture step after a partitioning step as described herein. The capture set may include DNA corresponding to a sequence-variable target region group, DNA corresponding to an epigenetic target region group, or a combination thereof. In some embodiments, the amount of captured sequence-variable target region DNA is greater than the amount of captured epigenetic target region DNA when normalized for differences in target region size (footprint size).

[0128] Optionally, a first capture group and a second capture group may be provided, comprising DNA corresponding to a sequence-variable target region group and DNA corresponding to an epigenetic target region group, respectively. The first capture group and the second capture group may be combined to provide a combined capture group.

[0129] In some embodiments, where the capture set, which includes DNA corresponding to both sequence-variable target regions and epigenetic target regions, comprises a combination of capture sets as discussed above, the DNA corresponding to the sequence-variable target regions may be present at a higher concentration than the DNA corresponding to the epigenetic target regions, for example, 1.1 to 1.2 times higher, 1.2 to 1.4 times higher, 1.4 to 1.6 times higher, 1.6 to 1.8 times higher, 1.8 to 2.0 times higher, or 2.0 to 2.2 times higher. Concentration, 2.2 to 2.4 times larger concentration, 2.4 to 2.6 times larger concentration, 2.6 to 2.8 times larger concentration, 2.8 to 3.0 times larger concentration, 3.0 to 3.5 times larger concentration, 3.5 to 4.0 times larger concentration, 4.0 to 4.5 times larger concentration, 4.5 to 5.0 times larger concentration, 5.0 to 5.5 times larger concentration, 5.5 to 6.0 times larger concentration, 6.0 to 6.5 times larger concentration, 6.5 to 7.0 times larger concentration, 7.0 to 7. 5 times larger concentration, 7.5 to 8.0 times larger concentration, 8.0 to 8.5 times larger concentration, 8.5 to 9.0 times larger concentration, 9.0 to 9.5 times larger concentration, 9.5 to 10.0 times larger concentration, 10 to 11 times larger concentration, 11 to 12 times larger concentration, 12 to 13 times larger concentration, 13 to 14 times larger concentration, 14 to 15 times larger concentration, 15 to 16 times larger concentration, 16 to 17 times larger concentration, 17 to 18 times larger concentration, 18 times larger to Concentrations ranging from 19 to 20 times, 20 to 30 times, 30 to 40 times, 40 to 50 times, 50 to 60 times, 60 to 70 times, 70 to 80 times, 80 to 90 times, 90 to 100 times, 10 to 20 times, 10 to 40 times, 10 to 50 times, 10 to 70 times, or 10 to 100 times. The degree of concentration variation is calculated based on normalization for the target footprint size, as discussed in the definitions section.

[0130] Epigenetic target region group An epigenetic target set may include one or more types of target regions that can distinguish DNA from DNA from vegetative (e.g., tumor or cancer) cells from DNA from healthy cells (e.g., non-vegetative circulating cells). Example types of such regions are discussed in detail herein. An epigenetic target set may also include one or more control regions, such as those described herein. In some embodiments, the epigenetic target set has a footprint of at least 100 kb, for example, at least 200 kb, at least 300 kb, or at least 400 kb. In some embodiments, the epigenetic target set has a footprint in the range of 100-1000 kb, for example, 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 900-1000 kb.

[0131] Highly methylated variable target region In some implementations, the epigenetic target set includes one or more hypermethylated variable target regions. Typically, a hypermethylated variable target region refers to a region in which, for example in a cfDNA sample, an observed increase in methylation levels indicates an increased likelihood that the sample (e.g., cfDNA) contains DNA produced by cytotoxic cells (such as tumor cells or cancer cells). For example, hypermethylation of tumor suppressor gene promoters has been repeatedly observed. See, for example, Kang et al., Genome Biol. 18:53 (2017) and the references cited therein. In examples, a hypermethylated variable target region may include a region in which the methylation is not necessarily different from that of DNA from the same type of healthy tissue, but is indeed different from that of typical cfDNA in healthy subjects (e.g., having more methylation). For example, such a hypermethylated variable target region can be used, at least in part, to detect cancer when the presence of cancer leads to increased cell death (such as apoptosis corresponding to the tissue type of cancer). In some implementations, the hypermethylated variable target region includes one or more genomic regions in which the methylation status of cfDNA molecules in these regions is not different from that of cfDNA from healthy subjects, but the presence / increased amount of hypermethylated cfDNA in these regions indicates a specific tissue type (e.g., cancer origin) and is presented as cfDNA entering circulation due to increased apoptosis (e.g., tumor shedding).

[0132] Hypermethylated target regions can be obtained, for example, from the Cancer Genome Atlas. Kang et al., Genome Biology 18:53 (2017) describe a probabilistic approach to constructing a cancer locator using hypermethylated target regions from the breast, colon, kidney, liver, and lung. In some embodiments, the hypermethylated target regions can be specific to one or more types of cancer. Thus, in some embodiments, the hypermethylated target regions comprise one, two, three, four, or five subgroups of hypermethylated target regions that collectively exhibit hypermethylation in one, two, three, four, or five of the following cancers: breast cancer, colon cancer, kidney cancer, liver cancer, and lung cancer.

[0133] In some embodiments, probes targeting an epigenetic target set include probes specific to one or more hypermethylated variable target regions. Hypermethylated variable target regions can be any of the hypermethylated variable target regions listed above. For example, in some embodiments, probes specific to hypermethylated variable target regions include probes specific to more than one locus listed in Table 1 (e.g., at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% of the loci listed in Table 1). In some embodiments, probes specific to hypermethylated variable target regions include probes specific to more than one locus listed in Table 2 (e.g., at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% of the loci listed in Table 2). In some embodiments, probes specific to hypermethylated variable target regions include probes specific to more than one locus listed in Table 1 or Table 2 (e.g., at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% of the loci listed in Table 1 or Table 2). In some embodiments, for each locus included as a target region, there may be one or more probes having hybridization sites that bind between the transcription start site and the stop codon (or the final stop codon for alternatively spliced ​​genes) of that gene. In some embodiments, one or more probes bind within 300 bp (e.g., within 200 bp or 100 bp) of the listed positions. In some embodiments, the probes have hybridization sites that overlap with the positions listed above. In some implementations, probes specific to hypermethylated target regions include probes specific to one, two, three, four, or five subgroups of hypermethylated target regions that collectively exhibit hypermethylation in one, two, three, four, or five of the following cancers: breast cancer, colon cancer, kidney cancer, liver cancer, and lung cancer.

[0134] Hypomethylated variable target region Universal hypomethylation is a phenomenon commonly observed in a variety of cancers. See, for example, Hon et al., GenomeRes. 22:246-258 (2012) (breast cancer); Ehrlich, Epigenomics 1:239-259 (2009) (a review article noting observations of hypomethylation in colon cancer, ovarian cancer, prostate cancer, leukemia, hepatocellular carcinoma, and cervical cancer). For example, regions that are normally methylated in healthy cells (such as repetitive elements (e.g., LINE1 elements, Alu elements, centromere tandem repeats, pericentromere tandem repeats, and satellite DNA) and intergenic regions) may show reduced methylation in tumor cells. Therefore, in some embodiments, the epigenetic target set includes hypomethylated variable target regions, where the observed reduction in methylation levels indicates an increased likelihood that the sample (e.g., cfDNA) contains DNA generated by cytoplasmic cells (such as tumor cells or cancer cells). In examples, a hypomethylated variable target region may include a region whose methylation state is not necessarily different in cancerous tissue relative to DNA from the same type of healthy tissue, but is indeed different in methylation (e.g., less methylated) relative to cfDNA typical of healthy subjects. For example, such a hypomethylated variable target region can be used at least in part to detect cancer when the presence of cancer leads to increased cell death (such as apoptosis corresponding to the tissue type of cancer). In some embodiments, the hypomethylated variable target region comprises one or more genomic regions in which the methylation state of cfDNA molecules in these regions is not different from cfDNA from healthy subjects, but the amount of hypomethylated cfDNA present / increased in these regions indicates a specific tissue type (e.g., cancer origin) and is presented as cfDNA entering circulation accompanied by increased apoptosis (e.g., tumor shedding).

[0135] In some embodiments, the hypomethylated variable target region includes repeating elements and / or intergenic regions. In some embodiments, repeating elements include one, two, three, four, or five of the following: LINE1 elements, Alu elements, centromere tandem repeat sequences, pericentromere tandem repeat sequences, and / or satellite DNA.

[0136] Example specific genomic regions exhibiting cancer-related hypomethylation include nucleotides 8403565-8953708 and 151104701-151106035 on human chromosome 1. In some embodiments, the hypomethylated variable target region overlaps with or includes one or both of these regions.

[0137] In some implementations, probes targeting a set of epigenetic target regions include probes specific to one or more hypomethylated variable target regions. Hypomethylated variable target regions can be any of the hypomethylated target regions listed above. For example, probes specific to one or more hypomethylated variable target regions can include probes targeting regions such as repetitive elements (e.g., LINE1 elements, Alu elements, centromere tandem repeats, pericentromere tandem repeats, and satellite DNA) and intergenic regions that are normally methylated in healthy cells but may exhibit reduced methylation in tumor cells.

[0138] In some embodiments, probes specific to hypomethylated variable target regions include probes specific to repetitive elements and / or intergenic regions. In some embodiments, probes specific to repetitive elements include probes specific to one, two, three, four, or five of the following: LINE1 elements, Alu elements, centromere tandem repeat sequences, pericentromere tandem repeat sequences, and / or satellite DNA.

[0139] Example probes specific to genomic regions exhibiting cancer-related hypomethylation include probes specific to nucleotides 8403565-8953708 and / or 151104701-151106035 of human chromosome 1. In some embodiments, probes specific to hypomethylated variable target regions include probes specific to regions that overlap with or contain nucleotides 8403565-8953708 and / or 151104701-151106035 of human chromosome 1.

[0140] Probes used to detect this set of regions may include probes for detecting genomic regions of interest (hotspot regions) and nucleosome-sensing probes (e.g., KRAS codons 12 and 13), and can be designed to optimize capture based on analysis of cfDNA coverage and fragment size variation and GC sequence composition affected by nucleosome binding patterns. The regions used in this paper may also include non-hotspot regions optimized based on nucleosome location and GC models.

[0141] Subjects In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject with cancer. In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject suspected of having cancer. In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject with 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 with a growth. In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject suspected of having a growth. In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject in remission from a tumor, cancer, or growth (e.g., after chemotherapy, surgical resection, radiation, or a combination thereof). In any of the foregoing embodiments, the cancer, tumor, or growth, or suspected cancer, tumor, or growth, may be of the lung, colon, rectum, kidney, breast, prostate, or liver. In some embodiments, the cancer, tumor, or growth, or suspected cancer, tumor, or growth, is of the lung. In some embodiments, the cancer, tumor, or growth, or suspected cancer, tumor, or growth, is of the colon or rectum. In some embodiments, the cancer, tumor, or growth, or suspected cancer, tumor, or growth, is of the breast. In some embodiments, the cancer, tumor, or growth, or suspected cancer, tumor, or growth, is of the prostate. In any of the foregoing embodiments, the subject may be a human subject.

[0142] In some embodiments, the sequence-variable target 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 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).

[0143] In some embodiments, the probes specific to the sequence-variable target set include probes specific to the target regions of at least 10, 20, 30, or 35 cancer-related genes, such as AKT1, ALK, BRAF, CCND1, CDK2A, CTNNB1, EGFR, ERBB2, ESR1, FGFR1, FGFR2, FGFR3, FOXL2, GATA3, GNA11, GNAQ, GNAS, HRAS, IDH1, IDH2, KIT, KRAS, MED12, MET, MYC, NFE2L2, NRAS, PDGFRA, PIK3CA, PPP2R1A, PTEN, RET, STK11, TP53, and U2AF1.

[0144] Composition containing captured DNA This document provides a combination of a first group and a second group comprising captured DNA. The first group may comprise or be derived from DNA with a greater proportion of cytosine modifications than the second group. The first group may comprise a form of a first nucleobase initially 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 initially present in the DNA before the alteration of base pairing specificity is a modified or unmodified nucleobase, and the second nucleobase is a modified or unmodified nucleobase different from the first nucleobase, and the form of the first nucleobase initially present in the DNA before the alteration of base pairing specificity and the second nucleobase have the same base pairing specificity. The second group does not comprise a form of the first nucleobase initially 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 modified or unmodified cytosine, and the second nucleobase is modified or unmodified cytosine. The first and second nucleobases can be any nucleobases discussed in the overview or regarding procedures that subject the first subsample to different effects on the first and second nucleobases in the DNA of the first subsample.

[0145] In some implementations, the first group includes sequence tags selected from one or more sequence tags in the first group, and the second group includes sequence tags selected from one or more sequence tags in the second group, wherein the sequence tags in the second group are different from the sequence tags in the first group. The sequence tags may include barcodes.

[0146] In some embodiments, the first population comprises protected hmC, such as glucosylated hmC. In some embodiments, the first population undergoes any of the transformation procedures discussed herein, such as bisulfite transformation, Ox-BS transformation, TAB transformation, ACE transformation, TAP transformation, TAPSβ transformation, or CAP transformation. In some embodiments, the first population is protected by hmC followed by deamination of mC and / or C. In some combined embodiments, the first population comprises or is derived from DNA having a larger proportion of cytosine modification than the second population, and the first population comprises both first and second subpopulations, and the first nucleotide is a modified or unmodified nucleotide, the second nucleotide is a modified or unmodified nucleotide different from the first nucleotide, and the first and second nucleotides have the same base-pairing specificity. In some embodiments, the second population does not contain the first nucleotide. In some embodiments, the first nucleotide is a modified or unmodified cytosine, and the second nucleotide 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.

[0147] In some embodiments, the first nucleobase (e.g., modified cytosine) is biotinylated. In some embodiments, the first nucleobase (e.g., modified cytosine) is a product of Huisgen cycloaddition of β-6-azido-glucosyl-5-hydroxymethylcytosine, the product containing an affinity tag (e.g., biotin).

[0148] In any of the combinations described herein, the captured DNA may include cfDNA. The captured DNA may have any of the characteristics described herein regarding the capture set, including, for example, a higher concentration of DNA corresponding to a sequence-variable target region set than the concentration of DNA corresponding to an epigenetic target region set (as normalized for footprint size as discussed above). In some embodiments, the DNA of the capture set contains a sequence tag, which may be added to the DNA as described herein. Typically, the inclusion of a sequence tag results in DNA molecules that differ from their naturally occurring, untagged form.

[0149] This combination may also include the probe set or sequencing primers described herein, each of which may differ from naturally occurring nucleic acid molecules. For example, the probe set described herein may include a capture portion, and the sequencing primers may contain a non-naturally occurring marker.

[0150] Computer systems and processing of real-world evidence (RWE) The methods of this disclosure can be implemented using or by means of a computer system. For example, such a method may include: partitioning a sample into more than one subsample, said more than one subsample including a first subsample and a second subsample, wherein the first subsample contains DNA with a larger proportion of cytosine modification than the second subsample; subjecting the first subsample to procedures that differently affect a first nucleobase and 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 and second nucleobases have the same base-pairing specificity; and sequencing the DNA in the first subsample and the DNA in the second subsample in a manner that distinguishes the first and second nucleobases in the DNA of the first subsample.

[0151] In one aspect, this disclosure provides a non-transitory computer-readable medium including computer-executable instructions that, when executed by at least one electronic processor, perform at least a portion of a method comprising: collecting cfDNA from a test subject; capturing more than one set of target regions from the cfDNA, wherein the more than one set of target regions includes a sequence-variable target region set and an epigenetic target region set, thereby generating a set of captured cfDNA molecules; sequencing the captured cfDNA molecules, wherein the captured cfDNA molecules of the sequence-variable target region set are sequenced to a deeper sequencing depth than the captured cfDNA molecules of the epigenetic target region set; obtaining more than one sequence read generated by a nucleic acid sequencer through sequencing the captured cfDNA molecules; mapping the more than one sequence read 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 the epigenetic target region set to determine the likelihood that the subject has cancer.

[0152] The code can be pre-compiled and configured for use with machines having processors suitable for executing the code, or it can be compiled during runtime. The code can be provided in a programming language, which can be selected to enable the code to be executed in a pre-compiled or just-in-time (JIT) compiled manner.

