Hypermethylation and hypomethylation analysis method for disease detection

Abstract

Some aspects of the present disclosure provide methods and compositions for hypermethylation and hypomethylation analysis of nucleic acid molecules. In some specific aspects, the nucleic acid molecule includes cell free DNA. The methods may include ligation of a first set of adapters, methylation-sensitive or methylation-dependent restriction enzyme digestion, ligation of a second set of adapters, amplification and sample distribution, enrichment of nucleic acid molecules labeled with specific adapters, sequencing and analysis of sequencing data, and detection of diseases such as cancer.

Description

[0001] Cross-references

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 441,680, filed January 27, 2023, which is incorporated herein by reference in its entirety.

[0003] Statement regarding federally funded research or development

[0004] This invention was developed with government support under CA230705 granted by the National Institutes of Health (NIH). The government owns certain rights to this invention. Background of the Invention I. Technical Field

[0005] Some aspects of this disclosure include, at least, the fields of nucleic acid preparation and analysis, sequencing, molecular biology, cell biology, and medicine. II. Background Technology

[0006] With the rapid development of next-generation sequencing (NGS) technology, genomic alterations in DNA can be analyzed to provide diagnostic information about diseases (e.g., cancer) or other physiological states (e.g., fetal genetic material in maternal blood). Some physiological conditions (including diseases or conditions such as cancer or infectious diseases) can cause DNA to be released into circulation (e.g., blood or lymphatic system), where tumor DNA or microbiome DNA can become part of circulating cell-free DNA (cfDNA) in bodily fluids (e.g., plasma or urine). Such cfDNA can be subjected to genomic or epigenomic profiling for clinical applications such as cancer screening, microbial detection, or prenatal testing. For example, analysis of the hypermethylation or hypomethylation status of cfDNA can be used for the early detection of cancer.

[0007] Silva et al., British Journal of Cancer 80, 1262 (1999); Kang et al., Genome Biology 18:53 (2018); Li et al., Nucleic Acids Research 46:e89 (2018); Guo et al., Nature Genetics 49:635 (2017); Liu et al., Annals of Oncology 31:745(2020); Chen et al., Nature Communications 11:3475(2020); Stackpole et al., Nature Communications 13:5566(2022)

[0008] (Each of these references is incorporated herein by reference in its entirety). DNA samples from biological samples (e.g., blood) may include mixtures of DNA from white blood cells (WBCs) and various tissues. The target DNA may be present in a background of abundant non-informative DNA. For example, cell-free DNA from cancer patients may contain only a small fraction of tumor DNA, while most of the DNA may originate from WBCs and various normal organs / tissues. In tissue biopsies, DNA from pathologically diseased tissue samples may contain a large background of DNA from healthy tissues. Disease-specific methylation information can be masked by background DNA methylation. To enrich the target high / low methylated nucleic acid molecules, such as cell-free DNA from tumor cells, from hypomethylated / hypermethylated background DNA in a DNA sample, the DNA sample can be divided into multiple aliquots, and restriction enzymes can be used to remove background DNA. For example, methylation-sensitive restriction enzymes can be used to remove hypomethylated background DNA for hypermethylation analysis of target DNA (as described, for example, in U.S. Patent No. 8,088,581B2; and International Publication No. WO 2022 / 073011 A1, each of which is incorporated herein by reference in its entirety). In some applications, such as the detection of cell-free DNA, only a limited amount of DNA may be available. Therefore, there remains a need for improved methods and compositions that can obtain hypermethylation and hypomethylation information from the entire informative portion of a DNA sample, rather than from sub-portions.

[0009] This disclosure provides improved methods and compositions for high and low DNA methylation analysis for disease detection. Summary of the Invention

[0010] Some aspects of this disclosure provide improvements to methods and compositions for hypermethylation and hypomethylation analysis by enriching both target hypermethylated and hypomethylated nucleic acid molecules from hypomethylated and hypermethylated background DNA in a DNA mixture sample. In liquid biopsy applications with disease-specific (e.g., tumor-derived) cell-free DNA, the background DNA can be derived from cell-free DNA from leukocytes (WBCs) or healthy tissue. In tissue biopsy applications, such as diagnostic applications using DNA from any type of tissue, the background DNA can contain DNA from surrounding healthy tissue, such as DNA from organs containing both diseased and healthy tissue. Some aspects of this disclosure provide methods for enriching target DNA or eliminating such background DNA based on its specific methylation pattern using methylation-sensitive and / or methylation-dependent restriction enzymes. In some aspects, in the methods of this disclosure, hypomethylated background DNA is eliminated for hypermethylation analysis. In some aspects, in the methods of this disclosure, hypermethylated background DNA is eliminated for hypomethylation analysis. The target hypermethylated and hypomethylated DNA can be used for downstream analyses, such as next-generation sequencing, to detect disease-specific methylation. In certain specific aspects, this disclosure provides methods for analyzing hypermethylation and hypomethylation patterns of cell-free DNA (cfDNA) molecules by eliminating background methylation signals from the DNA of leukocytes or healthy tissue, for example, to provide information about cancer and other physiological states. In specific cases, this method is used for cancer detection.

[0011] In one aspect, this disclosure provides a method for enriching hypermethylated and hypomethylated nucleic acid molecules in a plurality of nucleic acid molecules, comprising: (a) ligating a first set of adaptors to the ends of the plurality of nucleic acids; (b) digesting the plurality of nucleic acid molecules with one or more methylation-sensitive or methylation-dependent restriction enzymes to produce a plurality of nucleic acid molecules having enzyme-specific overhangs, and ligating a second set of adaptors to the plurality of nucleic acid molecules having enzyme-specific overhangs; (c) amplifying the multiple nucleic acid molecules ligated by adaptors using one or more primers that bind to the first and second sets of adaptors to produce amplified adaptor-ligated DNA fragments; (d) distributing the amplified adaptor-ligated DNA fragments into at least two partitions; (e) enriching the amplified DNA fragments having the first set of adaptors at both ends in the first partition and enriching the amplified DNA fragments having the second set of adaptors at both ends in the second partition; and (f) processing the enriched DNA fragments to determine the methylation state of the enriched DNA fragments.

[0012] In one aspect, this disclosure provides a method for enriching highly methylated and / or hypomethylated nucleic acid molecules in a plurality of nucleic acid molecules, comprising: (a) linking a first set of adaptors to each end of each nucleic acid in the plurality of nucleic acid molecules to produce a first set of linked molecules; (b) digesting the first set of linked molecules with one or more methylation-sensitive restriction enzymes and / or methylation-dependent restriction enzymes to produce a plurality of nucleic acid molecules having enzyme-specific overhangs, and linking a second set of adaptors to the plurality of nucleic acid molecules having enzyme-specific overhangs to produce a second set of linked molecules, wherein the second set of adaptors... (c) The molecules of the first and second sets of intermolecular linkages do not have a sequence that is recognized by one or more methylation-sensitive restriction enzymes; (d) the molecules of the first and second sets of intermolecular linkages are amplified at least partially using one or more primers that bind to the first set of intermolecular linkages and one or more primers that bind to the second set of intermolecular linkages to produce amplified intermolecular linkage DNA fragments; (e) the amplified intermolecular linkage DNA fragments are allocated to at least two partitions; and (f) the amplified intermolecular linkage DNA fragments having the first set of intermolecular linkages at both ends are enriched in the first partition to produce enriched DNA fragments in the first partition.

[0013] In some embodiments, the method further includes enriching highly methylated nucleic acid molecules, and (b) further includes digesting multiple nucleic acid molecules with one or more methylation-sensitive restriction enzymes to produce multiple nucleic acid molecules with enzyme-specific overhangs. In some embodiments, (b) further includes linking a second set of adaptors to multiple nucleic acid molecules with enzyme-specific overhangs to produce a second set of linked molecules. In some embodiments, the second set of linked molecules does not have a sequence recognized by one or more methylation-sensitive restriction enzymes.

[0014] In some embodiments, the method further includes enriching hypomethylated nucleic acid molecules, and (b) further includes digesting multiple nucleic acid molecules with one or more methylation-dependent restriction enzymes to produce multiple nucleic acid molecules with digestion enzyme-specific overhangs. In some embodiments, (b) further includes linking a second set of adaptors to multiple nucleic acid molecules with digestion enzyme-specific overhangs to produce a second set of linked molecules. In some embodiments, the second set of linked molecules does not have a sequence recognized by one or more methylation-sensitive restriction enzymes.

[0015] In some embodiments, the second set of adaptors has a protrusion complementary to the protrusion produced by a methylation-sensitive restriction enzyme and / or a methylation-dependent restriction enzyme, and (b) further includes digesting the adaptors from the first set of adaptors and / or the second set of adaptors that have been linked together with one or more additional restriction enzymes, without digesting the DNA fragments and the linkage between the adaptors.

[0016] In some embodiments, the first set of ligated molecules and the second set of ligated molecules are amplified to produce an amplified adaptor-ligated DNA fragment, including 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more (or any range thereof) amplification cycles. In some embodiments, the first set of ligated molecules and the second set of ligated molecules are amplified to produce an amplified adaptor-ligated DNA fragment, including 1 to 3, 1 to 5, or 1 to 10 (or any range thereof) amplification cycles.

[0017] In some embodiments, (e) further includes enriching the amplified DNA fragment having a second set of adaptors at both ends in the second partition to generate an enriched DNA fragment in the second partition. Enriching the amplified adaptor-ligated DNA fragment may include amplifying the amplified adaptor-ligated DNA fragment of the first partition using primers capable of initiating polymerization at the first set of adaptors. Enriching the amplified adaptor-ligated DNA fragment may include amplifying the amplified adaptor-ligated DNA fragment of the second partition using primers capable of initiating polymerization at the second set of adaptors. Enriching the amplified adaptor-ligated DNA fragment may include amplifying the amplified adaptor-ligated DNA fragment of any partition using primers capable of initiating polymerization at the first and / or second sets of adaptors.

[0018] In some embodiments, (d) further includes allocating the amplified adaptor-ligated DNA fragment to three or more partitions. In some embodiments, (e) further includes enriching the amplified adaptor-ligated DNA fragment having a first set of adaptors at one end and a second set of adaptors at the other end in a third partition to generate an enriched DNA fragment in the third partition. In some embodiments, (e) further includes using primers to amplify the amplified adaptor-ligated DNA fragment having a first set of adaptors at one end and a second set of adaptors at the other end in the third partition, said primers being capable of initiating polymerization at one end of the amplified adaptor-ligated DNA fragment at the first set of adaptors and at the other end of the amplified adaptor-ligated DNA fragment at the second set of adaptors.

[0019] In some embodiments, (e) further includes capturing at least one subset of the amplified adaptor-linked DNA fragments in one or more targeted genomic regions through purification. Purification can be performed by immunoprecipitation and / or column chromatography. In some embodiments, a hybridization capture probe is used to capture the subset of the amplified adaptor-linked DNA fragments. The hybridization capture probe may cover one or more restriction enzyme cleavage sites in the targeted genomic region.

[0020] In some embodiments, the method further includes (f) processing the enriched DNA fragments to determine the methylation status of the enriched DNA fragments. This processing may include generating sequencing data that provides a count of the enriched DNA fragments. In some embodiments, (b) is performed using one or more methylation-sensitive restriction enzymes, and (f) further includes determining the methylation status by counting hypermethylated DNA fragments in a first partition and hypomethylated DNA fragments in a second partition. In some embodiments, (b) is performed using one or more methylation-dependent restriction enzymes, and (f) further includes determining the methylation status by counting hypomethylated DNA fragments in a first partition and hypermethylated DNA fragments in a second partition.

[0021] In some embodiments, multiple nucleic acid molecules are derived from a biological sample. The biological sample may be derived from an individual, such as a subject or patient. The biological sample may contain blood. The biological sample may contain cell-free DNA. The biological sample may contain a biopsy. The biopsy may contain a tumor biopsy. In some embodiments, the multiple nucleic acid molecules contain cell-free DNA. In some embodiments, the nucleic acid molecules are fragmented, including fragmenting and cleaving the nucleic acid molecules using different methods (e.g., sonication with a cutting device and / or digestion with restriction enzymes). In some embodiments, fragmentation breaks down at least a portion of the nucleic acid molecule into smaller sizes for further analysis.

[0022] In some embodiments, a first set of adaptors is ligated to the ends of the plurality of nucleic acid molecules. Each adaptor may contain a functional sequence configured to be coupled to a flow cell of a nucleic acid sequencer. In some embodiments, the method further includes end repair or tailing of the plurality of nucleic acid molecules prior to adaptor ligation. Adaptor ligation includes ligation of sequencing adaptors using any ligase (e.g., T4 DNA).

[0023] In some embodiments, digesting multiple nucleic acid molecules with one or more methylation-sensitive restriction enzymes includes digesting at least a subset of the multiple nucleic acid molecules with a methylation-sensitive enzyme that cannot cleave methylated cytosine residues. In some embodiments, the method includes using one or more methylation-sensitive restriction enzymes (MSREs), said methylation-sensitive restriction enzymes comprising one or more of the following:

[0024] AcII,HindIII,MluCI,PciI,AgeI,BspMI,BfuAI,SexAI,MluI,BceAI,HpyCH4IV,HpyCH4III,BaeI,BsaXI,AflIII,SpeI,BsrI,BmrI,BglDI,BspDIB PI-SceI,NsiI,AseI,CspCI,MfeI,BssSαI,DraIII,EcoP15I,AlwNI,BtsI MutI,NdeI,CviAII,FatI,NlaIII,FspEI,XcmI,BstXI,PflMI,BccI,NcoI ,BseYI,FauI,TspMI,XmaI,LpnPI,AclI,ClaI,SacII,HpaII,MspI,ScrFI ,StyD4I,BsaJI,BslI,BtgI,NciI,AvrII,MnlI,BbvCI,SbfI,Bpu10I,Bsu 36I,EcoNI,HpyAV,BstNI,PspGI,StyI,BcgI,PvuI,EagI,RsrII,BsiEI,B siWI,BsmBI,Hpy99I,AbaSI,MspJI,SgrAI,BfaI,BspCNI,XhoI,PaeR7I,E arI,AcuI,PstI,BpmI,DdeI,SfcI,AflII,BpuEI,SmlI,AvaI,BsoBI,MboI I,BbsI,BsmI,EcoRI,HgaI,AatII,PflFI,Tth111I,AhdI,DrdI,SacI,Bse RI,PleI,HinfI,Sau3AI,MboI,DpnII,TfiI,BsrDI,BbvI,BtsαI,BstAPI, SfaNI,SphI,NmeAIII,NgoMIV,BglI,AsiSI,BtgZI,HhaI,HinP1I,BssHII ,NotI,Fnu4HI,MwoI,BmtI,NheI,BspQI,BlpI,TseI,ApeKI,Bsp1286I,Al wI,BamHI,BtsCI,FokI,FseI,SfiI,NarI,PluTI,CasI,AscI,EciI,BsmFI ,ApaI,PspOMI,Sau96I,KpnI,Acc65I,BsaI,HphI,BstEII,AvaII,BanI,B aeGI,BsaHI,BanII,CviQI,BciVI,SalI,BcoDI,BsmAI,ApaLI,BsgI,AccI,Tsp45I, BsiHKAI, TspRI, ApoI, NspI, BsrFαI, BstYI, HaeII, EcoO109I, PpuMI, I-CeuI, I-SceI, BspHI, BspEI, MmeI, TaqαI, Hpy188I, Hpy188III, XbaI, BclI, PI-PspI, BsrGI, MseI, PacI, BstBI, PspXI, BsaWI, EaeI, HpyF30I, Sfr2741,

[0025] Digestion of nucleic acid molecules, or their functional analogues, or combinations thereof, or mixtures thereof. In some embodiments, digestion of multiple nucleic acid molecules produces digestive enzyme-specific 5'- or 3'-overhead ends at the ends of said multiple nucleic acid molecules. For example, HpaII digestion can produce 5'-CG overhead ends.

[0026] In some embodiments, digesting multiple nucleic acid molecules with one or more methylation-dependent restriction enzymes includes digesting at least a subset of the multiple nucleic acid molecules with a methylation-dependent enzyme capable only of cleaving methylated cytosine residues. In some embodiments, the method includes using one or more methylation-dependent restriction enzymes (MSREs), including one or more of LpnPI, McrBC, GlaI, PkrI, MteI, AoxI, or their functional analogs, combinations thereof, or mixtures thereof. In some embodiments, digestion of the multiple nucleic acid molecules produces enzyme-specific 5' or 3' overhangs at the ends of the multiple nucleic acid molecules.

[0027] In some embodiments, digestion of multiple nucleic acid molecules and ligation of a second set of adaptors occur in the same reaction, and the second set of adaptors has overhangs complementary to enzyme-specific 5'- or 3'- overhangs at the ends of said multiple nucleic acid molecules, and (b) further includes digestion of the ligation between one adaptor end and another adaptor end with one or more additional restriction enzymes, but not digestion of the ligation between DNA fragment ends and adaptor ends. One or more additional restriction enzymes may include one or more of BspDI, ClaI, AclI, NarI, XhoI, SmlI, HpyF30I, PaeR7I, Sfr274I, or their functional analogs or mixtures thereof. Each of the second set of adaptors may contain a functional sequence configured to be coupled to the flow cell of a nucleic acid sequencer and distinguishable from the functional sequence of the first set of adaptors by the sequencer.

[0028] In some implementations, the digestion of multiple nucleic acid molecules and the ligation of a second set of adaptors occur in different reactions. In some implementations, the digestion of multiple nucleic acid molecules and the ligation of a second set of adaptors occur in different reaction vessels.

[0029] In some embodiments, after digesting multiple nucleic acid molecules and ligating a second set of adaptors, and before amplifying the multiple nucleic acid molecules ligated by the adaptors, the method further includes subjecting the multiple nucleic acid molecules to conditions sufficient to allow differentiation between methylated and unmethylated nucleic acid bases. In some embodiments, subjecting the nucleic acid molecules to conditions sufficient to allow differentiation between methylated and unmethylated bases includes bisulfite conversion of the nucleic acid molecules. In some embodiments, subjecting the nucleic acid molecules to conditions sufficient to allow differentiation between methylated and unmethylated bases includes enzymatic and / or chemical reactions to oxidize methylated cytosine nucleic acid bases and / or hydroxymethylated cytosine nucleic acid bases, followed by reduction and / or deamination of the oxidation products.

