Method for sequencing using distributed nucleic acids

Abstract

A method for sequencing using distributed nucleic acids is provided. [Solution] DNA damage (e.g., cytosine deamination) can occur more frequently in the highly methylated fraction of a DNA (e.g., cell-free DNA) sample than in the low-methylated fraction. Embodiments include the steps of sequencing the highly methylated and low-methylated fractions, wherein the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequence of molecules from the highly methylated fraction requires the observation of transition mutations in a greater number of molecules than the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequence of molecules from the low-methylated fraction, or C-to-T or G-to-A transition mutations are not called compared to the reference sequence based on the sequence of molecules in the highly methylated fraction.

Description

[Technical Field]

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 018,363, filed on 30 April 2020, which is incorporated herein by reference in its entirety for all purposes. [Background technology]

[0002] background Cancer is the cause of millions of deaths worldwide every year. Early detection of cancer can lead to improved outcomes because early-stage cancers tend to be more responsive to treatment.

[0003] Improperly controlled cell growth can lead to copy number variations (CNVs), single nucleotide variations (SNVs), gene fusions, insertions and / or deletions (indels), These are characteristics of cancer generally caused by the accumulation of genetic and epigenetic changes, including DNA methylation such as cytosine 5-methylation (5-methylcytosine), and epigenetic variations involving the association of DNA with chromatin proteins and transcription factors.

[0004] A biopsy represents a traditional approach to detecting or diagnosing cancer, in which cells or tissue are extracted from a potentially cancerous site and analyzed for relevant phenotypic and / or genotypic characteristics. A drawback of biopsies is that they are invasive.

[0005] Cancer detection based on the analysis of bodily fluids such as blood ("liquid biopsy") is an interesting alternative method based on the observation of DNA derived from cancer cells being released into bodily fluids. Liquid biopsy is non-invasive (possibly requiring only blood collection). However, given the low concentration and heterogeneity of cell-free DNA, developing accurate and highly sensitive methods for analyzing liquid biopsy material has been a challenging task. It has now been determined that DNA exhibiting high methylation can have damaged bases, such as deaminations, at a greater frequency than other DNA, independently of the actual genome sequence, which can have a detrimental effect on sequencing accuracy. Therefore, the aforementioned challenges are particularly applicable to procedures in which epigenetic changes, including DNA methylation, are analyzed by sequencing of high-methylation and low-methylation fractions. Thus, there is a need for improved methods for sequencing using distributed nucleic acids. [Overview of the Initiative] [Means for solving the problem]

[0006] Abstract This disclosure relates to a method for analyzing a DNA sample, such as cell-free DNA (cfDNA), wherein the sample is distributed into multiple fractions, including a highly methylated fraction (partition) and a lowly methylated fraction. The present disclosure provides embodiments including a method. This disclosure is based in part on the following embodiment: In a highly methylated fraction, DNA (e.g., cfDNA) may have a greater amount of damage, such as cytosine deamination, which does not reflect the actual mutation in the cell from which the DNA originates. Such DNA damage can result in an increased frequency of apparent, but false-positive, C-to-T and complementary G-to-A transition mutations. Therefore, it may be beneficial to use stricter requirements to identify such transition mutations based on sequences derived from a highly methylated fraction than to identify such transition mutations based on sequences derived from a low-methylated fraction. Accordingly, the following embodiments are provided. Embodiment 1 is a method for analyzing a sample of DNA, A step of distributing a DNA sample into a plurality of fractions, wherein the plurality of fractions include a hypermethylated fraction and a hypomethylated fraction; A step of tagging DNA in the hypermethylated and hypomethylated fractions to generate tagged nucleic acids, wherein the tagged nucleic acids include molecular barcodes; A step of obtaining sequence reads of molecules from the hypermethylated fraction and sequence reads of molecules from the hypomethylated fraction, wherein the sequence reads include a molecular barcode sequence and a sample sequence; A step of grouping sequence reads into families based on at least one of the genomic positions corresponding to the first and last nucleotides of (a) the molecular barcode sequence and (b) the sample sequence, wherein a family includes sequence reads derived from a single DNA molecule in the sample; A step of determining a first set of sequences of molecules from the hypermethylated fraction and a second set of sequences of molecules from the hypomethylated fraction; and A step of calling a plurality of bases based on the first and second sets of sequences, (i) Calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on the sequence of the molecules in the first set requires observing a greater number of transition mutations in a greater number of molecules than calling a C-to-T or G-to-A transition mutation compared to the reference sequence based on the sequence of the molecules in the second set; or (ii) A C-to-T or G-to-A transition mutation is not called compared to the reference sequence based on the sequence of the molecules in the first set, or a C-to-T or G-to-A transition mutation is called compared to the reference sequence based on the sequence of the molecules in the second set without using the sequence of the molecules in the first set, or a C-to-T or G-to-A transition mutation is called compared to the reference sequence if at least one sequence of the molecules in the second set includes a C-to-T or G-to-A transition mutation. A method comprising the step.

[0007] Embodiment 2 is the method according to the immediately preceding embodiment, wherein the step of calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on the sequences of the first set of molecules requires observation of the transition mutation in a greater number of molecules than the step of calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on the sequences of the second set of molecules.

[0008] Embodiment 3 is the method according to any one of the preceding embodiments, wherein the step of calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on the sequences of the first set of molecules requires observation of the transition mutation in at least three molecules.

[0009] Embodiment 4 is the method according to the immediately preceding embodiment, wherein the step of calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on the sequences of the first set of molecules requires observation of the transition mutation in at least four molecules.

[0010] Embodiment 5 is the method according to the immediately preceding embodiment, wherein the step of calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on the sequences of the first set of molecules requires observation of the transition mutation in at least five molecules.

[0011] Embodiment 6 is the method according to any one of the preceding embodiments, wherein the step of calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on the sequences of the second set of molecules requires observation of the transition mutation in at least two molecules.

[0012] Embodiment 7 is the method according to the immediately preceding embodiment, wherein the step of calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on the sequences of the second set of molecules requires observation of the transition mutation in at least three molecules.

[0013] Embodiment 8 is a method according to any one of the preceding embodiments, wherein the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequences of a first set of molecules requires observation of transition mutations in at least two more molecules than the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequences of a second set of molecules.

[0014] Embodiment 9 is a method according to any one of the preceding embodiments, wherein a first threshold is used to call a C-to-T or G-to-A transition based on the arrangement of a first set of molecules, and a second threshold is used to call a C-to-T or G-to-A transition based on the arrangement of a second set of molecules; the first threshold provides a first level of singularity for calling a C-to-T or G-to-A transition; the second threshold provides a second level of singularity for calling a C-to-T or G-to-A transition; and the first level of singularity is approximately equal to the second level of singularity, or the first level of singularity is within 10%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.2%, or 0.1% of the second level of singularity.

[0015] Embodiment 10 is the method of the immediately preceding embodiment, wherein the first and second thresholds are specific to transitions from C to T and / or from G to A.

[0016] Embodiment 11 is the method of Embodiment 9 or 10, wherein the first and second thresholds are determined from at least one or more control samples, and optionally, at least one or more control samples are derived from individuals not suspected of having cancer.

[0017] Embodiment 12 is the method according to any one of Embodiments 1 to 8, wherein a first group of position-specific background error rates is used for multiple positions for the sequence of a first set of molecules; a second group of position-specific background error rates is used for multiple positions for the sequence of a second set of molecules; the second group includes position-specific background error rates higher than the corresponding position-specific background error rates of the first group; and the step of calling a C-to-T or G-to-A transition mutation based on the sequence of the first set of molecules requires the observation of C-to-T or G-to-A transition mutations at a frequency exceeding the corresponding rates derived from the first group of position-specific background error rates.

[0018] Embodiment 13 is the method of the immediately preceding embodiment, wherein the step of calling C-to-T or G-to-A transition mutations based on the sequence of a first set of molecules requires the observation of C-to-T or G-to-A transition mutations at a frequency at least 2, 3, 4, or 5 times higher than the corresponding rate from the first group of position-specific background error rates.

[0019] Embodiment 14 is the method of the immediately preceding embodiment, wherein the step of calling C-to-T or G-to-A transition mutations based on the sequence of a first set of molecules requires the observation of C-to-T or G-to-A transition mutations at a frequency exceeding the corresponding rate from the first group of site-specific background errors, by an amount consistent with confidence levels of at least 95%, 98%, 99%, 99.5%, or 99.9%.

[0020] Embodiment 15 is the method according to any one of Embodiments 12 to 14, wherein the first and second groups of position-specific background error rates are determined from a plurality of control samples, and the control samples, if necessary, are derived from individuals not suspected of having cancer.

[0021] Embodiment 16 is the method according to any one of Embodiments 12 to 14, wherein the first and second groups of position-specific background error rates are determined using multiple control samples, and optionally the control samples are derived from individuals not suspected of having cancer.

[0022] Embodiment 17 is a method according to any one of Embodiments 12 to 14, wherein the first and second groups of location-specific background error rates are determined using medical history data.

[0023] Embodiment 18 is the method according to any one of Embodiments 12 to 14, wherein the first and second groups of position-specific background error rates are determined using reads and / or sequences of molecules derived from the highly methylated and low methylated fractions, respectively.

[0024] Embodiment 19 involves the step of obtaining sequence reads of molecules derived from the moderate fraction; A step of determining a third set of molecular sequences derived from the moderate fraction; and The method according to any one of the prior embodiments, further comprising the step of calling multiple bases based on a third set of sequences.

[0025] Embodiment 20 is the method of the immediately preceding embodiment, wherein the C-to-T and G-to-A transition mutations are called based on a third set of sequences with less strictness than when the C-to-T and G-to-A transition mutations are called based on a first set of molecular sequences.