[0153] Additional details relating to computer systems and networks, databases, and computer program products are provided, for example, in the following: 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), all of which are incorporated herein by reference in their entirety. Further information can be found in PCT Publication No. US2022032250 and U.S. Application No. 17832498.

[0154] This paper describes a method for generating an integrated data repository comprising multiple types of healthcare data, based on one or more implementations. The architecture may include a data integration and analysis system. This system can obtain data from multiple data sources and integrate the data from these sources into the integrated data repository. For example, the system may obtain data from a health insurance claims data repository. In various examples, the data integration and analysis system and the health insurance claims data repository may be created and maintained by different entities. In one or more additional examples, the system and the health insurance claims data repository may be created and maintained by the same entity.

[0155] Data integration and analysis systems can be implemented using one or more computing devices. These devices may include one or more server computing devices, one or more desktop computing devices, one or more laptop computing devices, one or more tablet computing devices, one or more mobile computing devices, or combinations thereof. In some implementations, at least a portion of the computing devices may be implemented in a distributed computing environment. For example, at least a portion of the computing devices may be implemented in a cloud computing architecture. In scenarios where the computing system used to implement the data integration and analysis system is configured in a distributed computing architecture, processing operations can be executed concurrently by multiple virtual machines. In various examples, the data integration and analysis system can implement multithreading techniques. The implementation of distributed computing architecture and multithreading techniques allows the data integration and analysis system to utilize fewer computing resources compared to computing architectures that do not implement these techniques.

[0156] A health insurance claims data repository may store information obtained from one or more health insurance companies, corresponding to insurance claims filed by subscribers of one or more health insurance companies. The health insurance claims data repository may be organized (e.g., sorted) by patient identifiers. Patient identifiers may be based on a patient's first name, last name, date of birth, Social Security number, address, employer, etc. The data stored by the health insurance claims data repository may include structured data arranged in one or more data tables. The one or more data tables storing structured data may include multiple rows and columns indicating information about health insurance claims filed by subscribers of one or more health insurance companies related to procedures and / or treatments received by the subscribers from healthcare providers. At least a portion of the rows and columns of the data tables stored by the health insurance claims data repository may include a health insurance code that may indicate a diagnosis and treatment and / or procedure for a biological condition obtained by a subscriber of one or more health insurance companies. In various examples, the health insurance code may also indicate a diagnostic procedure obtained by an individual related to one or more biological conditions that may be present in the individual. In one or more examples, the diagnostic procedure may provide information for detecting the presence of a biological condition. The diagnostic procedure may also provide information for determining the progression of the biological condition. In one or more illustrative examples, a diagnostic procedure may include one or more imaging procedures, one or more assays, one or more laboratory procedures, one or more combinations thereof, etc.

[0157] The data integration and analysis system can also obtain information from a molecular data repository. The molecular data repository can store data related to genomic information, genetic information, metabolomics information, transcriptomics information, fragmentomic information, immune receptor information, methylation information, epigenomic information, and / or proteomics information for multiple individuals. In one or more examples, the data integration and analysis system and the molecular data repository may be created and maintained by different entities. In one or more additional examples, the data integration and analysis system and the molecular data repository may be created and maintained by the same entity.

[0158] Genomic information can indicate one or more mutations corresponding to an individual's genes. Mutations in an individual's genes may correspond to differences between the individual's nucleic acid sequence and one or more reference genomes. Reference genomes may include known reference genomes, such as hg19. In various examples, mutations in an individual's genes may correspond to differences in the individual's germline genes relative to the reference genome. In one or more additional examples, the reference genome may include the individual's germline genome. In one or more further examples, mutations in an individual's genes may include somatic mutations. Mutations in an individual's genes may be associated with insertions, deletions, single nucleotide variants, heterozygous deletions, doubling, amplifications, translocations, fusion genes, or one or more combinations thereof.

[0159] In one or more illustrative examples, the genomic information stored by a molecular data repository may include a genomic map of tumor cells present within an individual. In these cases, the genomic information may be derived from analysis of genetic material, such as deoxyribonucleic acid (DNA) and / or ribonucleic acid (RNA) from a sample, including but not limited to tissue samples or tumor biopsies, circulating tumor cells (CTCs), exosomes, or efferosomes, or from circulating nucleic acids (e.g., cell-free DNA) found in a blood sample of the individual, present due to degradation by tumor cells present in the individual. In one or more examples, the genomic information of the individual's tumor cells may correspond to one or more target regions. One or more mutations in one or more target regions may indicate the presence of tumor cells in the individual. The genomic information stored by the molecular data repository may be generated in association with assays or other diagnostic tests that can identify one or more mutations in one or more target regions of a reference genome.

[0160] In one or more additional examples, the data integration and analysis system may obtain information from one or more additional data repositories. One or more additional data repositories may store data related to electronic medical records of individuals whose data resides in at least one health insurance claims data repository or molecular data repository. Furthermore, one or more additional data repositories may store data related to pathology reports of individuals whose data resides in at least one health insurance claims data repository or molecular data repository. In various examples, one or more additional data repositories may store data related to biological conditions and / or treatment of biological conditions. In one or more examples, the data integration and analysis system and at least a portion of one or more additional data repositories may be created and maintained by different entities. In one or more further examples, the data integration and analysis system and at least a portion of one or more additional data repositories may be created and maintained by the same entity.

[0161] In one or more further implementations, the data integration and analysis system may obtain information from one or more reference information data repositories. The one or more reference information data repositories may store information including definitions, standards, protocols, vocabularies, one or more combinations thereof, etc. In various examples, the information stored by one or more reference information data repositories may correspond to biological conditions and / or treatments for biological conditions. In one or more illustrative examples, the one or more reference information data repositories may include RxNorm. (RxNorm provides standardized names for clinical drugs and links their names to numerous drug vocabularies used in pharmacy management and drug interaction software.) In one or more examples, the data integration and analysis system and at least a portion of the one or more reference information data repositories may be created and maintained by different entities. In one or more further examples, the data integration and analysis system and at least a portion of the one or more reference information data repositories may be created and maintained by the same entity.

[0162] The data integration and analysis system can obtain data from at least one of a health insurance claims data repository, a molecular data repository, one or more supplementary data repositories, or a reference information data repository via one or more communication networks, which are accessible to the system and can be accessed by at least one of these repositories. The system can also obtain data from at least one of these repositories via one or more secure communication channels. Furthermore, the system can obtain data from at least one of these repositories via one or more calls to an application programming interface (API).

[0163] Data integration and analysis systems can include data integration systems. A data integration system can obtain data from health insurance claims data repositories and molecular data repositories to generate an integrated data repository. A data integration system can also obtain data from one or more additional data repositories to generate an integrated data repository. In various examples, a data integration system can implement one or more natural language processing techniques to integrate data from one or more additional data repositories into the integrated data repository.

[0164] In one or more examples, the data integration system can generate one or more tokens to identify individuals with data stored in a health insurance claims data repository and individuals with data stored in a molecular data repository. In various examples, the data integration system can generate one or more tokens by implementing one or more hash functions. The data integration system can implement one or more hash functions to generate one or more tokens based on information stored in at least one of the health insurance claims data repository or the molecular data repository. For example, the information used by the data integration system to generate the various tokens by implementing hash functions may include at least one of the individual's identifier, the individual's date of birth, the individual's postal code, or the individual's gender. In one or more illustrative examples, the individual's identifier may include a combination of at least a portion of the individual's first name and at least a portion of the individual's last name. Tokens generated using data from different data repositories may correspond to the same or similar information or the same or similar types stored in those different data repositories. For illustration, tokens can be generated using at least a portion of an individual's name, date of birth, postal code, and gender obtained from both the health insurance claims data repository and the molecular data repository.

[0165] Data integration systems can integrate data from multiple disparate data sources by analyzing tokens generated using data obtained from these sources through the implementation of one or more hash functions. For example, a data integration system might obtain one or more first tokens generated from data stored in a health insurance claims data repository, and one or more second tokens generated from data stored in a molecular data repository. The data integration system can analyze one or more first tokens relative to one or more second tokens to determine a single first token corresponding to a single second token. In one or more illustrative examples, the data integration system can identify a single first token that matches a single second token. A first token can match a second token when the data of the first token has at least a threshold amount of similarity to the data of the second token. In one or more examples, a first token can match a second token when the data of the first token is identical to the data of the second token. For illustration, a first token can match a second token when the alphanumeric string of the first token is identical to the alphanumeric string of the second token.

[0166] By determining a first token generated using data stored in a health insurance claims data repository, corresponding to a second token generated using data stored in a molecular data repository, the data integration system can identify individuals with data stored in both the health insurance claims data repository and the molecular data repository. In this way, the data integration system can obtain data from multiple individuals from the health insurance claims data repository and data from the same multiple individuals from the molecular data repository, storing the health insurance claims data and molecular data of these multiple individuals in the integrated data repository.

[0167] The data integration system can also integrate data stored by one or more additional data repositories with data from a health insurance claims data repository and a molecular data repository to generate an integrated data repository. For illustration, the data integration system can obtain one or more third tokens generated based on data stored by additional data repositories, such as those storing data corresponding to pathology reports. The data integration system can analyze the one or more third tokens relative to a first token generated using information stored in the health insurance claims data repository and a second token generated using information stored in the molecular data repository to determine the corresponding third token corresponding to the individual first and second tokens. In one or more illustrative examples, the data integration system can identify third tokens generated using one or more hash functions and a common set of information obtained from the health insurance claims data repository, the molecular data repository, and the additional data repositories.

[0168] By determining a third token—generated using data from an additional data repository—corresponding to a first token generated using data stored in the health insurance claims data repository and a second token generated using data stored in the molecular data repository, the data integration system can identify individuals with data stored in the health insurance claims data repository, the molecular data repository, and the additional data repository. In this way, the data integration system can obtain data from multiple individuals from the health insurance claims data repository, and data from the same multiple individuals from the molecular data repository and the additional data repository, and store the health insurance claims data, molecular data, and additional data of these multiple individuals in the integrated data repository.

[0169] Data on multiple individuals stored in an integrated data repository can be accessed using a corresponding identifier for each individual. The data integration system can implement various techniques as part of a de-identification process for storing and retrieving information about individuals in the integrated data repository. An individual's identifier can correspond to a key generated using at least one hash function. An individual's identifier can also be generated using one or more salting processes on a key generated using at least one hash function, including tokens generated using one or more hash functions and a set of public information obtained from a health insurance claims data repository, a molecular data repository, and / or additional data repositories. In one or more illustrative examples, the identifier generated by the data integration system for accessing information about a corresponding individual stored in the integrated data repository can be unique to each individual. In one or more examples, an individual's identifier can be generated using at least a portion of the information used to generate an individual-associated token. In one or more additional examples, an individual's identifier can be generated using information different from the information used to generate an individual-associated token.

[0170] Data integration systems can also generate integrated data repositories in a similar manner, based on multiple different combinations of data repositories. For example, a data integration system can obtain tokens generated based on information stored in a health insurance claims data repository, and additional tokens generated based on information stored in one or more additional data repositories. The data integration system can determine a separate token generated based on information stored in the health insurance claims data repository, corresponding to the separate additional token generated based on information stored in one or more additional data repositories. By determining the token generated using data stored in the health insurance claims data repository, corresponding to the additional token generated using data stored in the additional data repositories, the data integration system can identify individuals with data stored in both the health insurance claims data repository and the additional data repositories. In this way, the data integration system can obtain data from multiple individuals from the health insurance claims data repository and data from the same multiple individuals from the additional data repositories, and store the health insurance claims data and additional data of these multiple individuals in the integrated data repository. The health insurance claims data and additional data of the multiple individuals stored in the integrated data repository can be accessed using the individual's corresponding identifier.

[0171] In one or more further examples, the data integration system can obtain tokens generated based on information stored in a molecular data repository and tokens generated based on information stored in one or more additional data repositories. The data integration system can determine a separate token generated based on the information stored in the molecular data repository, corresponding to a separate additional token generated based on the information stored in the one or more additional data repositories. By determining the token generated using the data stored in the molecular data repository, corresponding to the additional token generated using the data stored in the additional data repositories, the data integration system can identify individuals having data stored in both the molecular data repository and the additional data repositories. In this way, the data integration system can obtain data from multiple individuals from the molecular data repository and data from the same multiple individuals from the additional data repositories, and store the molecular data and additional data of these multiple individuals in an integrated data repository. The molecular data and additional data of the multiple individuals stored in the integrated data repository can be accessed using the individual's corresponding identifier.

[0172] Data stored in an integrated data repository can be stored under one or more regulatory frameworks that protect privacy and ensure the security of an individual's medical records, health information, and insurance information. For example, data can be stored by the integrated data repository under one or more government regulatory frameworks designed to protect personal information, such as the Health Insurance Portability and Accountability Act (HIPAA) and / or the General Data Protection Regulation (GDPR). The integrated data repository also stores data in an anonymized and de-identified manner to ensure the privacy of individuals who have data stored in it. To further ensure the privacy of individuals who have data stored in the integrated data repository, the data integration system can periodically regenerate the integrated data repository. For example, the data integration system can create an integrated data repository quarterly. In one or more additional examples, the data integration system can generate an integrated data repository monthly, weekly, or bi-weekly. By periodically regenerating the integrated data repository, rather than simply refreshing it when new data becomes available, the integrated data repository enhances privacy protection for data stored in it. In other words, it may be easier to track individuals associated with newly added data when simply refreshing the data repository with new data, since the number of new individuals added at a given time is usually less than the number of existing individuals with data stored in the data repository.

[0173] In various examples, the data stored by the integrated data repository can be accessed via a database management system. Furthermore, the integrated data repository can store data according to one or more database models. In one or more examples, the integrated data repository can store data according to one or more relational database technologies. For example, the integrated data repository can store data according to a relational database model. In one or more additional examples, the integrated data repository can store data according to an object-oriented database model. In one or more further examples, the integrated data repository can store data according to an Extensible Markup Language (XML) database model. In an additional example, the integrated data repository can store data according to a Structured Query Language (SQL) database model. In an even further example, the integrated data repository can store data according to an image database model.

[0174] Data integration systems can generate an integrated data repository by creating multiple data tables and establishing links between them. Links can indicate logical coupling between data tables. A data integration system can generate data tables by extracting a specified dataset based on information obtained from the data repository and storing the data in rows and columns of the corresponding data tables. In various examples, the logical coupling between data tables can include at least one of a one-to-one link (where one row in one data table corresponds to one row in another), a one-to-many link (where one row in one data table corresponds to multiple rows in another), or a many-to-many link (where multiple rows in one data table correspond to multiple rows in another).

[0175] Multiple data tables can be arranged according to a data repository pattern. In the illustrative example, the data repository pattern includes a first data table, a second data table, a third data table, a fourth data table, and a fifth data table. Although the illustrative example includes five data tables, in additional implementations, the data repository pattern may include more or fewer data tables. The data repository pattern may also include links between data tables. Links between data tables can indicate that information retrieved from one data table leads to the retrieval of additional information stored in one or more additional data tables. Furthermore, not all data tables can be linked to every single other data table. In the illustrative example, the first data table is coupled to the second data table via a first link logic, and the first data table is coupled to the fourth data table via a second link logic. Furthermore, the second data table is coupled to the third data table via a third link logic, and the fourth data table is coupled to the fifth data table via a fourth link logic. Additionally, the third data table is coupled to the fifth data table via a fifth link logic.

[0176] In various examples, when a data table is added to and / or removed from a data repository schema, additional links between data tables can be added to or removed from the data repository schema. In one or more illustrative examples, an integrated data repository can store data tables for at least a portion of individuals whose information is obtained from a combination of at least two of a health insurance claims data repository, a molecular data repository, one or more other data repositories, and one or more reference information data repositories, based on the data repository schema. As a result, an integrated data repository can store corresponding instances of data tables for thousands, tens of thousands, or even hundreds of thousands of individuals, based on the data repository schema.

[0177] Data integration and analysis systems may also include data pipeline systems. Data pipeline systems may include multiple algorithms, software code, scripts, macros, or other packages of computer-executable instructions that process information stored in an integrated data repository to generate additional datasets. Additional datasets may include information obtained from one or more data tables. Additional datasets may also include information derived from data obtained from one or more data tables. The components of the data pipeline system used to generate the first additional dataset may differ from the components of the data pipeline system used to generate the second additional dataset.