[0030] In some embodiments, amplifying multiple nucleic acid molecules linked by an adaptor includes amplification, such as PCR. In some embodiments, primers for PCR are designed to recognize both a first set of adaptors and a second set of adaptors, and a limited number of PCR cycles are performed to amplify the multiple nucleic acid molecules to a sufficient amount for the partitioning step. Some examples of limited PCR cycles may include about 1 cycle, about 2 cycles, about 3 cycles, about 4 cycles, and about 5 cycles.

[0031] In some implementations, the amplified DNA fragment enriched at both ends with a first set of adapters includes amplifying the amplified DNA fragment of the first region using primers capable of initiating polymerization at the first set of adapters, and the amplified DNA fragment enriched at both ends with a second set of adapters includes amplifying the amplified DNA fragment of the second region using primers capable of initiating polymerization at the second set of adapters.

[0032] In some embodiments, (d) further includes allocating the amplified adaptor-ligated DNA fragment to three or more partitions, and (e) further includes enriching the amplified DNA fragment having a first set of adaptors at one end and a second set of adaptors at the other end in the third partition. In some embodiments, enriching the amplified DNA fragment having a first set of adaptors at one end and a second set of adaptors at the other end in the third partition comprises amplifying the amplified DNA fragment in the third partition using primers capable of initiating polymerization at the first set of adaptors at one end (e.g., the 3' end) of the amplified adaptor-ligated DNA fragment and at the second set of adaptors at the other end (e.g., the 5' end). In some embodiments, the primers are complementary to at least a portion of the first set of adaptors at the 3' end of the amplified adaptor-ligated DNA fragment and to at least a portion of the complementary sequence of the second set of adaptors at the 5' end of the amplified adaptor-ligated DNA fragment.

[0033] In some embodiments, (e) further includes capturing at least a subset of enriched DNA fragments in one or more targeted genomic regions. Capture may include hybridization capture and optional processing operations, such as amplification. Hybridization capture may include hybridization-based targeted capture using hybridization capture probes to cover one or more restriction enzyme cleavage sites in the target region. In some embodiments, pre-amplification is performed before hybridization capture, and post-amplification is performed after hybridization capture. In some embodiments, amplification is performed after hybridization capture, and pre-amplification is omitted. In some cases, such as PCR-free library preparation, PCR amplification before or after hybridization capture is omitted.

[0034] In some embodiments, processing the enriched DNA fragments includes sequencing the enriched DNA fragments, such as next-generation sequencing, to generate sequencing data. The sequencing data, which provides a count of the enriched DNA fragments, can be used for one or a series of downstream analyses. In some embodiments, one or more methylation-sensitive restriction enzymes are used (b), and determining the methylation status includes obtaining a count of highly methylated DNA fragments in a first partition and a count of low-methylated DNA fragments in a second partition from the sequencing data. In some embodiments, one or more methylation-dependent restriction enzymes are used (b), and determining the methylation status includes obtaining a count of low-methylated DNA fragments in a first partition and a count of highly methylated DNA fragments in a second partition from the sequencing data. In some embodiments, the count includes a count of all sequencing reads from the target region. In some embodiments, for example, when multiple nucleic acid molecules are subjected to conditions sufficient to allow methylated nucleic acid bases to be distinguished from unmethylated nucleic acid bases, the counting of highly methylated DNA fragments may be included in the counting of DNA fragments having at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90%, or about 100% methylated cytosine residues in the CpG dinucleotide of a single DNA fragment, and the counting of low-methylated DNA fragments may be included in the counting of DNA fragments having at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90%, or about 100% unmethylated cytosine residues in the CpG dinucleotide of a single DNA fragment.

[0035] In some implementations, hypermethylation or hypomethylation states indicate the presence or absence of disease or its risk in an individual. Counts of hypermethylated and / or hypomethylated DNA fragments can serve as feature input for a trained single-class or multi-class machine learning classifier or artificial intelligence algorithm to measure, detect, or predict the presence or absence of disease in an object. In some implementations, the counts can be preprocessed before being input to the classifier. In some implementations, preprocessing methods may include, but are not limited to, logarithmic transformation, standardization, discretization, feature selection, dimensionality reduction, or any combination thereof. Exemplary single-class or multi-class classifiers may include support vector machines, random forests, k-nearest neighbors, Naive Bayes, Gaussian processes, decision trees, XGBoost, neural networks, linear and quadratic discriminant analysis, logistic regression, general linear models, or similar methods, or any combination thereof. In some cases, preprocessing methods may include normalizing the counts using counts from reference genomic regions that do not have methylation-sensitive restriction enzyme and / or methylation-dependent restriction enzyme digestion sites. In some implementations, multiple nucleic acid molecules include cell-free DNA, and the disease subjects include cancer subjects or those at increased risk of having cancer (relative to the general population) or suspected of having cancer. Measurements of hypermethylation and hypomethylation in the method can be metrics for cancer detection. Detecting cancer from a subject's cell-free DNA can include screening the subject for the presence of cancer, and this screening can occur from routine healthcare maintenance or when cancer is suspected. This screening can lead to further diagnostic testing or intervention, such as for early detection of cancer. Detecting cancer from a subject's cell-free DNA can be used to detect trace residual disease and / or predict cancer recurrence. Treatment decisions can be made based on the cancer status.

[0036] In another aspect, this disclosure provides kits for carrying out any of the methods described herein. In some embodiments, the kit includes reagents for performing any of the methods described herein.

[0037] In another aspect, this disclosure provides methods for treating a subject. In some embodiments, the method includes performing one or more methods disclosed herein on a biological sample from the subject to determine whether the subject has a condition sensitive to clinical intervention. In some embodiments, if the subject has been determined to have a condition sensitive to clinical intervention, a clinical intervention is administered to the subject. The subject may have or be suspected of having cancer, an infectious disease, or a non-infectious disease. A condition sensitive to clinical intervention can be a growth, metastasis, infection, or autoimmune disease. In some embodiments, the clinical intervention includes an effective amount of chemotherapy, radiation therapy, hormone therapy, targeted therapy, immunotherapy, biologics, cell therapy, antibiotics, antiviral drugs, radiation, cryoablation, or a combination thereof, and / or surgical intervention. In some embodiments involving treatment methods, the method further includes processing enriched DNA fragments to determine the methylation status of the enriched DNA fragments. In some embodiments, the methylation status indicates whether the subject has a condition sensitive to clinical intervention.

[0038] In some embodiments, the method includes monitoring the subject's response to a first clinical intervention. In some embodiments, the method includes performing any of the methods described herein on a biological sample from the subject, wherein the subject has received at least one round of the first clinical intervention. In some embodiments, the method further includes processing enriched DNA fragments to determine the methylation status of the enriched DNA fragments. In some embodiments, the method further includes administering a further round of the first or second clinical intervention to the subject, at least in part based on the methylation status of the enriched DNA fragments. In some embodiments, the first clinical intervention includes an effective amount of chemotherapy, radiation therapy, hormone therapy, targeted therapy, immunotherapy, biologics, cell therapy, antibiotics, antiviral drugs, radiation, cryoablation, or a combination thereof, and / or surgical intervention. In some embodiments, the second clinical intervention differs from the first clinical intervention. In some embodiments, the second clinical intervention includes an effective amount of chemotherapy, radiation therapy, hormone therapy, targeted therapy, immunotherapy, biologics, cell therapy, antibiotics, antiviral drugs, radiation, cryoablation, or a combination thereof, and / or surgical intervention, or discontinuation of the first clinical intervention.

[0039] In another aspect, this disclosure provides methods for diagnosing or prognosing a disease in a subject. In some embodiments, the method includes performing any of the methods described herein to determine whether the subject has a disease or the severity of a disease in the subject. The disease may be cancer, an infectious disease, or a non-infectious disease.

[0040] It is specifically contemplated that any limitations discussed with respect to one aspect of this disclosure may be applied to any other aspect of this disclosure. Furthermore, any composition of this disclosure may be used in any method of the invention, and any method of this disclosure may be used to produce or utilize any composition of this disclosure. Some aspects set forth in the embodiments are also aspects that may be practiced elsewhere in different embodiments or in the context of aspects discussed elsewhere in this application (e.g., in the summary, detailed description, claims, and description of drawings).

[0041] The foregoing discussion has provided a fairly broad overview of the features and technical advantages of this disclosure in order to better understand the detailed description that follows. Further features and advantages that form the subject matter of the claims herein will be described below. Those skilled in the art will understand that the disclosed concepts and specific aspects can be readily used as the basis for modifying or designing other structures for the same purposes of carrying out the inventive design. Those skilled in the art will also recognize that such equivalent constructions do not depart from the spirit and scope set forth in the appended claims. The novel features, as well as additional objects and advantages, considered to be characteristics of the designs disclosed herein in terms of both organization and manner of operation, will be better understood from the following description when considered in conjunction with the accompanying drawings. However, it should be clearly understood that each drawing is provided for illustrative and descriptive purposes only and is not intended to be limiting of the disclosure. Further objects, features, aspects, and advantages of the invention will be set forth in part in the following description and in part will be apparent from the description or may be learned by practice of the invention. Various aspects of this disclosure will be described in sufficient detail to enable those skilled in the art to practice the invention, and it should be understood that other aspects may be utilized and changes may be made without departing from the scope of the invention.

[0042] By incorporating via reference

[0043] All publications, patents, and patent applications mentioned in this specification are incorporated herein by reference to the same extent as each individual publication, patent, or patent application is expressly and individually indicated to be incorporated by reference. In the event of any publication or patent or patent application incorporated by reference that contradicts the disclosure contained in this specification, this specification is intended to supersede and / or take precedence over any such contradictory material. Attached Figure Description

[0044] The novel features of the invention are specifically set forth in the appended claims. A better understanding of the features and advantages of this disclosure will be obtained by referring to the following detailed description and accompanying drawings (also referred to herein as “drawings” and “figures”) illustrating illustrative aspects utilizing the principles of the invention, in which:

[0045] Figure 1 A flowchart of hypermethylation and hypomethylation analysis with background DNA elimination is shown.

[0046] Figure 2 An example of the method of this disclosure is shown, in which highly methylated and lowly methylated nucleic acid molecules are enriched for analysis.

[0047] Figure 3 A computer system is shown that is programmed or otherwise configured to implement the methods provided herein.

[0048] Although many aspects of this disclosure have been shown and described herein, it will be apparent to those skilled in the art that such aspects are provided only by way of example. Many variations, modifications, and substitutions will occur to those skilled in the art without departing from the invention. It should be understood that various alternatives may be made to some aspects of this disclosure described herein. Detailed Implementation

[0049] I. Defined Instances

[0050] The terms “or” and “and / or / or” as used herein are used to describe multiple components that are combined or mutually exclusive. For example, “x, y and / or z” can refer to “x” alone, “y” alone, “z” alone, “x, y and z”, “(x and y) or z”, “x or (y and z)”, or “x or y or z”. In particular, it is anticipated that x, y, or z may be specifically excluded from one aspect.

[0051] The term "comprising / including," as used herein, is inclusive or open-ended and does not exclude additional unlisted elements or method steps. The phrase "consisting of" excludes any unspecified element, step, or component. The phrase "substantially consisting of" limits the scope of the subject matter to the specified substance or step and those that do not substantially affect its essential and novel characteristics. It is contemplated that some aspects described in the context of the term "comprising / including" may also be implemented in the context of the terms "consisting of" or "substantially consisting of."

[0052] As used herein, the terms "one embodiment," "an embodiment," "specific embodiment," "related embodiment," "specific embodiment," "a particular embodiment," "an additional embodiment," "a further embodiment," "one aspect," "an aspect," "a specific aspect," "related aspect," "specific aspect," "a certain aspect," "an additional aspect," "a further aspect," "some aspects," "some aspects," or combinations thereof generally indicate that a particular feature, structure, or characteristic described in connection with that aspect is included in at least one aspect of the invention. Therefore, the foregoing phrases appearing in various places throughout this specification do not necessarily refer to the same aspect. Furthermore, a particular feature, structure, or characteristic may be combined in any suitable manner in one or more aspects.

[0053] Several aspects of this disclosure may be presented in range form. It should be understood that range-based descriptions are for convenience and brevity only and should not be construed as an inflexible limitation on the scope of this disclosure. Therefore, a range description should be regarded as specifically disclosing all possible subranges within that range, as well as individual numerical values, as if explicitly written. For example, a description of a range such as 1 to 6 should be regarded as specifically disclosing subranges within that range, such as 1 to 3, 1 to 4, 1 to 5, 2 to 4, 2 to 6, 3 to 6, etc., as well as individual numbers, such as 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range. When ranges exist, they may include range endpoints.

[0054] As used herein, the term "object" generally refers to an individual having a biological sample being processed or analyzed. An object can be an animal or plant. An object can be a mammal, such as a human, dog, cat, horse, pig, or rodent. An object can be a patient, for example, someone who has or is suspected of having a disease or has a risk of having a disease (e.g., an elevated risk), such as one or more cancers (e.g., brain cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, stomach cancer, hepatobiliary cancer, leukemia, liver cancer, lung cancer, lymphoma, ovarian cancer, pancreatic cancer, skin cancer, urinary tract cancer, testicular cancer, kidney cancer, sarcoma, bile duct cancer, thyroid cancer, gallbladder cancer, spleen cancer, or prostate cancer, and cancer may or may not include solid tumors), one or more infectious diseases, one or more hereditary conditions, or one or more tumors, or any combination thereof. For an object who has or is suspected of having one or more tumors, the tumors can be of one or more types. An object may have or is suspected of having a disease. An object may be asymptomatic. The individual may be at risk of developing the disease, for example, having a higher risk than the general population.

[0055] As used herein, the term "sample" generally refers to a biological sample. A sample may be taken from tissues and / or cells or from the environment, including tissues and / or cells and / or the circulatory system. In some instances, a sample may contain or originate from tissue biopsies, blood (e.g., whole blood), plasma, serum, bone marrow, cerebrospinal fluid, pleural fluid, saliva, feces, urine, extracellular fluid, dried blood spots, cultured cells, culture media, waste tissue, plant matter, synthetic proteins, bacterial and / or viral samples, fungal tissue, archaea, or protozoa. The sample may be isolated from its source prior to collection. A sample may contain forensic evidence. Some non-limiting examples include fingerprints, saliva, urine, blood, feces, semen, or other bodily fluids isolated from their primary source prior to collection. In some instances, the sample is isolated from its primary source (cells, tissues, bodily fluids (e.g., blood), environmental samples, etc.) during sample preparation. Samples may originate from extinct species, including but not limited to samples derived from fossils. A sample may or may not be purified or otherwise enriched from its primary source. In some cases, the primary source is homogenized before further processing. Samples may be filtered or centrifuged to remove the erythrocyte sedimentation rate (ESR) layer, lipids, or particulate matter. Nucleic acids in the sample may also be purified or enriched, or the sample may be treated with RNases or DNases. Samples may contain intact, fragmented, or partially degraded tissues and / or cells.

[0056] Samples may be obtained from subjects who have a disease or condition, subjects suspected of having a disease or condition, and / or subjects who may or may not have a diagnosis of a disease or condition. Subjects may require a second opinion. A disease or condition can be an infectious disease, an immune disorder or disease, cancer, a genetic disease, a degenerative disease, a lifestyle disease, or an injury. Infectious diseases may be caused by bacteria, viruses, fungi, and / or parasites. Some non-limiting examples of cancer include pancreatic cancer, liver cancer, lung cancer, colorectal cancer, leukemia, bladder cancer, bone cancer, brain cancer, breast cancer, cervical cancer, endometrial cancer, esophageal cancer, stomach cancer, head and neck cancer, melanoma, ovarian cancer, testicular cancer, kidney cancer, thyroid cancer, gallbladder cancer, spleen cancer, and prostate cancer. Examples of hereditary diseases or conditions include, but are not limited to, cystic fibrosis, Charcot–Marie–Tooth disease, Huntington's disease, Peutz-Jeghers syndrome, Down syndrome, rheumatoid arthritis, and Tay-Sachs disease. Some non-limiting examples of lifestyle diseases include obesity, diabetes, arteriosclerosis, heart disease, stroke, hypertension, cirrhosis, nephritis, cancer, chronic obstructive pulmonary disease (COPD), hearing problems, and chronic back pain. Examples of injuries include, but are not limited to: abrasions, brain injury, contusions, burns, concussion, congestive heart failure, construction injury, dislocation, flail chest, fracture, hemothorax, herniated disc, hip pointer contusion, hypothermia, lacerations, pinched nerve, pneumothorax, rib fractures, sciatica, spinal cord injury, tendon, ligament, and fascia injury, traumatic brain injury, and whiplash injury. Samples may be obtained before and / or after treatment of a subject with a disease or condition. Samples may be obtained during treatment or a treatment protocol. Multiple samples may be obtained from a subject to monitor treatment effectiveness over time, including starting before treatment begins. Samples may be taken from subjects with known or suspected infectious diseases for which diagnostic reagents (e.g., antibodies) may or may not be available. Samples may be obtained from subjects to monitor abnormal tissue-specific cell death or organ transplantation.

[0057] Samples may be taken from subjects who have or are suspected of having a disease or condition. Samples may be taken from subjects experiencing unexplained symptoms (e.g., fatigue, nausea, weight loss, pain, aches, weakness, abnormal growth, or memory loss). Samples may be taken from subjects with explained symptoms. Samples may be taken from subjects who are at increased risk of developing a disease or condition due to one or more factors, such as family and / or personal medical history, age, environmental exposure, lifestyle risk factors, the presence of other known risk factors, or a combination thereof.

[0058] Samples may be taken from healthy individuals. In some cases, samples may be taken longitudinally from the same individual. In some cases, longitudinally acquired samples may be analyzed for the purpose of monitoring individual health and early detection of healthy tissues (e.g., early diagnosis of cancer). In some aspects, samples may be collected in a home or point-of-care setting and subsequently transported by mail, courier, or other means of transport for analysis. For example, a home user may collect a bloodstain sample by pricking their finger, and the bloodstain sample may be dried and then transported by mail for analysis. In some cases, longitudinally acquired samples may be used to monitor responses to stimuli expected to affect health, motor performance, or cognitive performance. Some non-limiting examples include responses to medications, diets, and / or exercise programs. In some cases, individual samples are multipurpose and allow for high / low methylation profiling to obtain clinically relevant information, but are also used to obtain information about an individual's personal or ancestry. In some cases, samples may be collected from pregnant women and / or their fetuses.