[0026] Embodiment 21 is the method of the immediately preceding embodiment, wherein the C-to-T and G-to-A transition mutations are called based on a third set of sequences in the same way that the C-to-T and G-to-A transition mutations are called based on a second set of sequences, or with greater strictness than that that the C-to-T and G-to-A transition mutations are called based on a second set of sequences.

[0027] Embodiment 22 is a method for analyzing a DNA sample, The steps of obtaining first and second sets of sequence reads derived from the highly methylated and low methylated fractions of the sample, respectively; and A step of determining the sequences derived from a first and second set of sequence reads, (i) The step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a first set of reads requires observation of transition mutations in a greater number of reads than the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a second set of reads; or (ii) A method comprising the step that a C-to-T or G-to-A transition mutation is not called compared to a reference sequence based on reads from a first set, or a C-to-T or G-to-A transition mutation is called compared to a reference sequence based on sequences from a second set of molecules without using sequences from a first set of molecules, or a C-to-T or G-to-A transition mutation is called compared to a reference sequence only if at least one sequence from the second set of molecules contains a C-to-T or G-to-A transition mutation.

[0028] Embodiment 23 is the method of the immediately preceding embodiment, wherein the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a first set of reads requires observation of transition mutations in a greater number of reads than the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a second set of reads.

[0029] Embodiment 24 is a method according to either Embodiment 22 or 23, wherein the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a first set of reads requires observation of transition mutations in at least three reads.

[0030] Embodiment 25 is the method of the immediately preceding embodiment, wherein the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a first set of reads requires observation of transition mutations in at least four reads.

[0031] Embodiment 26 is the method of the immediately preceding embodiment, wherein the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a first set of reads requires observation of transition mutations in at least five reads.

[0032] Embodiment 27 is a method according to any one of Embodiments 22-26, wherein the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a second set of reads requires observation of transition mutations in at least two reads.

[0033] Embodiment 28 is the method of the immediately preceding embodiment, wherein the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a second set of reads requires observation of transition mutations in at least three reads.

[0034] Embodiment 29 is a method according to any one of Embodiments 22 to 28, wherein the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a first set of reads requires observation of transition mutations in at least two more reads than the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a second set of reads.

[0035] Embodiment 30 is a method according to any one of the prior embodiments, further comprising the step of obtaining a third set of sequence reads derived from the moderate fraction, wherein the sequence is determined from the third set in addition to the first and second sets.

[0036] Embodiment 31 is the method of the immediately preceding embodiment, wherein transition mutations from C to T and G to A are called based on a third set of reads with lower strictness than in which transition mutations from C to T and G to A are called based on a first set of reads.

[0037] Embodiment 32 is the method of the immediately preceding embodiment, wherein the transition mutations from C to T and G to A are called based on a third set of reads, in the same way that the transition mutations from C to T and G to A are called based on a second set of reads.

[0038] Embodiment 33 is a method according to any one of the prior embodiments, wherein the DNA in the high-methylation fraction and the DNA in the low-methylation fraction are differentially tagged.

[0039] Embodiment 34 is a method according to any one of the prior embodiments, wherein the DNA of the high-methylation fraction and the DNA of the low-methylation fraction are differentially tagged with sequence tags including barcodes.

[0040] Embodiment 35 is a method according to any one of the preceding embodiments, wherein the highly methylated and low methylated fractions are prepared by contacting the DNA of the sample with a methyl-binding reagent immobilized on a solid support.

[0041] Embodiment 36 is the method of the immediately preceding embodiment, wherein the methyl-binding reagent includes MBD.

[0042] Embodiment 37 is the method according to Embodiment 36, wherein the methyl bonding reagent includes MeCP.

[0043] Embodiment 38 is the method of Embodiment 36, wherein the methyl-binding reagent comprises an antibody that binds to a methylated nucleotide, and optionally the methylated nucleotide is methylated cytosine.

[0044] Embodiment 39 is a method according to any one of Embodiments 35 to 38, comprising the step of contacting the DNA of a sample with a methyl-binding reagent immobilized on a solid support to obtain a low-methylated fraction and a high-methylated fraction based on differential binding to the methyl-binding reagent.

[0045] Embodiment 40 is a method according to any one of Embodiments 35 to 39, which includes the step of adding differential tags to the DNA of the high-methylation fraction and the DNA of the low-methylation fraction before sequencing.

[0046] Embodiment 41 is a method according to any one of the prior embodiments, wherein the step of determining the sequence includes mapping first and second sets of sequence reads to a reference sequence to produce mapped sequence reads.

[0047] Embodiment 42 is a method according to any one of the prior embodiments, wherein the DNA of the sample or the highly methylated and low methylated fractions includes a region of interest that is enriched or captured.

[0048] Embodiment 43 is a method according to any one of the prior embodiments, comprising the steps of enriching the DNA of a sample or hypermethylated and hypomethylated fractions with respect to a region of interest, or capturing a region of interest from a sample or hypermethylated and hypomethylated fractions.

[0049] Embodiment 44 is the method of the immediately preceding embodiment, wherein the enrichment or capture step includes contacting DNA with a set of target-specific probes, thereby producing a captured set of DNA molecules.

[0050] Embodiment 45 is a method according to any one of Embodiments 42 to 44, wherein the region of interest includes a sequence-variable target region.

[0051] Embodiment 46 is the method of the preceding embodiment, wherein the set of target-specific probes includes target-binding probes specific to a sequence-variable target set.

[0052] Embodiment 47 is the method of the preceding embodiment, wherein the footprint of the sequence variable target region set is at least 25kB or at least 50kB.

[0053] Embodiment 48 is a method according to any one of Embodiments 42 to 47, wherein the region of interest includes an epigenetic target region.

[0054] Embodiment 49 is the method of the preceding embodiment, wherein the set of target-specific probes includes target-binding probes specific to an epigenetic target set.

[0055] Embodiment 50 is a method according to any one of Embodiments 42 to 49, wherein the region of interest includes a sequence-variable target region set and an epigenetic target region set.

[0056] Embodiment 51 is the method according to the previous embodiment, wherein the sequence variable target region set contains at least 10 regions and the epigenetic target region set contains at least 100 regions.

[0057] Embodiment 52 is the method according to any one of Embodiments 50 to 51, wherein the footprint of the epigenetic target region set is at least twice as large as the size of the sequence variable target region set.

[0058] Embodiment 53 is the method of the immediately preceding embodiment, wherein the footprint of the epigenetic target region set is at least 10 times larger than the size of the sequence variable target region set.

[0059] Embodiment 54 is the method according to Embodiment 52 or 53, wherein the target-specific probe set is configured to capture cfDNA corresponding to a sequence-variable target set with a larger capture yield than cfDNA corresponding to an epigenetic target set.

[0060] Embodiment 55 is a method according to any one of Embodiments 50 to 54, wherein the sequence variable target region set has a footprint in the range of 10 to 30 kilobases.

[0061] Embodiment 56 is a method according to any one of Embodiments 50 to 54, wherein the sequence variable target region set has a footprint in the range of 30 to 60 kilobases.

[0062] Embodiment 57 is a method according to any one of Embodiments 50 to 54, wherein the sequence variable target region set has a footprint in the range of 60 kilobases to 1 megabase.

[0063] Embodiment 58 is a method according to any one of Embodiments 50 to 54, wherein the sequence variable target region set has a footprint in the range of 1 to 2 megabases.

[0064] Embodiment 59 is a method according to any one of Embodiments 50 to 58, wherein the epigenetic target region set has a footprint in the range of 0.2 to 0.8 megabases.

[0065] Embodiment 60 is a method according to any one of Embodiments 50 to 58, wherein the epigenetic target region set has a footprint in the range of 0.8 to 1.5 megabases.

[0066] Embodiment 61 is a method according to any one of Embodiments 50 to 58, wherein the epigenetic target region set has a footprint in the range of 1.5 to 3 megabases.

[0067] Embodiment 62 is a method according to any one of Embodiments 50 to 58, wherein the epigenetic target region set has a footprint in the range of 3 to 8 megabases.

[0068] Embodiment 63 is a method according to any one of Embodiments 50 to 62, wherein the epigenetic target region set includes a highly methylated variable target region set.

[0069] Embodiment 64 is the method according to any one of Embodiments 50 to 63, wherein the epigenetic target region set includes a low-methylation variable target region set.

[0070] Embodiment 65 is a method according to any one of Embodiments 50 to 64, wherein the epigenetic target region set includes a fragmentation variable target region set.

[0071] Embodiment 66 is the method of the immediately preceding embodiment, wherein the fragmentation variable target region set includes a transcription start site region.

[0072] Embodiment 67 is the method according to Embodiment 65 or 66, wherein the fragmentation variable target region set includes a CTCF binding region.

[0073] Embodiment 68 is a method according to any one of Embodiments 50 to 67, wherein the captured DNA of the sequence variable target set is sequenced to a higher sequencing depth than the captured DNA of the epigenetic target region set.

[0074] Embodiment 69 is the method of the immediately preceding embodiment, wherein the captured DNA of the sequence variable target set is sequenced to at least 2, 3, or 4 times higher sequencing depth than the captured cfDNA molecules of the epigenetic target region set, or to 4 to 10 or 4 to 100 times higher sequencing depth.

[0075] Embodiment 70 is a method according to any one of Embodiments 50 to 69, wherein the captured DNA of the sequence variable target set is pooled together with the captured DNA of the epigenetic target region set before sequencing.

[0076] Embodiment 71 is a method according to any one of Embodiments 50 to 70, wherein the captured DNA of the sequence variable target set and the captured DNA of the epigenetic target region set are sequenced in the same sequencing cell.