[0178] In one or more examples, the data pipeline system can generate a dataset indicating drug treatments received by multiple individuals. In one or more illustrative examples, the data pipeline system can analyze information stored in at least one of the data tables to determine the health insurance codes corresponding to the drug treatments received by the multiple individuals. The data pipeline system can analyze the health insurance codes corresponding to the drug treatments relative to a library indicating the specific drug treatments corresponding to one or more health insurance codes to determine the names of the drug treatments received by the individuals. In one or more additional examples, the data pipeline system can analyze information stored in an integrated data repository to determine medical procedures received by multiple individuals. For illustration, the data pipeline system can analyze information stored in one of the data tables to determine treatments received by individuals via at least one injection or intravenous route. In one or more further examples, the data pipeline system can analyze information stored in an integrated data repository to determine an individual's episodes of care, the individual's treatment line, the progression of the biological condition, or the time of the next treatment. In the various examples, the datasets generated by the data pipeline system can be different for different biological conditions. For example, a data pipeline system can generate a first dataset of data on a first type of cancer (e.g., lung cancer) and a second dataset of data on a second type of cancer (e.g., colorectal cancer).

[0179] The data pipeline system can also determine one or more confidence levels to assign to information associated with individuals having data stored in an integrated data repository. The corresponding confidence levels may correspond to different accuracy measures of the information associated with individuals having data stored in the integrated data repository. The information associated with a corresponding confidence level may correspond to one or more features of an individual derived from the data stored in the integrated data repository. The data pipeline system may combine the generation of one or more datasets based on the integrated data repository to generate confidence level values ​​for one or more features. In one or more examples, a first confidence level may correspond to a first range of accuracy measures, a second confidence level may correspond to a second range of accuracy measures, and a third confidence level may correspond to a third range of accuracy measures. In one or more additional examples, the second range of accuracy measures may include values ​​smaller than the first range of accuracy measures, and the third range of accuracy measures may include values ​​smaller than the second range of accuracy measures. In one or more illustrative examples, the information corresponding to the first confidence level may be referred to as gold standard information, the information corresponding to the second confidence level may be referred to as silver standard information, and the information corresponding to the third confidence level may be referred to as bronze standard information.

[0180] A data pipeline system can determine the confidence level of an individual's characteristics based on numerous factors. For example, a corresponding set of information can be used to determine the individual's characteristics. The data pipeline system can determine the confidence level of an individual's characteristics based on the amount of completeness of the corresponding set of information used to determine the individual's characteristics. If one or more pieces of information are missing from the set of information associated with a first number of individuals, the confidence level of the characteristic may be lower than the confidence level of a second number of individuals in the set where no information is missing. In one or more examples, the data pipeline system can use the amount of missing information to determine the confidence level of an individual's characteristics. To illustrate, a larger amount of missing information used to determine a characteristic may result in a lower confidence level for that characteristic compared to a lower amount of missing information used to determine the individual's characteristics. Furthermore, different types of information can correspond to various confidence levels for a characteristic. In one or more examples, the presence of a first piece of information used to determine a characteristic may result in a higher confidence level for that characteristic compared to the presence of a second piece of information used to determine the individual's characteristics.

[0181] In one or more illustrative examples, a data pipeline system can identify multiple individuals included in a group with a preliminary diagnosis of lung cancer (or other biological condition). The data pipeline system can determine the confidence level for each individual regarding being classified as having a preliminary diagnosis of lung cancer. The data pipeline system can use information from multiple columns included in a data table to determine the confidence level of an individual being included in the lung cancer group. These multiple columns may include health insurance codes associated with the diagnosis and / or treatment of the biological condition. Furthermore, these multiple columns may correspond to the date of the diagnosis and / or treatment of the biological condition. The data pipeline system can determine that, if information is available in each of these multiple columns, or at least a threshold number of columns, the confidence level of an individual being characterized as part of the lung cancer group is higher than the confidence level if information is available in fewer than a threshold number of columns. Furthermore, the data pipeline system can determine the confidence level of an individual being included in the lung cancer group based on the type of information and the availability of information associated with one or more columns. To illustrate, in the case where one or more diagnostic codes exist for a group of individuals over one or more time periods and one or more treatment codes do not exist, the data pipeline system can determine that the confidence level for including that group of individuals in the lung cancer group is greater than the confidence level in the case where at least one diagnostic code does not exist and the treatment code used to determine whether an individual is included in the lung cancer group does exist.

[0182] A data integration and analysis system may include a data analysis system. The data analysis system may receive integrated data repository requests from one or more computing devices (e.g., the example computing device). One or more integrated data repository requests may result in the retrieval of data from the integrated data repository. In various examples, one or more integrated data repository requests may result in the retrieval of data from one or more datasets generated by a data pipeline system. An integrated data repository request may specify the data to be retrieved from the integrated data repository and / or the one or more datasets generated by the data pipeline system. In one or more additional examples, an integrated data repository request may include one or more pre-built queries corresponding to computer-executable instructions for retrieving a specified dataset from the integrated data repository and / or the one or more datasets generated by the data pipeline system.

[0183] In response to one or more integrated data repository requests, a data analytics system can analyze data retrieved from at least one of the integrated data repositories or datasets generated by a data pipeline system to generate data analytics results. The data analytics results can be sent to one or more computing devices, such as the example computing device. Although the illustrative example shows one or more integrated data repository requests originating from one computing device and the data analytics results being sent to another computing device, in one or more other implementations, the data analytics results can be received by the same computing device that sent the one or more integrated data repository requests. The data analytics results can be displayed through one or more user interfaces presented by or rendered by the computing device.

[0184] In one or more examples, the data analysis system may implement at least one of one or more machine learning techniques or one or more statistical techniques to analyze data retrieved in response to requests from one or more integrated data repositories. In one or more examples, the data analysis system may implement one or more artificial neural networks to analyze data retrieved in response to requests from one or more integrated data repositories. For illustration, the data analysis system may implement at least one of one or more convolutional neural networks or one or more residual neural networks to analyze data retrieved from integrated data repositories in response to requests from one or more integrated data repositories. In at least some examples, the data analysis system may implement one or more random forest techniques, one or more support vector machines, or one or more hidden Markov models to analyze data retrieved in response to requests from one or more integrated data repositories. One or more statistical models may also be implemented to analyze data retrieved in response to requests from one or more integrated data repositories to identify at least one of the correlation or significance measures between individual characteristics. For example, a log-rank test may be applied to data retrieved in response to requests from one or more integrated data repositories. Furthermore, a Cox proportional hazards model can be implemented using data retrieved in response to requests from one or more integrated data repositories. Additionally, the Wilcoxon signed-rank test can be applied to the data retrieved in response to requests from one or more integrated data repositories. In other examples, z-score analysis can be performed on the data retrieved in response to requests from one or more integrated data repositories. In yet another example, Kaplan-Meier analysis can be performed on the data retrieved in response to requests from one or more integrated data repositories. In at least some examples, one or more statistical techniques can be combined to implement one or more machine learning techniques for analyzing the data retrieved in response to requests from one or more integrated data repositories.

[0185] In one or more illustrative examples, a data analysis system can determine the survival rate of an individual with lung cancer in response to one or more treatments. In one or more additional illustrative examples, a data analysis system can determine the survival rate of an individual with lung cancer having mutations in one or more genomic regions in response to one or more treatments. In various examples, the data analysis system can generate data analysis results if data retrieved from at least one of one or more datasets generated by an integrated data repository or a data pipeline system meets one or more criteria. For example, the data analysis system can determine whether at least a portion of the data retrieved in response to a request from one or more integrated data repositories meets a threshold confidence level. If the confidence level of at least a portion of the data retrieved in response to a request from one or more integrated data repositories is less than the threshold confidence level, the data analysis system can avoid generating at least a portion of the data analysis results. If the confidence level of at least a portion of the data retrieved in response to a request from one or more integrated data repositories is at least the threshold confidence level, the data analysis system can generate at least a portion of the data analysis results. In various examples, the threshold confidence level can be related to the type of data analysis results generated by the data analysis system.

[0186] In one or more illustrative examples, the data analytics system may receive an integrated data repository request to generate data analytics results indicating the survival rates of one or more individuals. In these cases, the data analytics system may determine whether the data stored in one or more datasets generated by the integrated data repository and / or by the data pipeline system meets a threshold confidence level, such as the gold standard confidence level. In one or more additional examples, the data analytics system may receive an integrated data repository request to generate data analytics results indicating the treatments received by one or more individuals. In these implementations, the data analytics system may determine whether the data stored in one or more datasets generated by the integrated data repository and / or by the data pipeline system meets a lower threshold confidence level, such as the bronze standard confidence level.

[0187] In one or more additional illustrative examples, a data analysis system may receive an integrated data repository request to identify individuals with one or more genomic mutations who have received one or more treatments for their biological condition. Continuing this example, the data analysis system may determine the survival rate of individuals with one or more genomic mutations relative to the one or more treatments received by the individuals. The data analysis system may then identify the effectiveness of treatments for individuals that may be associated with the genomic mutations present in the individuals, based on the individuals' survival rates. In this way, individual health outcomes can be improved by identifying anticipated treatments that are more effective for the population of individuals with one or more genomic mutations than the current treatments provided to the individuals.

[0188] This document describes a framework corresponding to the arrangement of data tables in an integrated data repository, depending on one or more implementations. In the illustrative example, the framework includes a data repository schema comprising a first data table, a second data table, a third data table, a fourth data table, a fifth data table, a sixth data table, and a seventh data table. Although the illustrative example includes seven data tables, in additional implementations, the data repository schema may include more or fewer data tables. The data repository schema may also include links between data tables. Links between data tables can indicate that information retrieved from one data table results in the retrieval of additional information stored in one or more additional data tables. Furthermore, not all data tables can be linked to every single one of the other data tables. In the illustrative example, the first data table is coupled to the second data table via a first link logic, and the third data table is coupled to the second data table via a second link logic. The second data table is also coupled to the fourth data table via a third link logic, the second data table is coupled to the fifth data table via a fourth link logic, and the second data table is coupled to the sixth data table via a fifth link logic. Furthermore, the fifth data table is coupled to the sixth data table via a sixth link logic, and the sixth data table is coupled to the seventh data table via a seventh link logic. Additionally, the seventh data table is coupled to the fourth data table via an eighth link logic. In various examples, additional links between data tables can be added to or removed from the data repository schema when data tables are added to and / or removed from it. In one or more illustrative examples, the integrated data repository can store data tables for at least a portion of individuals whose information is obtained from a combination of at least two of a health insurance claims data repository, a molecular data repository, and one or more additional data repositories, according to the data repository schema. As a result, the integrated data repository can store corresponding instances of data tables for thousands, tens of thousands, or even hundreds of thousands or more individuals, according to the data repository schema.

[0189] In one or more examples, the first data table may store data corresponding to genomics and genomic testing for an individual. For example, the first data table may include columns containing information corresponding to panels used to generate genomics data, mutations in genomic regions, mutation types, copy numbers of genomic regions, coverage data indicating the number of nucleic acid molecules identified in samples with one or more mutations, testing dates, and patient information. The first data table may also include one or more columns containing health insurance data codes that may correspond to one or more diagnostic codes. Furthermore, the information in the first data table may include at least one identifier of the individual associated with an instance of the first data table.

[0190] The second data table may store data related to one or more patient visits to one or more healthcare providers for an individual. The third data table may store information corresponding to the services provided to the individual regarding the patient visits to one or more healthcare providers indicated by the second data table. For illustration, an individual may visit a healthcare provider and multiple services may be performed on that individual during the visit. The second data table may include columns indicating information about each of the multiple services performed during the patient visit. Multiple third data tables may be generated regarding patient visits, including columns indicating a more granular level of information about the corresponding services provided during the patient visit, compared to the patient visit-related information stored in the second data table. For example, the second data table may include multiple columns indicating health insurance codes for the different services provided to the individual during the patient visit, and the third data table associated with one of the services may include multiple columns corresponding to additional health insurance codes for additional information associated with the corresponding service. The second and third data tables for patient visits may indicate the dates of one or more services corresponding to the patient visit.

[0191] The fourth data table may include columns indicating information about individuals for whom the integrated data repository stores information. For example, the fourth data table may include columns indicating information related to at least one of the individual's location, gender, date of birth, date of death (if applicable), or one or more keys associated with the individual. In one or more examples, the fourth data table may include one or more columns related to whether erroneous data has been identified for the individual. In various examples, a single fourth data table may be generated for the corresponding individual. Therefore, the data repository schema may include multiple instances of fourth data tables, such as thousands, tens of thousands, or even hundreds of thousands or more.

[0192] The fifth data table may include columns indicating information relating to a health insurance company or government entity that makes payments for one or more services provided to the individual. For example, the fifth data table may include one or more payer identifiers. The sixth data table may include columns containing information corresponding to the individual's health insurance coverage information. In one or more examples, the sixth data table may include columns indicating the individual's medical coverage, pharmacy coverage, and the type of health insurance plan associated with the individual (such as a Health Maintenance Organization (HMO), Preferred Provider Organization (PPO), etc.).

[0193] The seventh data table may include columns indicating information related to the medication treatment received by the respective individual. In one or more examples, the seventh data table may include one or more columns indicating a health insurance code corresponding to the medication treatment available at the pharmacy. The health insurance code may correspond to an individual's medication treatment. Furthermore, the health insurance code may indicate a diagnosis regarding the individual's biological condition. The seventh data table may also include at least one of the following additional information: dosage, number of days of supply, amount dispensed, number of authorized refills, service dates, or information related to the individual receiving the medication treatment.

[0194] In various examples, the data repository pattern can provide analytical results of information stored in data tables in a more efficient manner than a typical data repository pattern. For instance, logical joins between data tables are arranged to efficiently retrieve related data across different data tables. When data tables are arranged serially and / or when a large number of data tables are logically joined, retrieving data from one or more data tables from an integrated data repository in response to a request for information from the integrated data repository will be less efficient than when implementing the data repository pattern.

[0195] This document describes an architecture for generating one or more datasets based on information retrieved from a data repository that integrates health-related data from multiple sources, according to one or more implementations. The architecture may include a data integration and analysis system and an integrated data repository. Furthermore, the data integration and analysis system may include at least a data pipeline system and a data analysis system. The data pipeline system may include multiple sets of data processing instructions that are executable to generate corresponding datasets, and the data analysis system may analyze these datasets in response to requests from the integrated data repository to generate data analysis results.

[0196] The data pipeline system may include a first data processing instruction, a second data processing instruction, and so on up to an Nth data processing instruction. The data processing instructions may be executed by one or more processing units to perform multiple operations to generate corresponding datasets using information obtained from an integrated data repository. In one or more illustrative examples, the data processing instructions may include at least one of software code, scripts, API calls, macros, etc. The first data processing instruction may be executed to generate a first dataset. Furthermore, the second data processing instruction may be executed to generate a second dataset. Additionally, the Nth data processing instruction may be executed to generate a Nth dataset. In various examples, after the data integration and analysis system generates an integrated data repository, the data pipeline system may enable the execution of data processing instructions to generate datasets. In one or more examples, the datasets may be stored by the integrated data repository or by an additional data repository accessible to the data integration and analysis system. At least a portion of the data processing instructions may analyze health insurance codes to generate at least a portion of the dataset. Furthermore, at least a portion of the data processing instructions may analyze genomic data to generate at least a portion of the dataset.

[0197] In one or more examples, a first data processing instruction may be executed to retrieve data from one or more first data tables stored in an integrated data repository. The first data processing instruction may also be executed to retrieve data from one or more specified columns of one or more first data tables. In various examples, the first data processing instruction may be executed to identify an individual having a health insurance code stored in one or more combinations of columns and rows corresponding to one or more diagnostic codes. The first data processing instruction may then be executed to analyze one or more diagnostic codes to determine the biological condition to which the individual has been diagnosed. In one or more illustrative examples, the first data processing instruction may be executed to analyze one or more diagnostic codes with respect to a diagnostic code library indicating one or more biological conditions (corresponding to the corresponding diagnostic codes). The diagnostic code library may include hundreds to thousands of diagnostic codes. The first data processing instruction may also be executed to determine an individual diagnosed with a biological condition by analyzing the individual's timing information, such as treatment date, diagnosis date, death date, one or more combinations thereof, etc.