[0059] Biological samples can be nucleic acid samples containing one or more nucleic acid molecules. Nucleic acid molecules can be cell-free or substantially cell-free nucleic acid molecules, such as cell-free DNA (cfDNA) or cell-free RNA (cfRNA), or mixtures thereof. Nucleic acid molecules can be derived from a variety of sources, including human, mammalian, non-human mammalian, ape, monkey, chimpanzee, reptile, amphibian, or avian sources. Furthermore, samples can be extracted from a variety of animal fluids containing cell-free sequences, including but not limited to blood, serum, plasma, bone marrow, vitreous humor, sputum, feces, urine, tears, sweat, saliva, semen, mucosal excretions, mucus, cerebrospinal fluid, pleural fluid, amniotic fluid, and lymph. Samples can be taken from embryos, fetuses, or pregnant women. In some instances, samples can be isolated from maternal plasma. In some instances, samples can contain cell-free nucleic acids derived from the fetus (e.g., cfDNA) (through body samples obtained from the pregnant subject) or from the subject's own tissues.

[0060] Components of a sample (including nucleic acids) can be tagged, for example, with identifiable labels to allow for identification in sample detection or multiplexing. Some non-limiting examples of identifiable labels include fluorophores, magnetic nanoparticles, and nucleic acid barcodes. Fluorophores may include fluorescent proteins such as GFP, YFP, RFP, eGFP, mCherry, tdtomato, FITC, Alexa Fluor 350, Alexa Fluor 305, Alexa Fluor 488, Alexa Fluor 532, Alexa Fluor 546, Alexa Fluor 555, Alexa Fluor 568, Alexa Fluor 594, Alexa Fluor 647, Alexa Fluor 680, Alexa Fluor 750, Pacific Blue, Coumarin, BODIPY FL, Pacific Green, Oregon Green, Cy3, Cy5, Pacific Orange, TRITC, Texas Red, phycoerythrin, allophcocyanin, or other fluorophores. The intensity of a fluorescence signal can be used to quantify the abundance of nucleic acid molecules in a sample or to determine the presence or absence of nucleic acid molecules in the sample. Prior to sequencing, one or more barcode tags can be attached (e.g., by coupling or linking) to cell-free nucleic acids (e.g., cfDNA) in the sample. The barcode can uniquely label the cfDNA molecules in the sample. Alternatively, the barcode can non-uniquely label the cfDNA molecules in the sample. The barcode can non-uniquely label the cfDNA molecules in the sample such that additional information obtained from the cfDNA molecule, combined with the non-unique tag (e.g., at least a portion of the endogenous sequence of the cfDNA molecule), can be used as a unique identifier for the cfDNA molecule in the sample (e.g., for unique identification relative to other molecules). For example, a uniquely identified cfDNA sequence readout (e.g., from a given template molecule) can be detected based on sequence information containing one or more consecutive base regions at one or both ends of the sequence readout, the length of the sequence readout, and the sequence of the barcode attached to one or both ends of the sequence readout. By distributing a DNA (e.g., cfDNA) sample into many (e.g., at least about 50, at least about 100, at least about 500, at least about 1,000, at least about 5,000, at least about 10,000, at least about 50,000, or at least about 100,000) different discrete subunits (e.g., partitions, wells, or droplets) before amplification, it is possible to uniquely distinguish the amplified DNA molecules and identify them as originating from their respective individual DNA input molecules, enabling unique identification of DNA molecules without labeling.

[0061] Multiplexing of any number of samples is possible. For example, multiplexing analysis can include at least about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 11, about 12, about 13, about 14, about 15, about 16, about 17, about 18, about 19, about 20, about 25, about 30, about 35, about 40, about 45, about 50, about 55, about 60, about 65, about 70, about 75, about 80, about 85, about 90, about 95, about 100 or more samples. Identifiable tags can provide a method for tracing the origin of each sample, or can guide different samples to different regions or solid supports.

[0062] Any number of samples can be mixed before analysis without labeling or multiplexing. For example, multiplexed analysis can contain at least about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 11, about 12, about 13, about 14, about 15, about 16, about 17, about 18, about 19, about 20, about 25, about 30, about 35, about 40, about 45, about 50, about 55, about 60, about 65, about 70, about 75, about 80, about 85, about 90, about 95, about 100 or more samples. Pooling design can be used to multiplex samples without labeling, where samples are mixed into cells in a manner that allows the signal of an individual sample to be distinguished from the analytical cell using computational multiplexing.

[0063] Samples can be enriched prior to sequencing. For example, cfDNA molecules can be selectively or non-selectively enriched against one or more regions of the target genome or transcriptome. For instance, cfDNA molecules can be selectively enriched against one or more regions of the target genome or transcriptome via targeted sequence capture (e.g., using a panel), selective amplification, and / or targeted amplification (e.g., targeted polymerase chain reaction, PCR). As another example, cfDNA molecules can be non-selectively enriched against one or more regions of the target genome or transcriptome via universal amplification (e.g., universal PCR). In some aspects, amplification includes universal amplification, whole-genome amplification, or non-selective amplification. cfDNA molecules can be size-selected to select fragments of predetermined lengths. For example, DNA fragments can be size-selected prior to adaptor ligation to select lengths from approximately 40 base pairs (bp) to approximately 250 bp. Specific ranges include 40 to 250, 40 to 200, 40 to 150, 40 to 100, 50 to 250, 50 to 200, 50 to 150, 50 to 100, 100 to 250, 100 to 200, 100 to 150, 150 to 250, 150 to 200, or 175 to 200 bp. As another example, DNA fragments can be size-selected after adaptor ligation to choose lengths from approximately 160 bp to approximately 400 bp. Specific ranges include 160 to 400, 160 to 300, 160 to 200, 175 to 400, 175 to 300, 175 to 200, 200 to 400, 200 to 300, or 300 to 400 bp.

[0064] As used herein, the terms “nucleic acid” or “polynucleotide” generally refer to a molecule containing one or more nucleic acid subunits or nucleotides. Nucleic acids may contain one or more nucleotides selected from adenosine (A), cytosine (C), guanine (G), thymine (T), and uracil (U), or variants thereof. Nucleotides typically contain a nucleoside and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more phosphate (PO3) groups. Nucleotides may contain single or combined nucleobases, a pentose sugar (ribose or deoxyribose), and one or more phosphate groups.

[0065] As used in this article, the terms “nucleic acid molecule,” “nucleic acid sequence,” “nucleic acid fragment,” “oligonucleotide,” and “polynucleotide” generally refer to polynucleotides, such as deoxyribonucleotides (DNA) or ribonucleotides (RNA), or analogs thereof and / or combinations thereof (e.g., a mixture of DNA and RNA). Nucleic acid molecules can have a variety of lengths. Nucleic acid molecules may have a length of at least about 5 bases, 10 bases, 20 bases, 30 bases, 40 bases, 50 bases, 60 bases, 70 bases, 80 bases, 90, 100 bases, 110 bases, 120 bases, 130 bases, 140 bases, 150 bases, 160 bases, 170 bases, 180 bases, 190 bases, 200 bases, 300 bases, 400 bases, 500 bases, 1 kilobase (kb), 2kb, 3, 4kb, 5kb, 10kb, or 50kb, or may have any number of bases between any two of the above values. Oligonucleotides can contain a specific sequence of four nucleotide bases: adenine (A); cytosine (C); guanine (G); and thymine (T) (uracil (U) replaces thymine (T) when the polynucleotide is RNA). Therefore, the terms "nucleic acid molecule," "nucleic acid sequence," "nucleic acid fragment," "oligonucleotide," and "polynucleotide" are at least partially intended to be a letter representation of a polynucleotide molecule. Alternatively, the term can be applied to the polynucleotide molecule itself. This letter representation can be entered into a database in a computer with a central processing unit and / or used for bioinformatics applications such as functional genomics and homology searches. Oligonucleotides can contain one or more non-standard nucleotides, nucleotide analogs, and / or modified nucleotides.

[0066] As used herein, the term "probe" generally refers to a nucleotide sequence from a sample to which nucleic acids can hybridize. Probes specifically bind to target nucleotide sequences that are complementary, substantially complementary, or partially complementary. In some aspects, probes are labeled. In some aspects, the labels on probes are designed as fluorescent markers for detection. In some aspects, the labeling on probes includes the biotinylation of one or more nucleotides.

[0067] The term "methylation state" as used in this article generally refers to the methylated, unmethylated, hypermethylated, and hypomethylated states of cytosine residues in CpG dinucleotides.

[0068] As used herein, the term "hypermethylation" refers to the methylation of at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90%, or 100% of the cytosine residues in a CpG dinucleotide of a nucleic acid molecule. The term "hypomethylation" as used herein refers to the demethylation of at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90%, or 100% of the cytosine residues in a CpG dinucleotide of a nucleic acid molecule.

[0069] The term "target region" as used in this article generally refers to a nucleotide sequence in the genome that has a different methylation state in the target nucleic acid molecule. For example, cell-free DNA derived from tumor cells has a different methylation state than background nucleic acid molecules. The hypermethylation or hypomethylation of nucleic acid molecules in these regions can respectively indicate the presence or absence of disease or symptom in the target.

[0070] The term "target nucleic acid molecule" as used in this article generally refers to a disease-related nucleic acid molecule. For example, in cancer detection using cell-free DNA, the target nucleic acid molecule may refer to DNA released from tumor cells.

[0071] As used herein, the term "cell-free DNA" or "cfDNA" generally refers to DNA that circulates freely in bodily fluids (such as blood or plasma derived therefrom). In some aspects of the methods used herein, cfDNA encompasses specific types of cfDNA, such as circulating tumor DNA (ctDNA), which is fragmented DNA derived from a tumor and not associated with cells in the bloodstream. cfDNA can be double-stranded, single-stranded, or have characteristics of both.

[0072] The terms “clinical intervention” and “therapeutic intervention” are used interchangeably and can refer to compositions and / or methods used to treat an individual.

[0073] II. Examples of methods and compositions thereof disclosed herein

[0074] This disclosure provides methods and systems for hypermethylation and / or hypomethylation analysis by enriching either or both of target hypermethylated and hypomethylated nucleic acid molecules from a mixture of hypomethylated and / or hypermethylated background nucleic acid molecules. Some aspects of this disclosure include methods for generating sequencing libraries of hypermethylated and / or hypomethylated DNA fragments using a series of operations. In some specific aspects, the method includes ligating a first adaptor to the end of a DNA fragment, digesting the adaptor-ligated DNA fragment with a methylation-sensitive or methylation-dependent restriction enzyme, ligating a second adaptor to the digested DNA fragment, amplifying the DNA fragment with the adaptor at the end, distributing the amplified DNA fragment, enriching the hypermethylated or hypomethylated DNA fragment from different partitions, and processing the enriched DNA fragment for hypermethylation or hypomethylation analysis. In some specific aspects, the source of the nucleic acid molecules from which the library is generated includes any type of DNA, particularly cell-free DNA (cfDNA). In some specific aspects, the library is generated after DNA fragmentation using different methods (e.g., acoustic treatment with a shearing device and / or digestion with restriction enzymes). In some cases, the starting nucleic acid material itself may contain fragments (e.g., fragments of natural origin (from apoptosis or necrosis, including DNA from cancer cells)).

[0075] Figure 1A flowchart illustrating an example of applying the method for hypermethylation and / or hypomethylation analysis with background elimination is shown. In step 105, a nucleic acid molecule, such as cfDNA, can be obtained from the object to be tested. In step 110, a first set of adaptors is ligated to the ends of the nucleic acid molecule. In step 115, the adaptor-ligated nucleic acid molecule is digested with a methylation-sensitive or methylation-dependent restriction enzyme. For methylation-sensitive digestion, an adaptor-ligated nucleic acid molecule with a non-methylated cleavage site can be digested, such that the digested nucleic acid molecule has one or no adaptors from the first set at its end. For methylation-dependent digestion, an adaptor-ligated nucleic acid molecule with a methylated cleavage site can be digested, such that the digested nucleic acid molecule has one or no adaptors from the first set at its end. Digestion can produce a digestive enzyme-specific overhang at the end of the digested nucleic acid molecule, such as a 5'-CG overhang in a nucleic acid molecule digested by HpaII enzyme. A second set of adaptors with overhangs complementary to the enzyme-specific 5'-overhang or 3'-overhang can be ligated to the digested nucleic acid molecule. The second set of adaptors can be distinguished from the first set using a sequencer. Digestion and ligation of the second set of adaptors can be performed in the same reaction or in different reactions. For methylation-sensitive digestion, only nucleic acid molecules with methylation restriction enzyme cleavage sites or without any restriction enzyme cleavage sites can have the first set of adaptors at both ends, and only nucleic acid molecules with two non-methylation restriction enzyme cleavage sites can have the second set of adaptors at both ends. For methylation-dependent digestion, only nucleic acid molecules with non-methylation restriction enzyme cleavage sites or without any restriction enzyme cleavage sites can have the first set of adaptors at both ends, and only nucleic acid molecules with two methylation restriction enzyme cleavage sites can have the second set of adaptors at both ends.

[0076] In operation 120 (optional), nucleic acid molecules may optionally be subjected to conditions that allow methylated nucleic acid bases to be distinguished from unmethylated nucleic acid bases, such as bisulfite conversion and enzymatic conversion.

[0077] In step 125, after adding the first and second sets of adaptors, the adaptor-ligated nucleic acid molecules are then subjected to PCR amplification with a finite number of PCR cycles. Primers for the PCR reaction are designed to recognize both the first and second sets of adaptors. The finite number of PCR cycles is used to produce multiple copies of the nucleic acid molecules, such that each partition in the allocation step has at least one or more copies of the nucleic acid molecules. Next, in step 130, the amplified nucleic acid molecules are allocated (e.g., in this exemplary flowchart, the amplified nucleic acid molecules are allocated to four partitions) for further analysis.

[0078] In step 135, nucleic acid molecules with a first set of adaptors at both ends are enriched. Enrichment can be performed by PCR amplification using primers capable of initiating polymerization at the first set of adaptors. Hybridization-based targeted capture can be performed using probes after PCR amplification to cover one or more restriction enzyme cleavage sites in the target region. In step 140, the enriched nucleic acid molecules are sequenced using one of the sequencers (e.g., Illumina HiSeq 2000). Bioinformatics analysis is performed to determine the count and methylation status of the nucleic acid molecules. Because methylation-sensitive restriction enzymes are used in the digestion step, nucleic acid molecules with one or more unmethylated restriction enzyme cleavage sites are digested, and the digested nucleic acid molecules with a first set of adaptors at one end or none at either end are not enriched from partition 1. The count of nucleic acid molecules represents highly methylated nucleic acid molecules. Because methylation-dependent restriction enzymes are used in the digestion process, nucleic acid molecules with one or more methylation restriction enzyme cleavage sites are digested, and the digested nucleic acid molecules either have a first set of adaptors at one end or none at either end, therefore they cannot be enriched from partition 1. The count of nucleic acid molecules represents hypomethylated nucleic acid molecules.

[0079] In step 145, nucleic acid molecules with a second set of adaptors at both ends are enriched. Enrichment can be performed by PCR amplification using primers capable of initiating polymerization at the second set of adaptors. Following PCR amplification, probe-based hybridization-based targeted capture can be performed to cover one or more restriction enzyme cleavage sites in the target region, or probe-based hybridization-based targeted capture can be omitted. In step 150, the enriched nucleic acid molecules are sequenced using one of the sequencers (e.g., Illumina HiSeq2000). Bioinformatics analysis is performed to determine the count and methylation status of the nucleic acid molecules. Due to the use of methylation-sensitive restriction enzymes in the digestion step, only nucleic acid molecules with two or more non-methylated restriction enzyme cleavage sites can have a second set of adaptors at both ends of the digested fragment, and therefore can be enriched from partition 2. The count of nucleic acid molecules represents hypomethylated nucleic acid molecules. Due to the use of methylation-dependent restriction enzymes in the digestion operation, only nucleic acid molecules with two or more methylated restriction enzyme cleavage sites can have a second set of adaptors at both ends of the digested fragment, and therefore can be enriched from partition 2. The count of nucleic acid molecules represents highly methylated nucleic acid molecules.

[0080] In operation 155, nucleic acid molecules having a first set of adaptors at the 5' end and a second set of adaptors at the 3' end of the nucleic acid molecule linked by the amplified adaptor are enriched from partition 3. Enrichment can be performed by PCR amplification using primers that initiate polymerization at the second set of adaptors at the 3' end of the amplified adaptor-linked DNA fragment and at the first set of adaptors at the 5' end of the amplified adaptor-linked DNA fragment. The primers are complementary to at least a portion of the second set of adaptors at the 3' end of the amplified adaptor-linked DNA fragment and to at least a portion of the complementary sequence of the first set of adaptors at the 5' end of the amplified adaptor-linked DNA fragment. Following PCR amplification, probe-based hybridization-based targeted capture can be performed to cover one or more restriction enzyme cleavage sites in the target region, or probe-based hybridization-based targeted capture can be performed without such a process. In Operation 160, the enriched nucleic acid molecules are sequenced using one of the sequencers (e.g., Illumina HiSeq 2000). Bioinformatics analysis is performed to determine the count and methylation status of the nucleic acid molecules.

[0081] In operation 165, nucleic acid molecules having a first set of adaptors at the 3' end and a second set of adaptors at the 5' end of the nucleic acid molecules linked by the amplified adaptor are enriched from partition 4. Enrichment can be performed by PCR amplification using primers that initiate polymerization at the first set of adaptors at the 3' end of the amplified adaptor-linked DNA fragment and at the second set of adaptors at the 5' end of the amplified adaptor-linked DNA fragment. The primers are complementary to at least a portion of the first set of adaptors at the 3' end of the amplified adaptor-linked DNA fragment and to at least a portion of the complementary sequence of the second set of adaptors at the 5' end of the amplified adaptor-linked DNA fragment. Following PCR amplification, probe-based hybridization-based targeted capture can be performed to cover one or more restriction enzyme cleavage sites in the target region, or probe-based hybridization-based targeted capture can be performed without using probes. In operation 170, the enriched nucleic acid molecules are sequenced using one of the sequencers (e.g., Illumina HiSeq 2000). Bioinformatics analysis is performed to determine the count and methylation status of the nucleic acid molecules.