[0077] Embodiment 72 is a method according to any one of Embodiments 50 to 71, wherein the DNA in the highly methylated and low methylated fractions is amplified before capture.

[0078] Embodiment 73 is a method according to any one of the preceding embodiments, wherein the sample is obtained from biological tissue or biological fluid.

[0079] Embodiment 74 is a method according to any one of the preceding embodiments, wherein the sample is obtained from blood.

[0080] Embodiment 75 is a method according to any one of the prior embodiments, wherein the DNA of the sample includes cell-free DNA.

[0081] Embodiment 76 is a method according to any one of the prior embodiments, wherein the DNA of the sample is essentially derived from cell-free DNA.

[0082] Embodiment 77 is a method according to any one of the preceding embodiments, wherein the sample is derived from a subject having or suspected to have a proliferative disorder or a solid tumor.

[0083] Embodiment 78 is a method according to any one of the preceding embodiments, wherein the sample is derived from a subject that has undergone or has previously undergone treatment for a proliferative disorder or a solid tumor.

[0084] Embodiment 79 is a method according to any one of the prior embodiments, further comprising the step of determining the likelihood that a subject has a proliferative disorder or a solid tumor based on the sequence determined from the sequence reads.

[0085] Embodiment 80 is the method according to any one of the preceding three embodiments, wherein the proliferative disorder or solid tumor is cancer.

[0086] In some embodiments, the results of the methods disclosed herein are used as input for generating a report. The report may be in paper or electronic format. For example, the classification of C-to-T or G-to-A transition mutations obtained by the methods disclosed herein may be directly displayed in such a report. Alternatively, or on top of that, diagnostic information or treatment recommendations based on the classification of whether or not C-to-T or G-to-A transition mutations are present may be included in the report.

[0087] Various steps of the methods disclosed herein may be performed at the same or different times, in the same or different geographical locations, for example, in countries, and / or by the same or different people.

[0088] The accompanying drawings incorporated herein, and which constitute part thereof, illustrate certain embodiments and, together with the descriptions provided herein, are useful for illustrating certain principles of the methods, computer-readable media, and systems disclosed herein. The descriptions provided herein will be better understood when read in conjunction with the accompanying drawings, which are included as examples and are not limiting. It will be understood that, unless the context indicates otherwise, similar reference numbers refer to similar components throughout the drawings. It will also be understood that some or all of the drawings are schematic diagrams for illustrative purposes and do not necessarily depict the actual relative dimensions or locations of the elements shown. [Brief explanation of the drawing]

[0089] [Figure 1] Figure 1 shows a summary of the distribution method system.

[0090] [Figure 2] Figure 2 is a schematic diagram of an example of a system suitable for use in some embodiments of this disclosure.

[0091] [Figure 3] Figure 3 shows the single nucleotide variant (SNV) error rate per base for specific nucleotide substitutions. [Modes for carrying out the invention]

[0092] Detailed explanation Next, certain embodiments of the present invention will be described in detail. The present invention will be described in conjunction with such embodiments, but it will be understood that the present invention is not intended to be limited to these embodiments. On the contrary, the present invention is intended to cover all alternatives, modifications and equivalents that may be included within the present invention as defined by the appended claims.

[0093] Before describing this teaching in detail, please understand that this disclosure is not limited to specific compositions or process steps, as such may vary. Note that, as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly indicates otherwise. For example, the reference to “a nucleic acid” includes multiple nucleic acids, and the reference to “a cell” includes multiple cells. ,and so on.

[0094] A numerical range encompasses the numbers that define the range. Measured and measurable values ​​are understood to be approximations, taking into account the significant figures and errors associated with the measurement. Also, "comprise", "comprises", "comprising", and "include" are used. "contain", "contains", "containing", The use of “include,” “includes,” and “including” is not intended to be restrictive. Both the general and detailed explanations above are merely illustrative and descriptive, and should not be understood as limiting the teaching.

[0095] Unless otherwise specified in the foregoing specification, embodiments in this specification that enumerate various components "including" shall also be considered to "consist of" or "essentially consist of" the enumerated components; embodiments in this specification that enumerate various components "including" or "essentially consist of" the enumerated components; embodiments in this specification that enumerate various components "essentially consist of" or "essentially consist of" the enumerated components (this interchangeability does not apply to the use of these terms in the claims).

[0096] The section headings used herein are for orderly purposes only and should not be construed as limitations on the subject matter disclosed. In the event of any conflict between any document or other material incorporated herein by reference and any express content herein, including definitions, herein shall prevail. I. Definition

[0097] The step of “calling C-to-T or G-to-A transition mutations compared to a reference sequence” refers to the step of concluding that sequence reads from the sample, compared to a reference sequence with acceptable confidence, support the presence of a mutation at a given location in the nucleic acid being sequenced. In some embodiments, the conclusion is based on the number of reads in which a difference appears at the location, and / or is calculated by computer in combination with other parameters as necessary, such as a measure of sequence quality (e.g., Phred quality score). In some embodiments, the conclusion is based on the number of molecules in which a difference appears at the location, and / or is calculated by computer in combination with other parameters as necessary, such as a measure of variant allele fraction and / or background error rate, which may be estimated from data from a control group, such as medical history data or data from a healthy cohort. In these embodiments, molecular counts are estimated using the molecular barcode and / or genomic coordinates (co-ordinate) of the sequence reads. The steps of generating an assembled sequence containing the mutations and generating a report listing the mutations are non-exclusive examples of the steps of calling the mutations. The assembled sequence or report may be displayed, printed, or otherwise communicated to the user or other individuals.

[0098] "Cell-free DNA," "cfDNA molecules," or simply "cfDNA" includes DNA molecules that arise in the extracellular form of the subject (e.g., in blood, serum, plasma, or in other bodily fluids such as lymph, cerebrospinal fluid, urine, or sputum) and that are neither contained within cells nor otherwise bound to cells. DNA is inherently present in the cells(s) of large, complex organisms, such as mammals, but DNA has undergone release from cells(s) into fluids found in organisms. Typically, cfDNA can be obtained by obtaining a fluid sample without the need to perform an in vitro cell lysis step, which also includes the removal of cells present in the fluid (e.g., centrifugation of blood to remove cells).

[0099] The "capture yield" of a probe for a given set of target regions refers to the amount of nucleic acid corresponding to the set of target regions captured by the collectible under typical conditions (e.g., compared to another set of target regions, or in absolute terms). Exemplary and typical capture conditions are incubation of the sample nucleic acid and probe in a small reaction volume (approximately 20 μL) containing buffer for strict hybridization at 65°C for 10–18 hours. Capture yield can be expressed in absolute terms, or in relative terms for multiple probe collectibles. When capture yields for multiple sets of target regions are compared, the yield is normalized to the footprint size of the target region set (e.g., per kilobase). Therefore, for example, if the footprint sizes of the first and second target regions are 50 kb and 500 kb, respectively, the normalization factor is 0.1. When the mass-per-volume concentration of captured DNA corresponding to the first target region set exceeds 0.1 times the mass-per-volume concentration of captured DNA corresponding to the second target region set, the DNA corresponding to the first target region set is captured in a larger yield than the DNA corresponding to the second target region set. As yet another example, using the same footprint size, when the mass-per-volume concentration of captured DNA corresponding to the first target region set is 0.2 times the mass-per-volume concentration of captured DNA corresponding to the second target region set, the DNA corresponding to the first target region set is captured in twice the amount of captured DNA as the DNA corresponding to the second target region set.

[0100] The step of “capturing” or “enriching” one or more target nucleic acids refers to the step of preferentially isolating or separating one or more target nucleic acids from non-target nucleic acids.

[0101] A "captured set" of nucleic acids refers to the nucleic acids that have been captured.

[0102] A “target region set” or “target region” refers to multiple genomic loci or multiple genomic regions that are targeted (e.g., by sequence complementarity) by a set of probes targeted for capture and / or targeting.

[0103] "Corresponding to a target region set" means that the nucleic acid, such as cfDNA, originates from a gene locus in the target region set, or specifically binds to one or more probes corresponding to the target region set.

[0104] In the context of a probe or other oligonucleotide and a target sequence, "specifically binding" means that, under appropriate hybridization conditions, the oligonucleotide or probe hybridizes to its target sequence or a replica thereof to form a stable probe:target hybrid, while simultaneously minimizing the formation of a stable probe:non-target hybrid. Thus, the probe hybridizes to a sufficiently larger extent than the non-target sequence to enable the capture or detection of the target sequence. Appropriate hybridization conditions can be predicted based on sequence composition, as is well known in the art, or determined by using routine testing methods (e.g., Sambrook et al., Molecular, incorporated herein by reference). Cloning, A Laboratory Manual, 2nd ed. (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, 1989) §§1.90~1.91, 7.37~ See 7.57, 9.47–9.51 and 11.47–11.57, in particular §§9.50–9.51, 11.12–11.13, 11.45–11.47 and 11.55–11.57).

[0105] A "set of sequence-variable target regions" refers to a set of target regions in neoplastic cells (e.g., tumor cells and cancer cells) that can exhibit sequence changes such as nucleotide substitutions, insertions, deletions, gene fusions, or transpositions.

[0106] The “epigenetic target region set” refers to a set of target regions that can manifest non-sequence modifications in neoplastic cells (e.g., tumor cells and cancer cells) and non-tumor cells (e.g., immune cells, cells derived from the tumor microenvironment). Such modifications do not alter the DNA sequence. Examples of non-sequence modification changes include, but are not limited to, methylation (increase or decrease), nucleosome distribution, CTCF binding, transcription start sites, regulatory protein binding regions, and changes in any other protein that can bind to DNA. For the purposes of this end, loci sensitive to neoplasm, tumor, or cancer-related localized amplification and / or gene fusions may also be included in the epigenetic target region set for the following reasons: for example, localized amplification and / or gene fusions can be detected at relatively shallow sequencing depths because their detection does not depend on the precision of base calls at one or a few individual locations. Therefore, detecting copy number changes by sequencing or detecting fused sequences that map to two or more loci in the reference genome tends to be more similar to detecting the exemplary epigenetic changes described above than detecting nucleotide substitutions, insertions, or deletions. For example, the epigenetic target region set may include a set of target regions for analyzing fragment length or fragment endpoint location distributions.