[0198] A second data processing instruction can be executed to retrieve data from one or more second data tables stored in an integrated data repository. The second data processing instruction can also be executed to retrieve data from one or more specified columns of one or more second data tables. In various examples, the second data processing instruction can be executed to identify individuals having health insurance codes stored in one or more combinations of columns and rows corresponding to one or more treatment codes. These one or more treatment codes may correspond to treatments obtained from a pharmacy. In one or more additional examples, the one or more treatment codes may correspond to treatments received through medical procedures such as injection or intravenous administration. The second data processing instruction can be executed to determine one or more treatments corresponding to the corresponding health insurance codes included in one or more second data tables by analyzing health insurance codes in relation to a predetermined set of information. This predetermined set of information may include a database indicating one or more treatments corresponding to one of hundreds to thousands of health insurance codes. The second data processing instruction can generate a second dataset indicating the corresponding treatments received by a group of individuals. In one or more illustrative examples, this group of individuals may correspond to individuals included in a first dataset. The second dataset can be arranged in rows and columns, where one or more rows correspond to a single individual, and one or more columns indicate the treatment received by the corresponding individual.

[0199] The Nth processing instruction (where N can be any positive integer) can be executed to generate the Nth dataset by combining information from multiple previously generated datasets, such as the first dataset and the second dataset. Furthermore, the Nth processing instruction can be executed to generate the Nth dataset by retrieving additional information from one or more additional columns of the integrated data repository and merging the additional information from the integrated data repository with the information obtained from the first and second datasets. For example, the Nth processing instruction can be executed to identify individuals diagnosed with a biological condition included in the first dataset and analyze specified columns of one or more additional data tables in the integrated data repository to determine the date of treatment indicated in the second dataset corresponding to the individuals included in the first dataset. In one or more further examples, the Nth processing instruction can be executed to analyze columns of one or more additional data tables in the integrated data repository to determine the dose of treatment indicated in the second dataset received by the individuals included in the first dataset. In this way, the Nth processing instruction can be executed to generate a care event dataset based on information included in the group dataset and the treatment dataset.

[0200] In one or more illustrative examples, in response to receiving an integrated data repository request, the data analysis system can determine one or more datasets corresponding to the characteristics of a query regarding the integrated data repository request. For example, the data analysis system can determine that information included in a first dataset and a second dataset is suitable for responding to the integrated data repository request. In these scenarios, the data analysis system can analyze at least a portion of the data included in the first and second datasets to generate data analysis results. In one or more additional examples, the data analysis system can determine different datasets to respond to different queries included in the integrated data repository request in order to generate data analysis results.

[0201] Using a specific set of data processing instructions to generate corresponding datasets can reduce the amount of input from users of the data integration and analysis system, as well as the computational load, such as processing resources and memory, required to process integrated data repository requests. For example, in a specific architecture without a data pipeline system, the data used to respond to an integrated data repository request is aggregated from the data repository each time it is received. Conversely, by implementing a data pipeline system to execute data processing instructions to generate datasets, the data required to respond to various integrated data repository requests has already been aggregated and is accessible to the data analysis system in response. Therefore, the computational resources required to generate datasets in response to integrated data repository requests by implementing a data pipeline system are less than those required by a typical system that performs information parsing and collection processes for each integrated data repository request. Furthermore, without a data pipeline system, users of the data integration and analysis system may need to submit multiple integrated data repository requests to analyze the information they intend to analyze, because ad-hoc collection of data in response to integrated data repository requests in a typical system is inaccurate, or because the data analysis system is called multiple times in a typical system to perform information analysis that could be performed with a single integrated data repository request when the data pipeline system is implemented.

[0202] This document describes an architecture for generating an integrated data repository based on one or more implementations, comprising de-identified health insurance claims data and de-identified genomics data. The architecture may include a data integration and analysis system, a health insurance claims data repository, and a molecular data repository. The data integration and analysis system may obtain patient information from the molecular data repository. Patient information may include genomics data of an individual with data stored in the molecular data repository. The genomics data may indicate the results of one or more nucleic acid sequencing operations that analyze the sequences of nucleic acid molecules included in a sample obtained from the individual regarding one or more target genomic regions. In one or more examples, the sample may be obtained from the tissue of one or more individuals. In one or more additional examples, the sample may be obtained from the fluid (e.g., blood or plasma) of one or more individuals. The one or more target genomic regions may correspond to genomic regions corresponding to the presence of one or more biological conditions. For example, the target region may correspond to a genomic region of a reference genome with a mutation present in an individual with the biological condition. In one or more illustrative examples, the target region may correspond to a genomic region of a reference human genome in which one or more mutations are present in an individual with one or more forms of cancer. Patient information may also include information indicating personal information about an individual having data stored in a molecular data repository, and information corresponding to tests and analyses performed on samples provided to the individual.

[0203] The data integration and analysis system can perform a de-identification process that anonymizes personal information obtained from a molecular data repository. The system can implement one or more computational techniques as part of the de-identification process to anonymize individual-related data stored in the molecular data repository, ensuring that the de-identified data protects individual privacy and complies with one or more privacy regulatory frameworks. The de-identification process may include access tokens. In various examples, the token may include an alphanumeric string of characters. In one or more examples, the token may be generated by the data integration and analysis system. In one or more additional examples, the token may be generated by a third party and obtained by the system.

[0204] Tokens can be generated using one or more hash functions associated with a subset of patient information. For illustration, for an individual with information stored in a molecular data repository, a token can be generated using a combination of at least a portion of the individual's name, at least a portion of the individual's surname, at least a portion of the individual's date of birth, the individual's gender, and at least a portion of the individual's location identifier. The de-identification process can also include generating identifiers for individuals with data stored in the molecular data repository. Identifiers can be generated by a data integration and analysis system using one or more hash functions, which are different from the hash functions used to generate the tokens. In one or more illustrative examples, the data integration and analysis system can generate intermediate versions of the corresponding identifiers using one or more hash functions, and then apply one or more salting techniques to the intermediate versions of the identifiers to generate a final version of the identifier. The salt function includes a function configured to add at least one random bit to each intermediate identifier to generate the corresponding final identifier. In various examples, the data integration and analysis system can generate identifiers using at least a portion of the information of the corresponding individual stored in the molecular data repository. In one or more illustrative examples, identifiers can be generated based on patient identifiers included in patient information. Identifiers generated by data integration and analysis systems can be unique to the corresponding individuals with data stored in a molecular data repository.

[0205] In operation, the data integration and analysis system can generate modified patient information based on identifiers. The modified patient information may include genomic data about individuals associated with the molecular data repository and the corresponding individual identifiers. The modified patient information may have a data structure. The data structure may include columns containing the corresponding identifiers of individuals associated with the molecular data repository and multiple columns containing genomic data associated with these individuals, such as identifiers of one or more genes, and / or associations with one or more genes, alterations to one or more genes, and / or associations with one or more genes, the type of gene alteration, etc.

[0206] A data integration and analysis system can generate a token file. The token file may include a first token that, at operational time, is accessible to a corresponding individual having data stored in a molecular data repository. The token file may have a data structure comprising multiple columns containing information about the corresponding individual. The data structure may include columns indicating a corresponding identifier generated by the data integration and analysis system, and columns indicating one or more first tokens associated with the corresponding identifier. The data integration and analysis system may send the token file to a health insurance claims data management system coupled to a health insurance claims data repository. The health insurance claims data management system may analyze the first token relative to a corresponding second token. The second token may be accessed or generated by the health insurance claims data management system. The second token may be generated using a subset of information that is identical or similar to a subset of patient information for an individual having data stored in the health insurance claims data repository. For example, the second token may be generated using a combination of at least a portion of the corresponding individual's name, at least a portion of the corresponding individual's surname, at least a portion of the corresponding individual's date of birth, the corresponding individual's gender, and at least a portion of the corresponding individual's location identifier.

[0207] In various examples, the health insurance claims data management system can retrieve health insurance claim data from a health insurance claims data repository for individuals associated with a corresponding second token that matches a first token. A first token can match a second token when the data of the first token is at least a threshold amount of similarity to the data of the second token. In one or more examples, a first token can match a second token when the data of the first token is identical to the data of the second token.

[0208] In response to identifying health insurance claim data of individuals possessing a corresponding second token corresponding to a corresponding first token, the health insurance claim data management system can generate modified health insurance claim data. The health insurance claim data management system can then send the modified health insurance claim data to a data integration and analysis system. In one or more examples, the modified health insurance claim data can be formatted according to a data structure. The data structure may include columns containing a subset of the second token corresponding to the first token, and multiple columns containing the health insurance claim data.

[0209] In operation, the data integration and analysis system can integrate genomic data and health insurance claim data of individuals shared by both the molecular data repository and the health insurance claim data repository. The system can identify the individuals shared by both repositories by determining the genomic data and health insurance claim data corresponding to a shared token. The system can determine that a first token corresponds to a second token by determining a similarity measure between a first token associated with a portion of the genomic data and a second token associated with a portion of the health insurance claim data. If the first token has at least a threshold amount of similarity to the second token, the system can store the corresponding portions of the genomic data and the corresponding portions of the health insurance claim data in an integrated data repository, such as an integrated data repository, based on the individual's identifier.

[0210] The architecture can implement encryption protocols that enable the integration of de-identified information from different data repositories into a single data repository. This increases the security of data stored in the integrated data repository. Furthermore, the encryption protocols implemented by the architecture allow for more efficient retrieval and accurate analysis of information stored in the integrated data repository compared to not utilizing the architecture's encryption protocols. For example, by generating a token file containing a first token using encryption techniques based on a specified set of information stored in a molecular data repository, and by using a second token generated using the same or similar encryption techniques on a similar or identical set of information stored in a health insurance claims data repository, the data integration and analysis system can match information stored in different data repositories corresponding to the same individual. Without implementing the architecture's encryption protocols, the probability of misattributing information from one data repository to one or more individuals increases, reducing the accuracy of the results provided by the data integration and analysis system in response to requests from the integrated data repository sent to the system.

[0211] This paper describes a framework for generating datasets based on data stored in an integrated data repository through a data pipeline system, based on one or more implementations. The integrated data repository can store health insurance claims data and genomic data for a group of individuals. For example, the integrated data repository can store information obtained from health insurance claims records of a group of individuals. For each individual included in the group, the integrated data repository can store information obtained from multiple health insurance claims records. In various examples, the information stored by the integrated data repository can include thousands, tens of thousands, hundreds of thousands, or even millions of health insurance claims records for multiple individuals and / or derived from these health insurance claims records. Furthermore, each health insurance claim record can include multiple columns. As a result, the integrated data repository can be generated through the analysis of millions of columns of health insurance claims data.

[0212] Furthermore, while health insurance claim data can be organized according to structured data formats, it is typically arranged for viewing by health insurance providers, patients, and healthcare providers to display financial and insurance code information related to services provided to individuals by healthcare providers. Therefore, it is not easy to analyze health insurance claim data to obtain available insights related to the characteristics of individuals with biological conditions that could aid in the treatment of those conditions. An integrated data repository can be generated and organized by analyzing and modifying raw health insurance claim data in a way that allows the data stored in the integrated data repository to be further analyzed to determine trends, characteristics, features, and / or insights about individuals who may have one or more biological conditions. For example, health insurance codes can be stored in an integrated data repository in a way that allows at least one of a medical procedure, biological condition, treatment, dosage, drug manufacturer, drug distributor, or diagnosis to be determined for a given individual based on their health insurance claim data. In various examples, the data integration and analysis system can generate and implement one or more tables that indicate the correlation between health insurance claim data and various treatments, symptoms, or biological conditions corresponding to the health insurance claim data. Furthermore, an integrated data repository can be generated using the genomic data records of a set of individuals. In various examples, large amounts of health insurance claims data can be matched with the genomic data of a set of individuals to generate an integrated data repository.

[0213] By integrating the genomic data records of a group of individuals with health insurance claim records, data integration and analysis systems can determine the correlation between the presence of one or more biomarkers present in the genomic data records and other characteristics of the individual indicated by the health insurance claim records, which is typically not possible with existing systems. For example, a data integration and analysis system can identify one or more genomic characteristics of an individual that correspond to treatment received by the individual, the timing of treatment, the dosage of treatment, the individual's diagnosis, smoking status, the presence of one or more biological conditions, the presence of one or more symptoms of a biological condition, one or more combinations thereof, etc. Based on the correlations determined by the data integration and analysis system using an integrated data repository, groups of individuals who may benefit from one or more treatments can be identified that would not be identified in existing systems. In one or more examples, the processes and techniques implemented to integrate health insurance claim records and genomic data records to generate an integrated data repository can be complex, and efficiency enhancement techniques, systems, and processes are implemented to minimize the amount of computational resources used to generate the integrated data repository.

[0214] In one or more illustrative examples, the data pipeline system may access information stored by an integrated data repository to generate a dataset comprising multiple supplementary data records, which include information relating to at least a portion of a group of individuals. In the illustrative example, the supplementary data records include information indicating whether an individual is included in a cohort of individuals with lung cancer. The data pipeline system may execute more than one set of different data processing instructions to determine a cohort of individuals with lung cancer. In various examples, the supplementary data records may indicate information used to determine an individual's status with respect to lung cancer, such as one or more transaction insurance identifiers, one or more International Classification of Diseases (ICD) codes, and one or more health insurance transaction dates. In addition to including columns indicating whether an individual is included in a lung cancer cohort, the supplementary data records may also include columns indicating the confidence level of an individual's status with respect to the presence of lung cancer.

[0215] This article describes a schematic diagram of a computational architecture for merging medical record data into an integrated data repository. In various examples, at least a portion of the operations of the computational architecture can be performed by… Figure 1 , Figure 3 and Figure 4 The data integration and analysis system is executed. In one or more examples, at least a portion of the operation of the computing architecture may be performed by one or more additional computing systems, at least one of the control, maintenance, or implementation of these additional computing systems is performed by a service provider, which also performs at least one of the control, maintenance, or implementation of the data integration and analysis system. In one or more additional examples, at least a portion of the operation of the computing architecture may be performed by multiple servers in a distributed computing environment.

[0216] The computing architecture may include a medical record data repository. A medical record data repository can store medical record data from multiple individuals. Medical record data may include imaging information, laboratory test results, diagnostic test information, clinical observation results, dental health information, healthcare practitioner records, medical history forms, diagnostic request forms, medical procedure order forms, medical infographics, one or more combinations thereof, and so on. In various examples, for a given individual, the medical record data repository may store information related to that individual obtained from one or more healthcare practitioners.

[0217] The computing architecture can perform operations, including retrieving data packets from a medical record data repository. In one or more examples, data packets can be retrieved in response to one or more requests sent to the medical record data repository for medical records corresponding to one or more individuals. In one or more additional examples, the computing architecture can use one or more application programming interface (API) calls to retrieve data packets. In one or more illustrative examples, the computing architecture can be used to retrieve a first data packet, a second data packet, and so on up to an Nth data packet. Individual data packets can correspond to the medical records of the respective individuals. For example, a first data packet may include the medical records of the first individual, a second data packet may include the medical records of the second individual, and an Nth data packet may include the medical records of the Nth individual.

[0218] A single data packet may include multiple components. In one or more examples, a single data packet may include individual components corresponding to medical records from different healthcare providers. In one or more additional examples, a single data packet may include individual components corresponding to different sections of medical records corresponding to one or more healthcare providers. In illustrative examples, a second data packet may include a first component, a second component, and so on up to an Nth component. In one or more illustrative examples, a first component may include a first section of an individual's medical record, a second component may include a second section of an individual's medical record, and an Nth component may include an Nth section of an individual's medical record. In various examples, a first component may correspond to an individual's medical record from a first healthcare provider, a second component may correspond to an individual's medical record from a second healthcare provider, and a third component may correspond to an individual's medical record from a third healthcare provider. In one or more additional illustrative examples, a first component may include a first section of an individual's medical record, such as one or more forms related to diagnostic tests or procedures, and a second component may include a second section of an individual's medical record, such as an individual's pathology report.