[0082] Figure 2This illustrates an example of enriching highly methylated and low methylated cell-free DNA molecules for methylation analysis for applications such as cancer diagnosis. Operation 205 provides a mixture of DNA molecule types in cfDNA. In this example, in operation 210, adaptor 1 is ligated to the cfDNA molecule. Prior to adaptor ligation, DNA end repair (3'-end blunting and / or 3'-end A-tailing) and 5'-end phosphorylation reactions can be performed. These adaptor-ligated cfDNA molecules are then digested by one or more restriction enzymes. In this example, the methylation-sensitive restriction enzyme HpaII is used in operation 215, and adaptor 2 is added in the same reaction as HpaII digestion. The use of HpaII cleaves the adaptor-ligated cfDNA molecule containing the CCGG recognition site, where the second cytosine residue in the recognition site is unmethylated (in...). Figure 2 In the diagram, blue represents unmethylated C and red represents methylated C. The designed adaptor 2 contains a 3'-CG overhang, which can be attached to the HpaII-cleaved ends of nucleic acid molecules. Figure 2 In this example, the adaptor can form an adaptor dimer with a 5'-ATCGAT-3' sequence at the junction of the adaptor dimer. However, the junction between the adaptor and the end of the HpaII-cleaved cell-free DNA molecule contains a different sequence: 5'-ATCGG-3'. To avoid the formation of adaptor dimer products by adaptor-adaptor ligations, the restriction enzyme BspDI is added to the reaction. BspDI recognizes and cleaves the adaptor-adaptor ligations, while HpaII in the mixture digests the ligated cell-free DNA molecules. The second set of adaptors can be distinguished from the first set of adaptors by a sequencer. In this example, adaptor 1 is compatible with the Illumina Nextera adaptor, and adaptor 2 is compatible with the Illumina-TruSeq adaptor.

[0083] In step 220, the adaptor-ligated cfDNA molecule is subjected to several cycles (e.g., 1, 2, 3, 4, or 5 cycles) of PCR amplification. Multiple copies of the adaptor-ligated cfDNA molecule are produced. Prior to PCR amplification, the adaptor-ligated cfDNA molecule is not subjected to conditions that allow for the differentiation of methylated and unmethylated nucleic acid bases, such as bisulfite conversion and enzymatic conversion. The amplified cfDNA molecule is then partitioned into two partitions. In step 225, the first partition is amplified using adaptor-1 specific primers capable of initiating polymerization at the first set of adaptors. Next, in step 230, the amplified cfDNA molecule is hybridized to a probe that is complementary or substantially complementary to at least a portion of the cfDNA molecule in a target region with a high level of methylation, for example, where at least about 90% of the cytosine residues in the CpG dinucleotide of the nucleic acid molecule are methylated. One or more nucleotides of the probe may be biotinylated. In step 235, the captured DNA fragments can be post-amplified, for example using polymerase chain reaction (PCR), followed by nucleic acid sequencing. The sequence readout from the first partition represents a hypermethylated cfDNA fragment with the hypomethylated cfDNA fragment eliminated. In step 240, the second partition is amplified using adaptor-2 specific primers capable of initiating polymerization at the second set of adaptors, followed by nucleic acid sequencing. The cfDNA fragments from the second partition have unmethylated HpaII cleavage sites at both ends. Given the general nature of methylation, those cfDNA fragments may also be hypomethylated at other CpG sites. Therefore, the sequence readout from the second partition represents a hypomethylated cfDNA fragment with the hypermethylated cfDNA fragment eliminated.

[0084] The methods disclosed herein can be implemented in diagnostic tests encompassing reagents and machine learning classifiers. Reagents may include capture substrates, methylation-sensitive and / or methylation-dependent restriction enzymes, adaptors, and polymerases. Sequencing libraries can be prepared and analyzed using any of the methods disclosed, following the instructions in the test. Counts of hypermethylated and hypomethylated nucleic acid molecules can be used as feature inputs to a classifier, thereby generating the probability that an individual has or is suspected of having a disease or condition.

[0085] III. Nucleic acid molecules used for high / low methylation analysis

[0086] High / low methylation analysis can be performed on nucleic acid molecules (such as DNA or RNA). In some specific contexts, the nucleic acid molecule prepared for high / low methylation analysis is DNA, and in some cases, the DNA is cell-free DNA (cfDNA). cfDNA can be obtained from individuals (including mammals). cfDNA can be derived from individuals who require cfDNA analysis, for example, to provide a determination about their health, such as detecting disease conditions or risks or susceptibility to them. cfDNA can be derived from one or more samples from an individual. In some cases, samples can be derived from plasma, blood, serum, bone marrow, cerebrospinal fluid, pleural fluid, saliva, feces, or urine. The cfDNA prepared for high / low methylation analysis can be double-stranded, single-stranded, or a mixture thereof.

[0087] In some aspects, nucleic acid molecules intended for high / low methylation analysis may be modified prior to the methods used in this disclosure. For example, nucleic acid molecules may be enriched for specific types of nucleic acid molecules, specific sizes of nucleic acid molecules, or combinations thereof. In certain cases, the nucleic acid molecules are enriched cfDNA, for example, for molecules of a specific size.

[0088] IV. Applications of High / Low Methylation Analysis

[0089] Cancer cells can exhibit abnormal DNA methylation patterns. Highly methylated and / or lowly methylated tumor DNA fragments can be released into the bloodstream through processes such as apoptosis or necrosis, where they can become part of circulating cell-free cellular DNA (cfDNA) in bodily fluids (e.g., plasma or urine). Such cfDNA can be methylated for clinical diagnostic applications, such as cancer screening. The minimally invasive or non-invasive nature of cfDNA methylation profiling makes it an effective strategy for general cancer screening or for cancer diagnosis, prognosis, treatment selection, or treatment monitoring. For example, whole-genome bisulfite sequencing can provide a comprehensive view of the DNA methylome, but deep sequencing of the entire genome can be expensive.

[0090] Certain aspects of this disclosure relate to methods, systems, and compositions for analyzing the counting of highly / low methylated nucleic acid molecules, with the aim of measuring or detecting or determining the presence or absence of a disease or condition. In some specific aspects, the molecule contains cfDNA, and in some aspects, the cfDNA is derived from an individual (e.g., from a blood or plasma or urine sample (or a combination thereof) from the individual).

[0091] For some aspects of this disclosure related to diseases (such as cancer), analyzing cfDNA in suitable samples can be an effective method for obtaining information. For example, following hyper / hypomethylation analysis, the count of hyper / hypomethylated nucleic acid molecules in the target region can be used to determine whether an individual has a specific disease or medical condition, or is at elevated risk or susceptibility to it. In one instance, an individual has or is suspected of having cancer or is at elevated risk of having cancer, and hyper / hypomethylation analysis of the prepared cfDNA molecules helps determine whether the individual has or is suspected of having cancer or is at elevated risk of having cancer.

[0092] In some respects, high / low methylation analysis methods are involved in non-invasive cancer screening, including the identification of tumor-originating tissues. Unlike traditional tissue biopsies, liquid biopsies (also known as fluid biopsies or fluid-phase biopsies) (e.g., blood draws) are used to identify a variety of different malignancies and can be used in the methods covered in this disclosure.

[0093] In some aspects, multiple cfDNA molecules are obtained from a bodily sample of the subject. In some aspects, the bodily sample is selected from plasma, serum, bone marrow, cerebrospinal fluid, pleural fluid, saliva, feces, sputum, nipple aspiration, biopsy, buccal scraping, urine, and combinations thereof. In some aspects, the method further includes identifying molecules with high / low methylation in a target region to obtain their count (e.g., counting only those with certain methylation patterns). In some aspects, the method further includes processing the count of high / low methylated cfDNA molecules in the target region to generate a probability that the subject has or is suspected of having a disease or condition.

[0094] In some cases, the disease or condition for which information is sought is selected from cancer, multiple sclerosis, traumatic or ischemic brain injury, diabetes, pancreatitis, Alzheimer's disease, and fetal abnormalities. In other cases, the disease or condition is selected from the following cancers: pancreatic cancer, liver cancer, lung cancer, colorectal cancer, leukemia, bladder cancer, bone cancer, brain cancer, breast cancer, cervical cancer, endometrial cancer, esophageal cancer, stomach cancer, head and neck cancer, melanoma, ovarian cancer, testicular cancer, kidney cancer, sarcoma, bile duct cancer, thyroid cancer, gallbladder cancer, spleen cancer, and prostate cancer.

[0095] In some respects, high / low methylation analysis of cfDNA molecules obtained from bodily samples can be used to monitor abnormal tissue-specific cell death or organ transplantation.

[0096] In some respects, cfDNA hypermethylation / hypomethylation analysis can be used to diagnose patients with cancer symptoms, asymptomatic cancer, a family history or medical history of cancer, at elevated cancer risk, or who have already been diagnosed with cancer. The subjects can be mammalian subjects, such as humans. Cancer can be malignant, benign, metastatic, or precancerous. In other respects, cancer includes melanoma, non-small cell lung cancer, small cell lung cancer, lung cancer, liver cancer, retinoblastoma, astrocytoma, glioblastoma, gingival cancer, tongue cancer, leukemia, neuroblastoma, head cancer, neck cancer, breast cancer, pancreatic cancer, prostate cancer, kidney cancer, bone cancer, testicular cancer, ovarian cancer, liver cancer, mesothelioma, cervical cancer, gastrointestinal cancer, lymphoma, brain cancer, colon cancer, sarcoma, gallbladder cancer, thyroid cancer, spleen cancer, or bladder cancer. Cancer can include tumors composed of tumor cells.

[0097] In some aspects, there are methods and systems based on the high / low methylation analysis described herein for treating cancer in subjects (e.g., cancer patients) after determining their need for treatment. Such treatments may include administering an effective amount of chemotherapy, radiation therapy, hormone therapy, targeted therapy, or immunotherapy (or combinations thereof) to a patient after determining that the patient has cancer based on the methods disclosed herein. The origin of the cancer may be determined, in which case the treatment may be tailored to the cancer at that origin. In some aspects, tumor resection may be performed as a treatment, or may be part of a treatment with one of other therapies. Examples of chemotherapeutic agents include, but are not limited to: alkylating agents, such as bifunctional alkylating agents (e.g., cyclophosphamide, nitrogen mustard, chlorambucil, melphalan) or monofunctional alkylating agents (e.g., dacarbazine (DTIC), nitrosourea, temozolomide (oral dacarbazine)); anthracyclines (e.g., daunorubicin, doxorubicin, epirubicin, idarubicin, mitoxantrone, and pentorubicin); taxanes that disrupt the cytoskeleton (e.g., paclitaxel, docetaxel, abraxane, taxotere); epothilone; histone deacetylase inhibitors (e.g., vorinostat, romidepsin); topoisomerase I inhibitors (e.g., irinotecan, topotecan); topoisomerase II inhibitors (e.g., etoposide, teniposide, tazoline). Tafluposide; kinase inhibitors (e.g., bortezomib, erlotinib, gefitinib, imatinib, vemurafenib, and vismodegib); nucleotide analogs and nucleotide precursor analogs (e.g., azacitidine, azathioprine, capecitabine, cytarabine, doxifluridine, fluorouracil, gemcitabine, hydroxyurea, mercaptopurine, methotrexate, tioguanine). ine (formerly known as thioguanine); peptide antibiotics (e.g., bleomycin, actinomycin); platinum-based antitumor drugs (e.g., carboplatin, cisplatin, oxaliplatin); retinoids (e.g., retinoin, alitretinoin, bexarotene); and vinca alkaloids (e.g., vincristine, vinblastine, vinorelbine, and vinorelbine).Examples of immunotherapy include, but are not limited to, cell therapy, such as dendritic cell therapy (e.g., involving chimeric antigen receptors); antibody therapy (e.g., alemtuzumab, atezolizumab, ipilimumab, nivolumab, offatumumab, pembrolizumab, rituximab, or other antibodies with the same target as one of these antibodies, such as CTLA-4, PD-1, PD-L1, or other checkpoint inhibitors); and cytokine therapy (e.g., interferon or interleukin).

[0098] In some aspects, methods for diagnosing subjects using cfDNA hyper / hypomethylation analysis may also include performing a biopsy before or after the patient's cfDNA hyper / hypomethylation analysis, obtaining a computed tomography (CT) scan, obtaining a positron emission tomography (PET) scan, obtaining a magnetic resonance imaging (MRI) scan, obtaining a mammogram, obtaining an ultrasound scan, or otherwise evaluating tissue suspected of being cancerous. In some aspects, the detected cancer is classified according to cancer classification or staging (e.g., stage I, stage II, stage III, or stage IV).

[0099] In some respects, cfDNA hyper / hypomethylation analysis performed using the methods and systems disclosed herein is used to monitor treatment and / or tumor progression, including during and / or after treatment. For example, blood samples can be obtained from subjects at multiple time points to monitor tumor progression throughout one or more treatment regimens, and cfDNA derived from them can be measured.

[0100] In some respects, cfDNA hyper / hypomethylation analysis performed using the methods and systems of this disclosure can be used to assess disease stage or as a prognostic biomarker, for example, in cases where tumor samples that cannot be biopsied or archived cannot be used for genetic analysis.

[0101] In some respects, cfDNA hyper / hypomethylation analysis using the methods and systems presented in this article can be used for cancer screening and early detection. For example, blood can be drawn regularly from individuals without any cancer symptoms to detect cancer or determine cancer predisposition as early as possible.

[0102] In some respects, cfDNA hyper / hypomethylation analysis using the methods and systems provided herein can be used for prenatal testing of fetal DNA from maternal plasma or serum to identify fetal Down syndrome and other chromosomal abnormalities.

[0103] In some respects, cfDNA hyper / hypomethylation analysis obtained using the methods and systems provided in this paper can be used for organ transplantation monitoring.

[0104] In some respects, cfDNA hyper / hypomethylation analysis using the methods and systems provided in this article can be used to diagnose, detect or measure other types of diseases, such as multiple sclerosis, traumatic / ischemic brain injury, diabetes, pancreatitis or Alzheimer's disease, or infectious diseases (viruses, bacteria, fungi, etc.).

[0105] In some respects, cfDNA hyper / hypomethylation analysis performed using the methods and systems provided herein can be used to inform the composition of the microbiome (e.g., bacteria, fungi, viruses, and / or protozoa) in a subject, which can be used to inform the risk of infectious diseases or other health conditions.

[0106] It is anticipated that any aspect discussed in this specification can be implemented with respect to any method, system, kit, computer-readable medium, or device of this disclosure, and vice versa. Furthermore, the devices used in this disclosure can be used to implement the methods of this disclosure.

[0107] In some aspects, the method also includes generating a report, such as electronically outputting a report indicating a high / low methylation spectrum. In some embodiments, the method further includes processing the high / low methylation spectrum to generate a probability or risk that the subject has or is suspected of having at least one disease or condition. In some aspects, the disease or condition is selected from cancer, multiple sclerosis, traumatic or ischemic brain injury, diabetes, pancreatitis, Alzheimer's disease, and fetal abnormalities. In some aspects, the disease or condition is selected from the following cancers: pancreatic cancer, liver cancer, lung cancer, colorectal cancer, leukemia, bladder cancer, bone cancer, brain cancer, breast cancer, cervical cancer, endometrial cancer, esophageal cancer, gastric cancer, head and neck cancer, melanoma, ovarian cancer, testicular cancer, kidney cancer, sarcoma, bile duct cancer, thyroid cancer, spleen cancer, gallbladder cancer, and prostate cancer.

[0108] In some aspects, one or more computer processors are individually or jointly programmed to electronically output a report indicating a high / low methylation spectrum. In some aspects, one or more computer processors are individually or jointly programmed to process high / low methylation spectra to generate a probability or risk that the subject has or is suspected of having one or more diseases or conditions. In some aspects, the diseases or conditions are selected from cancer, multiple sclerosis, traumatic or ischemic brain injury, diabetes, pancreatitis, Alzheimer's disease, and fetal abnormalities. In some aspects, the diseases or conditions are selected from cancers including: pancreatic cancer, liver cancer, lung cancer, colorectal cancer, leukemia, bladder cancer, bone cancer, brain cancer, breast cancer, cervical cancer, endometrial cancer, esophageal cancer, gastric cancer, head and neck cancer, melanoma, ovarian cancer, testicular cancer, kidney cancer, sarcoma, bile duct cancer, thyroid cancer, spleen cancer, gallbladder cancer, and prostate cancer.

[0109] In another aspect, this disclosure provides a non-transitory computer-readable medium containing machine-executable code that, when executed by one or more computer processors, implements any of the methods disclosed herein. For example, this disclosure provides a non-transitory computer-readable medium containing machine-executable code that, when executed by one or more computer processors, implements the methods provided in this disclosure for processing or analyzing multiple cfDNA molecules undergoing high / low methylation analysis.

[0110] V. Trained Algorithm

[0111] After confirming the counts of highly / low methylated nucleic acid molecules in the target region from nucleotide sequence information from bodily samples (e.g., cell-free biological samples) from one or more subjects, a trained algorithm can be used to process the test dataset (e.g., the counts or normalized counts of highly / low methylated nucleic acid molecules in the target region obtained from or derived from the subject) to assess the disease or symptom status of the test subject (e.g., to detect the presence or absence of a disease or symptom). The trained algorithm can be configured to identify at least about 25, at least about 50, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or greater than 99% accuracy.

[0112] The trained algorithm can include supervised machine learning algorithms. A trained algorithm can include classification and regression tree (CART) algorithms. Supervised machine learning algorithms can include, for example, random forests, support vector machines (SVMs), neural networks, or deep learning algorithms. A trained algorithm can also include unsupervised machine learning algorithms.