[0107] Circulating tumor DNA, or ctDNA, is a component of cfDNA that originates from tumor cells or cancer cells. In some embodiments, cfDNA includes DNA originating from normal cells and DNA originating from tumor cells (i.e., ctDNA). Tumor cells are neoplastic cells that originate from a tumor, regardless of whether they remain within the tumor or have left the tumor (for example, as in the case of metastatic cancer cells and circulating tumor cells).

[0108] The term "hypermethylation" refers to an increased level or degree of methylation in a nucleic acid molecule(s) compared to other nucleic acid molecules in a population of nucleic acid molecules (e.g., a sample). In some embodiments, hypermethylated DNA may include DNA molecules containing at least one methylated residue, at least two methylated residues, at least three methylated residues, at least five methylated residues, at least ten methylated residues, at least twenty methylated residues, at least twenty-five methylated residues, or at least thirty methylated residues.

[0109] The term "hypomethylation" refers to a reduced level or degree of methylation in a nucleic acid molecule(s) compared to other nucleic acid molecules in a population of nucleic acid molecules (e.g., a sample). In some embodiments, hypomethylated DNA includes unmethylated DNA molecules. In some embodiments, hypomethylated DNA may include DNA molecules containing 0 methylated residues, at most 1 methylated residue, at most 2 methylated residues, at most 3 methylated residues, at most 4 methylated residues, or at most 5 methylated residues.

[0110] The term "methylated nucleotide" refers to a nucleotide to which a methyl group is attached, other than the methyl group attached to the pyrimidine ring of thymine. Examples of methylated nucleotides include nucleotides containing 5-methylcytosine or 7-methylguanine.

[0111] As used herein, “molecular sequence” and its grammatical variants refer to a sequence determined from multiple reads, including molecularly derived reads from the same original sample molecule. Reads can be determined to originate from the same original sample molecule based on, for example, the sequence of a tag or barcode; the genomic position corresponding to the first and last nucleotides of the sample sequence; and / or the sequence of multiple bases immediately following the 5' tag sequence and / or immediately following the 3' tag sequence, either one or more of these. In some embodiments, each base in the molecular sequence is determined based on the minimum number of matching reads at that position, e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 reads; the number of required reads may depend on whether the call is supported by reads for both strands of DNA or only one strand; for example, if there are reads for only one strand of the sequence compared to when there are reads for both strands of the molecular sequence, the number of required reads may increase by only 1, 2, 3, 4, or 5 reads.

[0112] The terms “or any combination thereof (singular)” and “or any combination thereof (plural)” as used herein refer to any sort and combination of the listed terms that precedes this term. For example, “A, B, C or any combination thereof” is intended to include at least one of A, B, C, AB, AC, BC, or ABC, and also intended to include BA, CA, CB, ACB, CBA, BCA, BAC, or CAB, where the order is important in a particular context. This example explicitly includes combinations containing repetitions of one or more items or terms, such as BB, AAA, AAB, BBC, AAABCCCC, CBBAAA, CABABB, and so on. A person skilled in the art will typically understand that there is no limit to the number of items or terms in any combination, unless otherwise evident from the context.

[0113] "Or" is used in an inclusive sense unless the context requires otherwise, i.e., it is equivalent to "and / or". II. Exemplary Methods

[0114] Methods for analyzing DNA samples are provided herein. In some embodiments, the method includes the step of obtaining first and second sets of sequence reads derived from high-methylated and low-methylated fractions, respectively. In some embodiments, the method includes the step of obtaining first and second sets of molecular sequences derived from high-methylated and low-methylated fractions, respectively. Molecular sequences can be obtained, for example, by the steps of: distributing a DNA sample into a plurality of fractions, the plurality of fractions comprising a high-methylated fraction and a low-methylated fraction; tagging the DNA in the high-methylated and low-methylated fractions to generate tagged nucleic acids comprising molecular barcodes; obtaining sequence reads of molecules derived from the high-methylated fraction and sequence reads of molecules derived from the low-methylated fraction, wherein the sequence reads comprise molecular barcode sequences and sample sequences; and grouping the sequence reads into families based on at least one of (a) molecular barcode sequences and (b) genomic positions corresponding to the first and last nucleotides of the sample sequences, wherein the families comprise sequence reads derived from a single DNA molecule in the sample. In some embodiments, the method includes the step of determining sequences derived from first and second sets of sequence reads or molecular sequences. The term “sequence” is used in a collective sense and does not necessarily imply a single continuous sequence. That is, it can refer to a whole genome sequence (e.g., including multiple chromosome sequences), a genomic locus or set of genes, any other set of sequences, base identity at individual locations, or a combination thereof. In some embodiments, the method includes the step of determining a first set of sequences of molecules derived from a highly methylated fraction and a second set of sequences of molecules derived from a low-methylated fraction. In some embodiments, the method includes the step of obtaining a set of sequences of molecules derived from a low-methylated fraction.The molecular sequence can be obtained, for example, by the steps of: distributing a DNA sample into multiple fractions, the fractions comprising a highly methylated fraction and a low-methylated fraction; tagging the DNA in the low-methylated fraction to generate tagged nucleic acids comprising a molecular barcode; obtaining sequence reads of molecules derived from the low-methylated fraction, the sequence reads comprising a molecular barcode sequence and a sample sequence; and grouping the sequence reads into families based on at least one of (a) a molecular barcode sequence and (b) a genomic position corresponding to the first and last nucleotides of the sample sequence, the family comprising sequence reads derived from a single DNA molecule in the sample. In some embodiments, the method includes the step of determining the sequence derived from a set of sequence reads or molecular sequences.

[0115] The method may include a step of calling C-to-T or G-to-A transition mutations compared to a reference sequence. The reference sequence may be a standard genome sequence for the organism from which the sample was obtained (e.g., a mammal such as a human). Alternatively, the reference sequence may be another sequence from the same subject from which the sample was obtained; in such a case, the reference sequence may be derived, for example, from healthy tissue or from an earlier point in time.

[0116] In some embodiments, the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a first set of reads requires observation of transition mutations in a greater number of reads than the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a second set of reads. In some embodiments, the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a first set of molecular sequences requires observation of transition mutations in a greater number of molecular sequences than the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a second set of molecular sequences. As described elsewhere in this specification, it has been determined that the highly methylated fraction (e.g., of cfDNA) contains more damaged (e.g., deaminated) DNA than the less methylated fraction, resulting in sequence reads with apparent C-to-T or G-to-A transition mutations that do not correspond to the actual sequence in the cell from which the DNA originates. Deaminations of bases that do not correspond to actual in vivo mutations compared to a reference sequence can be called anthropogenic deaminations. While we do not wish to be constrained by any particular theory, for example, highly methylated DNA may be more susceptible to damage such as deamination, or more likely to be exposed to damaging agents such as deamination agents, and therefore damaged (e.g., deaminated) DNA may be preferentially allocated to the highly methylated fraction. Consequently, the risk of anthropogenic deamination may increase, and therefore, when sequencing DNA from the highly methylated fraction, it may call false-positive C-to-T or G-to-A transition mutations. The requirement to observe transition mutations in a larger number of reads or molecules to call C-to-T or G-to-A transition mutations compared to a reference sequence based on a first set of reads or molecules can compensate for the increased frequency of anthropogenic deaminations and reduce or eliminate the increased risk of false-positive calls of transition mutations in sequences determined from the highly methylated fraction.

[0117] For example, the step of calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on a first set of reads may require observation of the transition mutation in at least three reads. In such embodiments, the step of calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on a first set of reads may require observation of the transition mutation in one or two reads, for example, two reads. In some embodiments, the step of calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on a first set of reads may require observation of the transition mutation in at least a portion of the reads containing that base. The portion may be, for example, three reads per 10,000 reads containing that base, or at least 0.1%, 0.5%, 1%, 2%, 5%, 10%, 20%, 30%, or 40% of the reads containing that base. In some embodiments, the portion of reads may be less than 0.1% of the reads containing that base. In some embodiments, the read portion may be at least 40% of the reads containing that base. If necessary, the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a second set of reads may require observation of transition mutations in one or two reads per 10,000 reads containing that base, for example, two reads per 10,000 reads containing that base, or at least 0.1%, 0.5%, 1%, 2%, 5%, 10%, 20%, or 30% of the reads containing that base, and the number of observations required is less than the number required for the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a first set of reads.

[0118] In another example, the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequences of a first set of molecules may require observation of transition mutations in at least three molecules. In such embodiments, the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequences of a first set of molecules may require observation of transition mutations in one or two molecules, for example, two molecules. In some embodiments, the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequences of a first set of molecules may require observation of transition mutations in the sequences of at least three molecules per 10,000 molecules containing that base, or in at least 0.1%, 0.5%, 1%, 2%, 5%, 10%, 20%, 30%, or 40% of the sequences of molecules containing that base. If necessary, the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequence of the first set of molecules may require observation of transition mutations in 0.1%, 0.5%, 1%, 2%, 5%, 10%, 20%, or 30% of the sequence of molecules containing the base, for example, 2 molecules per 10,000 molecules containing the base, or at least 3 per 10,000 molecules, and the number of observations required is less than the number required for the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequence of the first set of molecules.