[0219] During operation, the computational architecture can preprocess individual data packets to identify the corpus of information to be analyzed. In one or more examples, preprocessing of data packets obtained from a medical record data repository may include transforming the data included in the data packets. For example, preprocessing the data packets may include converting at least a portion of the data obtained from the medical record data repository into machine-encoded information. To illustrate, preprocessing the data packets may include performing one or more optical character recognition (OCR) operations on at least a portion of the data packets obtained from the medical record data repository. By converting at least a portion of the data packets obtained from the medical record data repository into machine-encoded information, the data packets may undergo multiple operations, such as one or more parsing operations for recognizing one or more characters or strings, or one or more editing operations that cannot be performed on at least a portion of the data packets obtained from the medical record data repository.

[0220] In one or more examples, preprocessing of individual data packets may include identifying information included in the individual data packets that will be excluded from further analysis by the computational architecture. In various examples, one or more components of an individual data packet may be excluded from the corpus of information to be analyzed. For example, relative to a second data packet, the computational architecture may determine that a first component will be excluded from further analysis by the computational architecture. In one or more examples, the computational architecture may analyze components relative to one or more keywords to identify at least one component for exclusion from further analysis by the computational architecture. In one or more illustrative examples, the computational architecture may parse components to identify one or more keywords, and in response to identifying one or more keywords in a component, the computational architecture may determine to exclude the corresponding component from further analysis by the computational architecture. For example, the computational architecture may determine that a first component of a second data packet is a test application form for one or more diagnostic procedures or tests. In these scenarios, the computational architecture may determine that the first component will be excluded from further analysis by the computational architecture. Furthermore, the computational architecture may determine, based on one or more keywords included in at least one of the second components or the Nth component, that at least one of the second components corresponds to one or more pathology reports of an individual. In these cases, the computational architecture can determine that at least a portion of the second component and / or at least a portion of the Nth component will be included in the information corpus to be further analyzed by the computational architecture.

[0221] Furthermore, subsets of the components of various data packets obtained from the medical record data repository can be included in the information corpus. In various examples, one or more additional operations can be performed to narrow down the information corpus. For example, one or more queries can be applied to a subset of information obtained from the medical record data repository. The one or more queries can extract information from one or more data packets that satisfy the one or more queries. In at least some examples, the one or more queries can be a set of queries applied to the various components of the data packets. In one or more illustrative examples, this set of queries can determine the information to be included in the information corpus and the additional information to be excluded from the information corpus. In one or more additional examples, one or more fragments of at least one component of the data packet can be excluded from the information corpus.

[0222] In one or more additional illustrative examples, after determining that a first component should be excluded from further analysis of the computational architecture, the computational architecture may then enable one or more queries to be implemented relative to at least one of the second or Nth components. In these scenarios, one or more queries may determine that fragments of the second component (such as fragments indicating a family history of one or more biological conditions) should be excluded from the information corpus. In various examples, one or more queries may involve identifying multiple keywords and / or combinations of keywords included in at least one of the second or Nth components. In these cases, the computational architecture may exclude one or more portions of the various components of a data packet that include one or more keywords or combinations of keywords from the information corpus. In one or more additional examples, the computational architecture may exclude multiple words, multiple characters, and / or multiple symbols included in one or more portions of the various components of a data packet following one or more keywords from the information corpus.

[0223] Furthermore, in operation, the computational architecture can analyze an information corpus to determine the characteristics of an individual. In one or more examples, the computational architecture can analyze an information corpus to identify individuals with one or more phenotypes. In various examples, the computational architecture can analyze an information corpus to identify one or more biomarkers indicating a biological condition. For example, the computational architecture can analyze an information corpus to identify individuals with one or more genetic traits. The one or more genetic traits may include at least one variant of one or more genomic regions corresponding to a biological condition. In one or more illustrative examples, the one or more genetic traits may correspond to one or more variants of a genomic region corresponding to a type of cancer. In one or more additional illustrative examples, one or more biomarkers may correspond to analyte levels outside a specified range. For illustration, the computational architecture can analyze an information corpus to identify individuals with levels of one or more proteins and / or one or more small molecules indicating a biological condition. In these scenarios, the computational architecture can analyze laboratory test results to determine the analyte levels of an individual. In one or more additional examples, the computational architecture can analyze an information corpus to identify individuals with the presence of one or more symptoms indicating a biological condition. In one or more further examples, the computational architecture can analyze imaging information included in an information corpus to identify individuals with one or more biomarkers.

[0224] In one or more examples, the computational architecture can implement one or more machine learning techniques to analyze the information corpus. For example, the computational architecture can implement one or more artificial neural networks, such as at least one of one or more convolutional neural networks or one or more residual neural networks, to analyze the information corpus. The computational architecture can also implement at least one of one or more random forest techniques, one or more hidden Markov models, or one or more support vector machines to analyze the information corpus.

[0225] In at least some implementations, a computational architecture can analyze an information corpus by executing one or more queries about the corpus. The one or more queries may correspond to one or more keywords and / or combinations of keywords. The one or more keywords and / or combinations of keywords may correspond to at least one of characters or symbols corresponding to one or more biological conditions. For example, a keyword may correspond to a character associated with a mutation in a genomic region, such as HER2. In one or more additional illustrative examples, one or more criteria may be associated with the combination of keywords. For example, the criteria corresponding to the combination of keywords may include multiple words in a portion of an individual's information corpus that are no more than a specified distance apart, such as the words "fatigue," "blood pressure," and "swelling" that appear no more than a certain number of characters apart. In these cases, the computational architecture can parse the information corpus against one or more keywords and / or combinations of keywords. In various examples, in response to determining the presence of one or more keywords and / or combinations of keywords based on one or more criteria, the computational architecture can determine the presence of a biological condition for a given individual.

[0226] In one or more additional examples, one or more queries may be image-based, and the computational architecture may analyze images included in the information corpus with respect to template images. Template images may be generated by analyzing multiple images in which biological conditions exist and aggregating those multiple images into a template image. In these scenarios, the computational architecture may analyze images included in the information corpus with respect to one or more template images to determine a similarity measure between images included in the information corpus and the template image. If the similarity measure for an individual is at least a threshold, the computational architecture may determine the characteristics of a biological condition present in that individual.

[0227] After identifying individuals with one or more characteristics, the computational architecture can, at runtime, generate a data structure that stores data about individuals with one or more characteristics. In one or more examples, the computational architecture can generate data tables that indicate individuals with individual characteristics and / or individuals with a set of characteristics. For example, the computational architecture can generate a first data table and a second data table. The first data table may indicate individuals with one or more first characteristics, and the second data table may indicate individuals with one or more second characteristics. In one or more illustrative examples, the first data table may indicate individuals with one or more first biomarkers for a biological condition, and the second data table may indicate individuals with one or more second biomarkers for said biological condition. The one or more first biomarkers may correspond to one or more first genomic variants associated with said biological condition, and the one or more second biomarkers may correspond to one or more second genomic variants associated with said biological condition. In various examples, the data tables may indicate whether one or more characteristics associated with a separate data table are present relative to a single individual. For illustration, the first data table may include a first indication for individuals in which one or more first genomic variants are present and a second indication for individuals in which one or more first genomic variants are not present. In one or more additional examples, the first data table may indicate an individual's smoking status, and the second data table may indicate whether a single individual has received one or more treatments for a biological condition.

[0228] In one or more illustrative examples, the first and second data tables may have rows corresponding to individual individuals. In at least some examples, an individual identifier may appear in a separate row. The individual identifier may include at least one of alphanumeric characters or symbols corresponding to the individual. In various examples, the individual identifier may be present in the data packet corresponding to the individual. Columns in the first and second data tables may indicate the state of an individual individual with respect to one or more characteristics. For example, columns in the data tables may include identifiers comprising at least one of alphanumeric characters or symbols indicating the presence or absence of one or more characteristics of a given individual. Furthermore, although the illustrative examples include a first and a second data table, the computational architecture may generate more or fewer data tables.

[0229] During operation, the computational architecture may store data structures in an additional data repository. For example, the computational architecture may store at least a first data table and / or a second data table in an intermediate data repository. In various examples, the first and second data tables may be temporarily stored in the intermediate data repository. In one or more illustrative examples, the first and second data tables may be stored in the intermediate data repository before being added to the integrated data repository. In one or more examples, the integrated data repository may be generated and / or updated periodically. In these scenarios, data structures generated by the computational architecture based on the analytical information corpus may be stored in the intermediate data repository until at least one of the integrated data repositories is generated or updated.

[0230] Before adding the data structure stored in the intermediate data repository to the integrated data repository, the computing architecture may perform one or more de-identification processes at operation. The data structure stored in the intermediate data repository can be de-identified to protect individual privacy. One or more de-identification processes may include applying one or more electronically implemented cryptographic techniques to the information of the individuals included in the data structure stored in the intermediate data repository. In one or more examples, the computing architecture may generate tokens corresponding to individual individuals with the information stored in the data structure in the intermediate data repository. Tokens can be generated by applying one or more hash functions to the information associated with the individual individual. In one or more examples, one or more de-identification processes may include applying a salt function to the information corresponding to the individual individual to generate tokens for that individual. In various examples, the one or more cryptographic techniques applied to de-identify the data structure stored in the intermediate data repository may be the same as or similar to the cryptographic techniques applied to information obtained from a health insurance claims data repository.

[0231] In operation, the computational architecture can incorporate an integrated data repository to store de-identified data structures. For example, information about a given individual stored in an intermediate data repository can be stored together with additional information about that individual in the integrated data repository. To illustrate, the integrated data repository can store information about a given individual obtained from at least two of the molecular data repository, the health insurance claims data repository, and the intermediate data repository. In this way, information about a given individual obtained from multiple different data repositories can be stored in the integrated data repository. As a result, information about an individual obtained from different data repositories can be analyzed together, rather than being analyzed separately as in many existing systems.

[0232] In various examples, information stored in an intermediate data repository can be used to verify one or more determinations made by a data integration and analysis system. For instance, the system can analyze information from health insurance claims data repositories and molecular data repositories to determine individual characteristics. The system can then analyze information from the intermediate data repository to determine whether predicted characteristics identified based on information from the health insurance claims data repository and the molecular data repository correspond to characteristics of the same individual associated with the information stored in the intermediate data repository.

[0233] The one or more cryptographic techniques applied to de-identify the data structure stored in the intermediate data repository can utilize information that is the same as or similar to the information used to generate at least one of the first or second tokens. For example, the operation can use a combination of at least a portion of the individual's name, at least a portion of the individual's surname, at least a portion of the individual's date of birth, the individual's gender, and at least a portion of the individual's location identifier to implement one or more cryptographic techniques to de-identify the data structure stored in the intermediate data repository. By utilizing cryptographic techniques and information subsets that are the same as or similar to those used to generate at least one of the first or second tokens to de-identify the data structure stored in the intermediate data repository, the information stored in the intermediate data repository can be synchronized with the information of the same individuals having information stored in the integrated data repository. Both the integrated data repository and the intermediate data repository can store information on thousands, tens of thousands, or even millions of individuals. Therefore, without the ability to synchronize individuals with records stored in an integrated data repository and an intermediate data repository by using the specified encryption protocol as described herein, the data structures of the integrated data repository and the intermediate data repository associated with the same individual may not be stored in a manner that allows information stored in both the integrated and intermediate data repositories to be retrieved together for a given individual. This could lead to inaccurate information provided by the data integration and analysis system. The lack of the specified encryption protocol as described herein could also result in the use of additional computational resources to determine information from other data sources stored in the integrated data repository and information corresponding to a given individual stored in the intermediate data repository. This invention illustrates an example process for generating an integrated data repository and generating a dataset for analyzing information stored in the integrated data repository. The example process is shown as a set of blocks in a logic flow diagram, representing a sequence of operations that can be implemented in hardware, software, or a combination thereof. These blocks are referred to by numbers. In the context of software, a block represents computer-executable instructions stored on one or more computer-readable media that, when executed by one or more processing units (e.g., hardware microprocessors), perform the operations. Typically, computer-executable instructions include routines, programs, objects, components, data structures, etc., which perform specific functions or implement specific data types. The order in which operations are described is not intended to be construed as a limitation, and any number of described blocks can be combined and / or parallelized in any order to implement the process.

[0234] This document describes a data flow diagram of an example process for generating an integrated data repository storing health insurance claims data and genomics data, based on one or more implementations. In operation, the process may include generating a data file containing tokens generated using a first hash function. Individual tokens may correspond to a corresponding individual in a set of individuals having data stored by the molecular data repository. In one or more examples, an individual having data stored by the molecular data repository may be associated with one or more tokens. Tokens can be generated by applying one or more first hash functions to a subset of information corresponding to a set of individuals stored by the genomics data repository. In various examples, individual tokens can be generated by applying one or more first hash functions to at least a portion of the first name of the corresponding individual in the set of individuals, at least a portion of the last name of the corresponding individual in the set of individuals, a location identifier of the corresponding individual in the set of individuals, the sex of the corresponding individual in the set of individuals, and one or more combinations of the birth date of the corresponding individual in the set of individuals. In one or more illustrative examples, tokens may be generated by a data integration and analysis system coupled to the genomics data repository. In one or more additional illustrative examples, tokens may be generated by a third-party system and accessed by a data integration and analysis system coupled to the molecular data repository. During operation, the process may also include sending a data file to a health insurance claims data management system. The health insurance claims data management system can then match a token included in the data file with a second token generated by the health insurance data management system based on information stored in the health insurance claims data repository.

[0235] Furthermore, in operation, the process may include obtaining first data corresponding to a group of individuals from a health insurance claims data management system in response to a data file, wherein the first data includes health insurance claims data. In some implementations, affirmative consent is obtained from members of the group of individuals for the transfer of their data from the health insurance claims data management system. In one or more examples, the data is transferred in an anonymous format, making it impossible to trace the data back to individual members. The health insurance claims data management system may be coupled to a health insurance claims data repository that stores health insurance claims information for multiple individuals. In one or more examples, the health insurance claims data management system may analyze a token in the data file relative to an additional token generated by the health insurance claims data management system. The additional token may be generated based on the same set of information used to generate the token included in the data file. However, the identity of an individual cannot be determined based on the token. In various examples, the health insurance claims data management system may match the token included in the data file with an additional token generated based on information stored by the health insurance claims data repository to identify individuals who have information stored by the health insurance claims data repository and also have information stored by a genomics data repository. The techniques disclosed herein comply with legal and best practice privacy standards such as HIPAA and GDPR.

[0236] In operation, the process may include generating multiple identifiers using a second hash function different from the first hash function. In one or more examples, a single identifier may correspond to one or more tokens associated with a corresponding individual in a set of individuals. The identifiers may be unique to a given individual in the set of individuals and may be de-identified. Furthermore, identifiers may be generated using information stored by a genomics data repository for a set of individuals, which may differ from the information stored by the genomics data repository for generating tokens. In various examples, intermediate identifiers may be generated by applying the second hash function to the information of the corresponding group of individuals, and the final version of the identifier may be generated by applying one or more salting techniques to the intermediate identifiers. The information stored by the genomics data repository for the corresponding individuals may be stored in association with the identifiers, such that at least a portion of the information about a given individual stored by the genomics data repository can be accessed using the corresponding identifier of the given individual.

[0237] Furthermore, in practice, the process may include obtaining second data about a group of individuals from a molecular data repository using multiple identifiers, and in practice, the process may include determining a corresponding portion of the first data that corresponds to a corresponding portion of the second data about the group of individuals. For example, for a given individual, in addition to the second data corresponding to the given individual's molecular data (e.g., genomic data), first data corresponding to the given individual's health insurance claim data may also be identified. In this way, for a given individual, health insurance claim data and molecular data can be identified.

[0238] In operation, the process may include generating an integrated data repository that stores corresponding portions of first and second data in association with corresponding identifiers among a plurality of identifiers. For example, the integrated data repository may store health insurance claim data and genomic data of a given individual in association with identifiers that can be used to access the given individual's health insurance claim data and genomic data. The information stored by the integrated data repository can be organized according to a data repository schema. For example, the integrated data repository may store health insurance claim data and genomic data of a group of individuals in multiple data tables. In one or more examples, information stored in multiple data tables may be linked. To illustrate, information related to a given individual stored in a first data table of the data repository schema may be linked to additional information related to a given individual stored in a second data table of the data repository schema. In this way, information accessed in one data table of the data repository schema may lead to access to additional information stored in another data table of the data repository schema.