[0113] The trained algorithm can be configured to accept multiple input variables and produce one or more output values ​​based on those input variables. The multiple input variables may include one or more datasets indicating control status or disease or symptom status. For example, input variables may include the count or normalized count of highly / low methylated nucleic acid molecules in target regions corresponding to a disease or symptom status (e.g., diseased samples vs. non-disease samples have different abundances). Multiple input variables may also include the subject's clinical health data.

[0114] The trained algorithm may include a classifier such that each of one or more output values ​​includes one of a fixed number of possible values ​​(e.g., a linear classifier, logistic regression classifier, etc.), indicating the classifier's classification of the cell-free biological sample. The trained algorithm may include a binary classifier such that each of one or more output values ​​includes one of two values ​​(e.g., {0, 1}, {positive, negative}, or {high risk, low risk}), indicating the classifier's classification of the cell-free biological sample. The trained algorithm may be another type of classifier such that each of one or more output values ​​includes one of more than two values ​​(e.g., {0, 1, 2}, {positive, negative, or uncertain}, or {high risk, medium risk, or low risk}), indicating the classifier's classification of the cell-free biological sample.

[0115] Output values ​​may include descriptive labels, numerical values, or combinations thereof. Some output values ​​may include descriptive labels. Such descriptive labels can provide identification or indication of a subject's disease or condition status and may include, for example, positive, negative, high-risk, intermediate-risk, low-risk, or indeterminate. Such descriptive labels can provide identification of treatment for a subject's disease or condition status and may include, for example, a suitable treatment intervention, the duration of the intervention, and / or the dosage. Such descriptive labels can provide identification of secondary clinical tests that may be suitable for the subject and may include, for example, imaging tests, blood tests, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound scans, chest X-rays, positron emission tomography (PET) scans, PET-CT scans, biopsy tests, cytology, or any combination thereof. For example, such descriptive labels can provide a prognosis for a subject's disease or condition status. As another example, such descriptive labels can provide a relative assessment of a subject's disease or condition status. Some descriptive labels may be mapped to numerical values, for example, by mapping "positive" to 1 and "negative" to 0.

[0116] Some output values ​​may include numeric values, such as binary, integer, or continuous values. Such binary output values ​​may include, for example, {0, 1}, {positive, negative}, or {high risk, low risk}. Such integer output values ​​may include, for example, {0, 1, 2}. Such continuous output values ​​may include, for example, probability values ​​that are at least 0 and no greater than 1. Such continuous output values ​​may include, for example, non-normalized probability values ​​that are at least 0. Such continuous output values ​​may indicate the prognosis of an object's disease or condition. Some numeric values ​​may be mapped to descriptive labels, for example, by mapping 1 to "positive" and 0 to "negative".

[0117] Output values ​​can be assigned based on one or more cutoff values. For example, if the probability that a sample indicates an object has a disease or condition (e.g., cancer) is at least 50%, the binary classification of the sample can be assigned an output value of "positive" or 1. Conversely, if the probability that a sample indicates an object has a disease or condition (e.g., cancer) is less than 50%, the binary classification of the sample can be assigned an output value of "negative" or 0. In this case, a single cutoff value of 50% is used to classify the sample into one of two possible binary output values. Some examples of individual cutoff values ​​may include approximately 1%, approximately 2%, approximately 5%, approximately 10%, approximately 15%, approximately 20%, approximately 25%, approximately 30%, approximately 35%, approximately 40%, approximately 45%, approximately 50%, approximately 55%, approximately 60%, approximately 65%, approximately 70%, approximately 75%, approximately 80%, approximately 85%, approximately 90%, approximately 91%, approximately 92%, approximately 93%, approximately 94%, approximately 95%, approximately 96%, approximately 97%, approximately 98%, and approximately 99%.

[0118] As another example, if the probability that a sample indicates that an object has a disease or condition (e.g., cancer) is at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or greater, the sample classification can be assigned an output value of "positive" or 1. If the probability that a sample indicates that the subject has a disease or condition (e.g., cancer) is greater than about 50%, greater than about 55%, greater than about 60%, greater than about 65%, greater than about 70%, greater than about 75%, greater than about 80%, greater than about 85%, greater than about 90%, greater than about 91%, greater than about 92%, greater than about 93%, greater than about 94%, greater than about 95%, greater than about 96%, greater than about 97%, greater than about 98%, or greater than about 99%, then the sample can be classified as "positive" or 1.

[0119] If the probability that a sample indicates that the subject has a disease or condition (e.g., cancer) is less than about 50%, less than about 45%, less than about 40%, less than about 35%, less than about 30%, less than about 25%, less than about 20%, less than about 15%, less than about 10%, less than about 9%, less than about 8%, less than about 7%, less than about 6%, less than about 5%, less than about 4%, less than about 3%, less than about 2%, or less than about 1%, the sample classification can be assigned an output value of "negative" or 0. If the probability that a sample indicates that the subject has a disease or condition (e.g., cancer) is no greater than about 50%, no greater than about 45%, no greater than about 40%, no greater than about 35%, no greater than about 30%, no greater than about 25%, no greater than about 20%, no greater than about 15%, no greater than about 10%, no greater than about 9%, no greater than about 8%, no greater than about 7%, no greater than about 6%, no greater than about 5%, no greater than about 4%, no greater than about 3%, no greater than about 2%, or no greater than about 1%, then the sample classification can be assigned an output value of "negative" or 0.

[0120] If a sample is not classified as “positive,” “negative,” 1, or 0, the sample classification can be assigned an output value of “uncertain” or 2. In this case, a set of two cutoff values ​​is used to classify the sample into one of three possible output values. Some examples of sets of cutoff values ​​can include {1%, 99%}, {2%, 98%}, {5%, 95%}, {10%, 90%}, {15%, 85%}, {20%, 80%}, {25%, 75%}, {30%, 70%}, {35%, 65%}, {40%, 60%}, and {45%, 55%}. Similarly, a set of n cutoff values ​​can be used to classify the sample into one of n+1 possible output values, where n is any positive integer.

[0121] The trained algorithm can be trained using multiple independent training samples. Each independent training sample may include a cell-free biological sample from an object, a relevant dataset obtained by measuring the cell-free biological sample (as described elsewhere herein), and one or more known output values ​​corresponding to the cell-free biological sample (e.g., clinical diagnosis, prognosis, absence, or therapeutic efficacy of the object's disease or symptom state). Independent training samples may include cell-free biological samples obtained from or derived from multiple different objects, along with relevant datasets and outputs. Independent training samples may include cell-free biological samples obtained from the same object at multiple different time points (e.g., regularly, such as weekly, bi-weekly, or monthly), along with relevant datasets and outputs. Independent training samples may be associated with the presence of a disease or symptom state (e.g., training samples may include cell-free biological samples obtained from or derived from multiple objects known to have a disease or symptom state, along with relevant datasets and outputs). Independent training samples may be associated with the absence of a disease or symptom state (e.g., training samples include cell-free biological samples obtained from or derived from multiple subjects who are known to be free of a disease or symptom state and have previously been diagnosed or have received negative test results for a disease or symptom state, along with associated datasets and outputs).

[0122] The trained algorithm can be trained with at least about 5, at least about 10, at least about 15, at least about 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 100, at least about 150, at least about 200, at least about 250, at least about 300, at least about 350, at least about 400, at least about 450, or at least about 500 independent training samples. Independent training samples may include cell-free biological samples associated with the presence of a disease or condition and / or cell-free biological samples associated with the absence of a disease or condition. The trained algorithm can be trained with no more than about 500, no more than about 450, no more than about 400, no more than about 350, no more than about 300, no more than about 250, no more than about 200, no more than about 150, no more than about 100, or no more than about 50 independent training samples associated with the presence of a disease or condition. In some respects, cell-free biological samples are independent of the samples used to train the trained algorithm.

[0123] The trained algorithm can be trained using a first number of independent training samples associated with the presence of a disease or symptom state and a second number of independent training samples associated with the absence of a disease or symptom state. The first number of independent training samples associated with the presence of a disease or symptom state may not exceed the second number of independent training samples associated with the absence of a disease or symptom state. The first number of independent training samples associated with the presence of a disease or symptom state may be equal to the second number of independent training samples associated with the absence of a disease or symptom state. The first number of independent training samples associated with the presence of a disease or symptom state may be greater than the second number of independent training samples associated with the absence of a disease or symptom state.

[0124] The trained algorithm can be configured to have at least approximately 50%, at least approximately 55%, at least approximately 60%, at least approximately 65%, at least approximately 70%, at least approximately 75%, at least approximately 80%, at least approximately 81%, at least approximately 82%, at least approximately 83%, at least approximately 84%, at least approximately 85%, at least approximately 86%, at least approximately 87%, at least approximately 88%, at least approximately 89%, at least approximately 90%, at least approximately 91%, at least approximately 92%, at least approximately 93%, at least approximately 94%, at least approximately 95%, at least approximately The algorithm identifies disease or condition states with an accuracy of 96%, at least about 97%, at least about 98%, at least about 99%, or higher, representing at least about 5, at least about 10, at least about 15, at least about 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 100, at least about 150, at least about 200, at least about 250, at least about 300, at least about 350, at least about 400, at least about 450, or at least about 500 independent training samples. The accuracy of identifying disease or condition states using the trained algorithm can be calculated as the percentage of independent test samples correctly identified or classified as having or not having a disease or condition state (e.g., subjects known to have a disease or condition state or subjects with negative clinical test results for a disease or condition state).

[0125] The trained algorithm can be configured to identify disease or condition status with a positive predictive value (PPV) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99% or higher. The PPV (Proportional Percentage Value) for identifying disease or symptom states using trained algorithms can be calculated as the percentage of cell-free biological samples identified or classified as having a disease or symptom state corresponding to objects that actually have the disease or symptom state.

[0126] The trained algorithm can be configured to identify disease or condition status with a negative predictive value (NPV) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99% or higher. The NPV (Net Value) for identifying disease or symptom states using trained algorithms can be calculated as the percentage of cell-free biological samples identified or classified as not having a disease or symptom state corresponding to objects that are truly not suffering from a disease or symptom state.

[0127] The trained algorithm can be configured to use at least approximately 5%, at least approximately 10%, at least approximately 15%, at least approximately 20%, at least approximately 25%, at least approximately 30%, at least approximately 35%, at least approximately 40%, at least approximately 50%, at least approximately 55%, at least approximately 60%, at least approximately 65%, at least approximately 70%, at least approximately 75%, at least approximately 80%, at least approximately 81%, at least approximately 82%, at least approximately 83%, at least approximately 84%, at least approximately 85%, at least approximately 86%, at least approximately 87%, at least approximately 88%, at least approximately 89%, and at least approximately 90%. Clinical sensitivity for identifying disease or condition states: % at least approximately 91%, at least approximately 92%, at least approximately 93%, at least approximately 94%, at least approximately 95%, at least approximately 96%, at least approximately 97%, at least approximately 98%, at least approximately 99%, at least approximately 99.1%, at least approximately 99.2%, at least approximately 99.3%, at least approximately 99.4%, at least approximately 99.5%, at least approximately 99.6%, at least approximately 99.7%, at least approximately 99.8%, at least approximately 99.9%, at least approximately 99.99%, at least approximately 99.99%, or higher. Clinical sensitivity for identifying disease or condition states using a trained algorithm can be calculated as the percentage of independent test samples (e.g., objects known to have a disease or condition state) that are correctly identified or classified as having a disease or condition state in relation to the presence of that disease or condition state.

[0128] The trained algorithm can be configured to use at least approximately 5%, at least approximately 10%, at least approximately 15%, at least approximately 20%, at least approximately 25%, at least approximately 30%, at least approximately 35%, at least approximately 40%, at least approximately 50%, at least approximately 55%, at least approximately 60%, at least approximately 65%, at least approximately 70%, at least approximately 75%, at least approximately 80%, at least approximately 81%, at least approximately 82%, at least approximately 83%, at least approximately 84%, at least approximately 85%, at least approximately 86%, at least approximately 87%, at least approximately 88%, at least approximately 89%, and at least approximately 90%. Clinical specificity for identifying a disease or condition state is defined as % or higher, at least approximately 91%, at least approximately 92%, at least approximately 93%, at least approximately 94%, at least approximately 95%, at least approximately 96%, at least approximately 97%, at least approximately 98%, at least approximately 99%, at least approximately 99.1%, at least approximately 99.2%, at least approximately 99.3%, at least approximately 99.4%, at least approximately 99.5%, at least approximately 99.6%, at least approximately 99.7%, at least approximately 99.8%, at least approximately 99.9%, at least approximately 99.99%, at least approximately 99.99%, or higher. Clinical specificity for identifying a disease or condition state using a trained algorithm can be calculated as the percentage of independent test samples (e.g., subjects with negative clinical test results for a disease or condition state) that are correctly identified or classified as not having a disease or condition state and are not associated with the absence of that disease or condition state.

[0129] The trained algorithm can be configured to identify disease or symptom states with an area under the curve (AUC) of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.81, at least about 0.82, at least about 0.83, at least about 0.84, at least about 0.85, at least about 0.86, at least about 0.87, at least about 0.88, at least about 0.89, at least about 0.90, at least about 0.91, at least about 0.92, at least about 0.93, at least about 0.94, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, at least about 0.99 or higher. AUC can be calculated as the integral (e.g., the area under the ROC curve) of the receiver operating characteristic (ROC) curve associated with the trained algorithm that classifies cell-free biological samples as having or not having a disease or symptom state.

[0130] The trained algorithm can be tuned or adjusted to improve one or more of the following: performance, accuracy, PPV, NPV, clinical sensitivity, clinical specificity, or AUC in identifying diseases or symptom states. The trained algorithm can be tuned or adjusted by modifying parameters of the trained algorithm (e.g., a set of cutoff values ​​for classifying cell-free biological samples, as described elsewhere herein, or the weights of the neural network). The trained algorithm can be tuned or adjusted continuously during or after training.

[0131] After initial training of the trained algorithm, a subset of the inputs can be identified as the most influential or important to be included for high-quality classification. For example, a subset of the counts or normalized counts of highly / low methylated nucleic acid molecules in a target region can be identified as the most influential or important to be included for high-quality classification or identification of a disease or condition (or subtype of a disease or condition). The set or subset of the counts or normalized counts of highly / low methylated nucleic acid molecules in the target region can be ranked based on a classification metric indicating the influence or importance of each count on the high-quality classification or identification of the disease or condition (or subtype of a disease or condition). In some cases, such a metric can be used to significantly reduce the number of input variables (e.g., predictor variables) that can be used to train the trained algorithm to the desired performance level (e.g., based on the desired minimum accuracy, PPV, NPV, clinical sensitivity, clinical specificity, AUC, or a combination thereof). For example, if training a trained algorithm with dozens or hundreds of input variables (e.g., counts or normalized counts of highly / low methylated nucleic acid molecules in the target region) results in a classification accuracy higher than 99%, then training the algorithm with a selected subset of the most influential or important input variables—no more than about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, or 100—can produce better results. The classification accuracy is reduced but still acceptable (e.g., at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%). A subset can be selected by sorting all multiple input variables (e.g., the count or normalized count of highly / low methylated nucleic acid molecules in the target region) and selecting a predetermined number of input variables with the best classification metric (e.g., no more than about 5, no more than about 10, no more than about 15, no more than about 20, no more than about 25, no more than about 30, no more than about 35, no more than about 40, no more than about 45, no more than about 50, or no more than about 100).

[0132] After processing a dataset using a trained algorithm, disease or symptom states (e.g., cancer) can be identified or monitored in objects. This identification can be based, at least in part, on the count or normalized count of highly / low methylated nucleic acid molecules in a target region that has differential power for a given disease or symptom.

[0133] Diseases or condition states can be identified in subjects with an accuracy of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or higher. The accuracy of identifying diseases or condition states using a trained algorithm can be calculated as the percentage of independent test samples (e.g., subjects known to have a disease or condition state or subjects with negative clinical test results for a disease or condition state) that are correctly identified or classified as having or not having a disease or condition state.

[0134] Diseases or condition states can be identified in subjects with a positive predictive value (PPV) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or higher. The PPV (Proportional Percentage Value) for identifying disease or symptom states using trained algorithms can be calculated as the percentage of cell-free biological samples identified or classified as having a disease or symptom state corresponding to objects that actually have the disease or symptom state.

[0135] Diseases or condition states can be identified in subjects with a negative predictive value (NPV) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or higher. The NPV (Net Value) for identifying disease or symptom states using trained algorithms can be calculated as the percentage of cell-free biological samples identified or classified as not having a disease or symptom state corresponding to objects that are truly not suffering from a disease or symptom state.

[0136] The percentage of each component in the object can be at least approximately 5%, at least approximately 10%, at least approximately 15%, at least approximately 20%, at least approximately 25%, at least approximately 30%, at least approximately 35%, at least approximately 40%, at least approximately 50%, at least approximately 55%, at least approximately 60%, at least approximately 65%, at least approximately 70%, at least approximately 75%, at least approximately 80%, at least approximately 81%, at least approximately 82%, at least approximately 83%, at least approximately 84%, at least approximately 85%, at least approximately 86%, at least approximately 87%, at least approximately 88%, at least approximately 89%, at least approximately 90%, or at least... Clinical sensitivity of approximately 91%, at least approximately 92%, at least approximately 93%, at least approximately 94%, at least approximately 95%, at least approximately 96%, at least approximately 97%, at least approximately 98%, at least approximately 99%, at least approximately 99.1%, at least approximately 99.2%, at least approximately 99.3%, at least approximately 99.4%, at least approximately 99.5%, at least approximately 99.6%, at least approximately 99.7%, at least approximately 99.8%, at least approximately 99.9%, at least approximately 99.99%, at least approximately 99.999%, or higher, is required to identify a disease or condition. Clinical sensitivity for identifying a disease or condition using a trained algorithm can be calculated as the percentage of independent test samples (e.g., objects known to have a disease or condition) that are correctly identified or classified as having a disease or condition in relation to the presence of that disease or condition.