[0119] In another example, the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a first set of reads may require observation of transition mutations in at least four reads. In such embodiments, the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a first set of reads may require observation of transition mutations in one, two, or three reads, for example, two or three reads.

[0120] In another example, the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequences of a first set of molecules may require observation of transition mutations in at least four molecules. In such embodiments, the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequences of a first set of molecules may require observation of transition mutations in one, two, or three molecules, for example, two or three molecules.

[0121] In another example, the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a first set of reads may require observation of transition mutations in at least five reads. In such embodiments, the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on a first set of reads may require observation of transition mutations in one, two, three, or four reads, for example, two, three, or four reads, or more specifically, two or three reads.

[0122] In another example, the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequences of a first set of molecules may require observation of transition mutations in at least five molecules. In such embodiments, the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequences of a first set of molecules may require observation of transition mutations in one, two, three, or four molecules, for example, two, three, or four molecules, or more specifically, two or three molecules. More generally, the number of observations required in the step of calling C-to-T or G-to-A transition mutations may be as shown in Table 1.

[0123] [Table 1]

[0124] Appropriate values ​​can be selected based on the quality of the sample, the depth of the sequence data, and the relative importance of one, more, or all of the following: specificity (avoiding false positives) and sensitivity (avoiding false negatives). In some embodiments, the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on reads from a first set requires observation of transition mutations in at least two more reads than the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on reads from a second set. In some embodiments, the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequence of molecules from a first set requires observation of transition mutations in at least two more molecules than the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequence of molecules from a second set.

[0125] In some embodiments, C-to-T or G-to-A transition mutations are not called based on a first set of reads compared to a reference sequence. In some embodiments, C-to-T or G-to-A transition mutations are not called based on a first set of molecular sequences compared to a reference sequence. In some embodiments, C-to-T or G-to-A transition mutations are called based on a second set of reads compared to a reference sequence without using a first set of reads. In some embodiments, C-to-T or G-to-A transition mutations are called based on a second set of molecular sequences compared to a reference sequence without using a first set of molecular sequences. For example, to the extent that C-to-T or G-to-A transition mutations are called, they may be called based only on evidence from a low-methylated fraction, or from a low-methylated fraction and one or more moderate fractions considered elsewhere in this specification. Such embodiments eliminate the risk of calling false-positive C-to-T or G-to-A transition mutations due to damaged (e.g., deaminated) DNA in the highly methylated fraction. In some embodiments, C-to-T or G-to-A transition mutations are called compared to the reference sequence only if at least one sequence of the second set of molecules (e.g., at least two sequences of the second set of molecules) contains the C-to-T or G-to-A transition mutation. In some embodiments, C-to-T or G-to-A transition mutations are called compared to the reference sequence only if at least one read of the second set (e.g., at least two reads of the second set) contains the C-to-T or G-to-A transition mutation.

[0126] In some embodiments, a third set of sequence reads derived from the moderate fraction can be obtained, from which a third set of molecular sequences derived from the moderate fraction can be determined. In some of these embodiments, C-to-T and G-to-A transition mutations can be called based on the sequences of the third set of molecules with lower strictness than when C-to-T and G-to-A transition mutations are called based on the sequences of the first set of molecules. In some of these embodiments, C-to-T and G-to-A transition mutations can be called based on the sequences of the third set of molecules in the same way that C-to-T and G-to-A transition mutations are called based on the sequences of the second set of molecules. In some embodiments, C-to-T and G-to-A transition mutations can be called based on the sequences of the third set of molecules with higher strictness than when C-to-T and G-to-A transition mutations are called based on the sequences of the second set of molecules.

[0127] In some embodiments, a third set of sequence reads derived from the moderate fraction is obtained, and then this third set of sequence reads derived from the moderate fraction can be determined in addition to the first and second sets. In some embodiments, C-to-T and G-to-A transition mutations can be called based on the third set of reads with lower strictness than C-to-T and G-to-A transition mutations can be called based on the reads of the first set. In some embodiments, C-to-T and G-to-A transition mutations are called based on the third set of reads in the same way that C-to-T and G-to-A transition mutations are called based on the reads of the second set. In some embodiments, C-to-T and G-to-A transition mutations are called based on the third set of reads with higher strictness than C-to-T and G-to-A transition mutations can be called based on the reads of the second set.

[0128] In some embodiments, thresholds may be used to call C-to-T or G-to-A transitions based on the arrangement of molecules. For example, in some embodiments, a first threshold is used to call C-to-T or G-to-A transitions based on the arrangement of a first set of molecules, and a second threshold is used to call C-to-T or G-to-A transitions based on the arrangement of a second set of molecules. In some embodiments, the first threshold provides a first level of specificity for calling C-to-T or G-to-A transitions, and the second threshold provides a second level of specificity for calling C-to-T or G-to-A transitions. In some embodiments, the first level of specificity is approximately equal to the second level of specificity. In other embodiments, the first level of specificity is within 10%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.2%, or 0.1% of the second level of specificity. In some embodiments, the first and second thresholds are specific to the transition from C to T and / or from G to A.

[0129] In some embodiments, the first and second thresholds may be determined from multiple control samples. In some embodiments, the first and second thresholds may be determined from at least one control sample. In some of these embodiments, the control sample may be derived from an individual not suspected of having cancer.

[0130] In some embodiments, background sequencing error rates may be incorporated into the methods of the present disclosure. For example, a first group of position-specific background error rates may be used for multiple positions for a first set of sequences of a first set. Some examples further include a second group of position-specific background error rates used for multiple positions for a second set of sequences. In these examples, the second group includes position-specific background error rates higher than the corresponding position-specific background error rates of the first group. In some of these embodiments, the step of calling C-to-T or G-to-A transition mutations based on the sequences of a first set of molecules requires the observation of C-to-T or G-to-A transition mutations at a frequency higher than the corresponding rates derived from the first group of position-specific background error rates.

[0131] In some embodiments, the step of calling C-to-T or G-to-A transition mutations based on the sequence of a first set of molecules requires the observation of C-to-T or G-to-A transition mutations at a frequency at least 2, 3, 4, or 5 times higher than the corresponding rate derived from the first group of position-specific background errors.

[0132] In some embodiments, the step of calling C-to-T or G-to-A transition mutations based on the sequence of a first set of molecules requires the observation of C-to-T or G-to-A transition mutations at a frequency exceeding the corresponding rate from the first group of position-specific background error rates by an amount consistent with a confidence level of at least 95%, 98%, 99%, 99.5%, or 99.9%. The confidence level may be determined based on appropriate statistics using statistical measures that may include, for example, standard deviation, standard error of the mean, confidence interval, t-score, and Z-score. In some embodiments, the first and second groups of position-specific background error rates were determined from multiple control samples. In some embodiments, the control samples may be from individuals not suspected of having cancer. In some embodiments, the first and second groups of position-specific background error rates were determined using medical history data, e.g., the frequency of obvious mutations that do not fit a predetermined confidence threshold in a previously obtained set of sequence data. In some embodiments, the first and second groups of position-specific background error rates were determined, for example, at runtime, using reads and / or sequences of molecules derived from the highly methylated and low methylated fractions, respectively. 1. Distribution step; analysis of epigenetic features

[0133] In certain embodiments described herein, the method includes the step of distributing a sample of DNA to provide, for example, highly methylated and hypomethylated fractions, and optionally one or more additional (e.g., moderately methylated) fractions and / or sub-fractions of the highly methylated and hypomethylated fractions. Generally, the captured set of DNA in a sample, e.g., cfDNA as described elsewhere herein, may be physically distributed based on one or more characteristics of the nucleic acid (e.g., methylation) before analysis, e.g., sequencing, or tagging and sequencing. This approach may be used, for example, to determine whether highly methylated variable epigenetic target regions exhibit hypermethylation characteristic of tumor cells, or whether hypomethylated variable epigenetic target regions exhibit hypomethylation characteristic of tumor cells. In addition, rare signals may be increased by distributing a heterogeneous population of nucleic acids, for example, by enriching rare nucleic acid molecules that are more abundant in one fraction (or part) of the population. For example, genetic variations that are present in highly methylated DNA but less abundant (or absent) in hypomethylated DNA (e.g., genetic variations other than transition mutations from C to T or G to A) can be more easily detected by distributing the sample into highly methylated and hypomethylated nucleic acid molecules. By analyzing multiple fractions of the sample, multidimensional analysis of a single gene locus or nucleic acid species in the genome can be performed, thus achieving higher sensitivity.

[0134] In some examples, heterogeneous nucleic acid samples are distributed into two or more fractions (e.g., at least three, four, five, six, or seven fractions). In some embodiments, each fraction is differentially tagged. The tagged fractions can then be pooled together for collective sample preparation and / or sequencing. The distribution-tagging-pooling steps may be performed more than once, and each round of distribution may be based on different characteristics (as in the examples provided herein) and tagged using differential tags that distinguish them from other fractions and distribution means.

[0135] Examples of features that may be used for distribution include sequence length, methylation level, nucleosome binding, sequence mismatch, immunoprecipitation, and / or DNA-binding proteins. The resulting fraction may contain one or more of the following nucleic acid forms: single-stranded DNA (ssDNA), double-stranded DNA (dsDNA), short DNA fragments, and long DNA fragments. In some embodiments, a heterogeneous population of nucleic acids is distributed between nucleic acids with one or more epigenetic modifications and nucleic acids without one or more epigenetic modifications. Examples of epigenetic modifications include the presence or absence of methylation; the level of methylation; the type of methylation (e.g., 5-methylcytosine versus other types of methylation, e.g., adenine methylation and / or cytosine hydroxymethylation); and association with one or more proteins, e.g., histones, and the level of association. Alternatively, the heterogeneous population of nucleic acids may be distributed between nucleic acid molecules associated with nucleosomes and nucleic acid molecules lacking nucleosomes. Alternatively, a heterogeneous population of nucleic acids may be distributed between single-stranded DNA (ssDNA) and double-stranded DNA (dsDNA). Alternatively, a heterogeneous population of nucleic acids may be distributed based on nucleic acid length (e.g., molecules up to 160 bp and molecules longer than 160 bp).