[0239] In one or more illustrative examples, a data repository schema may include a first data table storing genomic data for a group of individuals. For example, the first data table may store information corresponding to panels used to generate genomic data, mutations in genomic regions, mutation types, copy numbers of genomic regions, coverage data indicating the number of nucleic acid molecules identified in samples with one or more mutations, testing dates, and patient information. The data repository schema may also include second and third data tables, the second storing data related to one or more patient visits to one or more healthcare providers for an individual, and the third storing information corresponding to the services provided to the individual regarding one or more patient visits to one or more healthcare providers indicated by the second data table. Furthermore, the data repository schema may include a fourth and fifth data table, the fourth storing personal information for a group of individuals, and the fifth storing information about health insurance companies or government entities that pay for the services provided to the group of individuals. Additionally, the data repository schema may include a sixth data table storing information corresponding to health insurance coverage information for a group of individuals, such as the type of health insurance plan associated with that group of individuals. The data repository schema may also include a seventh data table that stores information related to drug treatments received by a group of individuals.

[0240] In one or more examples, the integrated data repository may also store medical records corresponding to at least a portion of a set of individuals. In these examples, medical records can be obtained from one or more data repositories storing medical records. One or more optical character recognition (OCR) operations can be performed relative to the medical records. Furthermore, the medical records can be analyzed to determine one or more portions of additional information to be removed, thereby generating an information corpus. In various examples, the information corpus can be analyzed to identify a subset of additional individuals corresponding to one or more biomarkers.

[0241] One or more data structures can be generated from an information corpus, storing identifiers of portions of an additional subset of individuals, and storing indications of one or more biomarkers for these portions of the additional subset of individuals. These one or more data structures can be stored in an intermediate data repository. Before modifying the integrated data repository to store at least a portion of the additional information of the medical records for portions of the additional subset of individuals in association with multiple identifiers, one or more de-identification operations can be performed on the identifiers of the portions of the additional subset of individuals. After de-identification of the information stored by the one or more data structures, the information stored by the integrated data repository can be added to the integrated data repository. In at least some examples, de-identified medical record information can be added to the integrated data repository in addition to or in lieu of health insurance claim data. In various examples, the one or more data structures storing de-identified medical record information about biomarker data can have one or more logical connections to other data structures stored in the integrated data repository. For illustration, one or more data structures storing de-identified medical record information about biomarker data may have one or more logical connections with at least one of the following data tables: a first data table that may store information corresponding to panels used to generate genomic data, mutations in genomic regions, mutation types, copy numbers of genomic regions, coverage data indicating the number of nucleic acid molecules identified in samples with one or more mutations, test dates, and patient information; a second data table that stores data related to an individual’s visits to one or more healthcare providers for one or more patients; a third data table that stores information corresponding to the services provided to an individual for one or more patients at one or more healthcare providers as indicated by the second data table; a fourth data table that stores personal information of a group of individuals; a fifth data table that stores information related to health insurance companies or government entities that pay for the services provided to the group of individuals; a sixth data table that stores information corresponding to health insurance coverage information of the group of individuals (e.g., the type of health insurance plan associated with the group of individuals); or a seventh data table that stores information related to drug treatments received by the group of individuals.

[0242] In various examples, medical record data can be added to an integrated data repository by generating a data file that includes a first token generated using a first hash function. A single first token can correspond to a corresponding individual in a group of individuals having data stored in a molecular data repository. Furthermore, the data file can be sent to a medical record data management system, and in response to the data file, medical record data corresponding to that group of individuals can be retrieved from the medical record data management system. Additionally, multiple identifiers can be generated using a second hash function different from the first hash function. Each identifier can correspond to one or more tokens associated with each individual in the group. Using multiple identifiers, second data for that group of individuals can be retrieved from the molecular data repository. In various examples, a corresponding portion of the first data can be determined to correspond to a corresponding portion of the second data for that group of individuals. In this way, an integrated data repository can be generated that stores the corresponding portions of the first and second data in association with corresponding identifiers among the multiple identifiers.

[0243] After generating an integrated data repository that stores medical record data, a request can be received to determine data about multiple individuals who have data stored in the integrated data repository. This request may include one or more search criteria. In one or more examples, a subset of the multiple individuals with one or more features corresponding to one or more search criteria can be determined, and information from this subset of individuals can be analyzed to determine the significance measure of the features among those features with respect to biological status.

[0244] In one or more illustrative examples, one or more genomic mutations may be identified as present in a subset of multiple individuals, and multiple treatments may be provided to the subset of multiple individuals. In various examples, corresponding survival rates, such as real-world survival rates, may be determined for the subset of multiple individuals. In at least some examples, a significance measure may correspond to the survival rate for treatments among multiple treatments and for genomic mutations among one or more genomic mutations. Based on the significance measure, the effectiveness of the treatments for the subset of multiple individuals can be determined. In one or more examples, untreated individuals within the subset of multiple individuals can be identified. Treatments of one or more effective amounts may be administered to the untreated individuals within the subset of multiple individuals.

[0245] This document describes a data flow diagram of an example process for generating multiple datasets, based on one or more implementations, for analyzing information stored in an integrated data repository containing health insurance claims data and genomics data. In operation, the process may include determining a first set of data processing instructions executable with respect to first data stored in the integrated data repository. The integrated data repository may store health insurance claims data and molecular data from a common set of individuals. In one or more examples, the first set of data processing instructions may be included in multiple sets of data processing instructions as part of a data processing pipeline. Each of the sets of data processing instructions in the data processing pipeline can be executed to generate a corresponding analysis-ready dataset. For example, a separate set of data processing instructions in the data processing pipeline may be executed to generate a dataset comprising a specified portion and / or combination of information stored in the integrated data repository. In one or more additional examples, a separate set of data processing instructions in the data processing pipeline may be executed to analyze and modify portions of the information stored in the integrated data repository, thereby generating a corresponding dataset. Furthermore, a separate set of data processing instructions may be executed relative to a separate subset of the information stored in the integrated data repository.

[0246] In operation, the process may further include executing a first set of data processing instructions to generate a first dataset. The first dataset may indicate a subset of biological conditions present in a group of individuals. The first set of data processing instructions may be executed to analyze data stored in an integrated data repository to identify groups of individuals with present biological conditions. In one or more illustrative examples, a biological condition may include cancer. For illustration, the first set of data processing instructions may be executed to analyze data stored in an integrated data repository to identify a group of individuals with lung cancer. In various examples, the data processing pipeline may include multiple sets of data processing instructions to identify groups of individuals with different biological conditions.

[0247] In one or more examples, a first set of data processing instructions may be executed to analyze at least one of health insurance claim data or molecular data to identify a group of individuals with a biological condition. For example, the first set of data processing instructions may be executed to identify individuals with one or more health insurance codes in the health insurance claim data to identify a group of individuals with a biological condition. Furthermore, the first set of data processing instructions may be executed to identify individuals with one or more mutations in a genomic region of a nucleic acid molecule derived from a sample obtained from the individual to identify a group of individuals with a biological condition.

[0248] Furthermore, during operation, the process may include determining a second set of data processing instructions executable with respect to the second data stored in the integrated data repository. The second dataset stored in the integrated data repository may differ from the first dataset stored in the integrated data repository and analyzed with respect to the first set of data processing instructions. For example, the first data may correspond to a first column of one or more first data tables stored in the integrated data repository, and the second data may correspond to a second column of one or more second data tables stored in the integrated data repository.

[0249] In operation, the process may include executing a second set of data processing instructions to generate a second dataset indicating one or more treatments provided to a second subset of a group of individuals. The second dataset may indicate a subset of individuals who have received one or more treatments. The one or more treatments may be provided to individuals exhibiting one or more biological conditions. In one or more examples, the second set of data processing instructions may be executed to analyze data stored in an integrated data repository to identify groups of individuals who have received one or more treatments. For illustration, the second set of data processing instructions may be executed to analyze at least one of health insurance claims data or genomic data to determine groups of individuals who have received one or more treatments. In one or more illustrative examples, the second set of data processing instructions may be executed to identify individuals with one or more health insurance codes in health insurance claims data to determine a group of individuals who have received one or more treatments.

[0250] Furthermore, in operation, the process may include determining a third subset of a group of individuals, which includes a portion of a first subset of the group of individuals that overlaps with a portion of a second subset of the group of individuals. As a result, the third subset of the group of individuals corresponds to individuals in which both a biological condition and one or more treatments are present. The process may include analyzing a first dataset and a second dataset relative to the third subset of the group of individuals to determine a significance measure of the features of the third subset of the group of individuals. In one or more examples, one or more machine learning techniques or statistical techniques may be applied relative to the third subset of the group of individuals to information included in at least one of the first and second datasets. The significance measure may correspond to a statistical measure of the significance of a feature. In one or more additional examples, the significance measure may correspond to the probability that a feature exists in individuals with a biological condition.

[0251] In one or more illustrative examples, the feature may include one or more treatments provided to an individual with a biological condition. In one or more additional illustrative examples, the feature may include the presence of mutations in genomic regions of nucleic acid molecules derived from samples obtained from an individual with a biological condition. In various examples, information included in at least one of a first dataset or a second dataset may be analyzed to determine the effect of the feature on one or more metrics. In one or more examples, information included in at least one of a first dataset or a second dataset may be analyzed to determine the magnitude of the effect of the treatment on the survival of an individual with a biological condition. In one or more further examples, information included in at least one of a first dataset or a second dataset may be analyzed to determine the magnitude of the effect of mutations in genomic regions on the survival of an individual with a biological condition. Furthermore, information included in both the first and second datasets may be analyzed to determine the magnitude of the effect of one or more treatments on an individual with a biological condition and also with one or more genomic mutations.

[0252] This document describes a machine in the form of a computer system implemented according to an example, within which a set of instructions can be executed to cause the machine to perform any or more of the methods discussed herein. For example, in the example form of a computer system, instructions (e.g., software, programs, applications, applets, or other executable code) for causing the machine to perform any or more of the methods discussed herein can be executed within that computer system. For example, the instructions can cause the machine to implement the previously described architecture and framework and perform the previously described methods.

[0253] Instructions transform a general, unprogrammed machine into a specific machine programmed to perform the described and illustrated functions in the described manner. In alternative implementations, the machine operates as a standalone device or may be coupled (e.g., networked) to other machines. In networked deployments, the machine may operate as a server machine or client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. A machine may include, but is not limited to, server computers, client computers, personal computers (PCs), tablet computers, laptop computers, netbooks, set-top boxes (STBs), personal digital assistants (PDAs), entertainment media systems, cellular phones, smartphones, mobile devices, wearable devices (e.g., smartwatches), smart home devices (e.g., smart appliances), other smart devices, web devices, network routers, network switches, bridges, or any machine capable of sequentially or otherwise executing instructions specifying the actions the machine should take. Furthermore, although only a single machine is shown, the term "machine" should also be understood to include a collection of machines that individually or jointly execute instructions to perform any or more of the methods discussed herein.

[0254] Examples of computing devices may include logic, one or more components, circuits (e.g., modules), or mechanisms. A circuit is a tangible entity configured to perform certain operations. In the examples, circuits may be arranged in a specified manner (e.g., internally or relative to external entities such as other circuits). In the examples, one or more computer systems (e.g., standalone client or server computer systems) or one or more hardware processors (processors) may be configured by software (e.g., instructions, application portions, or applications) to operate to perform certain operations described herein. In the examples, the software may (1) reside on a non-transitory machine-readable medium or (2) reside in a transmitted signal. In the examples, the software causes the circuit to perform certain operations when executed by the underlying hardware of the circuit.

[0255] In the examples, the circuit can be implemented mechanically or electronically. For instance, the circuit may include dedicated circuitry or logic specifically configured to perform one or more of the techniques described above, such as dedicated processors, field-programmable gate arrays (FPGAs), or application-specific integrated circuits (ASICs). In the examples, the circuit may include programmable logic (e.g., circuitry contained within a general-purpose processor or other programmable processor) that can be temporarily configured (e.g., via software) to perform certain operations. It should be understood that the decision to implement the circuit mechanically (e.g., in dedicated and permanently configured circuitry) or in temporarily configured circuitry (e.g., via software configuration) may be driven by cost and time considerations.

[0256] Therefore, the term "circuit" is understood to include tangible entities, which can be physically constructed, permanently configured (e.g., hardwired) or temporarily configured (e.g., programmed) entities that operate or perform specified operations in a specified manner. In the example, given multiple temporarily configured circuits, each circuit does not need to be configured or instantiated at any one instance of time. For example, in the case where the circuit includes a general-purpose processor configured by software, the general-purpose processor can be configured as corresponding different circuits at different times. The software can configure the processor accordingly, for example, constituting a specific circuit at one instance of time and different circuits at different instances of time.

[0257] In this example, a circuit can provide information to and receive information from other circuits. In this example, a circuit can be considered communicatively coupled to one or more other circuits. When multiple such circuits exist simultaneously, communication can be achieved through signal transmission connecting these circuits (e.g., via appropriate circuitry and buses). In implementations where multiple circuits are configured or instantiated at different times, communication between these circuits can be achieved, for example, by storing and retrieving information in a memory structure accessible to the multiple circuits. For example, one circuit can perform an operation and store the output of that operation in a memory device communicatively coupled to it. Another circuit can then access the memory device at a later time to retrieve and process the stored output. In this example, a circuit can be configured to initiate or receive communication with input or output devices and can operate on resources (e.g., a collection of information).

[0258] The various operations of the method examples described herein can be performed, at least in part, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors can constitute processor-implemented circuitry that operates to perform one or more operations or functions. In the examples, the circuitry mentioned herein can include processor-implemented circuitry.

[0259] Similarly, the methods described herein can be at least partially implemented by processors. For example, at least some or all of the operations of the methods can be performed by one or more processors or processor-implemented circuitry. The execution of certain operations can be distributed across one or more processors, which can reside not only within a single machine but also deployed across multiple machines. In the examples, one or more processors may be located in a single location (e.g., in a home environment, an office environment, or as a server farm), while in other examples, the processors may be distributed across multiple locations.

[0260] One or more processors may also operate to support the execution of related operations in a “cloud computing” environment or as “Software as a Service” (SaaS). For example, at least some operations may be performed by a set of computers (as an example of a machine that includes processors), and these operations may be accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., application programming interfaces (APIs)).

[0261] Example implementations (e.g., apparatus, system, or method) may be implemented in digital electronic circuits, computer hardware, firmware, software, or any combination thereof. Example implementations may be implemented using computer program products (e.g., computer programs tangibly embodied in an information carrier or machine-readable medium for execution by or control of a data processing apparatus such as a programmable processor, computer, or multiple computers).

[0262] Computer programs can be written in any programming language (including compiled or interpreted languages) and can be deployed in any form, including as standalone programs or as software modules, subroutines, or other units suitable for use in a computing environment. Computer programs can be deployed to execute on one or more computers at a single site, or distributed across multiple sites and interconnected via communication networks.

[0263] In the examples, the operations can be performed by one or more programmable processors that execute computer programs to perform functions by manipulating input data and generating output. Examples of the method operations can also be performed by special-purpose logic circuitry (e.g., field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs)), and the example devices can be implemented as special-purpose logic circuitry.

[0264] Computing systems can include clients and servers. Clients and servers are typically geographically isolated and usually interact via communication networks. The client-server relationship is established by computer programs running on the respective computers and having a client-server relationship with each other. In implementing a programmable computing system, it should be understood that both hardware and software architecture need to be considered. Specifically, it should be understood that the choice of implementing certain functionality in permanently configured hardware (e.g., ASIC), temporarily configured hardware (e.g., a combination of software and programmable processors), or a combination of permanently configured and temporarily configured hardware can be a design choice. The hardware (e.g., computing devices) and software architectures that can be deployed in example implementations are illustrated below.

[0265] In the example, the computing device can operate as a standalone device, or it can be connected (e.g., networked) to other machines.