[0137] It is permissible to include in the object at least approximately 5%, at least approximately 10%, at least approximately 15%, at least approximately 20%, at least approximately 25%, at least approximately 30%, at least approximately 35%, at least approximately 40%, at least approximately 50%, at least approximately 55%, at least approximately 60%, at least approximately 65%, at least approximately 70%, at least approximately 75%, at least approximately 80%, at least approximately 81%, at least approximately 82%, at least approximately 83%, at least approximately 84%, at least approximately 85%, at least approximately 86%, at least approximately 87%, at least approximately 88%, at least approximately 89%, at least approximately 90%, up to Clinical specificity for identifying a disease or condition is approximately 91%, at least approximately 92%, at least approximately 93%, at least approximately 94%, at least approximately 95%, at least approximately 96%, at least approximately 97%, at least approximately 98%, at least approximately 99%, at least approximately 99.1%, at least approximately 99.2%, at least approximately 99.3%, at least approximately 99.4%, at least approximately 99.5%, at least approximately 99.6%, at least approximately 99.7%, at least approximately 99.8%, at least approximately 99.9%, at least approximately 99.99%, at least approximately 99.99%, or higher. Clinical specificity for identifying a disease or condition using a trained algorithm can be calculated as the percentage of independent test samples (e.g., subjects with negative clinical test results for a disease or condition) that are correctly identified or classified as not having a disease or condition and are not associated with the absence of that disease or condition.

[0138] After identifying a disease or condition state in a subject, a subtype of the disease or condition state may be further identified (e.g., selected from multiple subtypes of the disease or condition state). The subtype of the disease or condition state may be determined at least in part based on the count or normalized count of highly / low methylated nucleic acid molecules in a target region with differential weighting for a given disease or condition. For example, a subject may be identified as being at an elevated risk for a cancer subtype (e.g., selected from multiple subtypes of a given cancer). After identifying a subject as being at an elevated risk for a disease subtype, a clinical intervention for the subject may be selected at least in part based on the disease subtype for which the subject is identified as being at an elevated risk. In some aspects, the clinical intervention is selected from a variety of clinical interventions (e.g., clinically applicable to different subtypes of cancer). For example, a clinical intervention may be chemotherapy, radiation therapy, targeted therapy, or immunotherapy that is clinically applicable to the identified subtype of a given cancer but not clinically applicable to other subtypes of a given cancer.

[0139] In some respects, the trained algorithm can determine that an object is at an elevated risk of disease or condition of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99% or higher.

[0140] The trained algorithm can achieve a success rate of at least approximately 50%, at least approximately 55%, at least approximately 60%, at least approximately 65%, at least approximately 70%, at least approximately 75%, at least approximately 80%, at least approximately 81%, at least approximately 82%, at least approximately 83%, at least approximately 84%, at least approximately 85%, at least approximately 86%, at least approximately 87%, at least approximately 88%, at least approximately 89%, at least approximately 90%, at least approximately 91%, at least approximately 92%, at least approximately 93%, at least approximately 94%, and at least approximately... A 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, at least about 99.1%, at least about 99.2%, at least about 99.3%, at least about 99.4%, at least about 99.5%, at least about 99.6%, at least about 99.7%, at least about 99.8%, at least about 99.9%, at least about 99.99%, at least about 99.999% or higher accuracy rate determines that the subject is at increased risk of disease or condition.

[0141] After identifying a subject as having a disease or condition, a therapeutic intervention may optionally be provided (e.g., prescisoring an appropriate treatment procedure to treat the subject's disease or condition). Therapeutic interventions may include administering an effective dose of medication, further testing or evaluation of the disease or condition, further monitoring of the disease or condition, induction or suppression of labor, or a combination thereof. If the subject is currently receiving a treatment procedure to treat a disease or condition, the therapeutic intervention may include a subsequent different treatment procedure (e.g., improving the efficacy of the current treatment procedure if it is ineffective).

[0142] Treatment interventions may include recommending secondary clinical testing to confirm the diagnosis of the disease or condition. This secondary clinical testing may include imaging tests, blood tests, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound scans, chest X-rays, positron emission tomography (PET) scans, PET-CT scans, biopsy tests, cytological examinations, or any combination thereof.

[0143] The counts or normalized counts of highly / low methylated nucleic acid molecules in a differentially weighted target region for a given disease or condition can be assessed over a period of time to monitor patients (e.g., those with a disease or condition or receiving treatment for a disease or condition). In this case, the counts or normalized counts of highly / low methylated nucleic acid molecules in the target region of a patient dataset can vary during the treatment process. For example, the counts or normalized counts of highly / low methylated nucleic acid molecules in the target region of a dataset of patients with a reduced risk of disease or condition due to effective treatment may shift towards the spectrum or distribution of healthy subjects (e.g., those without disease or condition). Conversely, quantitative measurements of a dataset of patients with an increased risk of disease or condition due to ineffective treatment may shift towards the spectrum or distribution of subjects with a higher risk of disease or condition or a more advanced disease or condition.

[0144] The disease or condition status of a subject can be monitored by monitoring the treatment process used to treat the subject. Monitoring may include assessing the subject's disease or condition status at two or more time points. The assessment may be based at least on the count or normalized count of highly / low methylated nucleic acid molecules in each identified target region at two or more time points.

[0145] In some respects, the difference in the count or normalized count of highly / low methylated nucleic acid molecules in a target region identified between two or more time points may indicate one or more clinical indications, such as (i) diagnosis of a subject's disease or condition, (ii) prognosis of a subject's disease or condition, (iii) increased risk of a subject's disease or condition, (iv) decreased risk of a subject's disease or condition, (v) efficacy of a treatment process for a subject's disease or condition, and (vi) ineffectiveness of a treatment process for a subject's disease or condition.

[0146] In some aspects, differences in the counts or treatment counts of highly / low methylated nucleic acid molecules in a target region identified between two or more time points can indicate a diagnosis of a disease or condition in a subject. For example, if a disease or condition was not detected in a subject at an earlier time point but was detected at a later time point, the difference indicates a diagnosis of the subject's disease or condition. Clinical actions or decisions can be made based on this indication of a diagnosis of the subject's disease or condition, such as directing a new therapeutic intervention for the subject. Clinical actions or decisions may include recommending the subject undergo secondary clinical testing to confirm the diagnosis of the disease or condition. Such secondary clinical testing may include imaging tests, blood tests, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound scans, chest X-rays, positron emission tomography (PET) scans, PET-CT scans, biopsy tests, cytology, or any combination thereof.

[0147] In some respects, differences in the counts or normalized counts of highly / low methylated nucleic acid molecules in a target region identified between two or more time points can indicate the prognosis of a subject's disease or condition.

[0148] In some aspects, differences in the counts or normalized counts of highly / low methylated nucleic acid molecules in a target region between two or more time points can indicate an increased risk of a subject having a disease or condition. For example, if a disease or condition is detected in a subject at both an earlier and a later time point, and if the difference is positive (e.g., the counts or normalized counts of highly / low methylated nucleic acid molecules in the target region increase from the earlier to the later time point), the difference can indicate an increased risk of a subject having a disease or condition. Clinical actions or decisions can be made based on this indication of increased risk of disease or condition, such as directing a new treatment intervention or switching treatment interventions for the subject (e.g., ending the current treatment and directing a new treatment). Clinical actions or decisions may include recommending a secondary clinical test for the subject to confirm an increased risk of disease or condition. This secondary clinical test may include imaging tests, blood tests, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound scans, chest X-rays, positron emission tomography (PET) scans, PET-CT scans, biopsy tests, cytology, or any combination thereof.

[0149] In some aspects, differences in the counts or normalized counts of highly / low methylated nucleic acid molecules in a target region identified between two or more time points can indicate a reduced risk of a subject having a disease or condition. For example, if a disease or condition is detected in a subject at both an earlier and a later time point, and if the difference is negative (e.g., the counts or normalized counts of highly / low methylated nucleic acid molecules in the target region decrease from the earlier to the later time point), the difference can indicate a reduced risk of the subject having a disease or condition. Clinical actions or decisions (e.g., continuing or ending the current treatment intervention) can be made based on this indication of a reduced risk of the subject's disease or condition. Clinical actions or decisions may include recommending the subject undergo secondary clinical testing to confirm a reduced risk of the disease or condition. Such secondary clinical testing may include imaging tests, blood tests, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound scans, chest X-rays, positron emission tomography (PET) scans, PET-CT scans, biopsy tests, cytology, or any combination thereof.

[0150] In some aspects, differences in the counts or normalized counts of highly / low methylated nucleic acid molecules in a target region identified between two or more time points can indicate the effectiveness of a treatment process for a disease or condition in a subject. For example, if a disease or condition is detected in a subject at an earlier time point but not at a later time point, the difference can indicate the effectiveness of a treatment process for the subject's disease or condition. Clinical actions or decisions can be made based on this indication of the effectiveness of the treatment process for the subject's disease or condition, such as continuing or ending the current treatment intervention for the subject. Clinical actions or decisions may include recommending the subject undergo secondary clinical testing to confirm the effectiveness of the treatment process for the disease or condition. Such secondary clinical testing may include imaging tests, blood tests, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound scans, chest X-rays, positron emission tomography (PET) scans, PET-CT scans, biopsy tests, cytology, or any combination thereof.

[0151] In some aspects, differences in the counts or normalized counts of highly / low methylated nucleic acid molecules in a target region identified between two or more time points can indicate ineffectiveness of a treatment process for a disease or condition in a subject. For example, if a disease or condition is detected in a subject at both an earlier and a later time point, and if the difference is positive or zero (e.g., the counts or normalized counts of highly / low methylated nucleic acid molecules in the target region increase or remain at a constant level from the earlier to the later time point), and if treatment effectiveness was indicated at the earlier time point, then the difference can indicate ineffectiveness of a treatment process for a subject's disease or condition. Clinical actions or decisions can be made based on this indication of ineffectiveness of a treatment process for a subject's disease or condition, such as ending the current treatment intervention and / or switching to (e.g., indicating) a different new treatment intervention. Clinical actions or decisions may include recommending a second clinical test for the subject to confirm ineffectiveness of the treatment process for the disease or condition. The secondary clinical test may include imaging tests, blood tests, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound scans, chest X-rays, positron emission tomography (PET) scans, PET-CT scans, biopsy tests, cytological examinations, or any combination thereof.

[0152] In some aspects, such as clinical health data, clinical health data includes one or more quantitative measurements of an individual, such as age, weight, height, body mass index (BMI), blood pressure, heart rate, glucose levels, and a history or family history of disease (e.g., cancer). As another example, clinical health data may include one or more categorical measurements, such as race, ethnicity, medication history or other clinical treatment history, smoking history, alcohol consumption history, daily activities or health level, genetic testing results, blood test results, and imaging results.

[0153] In some respects, the methods presented herein are performed using a computer or mobile device application. For example, an individual can use a computer or mobile device application to input their own clinical health data, including quantitative and / or categorical measurements. The computer or mobile device application can then use a trained algorithm to process the clinical health data. The computer or mobile device application can then display a report indicating the results of the computer-implemented method.

[0154] In some respects, a subject's detected disease or condition can be improved by performing one or more follow-up clinical tests. For example, a subject may be recommended one or more follow-up clinical tests by a physician based on the initially detected disease or condition. These follow-up clinical tests may include imaging tests, blood tests, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound scans, chest X-rays, positron emission tomography (PET) scans, PET-CT scans, biopsy tests, cytology examinations, or any combination thereof.

[0155] After identifying or monitoring a subject's disease or symptom state, a report indicating (e.g., identifying or providing an indication) the subject's disease or symptom state can be output electronically. The subject may not exhibit a disease or symptom state (e.g., have no symptoms of a disease or symptom state). This report can be presented on a user's electronic device's graphical user interface (GUI). The user can be the subject, caregiver, doctor, nurse, or other healthcare worker.

[0156] The report may include one or more clinical indications, such as (i) a diagnosis of the subject's disease or condition, (ii) the prognosis of the subject's disease or condition, (iii) an increased risk of the subject's disease or condition, (iv) a decreased risk of the subject's disease or condition, (v) the effectiveness of a treatment regimen for the subject's disease or condition, and (vi) the ineffectiveness of a treatment regimen for the subject's disease or condition. The report may include one or more clinical actions or decisions based on these clinical indications. Such actions or decisions may be directed at a therapeutic intervention or further clinical evaluation or testing of the subject's disease or condition.

[0157] For example, a clinical indication of a diagnosis of a disease or condition may be accompanied by a clinical action instructing the subject to a new treatment intervention. As another example, a clinical indication of an increased risk to a subject's disease or condition may be accompanied by a clinical action instructing the subject to a new treatment intervention or switching treatment interventions (e.g., ending the current treatment and instructing a new treatment). As another example, a clinical indication of a decreased risk to a subject's disease or condition may be accompanied by a clinical action instructing the subject to continue or end the current treatment intervention. As another example, a clinical indication of the effectiveness of a treatment process for a subject's disease or condition may be accompanied by a clinical action instructing the subject to continue or end the current treatment intervention. As another example, a clinical indication of the ineffectiveness of a treatment process for a subject's disease or condition may be accompanied by a clinical action instructing the subject to end the current treatment intervention and / or switch to (e.g., instruct) a different new treatment intervention.

[0158] VI. Reagent kits disclosed herein

[0159] This disclosure provides kits containing any of the compositions described herein. In one non-limiting example, one or more of the following may be included in the kit: a matrix (which may be referred to as a plate or array) for capturing nucleic acids; cfDNA; one or more devices for collecting cfDNA; a targeting probe; an enzyme; an adaptor; primers (e.g., PCR primers); deoxynucleoside triphosphate (dNTP); a hybridization buffer; a washing buffer; a 20× saline-sodium citrate (SSC) buffer; other chemicals and compositions, including adenosine triphosphate (ATP), dithiothreitol (DTT), etc.; and any combination thereof.

[0160] The kit components may be packaged in an aqueous medium or in lyophilized form. The kit may include containers, such as at least one vial, test tube, flask, bottle, or another container for holding and / or appropriately aliquoting the components. Where more than one component is present in the kit, the kit may include a second, third, or other additional containers into which additional components may be placed individually. However, vials may contain multiple combinations of components. The kits of this disclosure may include containers for tightly sealing and containing components for commercial sale. Such containers may include blow-molded plastic containers containing the desired vials.

[0161] The kits disclosed herein may include instructions for performing the methods provided herein, such as methods for hybridizing cfDNA with probes and preparing sequencing libraries for high / low methylation analysis. Such instructions may be in tangible form (e.g., printed instructions) or electronic form.

[0162] The kits disclosed herein may include software packages or network links to servers or cloud computing platforms for analyzing data generated using the kits. This analysis can provide information on kit quality control (e.g., hybridization efficiency) and provide high / low methylation counting profiles of cfDNA in the target region.

[0163] The kits disclosed herein may include reports generated by the software package provided with the kit or by a server or cloud computing platform. These reports may provide information on: (1) diagnosis and / or prevention of a medical condition; (2) treatment of a medical condition; (3) treatment monitoring; and so on. For example, the report may provide information on the presence or risk of cancer, including specific types of cancer.

[0164] VII. Computer System

[0165] This disclosure provides computer systems programmed to perform the methods of this disclosure. Figure 3 A computer system 301 is illustrated, which is programmed or otherwise configured to, for example, process sequencing or imaging data to identify counts or normalized counts of highly / low methylated nucleic acid molecules in each target region, input these counts in the target regions as features of one or more training classifiers, generate the probability that an object has or is suspected of having a disease or condition, analyze nucleotide sequence information, train a classifier using a training dataset and a set of counts or normalized counts of highly / low methylated nucleic acid molecules in the target regions, obtain or generate sequencing data of cfDNA samples, perform clustering methods to identify a set of counts, and determine the accuracy of the trained classifier in assessing disease status. Computer system 301 can regulate various aspects of the analysis, calculation, and generation of the present disclosure, such as processing sequencing to identify counts or normalized counts of highly / low-methylated nucleic acid molecules in each target region, inputting the counts as features of one or more trained classifiers, generating the probability that an object has or is suspected of having a disease or condition, analyzing nucleotide sequence information, training a classifier using a training dataset and a set of counts or normalized counts of highly / low-methylated nucleic acid molecules in the target region, obtaining or generating sequencing data of cfDNA samples, performing clustering methods to identify a set of counts, and determining the accuracy of the trained classifier in assessing disease status. Computer system 301 can be a user's electronic device or a computer system located remotely relative to an electronic device. The electronic device can be a mobile electronic device.

[0166] Computer system 301 includes a central processing unit (CPU, also referred to herein as a “processor” and “computer processor”) 305, which may be a single-core or multi-core processor, or multiple processors for parallel processing. Computer system 301 also includes memory or memory locations 310 (e.g., random access memory, read-only memory, flash memory), electronic storage units 315 (e.g., hard disks), communication interfaces 320 for communicating with one or more other systems (e.g., network adapters), and peripheral devices 325, such as cache memory, other memory, data storage, and / or electronic display adapters. Memory 310, storage units 315, interfaces 320, and peripheral devices 325 communicate with CPU 305 via a communication bus (solid line) (e.g., motherboard). Storage units 315 may be data storage units (or data repositories) for storing data. Computer system 301 may be operatively coupled to computer network (“network”) 330 by means of communication interface 320. Network 330 may be the Internet, an intranet, and / or an extranet, or an intranet and / or extranet communicating with the Internet. In some cases, network 330 is a telecommunications and / or data network. Network 330 may include one or more computer servers that enable distributed computing, such as cloud computing. For example, one or more computer servers may enable cloud computing on network 330 (“the cloud”) to perform various aspects of the analysis, computation, and generation of this disclosure, such as processing sequencing or imaging data to identify counts or normalized counts of highly / low methylated nucleic acid molecules in each target region, inputting the counts as features of one or more trained classifiers, generating the probability that an object has or is suspected of having a disease or condition, analyzing nucleotide sequence information, training a classifier using a training dataset and a set of counts or normalized counts of highly / low methylated nucleic acid molecules in the target region, obtaining or generating sequencing data of cfDNA samples, performing clustering methods to identify a set of counts or normalized counts of highly / low methylated nucleic acid molecules in the target region, and determining the accuracy of the trained classifier in assessing disease status. Such cloud computing can be provided by cloud computing platforms such as Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, and IBM cloud. In some cases, network 330 can implement a peer-to-peer network with the help of computer system 301, which allows devices coupled to computer system 301 to act as clients or servers.