[0136] In some cases, each fraction (representing a different nucleic acid morphology) is differentially labeled, and the fractions are pooled together before sequencing. In other cases, the different morphologies are sequenced separately.

[0137] Figure 1 shows an exemplary scheme including the distribution step. A population of different nucleic acids (101) is distributed into two or more different fractions (103a, b) (102). Each fraction (103a, b) is representative of a different nucleic acid morphology. Each fraction is explicitly tagged (104). The tagged nucleic acids are pooled together before sequencing (108) (107). Reads are analyzed in silico. Tags are used to sort reads from different fractions. Analysis for detecting gene variants can be performed at the fractional level and at the whole nucleic acid population level. Obvious C-to-T or G-to-A transition mutations may be analyzed separately for the hypermethylated fraction using more stringent parameters described in detail elsewhere herein, or using reads or sequences of molecules from the hypermethylated fraction that cannot be readily used to call C-to-T or G-to-A transition mutations. Exemplary analyses may include in silico analysis to determine gene variants, such as CNVs, SNVs, indels, and nucleic acid fusions in each fraction. In some cases, in silico analysis may include determining chromatin structure. For example, sequence read coverage may be used to determine the nucleosome fraction of chromatin. Higher coverage may correlate with higher nucleosome occupancy in genomic regions, while lower coverage may correlate with lower nucleosome occupancy or nucleosome-depleted regions (NDRs).

[0138] The sample may contain nucleic acids with different modifications, including nucleotides and post-replication modifications to binding to one or more proteins, which are typically non-covalent.

[0139] Any type of sample described elsewhere in this specification may be used. In some embodiments, the nucleic acid population is obtained from tissue, serum, plasma, or blood samples from a subject suspected of having a neoplasm, tumor, or cancer, or from a subject previously diagnosed with a neoplasm, tumor, or cancer. The nucleic acid population comprises nucleic acids having different levels of methylation. Methylation may result from one or more post-replication modifications or transcriptional modifications. Post-replication modifications include modifications of cytosine in nucleotides, particularly cytosine at the 5-position of the nucleic acid base, e.g., 5-methylcytosine, 5-hydroxymethylcytosine, 5-formylcytosine, and 5-carboxylcytosine.

[0140] In some embodiments, the nucleic acids in the original population may be single-stranded and / or double-stranded. Distribution based on the single-stranded versus double-stranded nature of nucleic acids can be achieved, for example, by using labeled capture probes to distribute ssDNA and double-stranded adapters to distribute dsDNA.

[0141] The dispersal step can be carried out using any suitable reagent, e.g., one of the reagents described elsewhere in this specification, which selectively binds to the nucleic acid or separates the nucleic acid based on differences in characteristics. The reagent may be an antibody with desired specificity, a natural binding partner or its variant (Bock et al., Nat Biotech 28: 1106-1114 (2010); Song et al., Nat Biotech 29: 68-72 (2011)), or, for example, an artificial peptide selected by phage display having specificity for a given target.

[0142] Examples of reagents intended herein include methyl-binding domains (MBDs) and methyl-binding proteins (MBPs) as described herein.

[0143] Similarly, the step of distributing different forms of nucleic acids can be carried out using histone-binding proteins that can separate histone-bound nucleic acids from free or unbound nucleic acids. Examples of histone-binding proteins that may be used in the methods disclosed herein include RBBP4 (RbAp48) and SANT domain peptides.

[0144] For some reagents and modifications, binding to the reagent may occur in an all-or-nothing manner, depending on whether the nucleic acid has the modification or not, but separation may be a matter of degree. In such cases, nucleic acids that are over-represented by the modification will bind to the reagent to a greater extent than nucleic acids that are under-represented by the modification. Alternatively, nucleic acids with modifications may bind in an all-or-nothing manner. However, varying levels of modification may be successively eluted from the binder.

[0145] For example, in some embodiments, the distribution may be binary or based on the degree / level of modification. For instance, all methylated fragments can be distributed from the unmethylated fragments using a methyl-binding domain protein (e.g., the MethylMiner methylated DNA enrichment kit (Thermo Fisher Scientific)). The additional distribution may then involve a step of eluting fragments with different levels of methylation by adjusting the salt concentration in the solution containing the methyl-binding domain and the bound fragments. As the salt concentration increases, fragments with higher levels of methylation are eluted.

[0146] In some cases, the final fraction represents nucleic acids with varying degrees of modification (over- or under-modification). Over- and under-modification can be defined by the number of modifications a nucleic acid has compared to the median number of modifications per strand in the population. For example, if the median number of 5-methylcytosine residues in nucleic acids in a sample is 2, nucleic acids containing more than 2 5-methylcytosine residues are over-modified, while nucleic acids with 1 or zero 5-methylcytosine residues are under-modified. The effect of affinity separation is to enrich nucleic acids that are over-modified in the modification in the conjugated phase and nucleic acids that are under-modified in the modification in the unconjugated phase (i.e., in solution). Nucleic acids in the conjugated phase can be eluted before subsequent processing.

[0147] When using the MethylMiner Methylated DNA Enrichment Kit (Thermo Fisher Scientific), various levels of methylation can be separated using sequential elution. For example, a low-methylated fraction (e.g., no methylation) can be separated from the methylated fraction by contacting the nucleic acid population with MBD from the kit bound to magnetic beads. The beads are used to separate methylated nucleic acids from unmethylated nucleic acids. Then, one or more elution steps are performed sequentially to elute nucleic acids with different levels of methylation. For example, a first set of methylated nucleic acids can be eluted at a salt concentration of 160 mM or higher, e.g., at least 200 mM, 300 mM, 400 mM, 500 mM, 600 mM, 700 mM, 800 mM, 900 mM, 1000 mM, or 2000 mM. After eluting such methylated nucleic acids, magnetic separation is used again to separate high-level methylated nucleic acids from nucleic acids with low levels of methylation. By repeating the elution and magnetic separation steps, various fractions can be prepared, such as a low-methylation fraction (e.g., representative of no methylation), a methylated fraction (representative of low levels of methylation), and a high-methylation fraction (representative of high levels of methylation).

[0148] In some methods, nucleic acids bound to the activator used for affinity separation are subjected to a washing step. The washing step washes away nucleic acids that are weakly bound to the affinity activator. Such nucleic acids can be enriched with nucleic acids that have been modified to a degree close to the mean or median (i.e., moderately between nucleic acids that remain bound to the solid phase and nucleic acids that do not bind to the solid phase when the activator is first brought into contact with the sample).

[0149] Affinity separation yields at least two, sometimes three or more, fractions of nucleic acids with varying degrees of modification. While the fractions remain distinct, the nucleic acids in at least one fraction, typically two or three (or more), are ligated to nucleic acid tags, usually provided as components of an adapter, so that nucleic acids in different fractions receive different tags that distinguish members of one fraction from members of another. Tags ligated to nucleic acid molecules in the same fraction can be identical or different from one another. However, if they are different, the tags may share a common code to identify the molecule to which they are ligated as belonging to a particular fraction.

[0150] For further details regarding the separation of nucleic acid samples based on characteristics such as methylation, please refer to WO2018 / 119452, which is incorporated herein by reference.

[0151] In some embodiments, nucleic acid molecules can be fractionated into different fractions based on nucleic acid molecules that bind to a specific protein or fragment thereof and nucleic acid molecules that do not bind to that specific protein or fragment thereof.

[0152] Nucleic acid molecules can be fractionated based on DNA-binding proteins. Protein-DNA complexes can be fractionated based on the specific properties of the proteins. Examples of such properties include various epitopes, modifications (e.g., histone methylation or acetylation), or enzymatic activity. Examples of proteins that can bind to DNA and serve as a basis for fractionation include, but are not limited to, protein A and protein G. Any preferred method can be used to fractionate nucleic acid molecules based on protein-binding regions. Examples of methods used to fractionate nucleic acid molecules based on protein-binding regions include, but are not limited to, SDS-PAGE, chromatin immunoprecipitation (ChIP), heparin chromatography, and asymmetric field flow fractionation (AF4).

[0153] In some embodiments, nucleic acid distribution is carried out by contacting the nucleic acid with the methylation-binding domain ("MBD") of a methylation-binding protein ("MBP"). The MBD binds to 5-methylcytosine (5mC). The MBD is coupled to paramagnetic beads, such as Dynabeads® M-280 streptavidin, via a biotin linker. Distribution to fractions with different degrees of methylation can be carried out by eluting the fractions by increasing the NaCl concentration.

[0154] Examples of MBPs intended herein include, but are not limited to, the following: (a) MeCP2 is a protein that preferentially binds to 5-methylcytosine rather than unmodified cytosine; (b) RPL26, PRP8, and the DNA mismatch repair protein MHS6 preferentially bind to 5-hydroxymethylcytosine rather than unmodified cytosine; (c)FOXK1, FOXK2, FOXP1, FOXP4, and FOXI3 bind more favorably to 5-formyl-cytosine than unmodified cytosine (Iurlaro et al., Genome Biol. 14: R119 (2013)); (d) An antibody specific to one or more methylated nucleotide bases.