[0266] In a networked deployment, a computing device can operate at the capacity of a server or client machine in a server-client network environment. In the example, the computing device can act as a peer machine in a peer-to-peer (or other distributed) network environment. A computing device can be a personal computer (PC), tablet PC, set-top box (STB), mobile phone, web device, network router, switch, or bridge, or any machine capable of executing instructions (sequentially or otherwise) specifying actions to be taken (e.g., performed) by the computing device. Furthermore, although only a single computing device is shown, the term "computing device" should also be understood to include any collection of machines that individually or jointly execute a set (or more) of instructions to perform any of the methods discussed herein.

[0267] Example computing devices may include processors (e.g., central processing unit (CPU), graphics processing unit (GPU), or both), main memory, and static memory, some or all of which may communicate with each other via a bus. The computing device may also include a display unit, an alphanumeric input device (e.g., a keyboard), and a user interface (UI) navigation device (e.g., a mouse). In the example, the display unit, input device, and UI navigation device may be a touchscreen display. The computing device may also include storage devices (e.g., a drive unit), signal generation devices (e.g., a speaker), network interface devices, and one or more sensors, such as a global positioning system (GPS) sensor, a compass, an accelerometer, or another sensor.

[0268] Storage devices may include machine-readable media on which one or more sets of data structures or instructions (e.g., software) are stored, embodying, or being utilized by any one or more methods or functions described herein. During execution of instructions by a computing device, the instructions may also reside wholly or at least partially in main memory, static memory, or a processor. In the examples, one or any combination of a processor, main memory, static memory, or storage device may constitute a machine-readable medium.

[0269] Although machine-readable media is indicated as a single medium, the term "machine-readable medium" can include a single or multiple media configured to store one or more instructions (e.g., a centralized or distributed database, and / or associated caches and servers). The term "machine-readable medium" can also be understood to include any tangible medium capable of storing, encoding, or carrying instructions for machine execution and enabling the machine to perform any one or more of the contents of this disclosure, or capable of storing, encoding, or carrying data structures utilized by or associated with those instructions. Therefore, the term "machine-readable medium" can be understood to include, but is not limited to, solid-state memory, optical and magnetic media. Specific examples of machine-readable media can include non-volatile memory, such as semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)) and flash memory devices; disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM discs.

[0270] Instructions can also be sent or received over a communication network via a transmission medium using any of a variety of transport protocols (e.g., Frame Relay, IP, TCP, UDP, HTTP, etc.) through a network interface device. Example communication networks may include local area networks (LANs), wide area networks (WANs), packet data networks (e.g., the Internet), mobile phone networks (e.g., cellular networks), conventional telephone (POTS) networks, and wireless data networks (e.g., the IEEE 802.11 standard family known as Wi-Fi®, the IEEE 802.16 standard family known as WiMax®), peer-to-peer (P2P) networks, etc. The term "transmission medium" should be understood to include any intangible medium capable of storing, encoding, or carrying instructions for machine execution, and includes digital or analog communication signals or other intangible media that facilitate communication of such software.

[0271] As used herein, a component can refer to a device, physical entity, or logic having boundaries defined by function or subroutine calls, branch points, APIs, or other technologies that provide specific processing or control functionality, such as partitions or modularization. Components can be combined with other components through their interfaces to perform machine processes. A component can be a packaged functional hardware unit designed for use with other components, or it can be part of a program that typically performs related functions. Components can constitute software components (e.g., code implemented on a machine-readable medium) or hardware components. A “hardware component” is a tangible unit capable of performing certain operations and can be configured or arranged in some physical manner. In various example implementations, one or more computer systems (e.g., standalone computer systems, client computer systems, or server computer systems) or one or more hardware components (e.g., processors or a group of processors) of a computer system can be configured as hardware components by software (e.g., an application or a portion of an application) that operates to perform certain operations described herein.

[0272] disease The methods of this invention can be used to diagnose the presence of a condition in a subject, characterize the condition, monitor the condition's response to treatment, and achieve a prognostic assessment of the risk of developing the condition or its subsequent progression. This disclosure can also be used to determine the efficacy of a particular treatment option. If treatment is successful, a successful treatment option can increase the amount of nucleic acids (such as cell-free nucleic acids) detected in the subject's blood due to disease and functional abnormalities resulting in the shedding of DNA, or otherwise manifesting chronic and acute signs of inflammation. In other instances, this may not occur. In yet another instance, perhaps certain treatment options may be correlated with the genetic spectrum of disease type and subtype over time. This correlation can be used to select a therapy.

[0273] In some implementations, the methods and systems disclosed herein can be used to identify customized or targeted therapies for treating a patient's specific disease or condition, based on classifying nucleic acid variations as somatic or germline. Typically, the disease under consideration is cancer.

[0274] Furthermore, the methods of this disclosure can be used to characterize the heterogeneity of abnormal conditions in subjects. Such methods may include, for example, generating genomic and epigenomic profiles of extracellular polynucleotides derived from the subject, wherein the genetic profile includes more than one type of data that can characterize dysfunctions and abnormalities (e.g., hypertrophy) associated with myocardial and valvular tissue, where reduced blood flow and oxygen supply to the heart is often a secondary symptom of weakening and / or deterioration of the blood flow and supply systems caused by physical and biochemical stress. Examples of cardiovascular diseases directly affected by these types of stress include atherosclerosis, coronary artery disease, peripheral vascular disease, and peripheral artery disease, as well as various heart diseases and arrhythmias that may represent other forms of disease and functional abnormalities. The methods of this invention can be used to generate our profiles, fingerprints, or datasets that are the sum of genetic information derived from different cells in heterogeneous diseases. This dataset may include individual or combined copy number variations, epigenetic variations, and mutation analyses.

[0275] The methods of this invention can be used for the diagnosis, prognosis, monitoring, or observation of cancer or other diseases. In some embodiments, the methods herein do not involve the diagnosis, prognosis, or monitoring of the fetus, and therefore do not involve noninvasive prenatal testing. In other embodiments, these methods can be used in pregnant subjects to diagnose, prognose, monitor, or observe cancer or other diseases in unborn subjects whose DNA and other polynucleotides may co-circulate with maternal molecules.

[0276] Other non-limiting examples of genetically based diseases, conditions, or states 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) disease, Cri-du-chat syndrome, Crohn's disease, cystic fibrosis, Dercum disease, Down syndrome, Duane syndrome, Duchenne muscular dystrophy, and factor V. Leiden's thrombotic syndrome, familial hypercholesterolemia, familial Mediterranean fever, Fragile X syndrome, Gaucher disease, hemochromatosis, hemophilia, holoprosencephaly, Huntington's disease, Cranfield syndrome, Marfan syndrome, myotonic dystrophy, neurofibromatosis, Noonan syndrome, osteogenesis imperfecta, Parkinson's disease, phenylketonuria, Poland's abnormality, porphyria, progeria, retinitis pigmentosa, severe combined immunodeficiency (SCID), sickle cell disease, spinal muscular atrophy, Tay-Sachs syndrome, thalassemia, trimethylamineuria, Turner syndrome, velocardiofacial syndrome, WAGR syndrome, Wilson's disease, etc.

[0277] Treatment and related applications In some embodiments, the methods disclosed herein involve identifying and administering a customized therapy to a patient based on the state of nucleic acid variation being of somatic or germline origin. In some embodiments, virtually any cancer therapy (e.g., surgical therapy, radiation therapy, chemotherapy therapy, and / or similar therapies) can be included as part of these methods. Typically, the customized therapy includes at least one immunotherapy (or immunotherapeutic agent). Immunotherapy generally refers to a method of enhancing the immune response against a given cancer type. In some embodiments, immunotherapy refers to a method of enhancing the T-cell response against a tumor or cancer.

[0278] In some implementations, the status of nucleic acid variants in a sample from a subject as somatic or germline can be compared with a database of comparator results from a reference population to identify a custom or targeted therapy for that subject. Typically, the reference population includes patients with the same type of cancer or disease as the subject being tested and / or those receiving or having received the same therapies as the subject being tested. When nucleic acid variants and comparator results meet certain classification criteria (e.g., a near-perfect or approximate match), a custom or targeted therapy (or more than one therapy) can be identified.

[0279] In some embodiments, the custom-made therapies described herein are typically administered parenterally (e.g., intravenously or subcutaneously). Pharmaceutical compositions containing immunotherapeutic agents are typically administered intravenously. Some therapeutic agents are administered orally. However, custom-made therapies (e.g., immunotherapeutic agents, etc.) can also be administered by methods such as, for example, sublingual, sublingual, rectal, vaginal, urethral, ​​topical, intraocular, intranasal, and / or intraauricular administration, which may include tablets, capsules, granules, aqueous suspensions, gels, sprays, suppositories, salves, ointments, etc.

[0280] Although preferred embodiments of the invention have been shown and described herein, it will be apparent to those skilled in the art that these embodiments are provided by way of example only. The invention is not intended to be limited to the specific instances provided herein. While the invention has been described with reference to the foregoing description, the description and illustration of the embodiments herein are not intended to be construed in a limiting sense. Various variations, modifications, and substitutions will now occur to those skilled in the art without departing from the invention. Furthermore, it should be understood that all aspects of the invention are not limited to the specific descriptions, configurations, or relative proportions set forth herein under various conditions and variables. It should be understood that various alternatives to the embodiments of this disclosure described herein may be employed in the practice of the invention. Therefore, it is contemplated that this disclosure should also cover any such alternatives, modifications, variations, or equivalents. The following claims are intended to define the scope of the invention and thereby cover the methods and structures within the scope of these claims and their equivalents.

[0281] While the foregoing disclosure has been described in detail with reference to illustrations and examples for clarity and understanding, it will be apparent to those skilled in the art, upon reading this disclosure, that various changes in form and detail may be made without departing from the true scope of this disclosure, and that it may be practiced within the scope of the appended claims. For example, all methods, systems, computer-readable media and / or component features, steps, elements, or other aspects may be used in various combinations.

[0282] Reagent test kit Kits comprising compositions as described herein are also provided. The kits can be used to perform the methods described herein. In some embodiments, the kit contains a first reagent for partitioning a sample into more than one subsample as described herein, such as any partitioning reagent described elsewhere herein. In some embodiments, the kit contains a second reagent (e.g., any reagent described elsewhere herein for converting nucleosides such as cytosine or methylated cytosine into different nucleosides), the second reagent being used to subject the first subsample to procedures that differently affect a first nucleobase and 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 and second nucleosides have the same base-pairing specificity. The kit may contain the first and second reagents, as well as additional elements discussed below and / or elsewhere herein.

[0283] The kit may also contain more than one oligonucleotide probe, which selectively hybridizes to at least 5, 6, 7, 8, 9, 10, 20, 30, 40, or all genes selected from the group consisting of: ALK, APC, BRAF, CDKN2A, EGFR, ERBB2, FBXW7, KRAS, MYC, NOTCH1, NRAS, PIK3CA, PTEN, RBI, TP53, MET, AR, ABL1, AKT1, ATM, CDH1, CSFIR, CTNNB1, ERBB4, EZ. H2, FGFR1, FGFR2, FGFR3, FLT3, GNA11, GNAQ, GNAS, HNF1A, HRAS, IDH1, IDH2, JAK2, JAK3, KDR, KIT, MLH1, MPL, NPM1, PDGFRA, PROC, PTPN11, RET, SMAD4, SMARCB1, SMO, SRC, STK11, VHL, TERT, CCND1, CDK4, CDKN2B, RAF1, BRCA1, CCND2, CDK6, NF1, TP53, ARID 1A, BRCA2, CCNE1, ESR1, RIT1, GATA3, MAP2K1, RHEB, ROS1, ARAF, MAP2K2, NFE2L2, RHOA, and NTRK1. The number of genes selectively hybridized by oligonucleotide probes can vary. For example, the number of genes may include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, or 54. The kit may contain a container with more than one oligonucleotide probe for performing any of the methods described herein and instructions.

[0284] Oligonucleotide probes can selectively hybridize with exon regions of genes (e.g., at least 5 genes). In some cases, oligonucleotide probes can selectively hybridize with at least 30 exons of genes (e.g., at least 5 genes). In some cases, more than one probe can selectively hybridize with each of the at least 30 exons. The probe hybridizing with each exon may have a sequence overlapping with at least one other probe. In some embodiments, oligonucleotide probes can selectively hybridize with non-coding regions of the genes disclosed herein (e.g., intronic regions of the genes). Oligonucleotide probes can also selectively hybridize with regions of genes that contain both exon and intronic regions of the genes disclosed herein.

[0285] Oligonucleotide probes can target any number of exons. For example, they can target at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, and 14 exons. 5, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, 275, 280, 285, 290, 295, 300, 400, 500, 600, 700, 800, 900, 1,000 or more exons.

[0286] The kit may contain at least 4, 5, 6, 7, or 8 different library adaptors with different molecular barcodes and the same sample barcode. Library adaptors may not be sequencing adaptors. For example, library adaptors may not contain flow cell sequences or sequences that allow the formation of hairpin loops for sequencing. Different variations and combinations of molecular and sample barcodes are described throughout the kit and apply to the kit. Furthermore, in some cases, the adaptor is not a sequencing adaptor. Additionally, the adaptors provided with the kit may also include sequencing adaptors. Sequencing adaptors may contain sequences that hybridize with one or more sequencing primers. Sequencing adaptors may also contain sequences that hybridize with a solid support, such as a flow cell sequence. For example, a sequencing adaptor may be a flow cell adaptor. Sequencing adaptors may be attached to one or both ends of a polynucleotide fragment. In some cases, the kit may contain at least 8 different library adaptors with different molecular barcodes and the same sample barcode. Library adaptors may not be sequencing adaptors. The kit may also contain a sequencing adaptor having a first sequence that selectively hybridizes with a library adaptor and a second sequence that selectively hybridizes with a flow-through sequence. In another example, the sequencing adaptor may be hairpin-shaped. For example, a hairpin-shaped adaptor may contain complementary double-stranded portions and circular portions, wherein the double-stranded portion may be attached (e.g., linked) to a double-stranded polynucleotide. The hairpin-shaped sequencing adaptor may be attached to both ends of a polynucleotide fragment to generate a circular molecule that can be sequenced multiple times. Sequencing adaptors can range from end-to-end in the number of 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, and 54 adaptors. One, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100 or more bases. Sequencing adaptors can contain 20-30, 20-40, 30-50, 30-60, 40-60, 40-70, 50-60, or 50-70 bases end-to-end. In specific instances, sequencing adaptors can contain 20-30 bases end-to-end.In another example, a sequencing adaptor may contain 50-60 bases end-to-end. A sequencing adaptor may contain one or more barcodes. For example, a sequencing adaptor may contain a sample barcode. A sample barcode may contain a predetermined sequence. A sample barcode can be used to identify the source of a polynucleotide. A sample barcode may be at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, or more nucleic acid bases (or any length as described throughout), for example, at least 8 bases. The barcode may be a continuous or non-continuous sequence, as described above.

[0287] Library adapters can be blunt-ended or Y-shaped, and their length can be less than or equal to 40 nucleic acid bases. Other variations of library adapters can be found in the full text and are applicable to this kit.

[0288] biomarkers This disclosure provides methods for diagnosing, prognosing, and selecting treatments for subjects suffering from diseases (e.g., heart failure, cardiovascular disease, cancer, etc.). A biomarker can be any gene or gene variant whose presence, mutation, deletion, substitution, copy number, or translation (i.e., translation into a protein) is an indicator of a disease state. Biomarkers in this disclosure may include the presence, mutation, deletion, substitution, copy number, or translation of any one or more of EGFR, KRAS, MET, BRAF, MYC, NRAS, ERBB2, ALK, Notch, PIK3CA, APC, and SMO.

[0289] Biomarkers are genetic variants. Several resources or methods can be used to identify biomarkers. Biomarkers can be previously discovered or discovered de novo using experimental or epidemiological techniques. When a biomarker is highly associated with a disease, its detection can indicate that disease. When a biomarker in a region or gene occurs at a frequency greater than that of a given background population or dataset, its detection can indicate cancer.