[0167] CPU 305 can execute a series of machine-readable instructions, which may be embodied in a program or software. The instructions may be stored in a memory location, such as memory 310. The instructions may be directed to CPU 305, which may subsequently be programmed or otherwise configured to implement the methods of this disclosure. Examples of operations performed by CPU 305 may include reading, decoding, executing, and writing back.

[0168] CPU 305 may be part of a circuit (e.g., an integrated circuit). One or more additional components of system 301 may be included in the circuit. In some cases, the circuit is an application-specific integrated circuit (ASIC).

[0169] Storage unit 315 may store files, such as drivers, libraries, and saved programs. Storage unit 315 may store user data, such as user preferences and user programs. In some cases, computer system 301 may include one or more additional data storage units located outside of computer system 301, such as on a remote server communicating with computer system 301 via an intranet or the Internet.

[0170] Computer system 301 can communicate with one or more remote computer systems via network 330. For example, computer system 301 can communicate with a remote computer system belonging to a user (e.g., a doctor, nurse, caregiver, patient, or object). Some examples of remote computer systems include personal computers (e.g., portable PCs), tablets, or tablet PCs (e.g., tablet computers). iPad Galaxy Tab), telephone, smartphone (e.g., iPhone and Android supported devices (or personal digital assistant). Users can access computer system 301 via network 330.

[0171] The methods described herein can be performed by machine-executable code stored at an electronic storage location (e.g., on memory 310 or electronic storage unit 315) in computer system 301. The machine-executable or machine-readable code may be provided in software form. During use, the code can be executed by processor 305. In some cases, the code can be retrieved from storage unit 315 and stored in memory 310 for access by processor 305 at any time. In some cases, electronic storage unit 315 may be excluded, and machine-executable instructions may be stored on memory 310.

[0172] The code can be pre-compiled and configured for use with machines that have processors suitable for executing the code, or it can be compiled at runtime. The code can be provided in a programming language, and the programming language can be selected to enable the code to be executed in a pre-compiled or just-in-time (JIT) compiled manner.

[0173] Some aspects of the systems and methods provided herein (e.g., computer system 301) can be embodied in programming. Multiple aspects of the technology can be viewed as “products” or “artifacts” typically carried or embodied in machine-readable media, in the form of machine (or processor) executable code and / or associated data. Machine-executable code can be stored on electronic storage units such as memory (e.g., read-only memory, random access memory, flash memory) or hard disks. Media of the “storage” type can include any or all tangible memory of computers, processors, etc., or related modules thereof, such as various semiconductor memories, tape drives, disk drives, etc., which can provide non-transitory storage for software programming at any time. All or part of the software can sometimes be communicated via the Internet or various other telecommunications networks. For example, such communication enables the loading of software from one computer or processor to another, such as from a management server or host computer platform to an application server. Therefore, another type of media that can carry software elements includes light waves, radio waves, and electromagnetic waves, for example, used between physical interfaces between local devices, via wired and optical landline networks, and via various air links. Physical elements carrying such waves, such as wired or wireless links, optical links, etc., can also be considered as media carrying software. Unless limited to non-transitory tangible "storage" media, the term "readable medium" as used herein, such as a computer or machine, refers to any medium that participates in providing instructions to a processor for execution.

[0174] Therefore, machine-readable media (e.g., computer-executable code) can take many forms, including but not limited to tangible storage media, carrier media, or physical transmission media. Non-volatile storage media include, for example, optical discs or disks, any storage device such as any computer, for example, which can be used to implement a database as shown in the accompanying drawings. Volatile storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wires and optical fibers, including lines that constitute a bus within a computer system. Carrier transmission media can take the form of electrical signals or electromagnetic signals or sound waves or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communication. Therefore, common forms of computer-readable media include, for example: floppy drives, floppy disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, DVDs or DVD-ROMs, any other optical media, punched cardboard tapes, any other physical storage media with perforated patterns, RAM, ROM, PROM and EPROM, FLASH-EPROM, any other memory chips or cassettes, carrier waves for transmitting data or instructions, cables or links for transmitting such carrier waves, or any other media from which a computer can read programming code and / or data. Many of these forms of computer-readable media may involve transmitting one or more sequences of one or more instructions to a processor for execution.

[0175] Computer system 301 may include or communicate with an electronic display 835, which includes a user interface (UI) 340 for providing, for example, high / low methylation count spectra, reports indicating count spectra, and / or the likelihood that an object has or is suspected of having a disease or condition. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces.

[0176] The methods and systems of this disclosure can be executed by one or more algorithms. The algorithms can be executed by software when performed by the central processing unit 305. For example, the algorithms can process sequencing or imaging data to identify counts or normalized counts of highly / low methylated nucleic acid molecules in each target region, inputting the counts as features of one or more trained classifiers, generating the probability that an object has or is suspected of having a disease or condition, analyzing nucleotide sequence information, training the classifier using a training dataset and a set of counts or normalized counts of highly / low methylated nucleic acid molecules in the target region, obtaining or generating sequencing data of cfDNA samples, performing clustering methods to identify a set of counts or normalized counts of highly / low methylated nucleic acid molecules in the target region, and determining the accuracy of the trained classifier in assessing disease status.

[0177] While preferred aspects of the invention have been shown and described herein, it will be apparent to those skilled in the art that such aspects are provided by way of example only. The invention is not intended to be limited by the specific examples provided in the specification. Although the invention has been described with reference to the foregoing description, the descriptions and illustrations of aspects herein are not intended to be construed as limiting. Many 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, and depend on a variety of conditions and variables. It should be understood that various alternatives to the aspects of the invention described herein can be used to practice the invention. Therefore, it is contemplated that the invention will also cover any such substitutions, modifications, variations, or equivalents. It is intended that the appended claims define the scope of the invention and therefore cover the methods and structures within the scope of these claims and their equivalents.

[0178] Although the present disclosure and its advantages have been described in detail, it should be understood that various changes, substitutions, and modifications may be made herein without departing from the spirit and scope of the invention as defined by the appended claims. Furthermore, the scope of this application is not intended to be limited to certain specific aspects of the processes, machines, articles, compositions of matter, means, methods, and steps described in the specification. As will be readily understood by those skilled in the art from the present disclosure, existing or future processes, machines, articles, compositions of matter, means, methods, or steps that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein may be used according to the present disclosure. Therefore, the appended claims are intended to include such processes, machines, articles, compositions of matter, means, methods, or steps within their scope.

[0179] VIII. Certain Determination Methods

[0180] A. Detection of methylated DNA

[0181] Some aspects of the method include measuring nucleic acids to determine their expression levels and / or methylation levels. Some aspects of this disclosure include detecting one or more CpG islands, for example, at least, at most, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 CpG islands (or any range thereof). Each biomarker may contain at least, at most, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 CpG islands (or any range thereof), or consist of them. Various assays can be used to detect methylated DNA. Exemplary methods are described herein.

[0182] 1. Bisulfite sequencing

[0183] Bisulfite treatment of DNA mediates the deamination of cytosine to uracil, and these transformed residues can be read as thymine, as determined by PCR amplification and subsequent Sanger sequencing analysis. However, the 5mC residue is resistant to this transformation and may therefore still be read as cytosine. Therefore, comparing Sanger sequencing reads from untreated DNA samples with those from the same bisulfite-treated sample allows for the detection of methylated cytosine. Using next-generation sequencing (NGS) technology, this method can be extended to the analysis of DNA methylation across the entire genome. To ensure complete transformation of unmethylated cytosine, a control can be added to the bisulfite reaction.

[0184] Whole genome bisulfite sequencing (WGBS) is similar to whole genome sequencing, except for the additional bisulfite conversion step. Sequencing the 5mC-enriched fractions of the genome is not only a lower-cost method but also makes it possible to increase sequencing coverage and thus improve the accuracy of revealing differentially methylated regions. Sequencing can be performed using any existing NGS platform; both Illumina and Life Technologies offer kits for such analyses.

[0185] Bisulfite sequencing methods include reduced representation bisulfite sequencing (RRBS), in which only a portion of the genome is sequenced. In RRBS, enrichment of CpG-rich regions is achieved by isolating short fragments after MspI digestion that recognizes CCGG sites (and their cleavage of methylated and unmethylated sites). This ensures the isolation of approximately 85% of the CpG islands in the human genome. Bisulfite transformation and library preparation are then performed in the same manner as in WGBS. The RRBS procedure typically requires approximately 100 ng to 1 μg of DNA.

[0186] 2. Hybridization

[0187] The methods of this disclosure can be used to hybridize nucleic acids with other nucleic acids under specific hybridization conditions. Various methods can be used to hybridize nucleic acids. See, for example, Current Protocols in Molecular Biology, John Wiley and Sons, NY (1989), 6.3.1–6.3.6, which are incorporated herein by reference in their entirety. The methods of this disclosure can be used with moderately stringent hybridization conditions, employing: a pre-wash solution comprising 5× sodium chloride / sodium citrate (SSC), 0.5% SDS, and 1.0 mM EDTA (pH 8.0); a hybridization buffer comprising approximately 50% formamide and 6× SSC; and a hybridization temperature of 55°C (or other similar hybridization solutions, such as a hybridization solution comprising approximately 50% formamide at a hybridization temperature of 42°C), and washing conditions at 60°C in 0.5× SSC and 0.1% SDS. Stringent hybridization conditions involve hybridization in 6× SSC at 45°C, followed by one or more washes in 0.1× SSC and 0.2% SDS at 68°C. Furthermore, hybridization and / or washing conditions can be manipulated to increase or decrease the stringency of hybridization, such that nucleic acids containing nucleotide sequences having at least 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% identity with each other can maintain hybridization with each other.

[0188] The parameters influencing the selection of hybridization conditions and the guidance for designing suitable conditions can be described, for example, by Sambrook, Fritsch, and Maniatis (Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, Chapters 9 and 11 (1989); Current Protocols in Molecular Biology, Ausubel et al., eds., John Wiley and Sons, Inc., Sections 2.10 and 6.3-6.4 (1995), each of which is incorporated herein by reference in its entirety), and can be readily determined based on, for example, the length and / or base composition of DNA.

[0189] 3. Probe

[0190] On the other hand, nucleic acid molecules are suitable for use as primers or hybridization probes for detecting or purifying nucleic acid sequences.

[0191] Probes based on desired nucleic acid sequences can be used to detect nucleic acids or similar nucleic acids, such as transcripts encoding target polypeptides. Probes may contain labeling groups, such as radioactive isotopes, fluorescent compounds, enzymes, or enzyme cofactors. Such probes can be used to isolate or purify certain nucleic acids.

[0192] B. Sequencing

[0193] DNA, including bisulfite-converted DNA, can be used to amplify a target region and then sequenced. Primers can be designed around CpG islands and used for PCR amplification of bisulfite-converted DNA. The resulting PCR product can be cloned and sequenced. Therefore, some aspects of this disclosure may include sequencing nucleic acids to detect methylation of nucleic acids and / or biomarkers. In some aspects, the methods of this disclosure include sequencing methods. Sequencing methods that can be used in some aspects may include those described below. Other sequencing methods may also be used in some aspects.

[0194] 1. Massively parallel signature sequencing (MPSS).

[0195] In the 1990s, Lynx Therapeutics developed the first next-generation sequencing technology, massively parallel-tag sequencing (or MPSS). MPSS is a bead-based approach that uses a complex method: adaptor ligation, followed by adaptor decoding, to read sequences in four-nucleotide increments. This method makes it susceptible to sequence-specific bias or the loss of specific sequences. Because of its complexity, MPSS was performed "internally" by Lynx Therapeutics and the DNA sequencer was not sold to independent laboratories. Lynx Therapeutics' merger with Solexa (later acquired by Illumina) in 2004 led to the development of sequencing-by-synthesis, rendering MPSS obsolete. Sequencing-by-synthesis was a simpler method derived from Manteia Predictive Medicine. However, the fundamental characteristics of MPSS output are those of later "next-generation" data types, including hundreds of thousands of short DNA sequences. In the case of MPSS, these can be used to sequence cDNA to measure gene expression levels. In fact, the powerful Illumina HiSeq2000, HiSeq2500 and MiSeq systems are all based on MPSS.

[0196] 2. Polony sequencing.

[0197] Developed by George M. Church's lab at Harvard University, the Polony sequencing method was one of the first next-generation sequencing systems and was used for whole-genome sequencing in 2005. It combines in vitro paired-tag libraries with emulsion PCR, automated microscopy, and ligation-based sequencing chemistry to sequence the *E. coli* genome with >99.9999% accuracy at approximately one-ninth the cost of Sanger sequencing. The technology was licensed to Agencourt Biosciences (later developed into Agencourt Personal Genomics) and eventually incorporated into the Applied Biosystems SOLiD platform, now owned by Life Technologies.

[0198] 3.454 pyrosequencing.

[0199] 454Life Sciences (acquired by Roche Diagnostics) developed a parallelized version of pyrosequencing. This method amplifies DNA within droplets in an oil solution (emulsion PCR), where each droplet contains a single DNA template attached to a single primer-coated bead and then forming a cloning colony. The sequencer contains numerous picoliter-volume wells, each containing a single bead and a sequencing enzyme. Pyrosequencing uses luciferase to generate light to detect single nucleotides added to the nascent DNA, and the combined data is used to generate sequence readouts. Compared to Sanger sequencing at one end and Solexa and SOLiD at the other, this technology offers moderate readout lengths and cost per base.

[0200] 4. Illumina (Solexa) sequencing.

[0201] Solexa (now part of Illumina) developed sequencing methods based on reversible dye terminator technology and modified polymerases, which were developed internally. Termination chemistry was developed in-house by Solexa, and the concept for the Solexa system was coined by Balasubramanian and Klennerman of the Department of Chemistry at Cambridge University. In 2004, Solexa acquired Manteia Predictive Medicine to gain access to massively parallel sequencing technology based on “DNA clusters,” which involves the clonal amplification of DNA on a surface. This cluster technology was acquired in conjunction with Lynx Therapeutics in California. Solexa Ltd. later merged with Lynx to form Solexa Inc.

[0202] In this method, DNA molecules and primers are first attached to a slide and amplified using polymerase to form localized clonal DNA colonies, later called "DNA clusters." To determine the sequence, four types of reversible terminator bases (RT-bases) are added, and unincorporated nucleotides are washed away. A camera images the fluorescently labeled nucleotides, and the dye is then chemically removed from the DNA along with a 3' end blocker, allowing the next cycle to begin. Unlike pyrosequencing, the DNA strand is extended one nucleotide at a time, and image acquisition can occur at delayed moments, allowing for the capture of very large arrays of DNA colonies through continuous images taken with a single camera.

[0203] Decoupling enzymatic reactions and image capture allows for optimal throughput and theoretically unlimited sequencing capabilities. Therefore, under optimal configuration, the final achievable instrument throughput is dictated solely by the following: the camera's analog-to-digital conversion rate, multiplied by the number of cameras, and then divided by the number of pixels per DNA colony required for optimal visualization (approximately 10 pixels / colony). In 2012, with cameras operating at A / D conversion rates greater than 10 MHz, and with available optical, fluidic, and enzymatic operations, throughput could be multiples of 1 million nucleotides / second, roughly equivalent to 1 × coverage / hour / instrument for one human genome and resequencing one human genome (approximately 30 ×) / day / instrument (equipped with a single camera).

[0204] 5. SOLiD sequencing.

[0205] Applied Biosystems' (now a trademark of Thermo Fisher Scientific) SOLiD technology employs sequencing by ligation. Here, a library of all possible oligonucleotides of fixed length is labeled according to the sequencing location. The oligonucleotides are annealed and ligated; the preferential ligation of matching sequences by DNA ligase results in a signal providing information about the nucleotide at that location. Prior to sequencing, the DNA is amplified by emulsion PCR. The resulting beads, each containing a single copy of the same DNA molecule, are deposited onto a glass slide. The result is a sequence quantity and length comparable to Illumina sequencing. This ligation-based sequencing method has reportedly some problems with sequencing palindromic sequences.

[0206] 6. Ion Torrent semiconductor sequencing.

[0207] Ion Torrent Systems Inc. (now owned by Thermo Fisher Scientific) developed a system based on standard sequencing chemistry but with a semiconductor-based detection system. Unlike optical methods used in other sequencing systems, this approach is based on the detection of hydrogen ions released during DNA polymerization. Micropores containing the template DNA strand to be sequenced are filled with a single type of nucleotide. If an introduced nucleotide is complementary to the leader template nucleotide, it is incorporated into the growing complementary strand. This results in the release of hydrogen ions, triggering a highly sensitive ion sensor, indicating that a reaction has occurred. If a homopolymer repeat sequence is present in the template sequence, multiple nucleotides can be incorporated into a single cycle. This results in a correspondingly higher number of hydrogen ions released and a proportionally higher electron signal.

[0208] 7. DNA nanosphere sequencing.

[0209] DNA nanosphere sequencing is a high-throughput sequencing technology used to determine the entire genome sequence of an organism. Complete Genomics uses this technology to sequence samples submitted by independent researchers. The method uses rolling circle replication to amplify small fragments of genomic DNA into DNA nanospheres. Then, ligated non-stranded sequencing is used to determine the nucleotide sequence. Compared to other next-generation sequencing platforms, this DNA sequencing method allows sequencing a large number of DNA nanospheres per run at a low reagent cost. However, only a short sequence of DNA is determined from each DNA nanosphere, making it difficult to map the short reads to a reference genome. This technology can be used for multiple genome sequencing projects.

[0210] 8. Heliscope single-molecule sequencing.

[0211] Heliscope sequencing is a single-molecule sequencing method developed by Helicos Biosciences. It uses DNA fragments with added poly-A tail adaptors attached to the surface of a flow cell. Subsequent steps involve extension-based sequencing, where the flow cell is cycled with fluorescently labeled nucleotides (one nucleotide type at a time, as with the Sanger method). Reads are performed using the Heliscope sequencer. Reads are short, allowing a maximum of 55 bases per run, but recent improvements allow for more accurate readings of one type of nucleotide segment. The genome of M13 bacteriophage has been sequenced using this sequencing method and instrument.

[0212] 9. Single molecule real-time (SMRT) sequencing.