[0155] Generally, elution is a function of the number of methylation sites per molecule, with molecules having more methylation eluting under increased salt concentrations. A series of elution buffers with increasing NaCl concentrations can be used to elute DNA into distinct populations based on the degree of methylation. Salt concentrations can range from approximately 100 mM to approximately 2500 mM NaCl. In one embodiment, the treatment yields three fractions. Molecules are brought into contact with a solution of a first salt concentration, and the molecules contained therein have methyl-binding domains, which can bind to a capture moiety such as streptavidin. At the first salt concentration, populations of molecules bind to the MBD, while other populations remain unbound. The unbound populations can be separated as a "low-methylated" population. For example, the first fraction representing the low-methylated form of DNA is the fraction that remains unbound at low salt concentrations, e.g., 100 mM or 160 mM. A second fraction, representing moderately methylated DNA, is eluted using a moderate salt concentration, e.g., 100 mM to 2000 mM. This fraction is also separated from the sample. A third fraction, representing the highly methylated form of DNA, is eluted using a high salt concentration, e.g., at least about 2000 mM. a. Tagging of images

[0156] In some embodiments, two or more fractions of a DNA sample, for example, each fraction, are differentially tagged or are differentially tagged. The tags may be molecules, such as nucleic acids, and contain information that characterizes the molecule to which the tag relates. For example, a molecule may have sample tags (distinguishing a molecule in one sample from a molecule in a different sample), fraction tags (distinguishing a molecule in one fraction from a molecule in a different fraction), or molecular tags (distinguishing different molecules from each other (in both unique and non-unique tagging scenarios)). In certain embodiments, a tag may include a single barcode or a combination of barcodes. As used herein, the term “barcode” means, depending on the context, a nucleic acid molecule having a particular nucleotide sequence, or the nucleotide sequence itself. A barcode may have, for example, 10 to 100 nucleotides. A collection of barcodes may have denatured sequences or sequences having a certain Hamming distance, as desired for a particular purpose. Therefore, for example, a sample index, fraction index, or molecular index may consist of one barcode or a combination of two barcodes attached to different ends of a molecule.

[0157] Tags may be used to label individual polynucleotide population fractions in order to correlate the tag (or multiple tags) with a specific fraction. Alternatively, tags may be used in embodiments of the invention that do not involve a distribution step. In some embodiments, a single tag may be used to label a specific fraction. In some embodiments, multiple different tags may be used to label a specific fraction. In embodiments where multiple different tags are used to label a specific fraction, the set of tags used to label one fraction can be readily identified with respect to the set of tags used to label other fractions. In some embodiments, tags may have additional functions, for example, tags may be used to index the sample source or as a unique molecular identifier (e.g., Kinde et al., Proc Nat'l Acad Sci USA 108: 9530-9535 (2011), Kou et al., PLoS ONE, 11: e0146638 (2016), for sequencing error Tags can be used to improve the quality of sequencing data by identifying mutations, or they can be used as non-unique molecular identifiers, for example, as described in U.S. Patent No. 9,598,731. Similarly, in some embodiments, tags may have additional functions, for example, they can be used to index sample sources or they can be used as non-unique molecular identifiers (to improve the quality of sequencing data by identifying sequencing errors from mutations).

[0158] In one embodiment, fraction tagging includes tagging molecules in each fraction with fraction tags. After the fractions are reassembled and the molecules are sequenced, the fraction tags identify the source fraction. In another embodiment, different fractions are tagged with different sets of molecular tags, for example, consisting of pairs of barcodes. Thus, each molecular barcode is useful for distinguishing the source fraction and the molecules within the fraction. For example, a first set of 35 barcodes may be used to tag molecules in a first fraction, while a second set of 35 barcodes may be used to tag molecules in a second fraction.

[0159] In some embodiments, molecules may be pooled for sequencing in a single trial after distribution and tagging with fraction tags. In some embodiments, sample tags are attached to molecules, for example, in a step after the addition of fraction tags and pooling. Sample tags can facilitate pooling material generated from multiple samples for sequencing in a single sequencing trial.

[0160] Alternatively, in some embodiments, fraction tags may correlate with samples and fractions. As a simple example, a first tag may represent a first fraction of a first sample; a second tag may represent a second fraction of a first sample; a third tag may represent a first fraction of a second sample; and a fourth tag may represent a second fraction of a second sample.

[0161] A tag may bind to a molecule that has already been distributed based on one or more features, but the final tagged molecule in the library may no longer possess those features. For example, a single-stranded DNA molecule may be distributed and tagged, but the final tagged molecule in the library may be double-stranded. Similarly, DNA may be subjected to distribution based on different levels of methylation, but the tagged molecules derived from these molecules in the final library may not be methylated. Therefore, a tag bound to a molecule in the library typically represents the features of the "parent molecule" from which the final tagged molecule originates, and not necessarily the features of the tagged molecule itself.

[0162] For example, molecules in a first fraction are tagged and labeled using barcodes 1, 2, 3, 4, etc.; molecules in a second fraction are tagged and labeled using barcodes A, B, C, D, etc.; and molecules in a third fraction are tagged and labeled using barcodes a, b, c, d, etc. Differentially tagged fractions can be pooled before sequencing. Differentially tagged fractions can be sequenced separately, or they can be sequenced together simultaneously in the same flow cell of an Illumina sequencer, for example.

[0163] After sequencing, read analysis to detect gene variants can be performed at the fractional level and the whole nucleic acid population level. Tags are used to sort reads from different fractions. Analysis may include in silico analysis to determine genetic and epigenetic variations (one or more of methylation, chromatin structure, etc.) using sequence information, genomic coordinate length, coverage, and / or copy number. In some embodiments, higher coverage may correlate with higher nucleosome occupancy in genomic regions, while lower coverage may correlate with lower nucleosome occupancy or nucleosome-depleted regions (NDRs). b. Determination of the 5-methylcytosine pattern of nucleic acids; bisulfite sequencing

[0164] Bisulfite-based sequencing and its variants provide means for determining the methylation patterns of nucleic acids, which can provide single-base resolution information regarding methylation status. In some embodiments, the step of determining the methylation pattern includes distinguishing 5-methylcytosine (5mC) from unmethylated cytosine. In some embodiments, the step of determining the methylation pattern includes distinguishing N-methyladenine from unmethylated adenine. In some embodiments, the step of determining the methylation pattern includes distinguishing 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxylcytosine (5caC) from unmethylated cytosine. Examples of bisulfite sequencing include, but are not limited to, oxidative bisulfite sequencing (OX-BS-seq), Tet-assisted bisulfite sequencing (TAB-seq), and reductive bisulfite sequencing (redBS-seq).

[0165] Oxidative bisulfite sequencing (OX-BS-seq) is used to distinguish between 5mC and 5hmC by first converting 5hmC to 5fC and then proceeding with bisulfite sequencing. Tet-assisted bisulfite sequencing (TAB-seq) can also be used to distinguish between 5mC and 5hmC. In TAB-seq, 5hmC is protected by glucosylation. The Tet enzyme is then used to convert 5mC to 5caC before proceeding with bisulfite sequencing. Reductive bisulfite sequencing is used to distinguish 5fC from modified cytosine.

[0166] Generally, bisulfite sequencing involves splitting a nucleic acid sample into two aliquots, with one aliquot treated with bisulfite. In some embodiments, a highly methylated fraction is split into two such aliquots. Bisulfite converts native cytosines and certain modified cytosine nucleotides (e.g., 5-formylcytosine or 5-carboxylcytosine) to uracil, while other modified cytosines (e.g., 5-methylcytosine, 5-hydroxymethylcytosine) are not. Comparison of the nucleic acid sequences of molecules from the two aliquots indicates which cytosines were converted to uracil and which were not. As a result, modified and unmodified cytosines can be determined. The initial splitting of a sample into two aliquots is disadvantageous for samples containing only small amounts of nucleic acids and / or samples consisting of heterogeneous cellular / tissue origins, such as body fluids containing cell-free DNA.

[0167] Therefore, in some embodiments, bisulfite sequencing is performed without first dividing the sample into two aliquots, for example, as follows. In some embodiments, the nucleic acids in the population are ligated to a capture portion, i.e., a label that can be captured or immobilized, such as one of the portions described herein. After the capture portion is ligated to the sample nucleic acid, the sample nucleic acid serves as a template for amplification. After amplification, the original template remains ligated to the capture portion, but the amplicon is not ligated to the capture portion.

[0168] The capture portion can be ligated to the sample nucleic acid as a component of an adapter, which may also provide amplification and / or sequencing primer binding sites. In some methods, the sample nucleic acid is ligated to adapters at both ends, and both adapters have a capture portion. Preferably, any cytosine residues in the adapter are modified, for example, with 5-methylcytosine, to protect against the action of bisulfites. In some examples, the capture portion is modified from the original template by a cleavable ligation (e.g., photocleavable desthiobiotin-TEG, or a uracil residue cleavable with USER® enzyme, Chem. Commun. (Camb). 51: 3266-3269 (2015)). It is connected to the other part, and in this case, the capture portion can be removed if desired.

[0169] The amplicon is denatured and brought into contact with an affinity reagent for the capture tag. The original template binds to the affinity reagent, but the nucleic acid molecule resulting from the amplification does not. Therefore, the original template can be separated from the nucleic acid molecule resulting from the amplification.

[0170] After separation of the original template from the nucleic acid molecules resulting from amplification, the original template can be subjected to bisulfite treatment. Alternatively, the amplified product can be subjected to bisulfite treatment, but the original template population can not. After such treatment, each population can be amplified (converting uracil to thymine in the case of the original template population). The populations can also be subjected to biotin probe hybridization for capture. Each population is then analyzed and its sequences are compared to determine which cytosines in the original sample were 5-methylated (or 5-hydroxymethylated). Detection of T nucleotides (corresponding to unmethylated cytosines converted to uracil) in the template population and C nucleotides at the corresponding positions in the amplified population indicates unmodified C. The presence of C at the corresponding positions in the original template and amplified population indicates modified C in the original sample.