[0290] Publicly available resources such as scientific literature and databases can describe genetic variants in detail. Scientific literature can describe experiments or genome-wide association studies (GWAS) that link one or more genetic variants. Databases can aggregate information collected from sources such as scientific literature to provide a more comprehensive resource for identifying one or more biomarkers. Non-limiting examples of databases include FANTOM, GTex, GEO, Body Atlas, INSiGHT, OMIM (Online Human Mendelian Genetics, omim.org), cBioPortal (cbioportal.org), CIVIC (Clinical Interpretation of Cancer Variants, civic.genome.wustl.edu), DOCM (Database of Selected Mutations, docm.genome.wustl.edu), and the ICGC Data Portal (dcc.icgc.org). In another example, the COSMIC (Catalogue of Somatic Mutations in Cancer) database allows searching for biomarkers by cancer, gene, or mutation type. Biomarkers can also be identified de novo by conducting experiments such as case-control or association studies (e.g., genome-wide association studies).

[0291] One or more biomarkers can be detected in a sequencing panel. Biomarkers can be one or more genetic variants. Biomarkers can be selected from single nucleotide variants (SNVs), copy number variants (CNVs), insertions or deletions (e.g., insertions / deletions), gene fusions, and inversions. Biomarkers can affect protein levels. Biomarkers can be in promoters or enhancers and can alter gene transcription. Biomarkers can affect gene transcription and / or translation efficiency. Biomarkers can affect the stability of transcribed mRNA. Biomarkers can lead to changes in the amino acid sequence of translated proteins. Biomarkers can affect splicing, can change amino acids encoded by specific codons, can cause frameshifts, or can cause premature stop codons. Biomarkers can lead to conserved substitutions of amino acids. One or more biomarkers can lead to conserved substitutions of amino acids. One or more biomarkers can lead to non-conserved substitutions of amino acids.

[0292] The frequency of biomarkers can be as low as 0.001%. The frequency of biomarkers can be as low as 0.005%. The frequency of biomarkers can be as low as 0.01%. The frequency of biomarkers can be as low as 0.02%. The frequency of biomarkers can be as low as 0.03%. The frequency of biomarkers can be as low as 0.05%. The frequency of biomarkers can be as low as 0.1%. The frequency of biomarkers can be as low as 1%.

[0293] A single biomarker may be absent in more than 50% of subjects with cancer. A single biomarker may be absent in more than 40% of subjects with cancer. A single biomarker may be absent in more than 30% of subjects with cancer. A single biomarker may be absent in more than 20% of subjects with cancer. A single biomarker may be absent in more than 10% of subjects with cancer. A single biomarker may be absent in more than 5% of subjects with cancer. A single biomarker may be present in 0.001% to 50% of subjects with cancer. A single biomarker may be present in 0.01% to 30% of subjects with cancer. A single biomarker may be present in 0.01% to 20% of subjects with cancer. A single biomarker may be present in 0.01% to 10% of subjects with cancer. A single biomarker may be present in 0.1% to 10% of subjects with cancer. A single biomarker can be present in 0.1% to 5% of subjects with cancer.

[0294] In some implementations, the biomarker from the subject is present in at least one exon of the group. One or more cancers selected from a group including the following cancers each exhibit a biomarker in at least one exon of the group: ovarian cancer, pancreatic cancer, breast cancer, colorectal cancer, non-small cell lung cancer (squamous or adenocarcinoma), or any other cancer. Each of at least three cancers may exhibit a biomarker in at least one exon of the group. Each of at least four cancers may exhibit a biomarker in at least one exon of the group. Each of at least five cancers may exhibit a biomarker in at least one exon of the group. Each of at least eight cancers may exhibit a biomarker in at least one exon of the group. Each of at least ten cancers may exhibit a biomarker in at least one exon of the group. All cancers may exhibit a biomarker in at least one exon of the group.

[0295] In some implementations, the biomarker from the subject is present in at least one exon or gene in the group. At least 85% of subjects with cancer may express the biomarker in at least one exon or gene in the group. At least 90% of subjects with cancer may express the biomarker in at least one exon or gene in the group. At least 92% of subjects with cancer may express the biomarker in at least one exon or gene in the group. At least 95% of subjects with cancer may express the biomarker in at least one exon or gene in the group. At least 96% of subjects with cancer may express the biomarker in at least one exon or gene in the group. At least 97% of subjects with cancer may express the biomarker in at least one exon or gene in the group. At least 98% of subjects with cancer may express the biomarker in at least one exon or gene in the group. At least 99% of subjects with cancer may express the biomarker in at least one exon or gene in the group. At least 99.5% of subjects with cancer can express a biomarker in at least one exon or gene in the group.

[0296] In some implementations, the biomarker from the subject is present in at least one region within the group. At least 85% of subjects with cancer may exhibit the biomarker in at least one region of the group. At least 90% of subjects with cancer may exhibit the biomarker in at least one region of the group. At least 92% of subjects with cancer may exhibit the biomarker in at least one region of the group. At least 95% of subjects with cancer may exhibit the biomarker in at least one region of the group. At least 96% of subjects with cancer may exhibit the biomarker in at least one region of the group. At least 97% of subjects with cancer may exhibit the biomarker in at least one region of the group. At least 98% of subjects with cancer may exhibit the biomarker in at least one region of the group. At least 99% of subjects with cancer may exhibit the biomarker in at least one region of the group. At least 99.5% of subjects with cancer may exhibit the biomarker in at least one region of the group.

[0297] Detection can be performed with high sensitivity and / or high specificity. Sensitivity can refer to a measure of the proportion of positive results correctly identified as positive. In some cases, sensitivity refers to the percentage of all detected biomarkers present. In some cases, sensitivity refers to the percentage of individuals correctly identified as having a certain disease. Specificity can refer to a measure of the proportion of negative results correctly identified as negative. In some cases, specificity refers to the proportion of correctly identified unaltered bases. In some cases, specificity refers to the percentage of individuals correctly identified as being in healthy individuals without a certain disease. The previously described non-unique labeling method significantly improves the specificity of detection by reducing noise generated by amplification and sequencing errors, which reduces the frequency of false positives. Detection can 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 can 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 can be performed with a specificity of at least 90%, 95%, 97%, 98%, 99%, 99.5%, 99.6%, 99.98%, 99.9%, or 99.95%. The method can detect biomarkers with at least 70% specificity and at least 70% sensitivity, at least 75% specificity and at least 75% sensitivity, at least 80% specificity and at least 80% sensitivity, at least 85% specificity and at least 85% sensitivity, at least 90% specificity and at least 90% sensitivity, at least 95% specificity and at least 95% sensitivity, at least 96% specificity and at least 96% sensitivity, at least 97% specificity and at least 97% sensitivity, at least 98% specificity and at least 98% sensitivity, at least 99% specificity and at least 99% sensitivity, or 100% specificity and 100% sensitivity. In some cases, the method can detect biomarkers with about 80% or higher sensitivity. In some cases, the method can detect biomarkers with about 95% or higher sensitivity. In some cases, the method can detect biomarkers with about 80% or higher sensitivity and about 95% or higher sensitivity.

[0298] The detection can be highly accurate. This accuracy can be applied to the identification of biomarkers ...

Claims

1. A method comprising: Detect methylation at at least one of more than one sites in the nucleic acids of cell-free DNA (cfDNA) samples from subjects; For each of the more than one site, generate more than one methylation determination; One or more measures are determined from the methylation determination; and The one or more metrics are processed to calculate the risk probability of a cardiac event in the subject.

2. The method according to any one of the preceding claims, wherein at least one of the more than one site comprises one or more genes selected from the group consisting of: SIX2, KKBTBD8, TAFA4, EVC, NCA, SNCA-AS, ARAP4, NT5E, XPO1, XPO1, TMEM181, NRG1, DYDC2, CACUL1, 0ORF888, FAM, FAM222A, MIR9-3HG, BCKDK, MAPK7, A1G, CACNA1, MXRA7, ADCYAP1, EPOR, MAST1, DKKL1, PCSK2, CGB7, EMB, TGFBI, COX6B1, ZNF347, and DKKL1.

3. The method according to any one of the preceding claims, wherein at least one of the more than one site comprises one or more genes selected from the group consisting of: SIX2, KKBTBD8, TAFA4, EVC, NCA, SNCA-AS, CGB7, EMB, TGFBI, COX6B1, ZNF347 and DKKL1.

4. The method according to any one of the preceding claims, wherein the cardiac event includes one or more of the following: stroke, transient ischemic attack (TIA), myocardial infarction (MI), angina pectoris, transient ischemic attack (TIA), stroke, and acute coronary syndrome.

5. The method according to any one of the preceding claims, wherein the cardiac event is associated with a cardiomyopathy selected from the group consisting of: dilated cardiomyopathy, hypertrophic cardiomyopathy, restrictive cardiomyopathy, and arrhythmogenic right ventricular dysplasia.

6. The method according to any one of the preceding claims, wherein the cardiomyopathy includes hereditary cardiomyopathy.

7. The method according to any one of the preceding claims, wherein the cardiomyopathy includes acquired cardiomyopathy.

8. The method according to any one of the preceding claims, wherein the cardiomyopathy is associated with a pharmaceutical agent.

9. The method according to any one of the preceding claims, wherein the agent is a drug.

10. The method according to any one of the preceding claims, wherein the drug is letrozole, palbociclib, capecitabine, tamoxifen, trastuzumab, T-DM1, pertuzumab, Ado-emtansine trastuzumab, neratinib, lapatinib, tucatinib, magruciximab, and detrastuzumab.

11. The method according to any one of the preceding claims, wherein determining one or more measures of methylation determination comprises determining a count of methylated molecules at at least one of the more than one site.

12. The method according to any one of the preceding claims, wherein the counting is peak counting.

13. The method according to any one of the preceding claims, wherein processing the one or more metrics comprises selection based on tumor score and / or methylation molecule count.

14. The method according to any one of the preceding claims, wherein processing the one or more metrics includes applying a model.

15. The method according to any one of the preceding claims, wherein the model is a probabilistic model.

16. The method according to any one of the preceding claims, wherein processing the one or more metrics includes determining a probability based on the one or more metrics.

17. The method according to any one of the preceding claims, wherein the calculation of the risk probability of the subject's cardiac event is generated based on the probability of the one or more measures.

18. The method according to any one of the preceding claims, wherein the risk probability of the subject is an increased risk of cardiac events.

19. The method according to any one of the preceding claims, wherein the cardiomyopathy is a comorbidity of another disease or condition in the subject.

20. The method according to any one of the preceding claims, wherein another disease or condition in the subject is selected from one or more diseases or conditions consisting of the group consisting of: cancer, coronary heart disease, heart attack, hypertension, diabetes, thyroid disease, viral hepatitis, HIV1, and viral infection.

21. A method comprising: Detects methylation at at least one site in the nucleic acids of cell-free DNA (cfDNA) samples from treated cancer subjects; For each of the more than one site, generate more than one methylation determination; One or more measures are determined from the methylation determination; and Process one or more of the metrics to calculate the risk probability of a cardiac event in the subject; as well as The decision to maintain or modify treatment is based on the calculated risk probability.

22. The method according to any one of the preceding claims, wherein the treatment comprises administering a pharmaceutical agent.

23. The method according to any one of the preceding claims, wherein the pharmaceutical agent comprises one or more of the following: letrozole, palbociclib, capecitabine, tamoxifen, trastuzumab, T-DM1, pertuzumab, Ado-emtansine trastuzumab, neratinib, lapatinib, tucatinib, magruciximab, and detrastuzumab.

24. The method according to any one of the preceding claims, comprising obtaining the sample from the subject or having already obtained the sample from the subject.

25. The method according to any one of the preceding claims, wherein at least one of the more than one site comprises one or more genes selected from the group consisting of: SIX2, KKBTBD8, TAFA4, EVC, NCA, SNCA-AS, ARAP4, NT5E, XPO1, XPO1, TMEM181, NRG1, DYDC2, CACUL1, 0ORF888, FAM, FAM222A, MIR9-3HG, BCKDK, MAPK7, A1G, CACNA1, MXRA7, ADCYAP1, EPOR, MAST1, DKKL1, PCSK2, CGB7, EMB, TGFBI, COX6B1, ZNF347, and DKKL1.

26. The method according to any one of the preceding claims, wherein at least one of the more than one site comprises one or more genes selected from the group consisting of: SIX2, KKBTBD8, TAFA4, EVC, NCA, SNCA-AS, CGB7, EMB, TGFBI, COX6B1, ZNF347 and DKKL1.

27. The method according to any one of the preceding claims, wherein the subject suffering from cancer is one of: HR+ / HER2-, HR+ / HER2+, HR- / HER2+, and HR- / HER2-.

28. A method comprising: Detect methylation at at least one of more than one sites in the nucleic acids of cell-free DNA (cfDNA) samples from subjects; For each of the more than one site, generate more than one methylation determination; One or more measures are determined from the methylation determination; and Process one or more of the metrics to calculate the risk probability of a cardiac event in the subject; as well as The diagnosis of a patient as susceptible to cardiomyopathy and / or cardiac events and / or prognosis indicates the likelihood of the patient developing cardiomyopathy and / or cardiac events.

29. The method according to any one of the preceding claims, wherein the cardiac event includes one or more of the following: stroke, transient ischemic attack (TIA), myocardial infarction (MI), angina pectoris, transient ischemic attack (TIA), stroke, and acute coronary syndrome.

30. The method according to any one of the preceding claims, wherein the cardiomyopathy is selected from the group consisting of: dilated cardiomyopathy, hypertrophic cardiomyopathy, restrictive cardiomyopathy, and arrhythmogenic right ventricular dysplasia.

31. The method according to any one of the preceding claims, wherein the cardiomyopathy includes hereditary cardiomyopathy.

32. The method according to any one of the preceding claims, wherein the cardiomyopathy includes acquired cardiomyopathy.

33. A method comprising: Detect one or more biomarkers in a sample containing cell-free DNA (cfDNA); as well as The sample is characterized based on the detection of one or more of the biomarkers in the sample.

34. The method according to any one of the preceding claims, wherein detecting the one or more biomarkers comprises measuring the methylation level of one or more at more than one site.

35. The method according to any one of the preceding claims, wherein the site comprises a customized group.

36. The method according to any one of the preceding claims, wherein the customized group is configured in a computer simulation group.

37. The method according to any one of the preceding claims, wherein the customized group is configured in the physical group.

38. The method according to any one of the preceding claims, wherein the customized set comprises a set of oncogenes, a set of promoter regions of oncogenes, HRR genes, immuno-oncology (IO) genes, cancer pathways, methylation peaks found in cancer or methylation peaks found in clinical samples.

39. The method according to any one of the preceding claims, wherein the biomarker comprises the presence, absence or increased expression of one or more genes selected from the group consisting of: SIX2, KKBTBD8, TAFA4, EVC, NCA, SNCA-AS, ARAP4, NT5E, XPO1, XPO1, TMEM181, NRG1, DYDC2, CACUL1, 0ORF888, FAM, FAM222A, MIR9-3HG, BCKDK, MAPK7, A1G, CACNA1, MXRA7, ADCYAP1, EPOR, MAST1, DKKL1, PCSK2, CGB7, EMB, TGFBI, COX6B1, ZNF347 and DKKL1.

40. The method according to any one of the preceding claims, wherein at least one of the more than one site comprises one or more genes selected from the group consisting of: SIX2, KKBTBD8, TAFA4, EVC, NCA, SNCA-AS, CGB7, EMB, TGFBI, COX6B1, ZNF347 and DKKL1.

41. A method comprising: Detection of methylation in samples obtained from subjects; Methylation detected by normalization; Apply probability models; The test statistic is calculated based on one or more expected values ​​and / or detected values; and A quantitative score is generated for the risk of cardiomyopathy and / or cardiac events in the subject.

42. The method according to any one of the preceding claims further includes replacing more than one sample and applying the test statistic.

43. The method according to any one of the preceding claims, wherein the statistic is a spurious test statistic.

44. The method according to any one of the preceding claims, wherein the probability model includes detecting methylation from more than one sample, each sample being obtained from more than one subject.

45. The method according to any one of the preceding claims, wherein the expected value includes a negative observation result.

46. ​​The method according to any one of the preceding claims, wherein the detected value comprises the sum of probabilities.

47. The method according to any one of the preceding claims, wherein the probability model comprises more than one probability for more than one sample.

48. The method according to any one of the preceding claims, comprising applying logistic regression.

49. A system comprising: One or more components, said one or more components being configured to perform the method according to any one of the preceding claims.

50. A computer-readable medium for performing the method according to any one of the preceding claims.

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