[0213] SMRT sequencing is based on a sequencing-by-synthesis approach. DNA is synthesized in a zero-mode waveguide (ZMW), a small, well-like container with a trapping device located at the bottom of the well. Sequencing is performed using fluorescently labeled nucleotides flowing freely in solution and unmodified polymerase (attached to the bottom of the ZMW). The wells are constructed in such a way that fluorescence present at the bottom of the well is detected only. As the nucleotides are incorporated into the DNA strand, the fluorescent label is separated from the nucleotides, leaving the unmodified DNA strand. According to Pacific Biosciences, the developer of SMRT technology, this method allows the detection of nucleotide modifications (such as cytosine methylation). This is achieved by observing polymerase kinetics. This method allows readouts of 20,000 nucleotides or more, with an average readout length of 5 kilobases.

[0214] C. Other detection methods

[0215] In some aspects, the method involves using at least one pair of primers specific to the target genomic region to amplify and / or sequence one or more target genomic regions. In some aspects, the primers are 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 or more nucleotides (or any range thereof). In other aspects, an enzyme (e.g., a primase / polymerase combinationase) is added to the amplification step to synthesize the primers.

[0216] In some aspects, arrays can be used to detect nucleic acids of this disclosure. The array includes a solid support to which nucleic acid probes are attached. The array may include multiple different nucleic acid probes coupled to the surface of a substrate at different known locations. These arrays (also referred to as “microarrays” or commonly as “chips”) can be described, for example, by U.S. Patent Nos. 5,143,854, 5,445,934, 5,744,305, 5,677,195, 6,040,193, 5,424,186, and Fodor et al., 1991, each of which is incorporated herein by reference in its entirety. Techniques for synthesizing these arrays using mechanosynthetic methods are described, for example, by U.S. Patent No. 5,384,261, which is incorporated herein by reference in its entirety. Although planar array surfaces are used in some aspects, arrays can be fabricated on surfaces of virtually any shape or even on multiple surfaces. The array can be nucleic acids on beads, gels, polymer surfaces, fibers (e.g., optical fibers), glass, or any other suitable matrix, see U.S. Patent Nos. 5,770,358, 5,789,162, 5,708,153, 6,040,193, and 5,800,992, each of which is incorporated herein by reference in its entirety.

[0217] In addition to using arrays and microarrays, many different assays are expected to be available for analyzing nucleic acids. Such assays include, but are not limited to, nucleic acid amplification, polymerase chain reaction, quantitative PCR, RT-PCR, in situ hybridization, digital PCR, dd PCR (digital droplet PCR), nCounter (nanoString), BEAMing (Beads, Emulsions, Amplifications, and Magnetics) (Inostics), ARMS (Amplification Refractory Mutation System), RNA-Seq, TAM-Seg (Tagged-Amplicon deep sequencing), PAP (Pyrophosphorolysis-activation polymerization), next-generation RNA sequencing, northern hybridization, hybridization protection assay (HPA) (GenProbe), branched DNA (bDNA) assay (Chiron), rolling circle amplification (RCA), single-molecule hybridization detection (US Genomics), and invader assay (ThirdWave). Technologies) and / or Bridge Litigation Assay (Genaco).

[0218] Amplification primers or hybridization probes can be prepared to complement the genomic regions, biomarkers, probes, or oligomers described herein. The terms "primer" or "probe" as used herein are intended to cover any nucleic acid capable of initiating the synthesis of nascent nucleic acids in a template-dependent process and / or pairing with a single strand or portion thereof with an oligomer of this disclosure. Primers can be oligonucleotides of 10 to 20 and / or 30 nucleic acids in length, but longer sequences may be used. Primers may be provided in double-stranded and / or single-stranded form.

[0219] Using probes or primers of 13 to 100 nucleotides in length, particularly 17 to 100 nucleotides, or in some respects 1 to 2 kilobases or longer, allows for the formation of stable and selective double-stranded molecules. Molecules with complementary sequences on contiguous segments longer than 20 bases can be used to improve the stability and / or selectivity of the resulting hybrid molecules. Nucleic acid molecules for hybridization can be designed to have one or more complementary sequences of 20 to 30 nucleotides (or even longer if desired). Such fragments can be readily prepared, for example, by direct chemical synthesis or by introducing selected sequences into recombinant vectors for recombination production.

[0220] In one aspect, each probe / primer contains at least 15 nucleotides. For example, each probe may contain at least, 25, 50, 75, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 400 or more nucleotides (or any range thereof). They may have these lengths and have sequences that are identical to or complementary to the genes described herein. In particular, each probe / primer has relatively high sequence complexity and contains no ambiguous residues (undetermined “n” residues). The probes / primers may hybridize with target genes, including their RNA transcripts, under stringent or highly stringent conditions. The probes or primers are expected to contain inosine or other designs adapted for the recognition of more than one person’s sequence of a particular biomarker.

[0221] For applications requiring high selectivity, relatively stringent conditions are desirable for hybridization. This includes, for example, relatively low salt and / or high temperature conditions, such as from 0.02 M to approximately 0.10 M NaCl at temperatures ranging from approximately 50°C to approximately 70°C. Such stringent conditions virtually eliminate the possibility of mismatches (if any) between the probe or primer and the template or target strand, and are particularly suitable for isolating specific genes or detecting specific mRNA transcripts. It is generally understood that conditions can be made even more stringent by adding increased amounts of formamide.

[0222] In one aspect, quantitative RT-PCR (e.g., TaqMan, ABI) is used to detect and compare the level or abundance of nucleic acids in a sample. In the linear portion of a PCR method, the concentration of target DNA is proportional to the initial concentration of the target before PCR begins. The relative concentration of a specific target sequence in the initial DNA mixture can be determined by identifying the concentration of the PCR product of the target DNA in a PCR reaction that has completed the same number of cycles and is within its linear range. This direct proportionality between the concentration of the PCR product and its relative abundance in the starting material is real in the linear portion of the PCR reaction. In the plateau portion of the curve, the final concentration of the target DNA is determined by the availability of reagents in the reaction mixture and is independent of the initial concentration of the target DNA. Therefore, when the PCR reaction is in the linear portion of its curve, the amplified PCR product can be sampled and quantified. Additionally, the relative concentration of amplifiable DNA can be normalized relative to some independent standards / controls, which can be based on internally present DNA species or externally introduced DNA species. The abundance of a specific DNA species can also be determined relative to the average abundance of all DNA species in the sample.

[0223] In one aspect, PCR amplification utilizes one or more internal PCR standards. Internal standards can be abundant housekeeping genes in the cell, or they can specifically be GAPDH, GUSB, and β-2 microglobulin. These standards can be used to normalize expression levels so that the expression levels of different gene products can be directly compared.

[0224] ***

[0225] According to this disclosure, all methods disclosed and claimed herein can be performed and implemented without excessive experimentation. Although the compositions and methods of the invention have been described according to some preferred aspects, it will be apparent to those skilled in the art that changes can be applied to the methods described herein and the steps or sequence of steps thereof without departing from the concept, spirit, and scope of the invention. More specifically, it will be apparent that certain agents relevant in both chemical and physiological respects can be substituted for the agents described herein, while achieving the same or similar results. All such similar substitutions and modifications that will be apparent to those skilled in the art are considered to be within the spirit, scope, and concept of the invention as defined by the appended claims.

Claims

1. A method for enriching highly methylated and / or hypomethylated nucleic acid molecules among multiple nucleic acid molecules, comprising: (a) Connecting the first set of adaptors to each end of each nucleic acid in the plurality of nucleic acid molecules to produce the first set of linked molecules; (b) Digest the first set of linked molecules with one or more methylation-sensitive restriction enzymes and / or methylation-dependent restriction enzymes to produce a plurality of nucleic acid molecules with digestion enzyme-specific overhangs, and link a second set of adaptors to the plurality of nucleic acid molecules with digestion enzyme-specific overhangs to produce a second set of linked molecules, wherein the second set of linked molecules does not have a sequence recognized by the one or more restriction enzymes. (c) Amplify the molecules of the first set of ligated molecules and the molecules of the second set of ligated molecules using at least partially one or more primers that bind to the first set of ligated molecules and one or more primers that bind to the second set of ligated molecules to produce amplified ligated DNA fragments of ligated molecules. (d) Assign the amplified adaptor-ligated DNA fragment to at least two partitions; as well as (e) Enriching the amplified adaptor-linked DNA fragment with the first set of adaptors at both ends in the first partition to generate enriched DNA fragments in the first partition.

2. The method of claim 1, wherein the plurality of nucleic acid molecules comprises cell-free DNA.

3. The method of claim 1 or 2, further comprising enriching highly methylated nucleic acid molecules, wherein (b) further comprises digesting the plurality of nucleic acid molecules with one or more methylation-sensitive restriction enzymes to produce a plurality of nucleic acid molecules having enzyme-specific overhangs, and linking the second set of adaptors to the plurality of nucleic acid molecules having enzyme-specific overhangs to produce a second set of linked molecules, wherein the second set of linked molecules does not have a sequence recognized by the one or more methylation-sensitive restriction enzymes.

4. The method of claim 1 or 2, further comprising enriching hypomethylated nucleic acid molecules, wherein (b) the method further comprises digesting the plurality of nucleic acid molecules with one or more methylation-dependent restriction enzymes to produce a plurality of nucleic acid molecules having enzyme-specific overhangs, and linking the second set of adaptors to the plurality of nucleic acid molecules having enzyme-specific overhangs to produce a second set of linked molecules, wherein the second set of linked molecules does not have a sequence recognized by the one or more methylation-dependent restriction enzymes.

5. The method of any one of claims 1 to 4, wherein the one or more methylation-sensitive restriction enzymes comprise one or more of the following, or their functional analogs, or mixtures thereof: HhaI, HpyCH4IV, AclI, AcII, AfeI, AgeI, AccII, AatII, Aor13HI, Aor51HI, AscI, AsiSI, AvaI, BceAI, BmgBI, BsaAI, BsaHI, BsiEI, BsiWI, BsmBI, BspDI, Bsp EI, BspT104I, BsrBI, BssHII, BstUI, Cfr10I, ClaI, CpoI, Eco52I, HaeII, HgaI, HinP1I, HpaII, Hpy99I, KasI, KroNI, MluI, NaeI, NarI, NgoMIV, NotI, NruI, NsbI, PaeR7I, PmaCI, PmlI, Psp1406I, PvuI, RsrII, SacII, SalI, SamI, SnaBI.

6. The method of any one of claims 1 to 5, wherein the one or more methylation-dependent restriction enzymes comprise one or more of LpnPI, McrBC, GlaI, PkrI, MteI, AoxI, or their functional analogs, or mixtures thereof.

7. The method of any one of claims 1 to 6, wherein the second set of adaptors has a protrusion complementary to the protrusion produced by the methylation-sensitive restriction enzyme and / or the methylation-dependent restriction enzyme, and wherein (b) further comprises digesting the adaptors from the first set of adaptors and / or the second set of adaptors that have been linked together with one or more other restriction enzymes, without digesting the DNA fragments and the linkage between the adaptors.

8. The method of claim 7, wherein the one or more additional restriction enzymes comprise one or more of BspDI, ClaI, AclI, NarI, XhoI, SmlI, HpyF30I, PaeR7I, Sfr274I, or their functional analogs, or mixtures thereof.

9. The method of any one of claims 1 to 8, wherein the digestion of the plurality of nucleic acid molecules and the ligation of the second set of adaptors occur in different reaction vessels.

10. The method of any one of claims 1 to 9, further comprising subjecting the plurality of nucleic acid molecules to conditions sufficient to allow methylated nucleic acid bases to be distinguished from unmethylated nucleic acid bases before (c) and after (b).

11. The method of claim 10, wherein subjecting the plurality of nucleic acid molecules to conditions sufficient to allow methylated nucleic acid bases to be distinguished from unmethylated nucleic acid bases includes subjecting the plurality of nucleic acid molecules to one or more enzymatic or chemical reactions.

12. The method of claim 10 or 11, wherein subjecting the plurality of nucleic acid molecules to conditions sufficient to allow the methylated nucleic acid bases to be distinguished from the unmethylated nucleic acid bases includes subjecting the plurality of nucleic acid molecules to bisulfite conversion.

13. The method of any one of claims 1 to 12, wherein the amplification in (c) comprises 1 to 3, 1 to 5, or 1 to 10 amplification cycles.

14. The method of any one of claims 1 to 13, further comprising enriching in a second partition an amplified adaptor-linked DNA fragment having the second set of adaptors at both ends, to generate an enriched DNA fragment in the second partition.

15. The method of any one of claims 1 to 14, further comprising using primers capable of initiating polymerization at the first set of adaptors to amplify the amplified adaptor-ligated DNA fragment of the first partition.

16. The method of claim 14 or 15, further comprising using primers capable of initiating polymerization at the second set of adaptors to amplify the amplified adaptor-linked DNA fragment of the second partition.

17. The method of any one of claims 1 to 16, wherein (d) further comprises distributing the amplified adaptor-ligated DNA fragment to three or more partitions, and wherein the method further comprises enriching the amplified adaptor-ligated DNA fragment having the first set of adaptors at one end and the second set of adaptors at the other end in a third partition to produce an enriched DNA fragment in the third partition.

18. The method of any one of claims 1 to 17, further comprising amplifying an amplified adaptor-linked DNA fragment having the first set of adaptors at one end and the second set of adaptors at the other end in a third partition using primers, the primers being capable of initiating polymerization at the first set of adaptors at one end of the amplified adaptor-linked DNA fragment and at the second set of adaptors at the other end of the amplified adaptor-linked DNA fragment.

19. The method of any one of claims 1 to 18, further comprising using a hybridization capture probe to capture at least a subset of the amplified adaptor-linked DNA fragments in one or more targeted genomic regions.

20. The method of claim 19, wherein the hybridization capture probe covers one or more restriction enzyme cleavage sites in the targeted genomic region.

21. The method of any one of claims 1 to 20, further comprising (f) processing the enriched DNA fragment to determine the methylation state of the enriched DNA fragment.

22. The method of claim 21, wherein (f) further comprises generating sequencing data that provides a count of the enriched DNA fragments.

23. The method of claim 21 or 22, wherein one or more methylation-sensitive restriction enzymes are used for (b), and wherein (f) further comprises determining the methylation state by counting highly methylated DNA fragments in a first partition and counting low-methylated DNA fragments in a second partition.

24. The method of any one of claims 21 to 23, wherein one or more methylation-dependent restriction enzymes are used in (b), and wherein (f) further comprises determining the methylation state by counting hypomethylated DNA fragments in a first partition and counting hypermethylated DNA fragments in a second partition.

25. The method of any one of claims 21 to 24, wherein the methylation state indicates the presence or absence of a disease or risk thereof in the individual.

26. The method of claim 25, wherein one or more trained machine learning classifiers are used to detect the presence or absence of a disease or symptom in the object.

27. The method of claim 26, wherein the one or more trained machine learning classifiers comprise features containing the counts of the enriched DNA fragments described in (e).

28. The method of claim 26 or 27, wherein the one or more trained machine learning classifiers comprise at least one of the following: support vector machine, random forest, k-nearest neighbor, Naive Bayes, Gaussian process, decision tree, XGBoost, neural network, linear and quadratic discriminant analysis, logistic regression, general linear model, and any combination thereof.

29. The method of any one of claims 25 to 28, further comprising administering a clinical intervention to the subject based at least in part on the presence or absence of a disease or risk thereof in the individual.

30. The method of claim 29, wherein the clinical intervention comprises an effective amount of chemotherapy, radiotherapy, hormone therapy, targeted therapy, immunotherapy, biologics, cell therapy, antibiotics, antiviral drugs, radiation, cryoablation or a combination thereof, and / or surgical intervention.

31. The method of any one of claims 25 to 29, wherein the disease includes cancer, an infectious disease, or a non-infectious disease.

32. A kit comprising reagents for performing the method of any one of claims 1 to 31.

33. Methods of treating the subject, including: (a) Performing the method of any one of claims 1 to 31 on a biological sample from the subject to determine whether the subject suffers from a condition sensitive to clinical intervention; as well as (b) If the subject has been identified as having a condition that is sensitive to the clinical intervention, the clinical intervention is administered to the subject.

34. The method of claim 33, wherein the subject has or is suspected of having cancer, an infectious disease, or a non-infectious disease.

35. The method of claim 33 or 34, wherein the clinically sensitive condition is a growth, metastasis, infection, or autoimmune disease.

36. The method of any one of claims 33 to 35, wherein the clinical intervention comprises an effective amount of chemotherapy, radiotherapy, hormone therapy, targeted therapy, immunotherapy, biological products, cell therapy, antibiotics, antiviral drugs, radiation, cryoablation or a combination thereof, and / or surgical intervention.

37. The method of any one of claims 33 to 36, further comprising processing the enriched DNA fragment to determine the methylation state of the enriched DNA fragment, wherein the methylation state indicates whether the patient has a condition sensitive to the clinical intervention.

38. Methods for monitoring the response of subjects to a primary clinical intervention, including: (a) Performing the method of any one of claims 1 to 31 on a biological sample from said object, wherein said object has received at least one round of the first clinical intervention; (b) Processing the enriched DNA fragment to determine the methylation status of the enriched DNA fragment; as well as (c) Based at least in part on the methylation status of the enriched DNA fragment, administer another round of the first clinical intervention to the subject.

39. The method of claim 38, wherein the subject has or is suspected of having cancer, an infectious disease, or a non-infectious disease.

40. The method of claim 38 or 39, wherein the clinical intervention comprises an effective amount of chemotherapy, radiotherapy, hormone therapy, targeted therapy, immunotherapy, biologics, cell therapy, antibiotics, antiviral drugs, radiation, cryoablation or a combination thereof, and / or surgical intervention.

41. The method of any one of claims 38 to 40, wherein the second clinical intervention is different from the first clinical intervention.

42. The method of any one of claims 38 to 41, wherein the second clinical intervention comprises an effective amount of chemotherapy, radiotherapy, hormone therapy, targeted therapy, immunotherapy, biological products, cell therapy, antibiotics, antiviral drugs, radiation, cryoablation or a combination thereof, and / or surgical intervention, or cessation of the first clinical intervention.

43. A method for diagnosing or prognosing a disease in a subject, the method comprising performing the method of any one of claims 1 to 31 to determine whether the subject has the disease or the severity of the subject's disease.

44. The method of claim 43, wherein the disease is cancer, an infectious disease, or a non-infectious disease.

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