[0171] In some embodiments, the method utilizes sequential DNA-seq and bisulfite-seq (BIS-seq) NGS library preparation of molecularly tagged DNA libraries (see WO2018 / 119452, e.g., Figure 4). This process is carried out by labeling of adapters (e.g., biotin), DNA-seq amplification of the entire library, recovery of parent molecules (e.g., streptavidin bead pulldown), bisulfite conversion, and BIS-seq. In some embodiments, the method identifies 5-methylcytosine at single-base resolution by sequential NGS-preparative amplification of parent library molecules with and without bisulfite treatment. This can be achieved by modifying a 5-methylated NGS-adapter (directional adapter; Y-shaped / fork-shaped with 5-methylcytosine substitution) used in BIS-seq, which has a label (e.g., biotin) on one of the two adapter strands. Sample DNA molecules are ligated with an adapter and amplified (e.g., by PCR). Since only parent molecules have labeled adapter ends, they can be selectively recovered from their amplified offspring by a label-specific capture method (e.g., streptavidin-magnetic beads). Because parent molecules retain a 5-methylation mark, bisulfite conversion in the captured library results in a single-base resolution 5-methylation state in BIS-seq, retaining molecular information for the corresponding DNA-seq. In some embodiments, the bisulfite-treated library can be combined with an untreated library before capture / NGS by adding a sample tagged DNA sequence in a standard multiplex NGS workflow. Bioinformatics analysis can be performed with respect to genomic alignment and identification of 5-methylated bases, as in a BIS-seq workflow. In other words, this method provides the ability to selectively recover ligated parent molecules with a 5-methylcytosine mark after library amplification, thereby enabling parallel processing of bisulfite-converted DNA. This overcomes the destructive characteristics of bisulfite treatment in terms of the quality / sensitivity of DNA-seq information extracted from the workflow.Using this method, the recovered ligated parental DNA molecules (via a labeling adapter) allow for amplification of a complete DNA library and enable the parallel application of treatments that induce epigenetic DNA modification. This disclosure considers the use of the BIS-seq method for identifying cytosine-5-methylated (5-methylcytosine), although the use of the BIS-seq method is not required in many embodiments. Variants of BIS-seq have been developed for identifying hydroxymethylated cytosine (5hmC; OX-BS-seq, TAB-seq), formylcytosine (5fC; redBS-seq), and carboxylcytosine. These methodologies can be performed in conjunction with the sequential / parallel library preparations described herein. c. Alternative methods for modified nucleic acid analysis

[0172] In some such methods, a population of nucleic acids having varying degrees of modification (e.g., 0, 1, 2, 3, 4, 5 or more methyl groups per nucleic acid molecule) is contacted with an adapter before fractionation of the population, depending on the degree of modification. The adapter binds to either one or both ends of the nucleic acid molecules in the population. Preferably, the adapter contains a sufficient number of different tags so that the number of tag combinations results in a low probability, for example, 95, 99, or 99.9% of two nucleic acids having the same start and end points receive the same tag combination. After binding to the adapter, the nucleic acid is amplified from a primer that binds to the primer binding site in the adapter. The adapter may contain the same or different primer binding sites, whether they have the same or different tags (e.g., tags with the same or different sequences), but preferably the adapter contains the same primer binding site. After amplification, the nucleic acid is contacted with an activator (e.g., one of the previously described activators) that preferably binds to the modified nucleic acid. The nucleic acids are separated from binding to the agonist into at least two fractions (e.g., a highly methylated fraction and a lowly methylated fraction) that differ in the degree to which the nucleic acids have modification. For example, if the agonist has affinity for nucleic acids with modification, nucleic acids that are over-represented by the modification (compared to the median occurrence in the population) will preferentially bind to the agonist, while nucleic acids that are under-represented by the modification will not bind to the agonist or will elute more readily from the agonist. After separation, the different fractions may then be subjected to further processing steps, which typically include further amplification and sequence analysis as described elsewhere herein, separately but in parallel. Sequence data from the different fractions may then be compared.

[0173] Such a separation scheme may be carried out using the following exemplary procedure: Nucleic acids are ligated to both ends of a Y-shaped adapter containing primer binding sites and a tag. The molecule is amplified. The amplified molecule is then fractionated by contact with an antibody that preferentially binds to 5-methylcytosine, resulting in two fractions. One fraction contains the original molecule lacking methylation and the amplified copy that has lost methylation. The other fraction contains the original DNA molecule with methylation. The two fractions are then processed and sequenced separately, along with further amplification of the methylated fraction. The sequence data of the two fractions can then be compared. In this example, the tag is not used to distinguish between methylated and unmethylated DNA, but is used to distinguish between different molecules within these fractions so that it can be determined whether reads with the same start and end points are based on the same or different molecules.

[0174] The methods described herein may further include the step of analyzing a population of nucleic acids (e.g., a highly methylated fraction) in which at least a portion of the nucleic acids contains one or more modified cytosine residues, e.g., 5-methylcytosine and any of the other modifications previously described. In these methods, the population of nucleic acids is brought into contact with an adapter containing one or more cytosine residues modified at the 5C position, e.g., 5-methylcytosine. Preferably, all cytosine residues in such an adapter are also modified, or all such cytosines in the primer-binding region of the adapter are modified. The adapter binds to both ends of the nucleic acid molecules in the population. Preferably, the adapter contains a sufficient number of different tags so that the number of tag combinations results in a low probability, e.g., 95, 99, or 99.9% of two nucleic acids having the same start and end points receive the same tag combination. The primer-binding sites in such an adapter may be the same or different, but are preferably the same. After binding to the adapter, the nucleic acid is amplified from a primer that binds to the primer-binding site of the adapter. The amplified nucleic acid is divided into first and second aliquots. The first aliquot is assayed for sequence data with or without further processing. The sequence data of the molecule in the first aliquot is thus determined regardless of the initial methylation state of the nucleic acid molecule. The nucleic acid molecule in the second aliquot is treated with bisulfite. This treatment converts unmodified cytosine to uracil. The bisulfite-treated nucleic acid is then subjected to amplification, primed by a primer to the original primer binding site of an adapter ligated to the nucleic acid. These nucleic acids retain cytosine at the primer binding site of the adapter, but since the amplified product loses methylation of these cytosine residues that have been converted to uracil in the bisulfite treatment, only the nucleic acid molecule originally ligated to the adapter (different from its amplified product) is amplified here. In this way, only the original molecule in a population that is at least partially methylated is amplified. After amplification, these nucleic acids are subjected to sequence analysis.A comparison of sequences determined from the first and second aliquots may indicate, among many possibilities, that cytosine in the nucleic acid population was subjected to methylation.

[0175] Such analysis may be performed using the following exemplary procedure: Methylated DNA is ligated to a Y-shaped adapter at both ends, containing a primer binding site and a tag. Cytosine in the adapter is 5-methylated. Methylation of the primer protects the primer binding site in the subsequent bisulfite step. After binding to the adapter, the DNA molecule is amplified. The amplified product is split into two aliquots for sequencing, with and without bisulfite treatment. The aliquot not subjected to bisulfite sequencing may be subjected to sequence analysis with or without further treatment. The other aliquot is treated with bisulfite, which converts unmethylated cytosine to uracil. Only the primer binding site protected by cytosine methylation can support amplification when in contact with a primer specific to the original primer binding site. In this way, only the original molecule, which is not a copy from the first amplification, is subjected to further amplification. The further amplified molecule is then subjected to sequence analysis. The sequences from the two aliquots may then be compared. As in the separation scheme discussed above, the nucleic acid tag in the adapter is not used to distinguish between methylated and unmethylated DNA, but is used to distinguish between nucleic acid molecules within the same fraction. 2. Target region; differential capture and sequencing depth

[0176] In some embodiments, the method includes a step of capturing cfDNA obtained from a test subject for multiple sets of target regions. The target regions include epigenetic target regions, which may exhibit differences in methylation levels and / or fragmentation patterns depending on whether they originate from tumors or healthy cells. The target regions also include sequence-variable target regions, which may exhibit differences in sequences depending on whether they originate from tumors or healthy cells. The capture step results in a captured set of cfDNA molecules, with cfDNA molecules corresponding to the sequence-variable target region set being captured in a higher capture yield of the captured set of cfDNA molecules than cfDNA molecules corresponding to the epigenetic target region set.

[0177] In some embodiments, the method includes the step of contacting cfDNA obtained from a test subject with a set of target-specific probes, the set of target-specific probes being configured to capture cfDNA corresponding to a sequence-variable target region set with a higher capture yield than cfDNA corresponding to an epigenetic target region set.

[0178] Since higher sequencing depths may be required to analyze sequence-variable target regions with sufficient reliability or accuracy than may be required to analyze epigenetic target regions, it may be beneficial to capture cfDNA corresponding to a set of sequence-variable target regions at a higher capture yield than cfDNA corresponding to a set of epigenetic target regions. Higher sequencing depths may yield more reads per DNA molecule and can be facilitated by capturing more unique molecules per region. The volume of data required to determine fragmentation patterns (e.g., to test for perturbations at transcription start sites or CTCF binding sites) or the abundance of fragments (e.g., in high-methylated and low-methylated fractions) is generally less than the volume of data required to determine the presence or absence of mutations in cancer-related sequences. Capturing target region sets at different yields may facilitate sequencing target regions to different sequencing depths in the same sequencing run (e.g., using a pooled mixture and / or in the same sequencing cell).

[0179] In various embodiments, the method further includes, in accordance with the above considerations, sequencing the captured cfDNA to different degrees of sequencing depth with respect to, for example, epigenetic and sequence-variable target region sets. a. Captured sets; differential capture and sequencing depth

[0180] In some embodiments, a captured set of DNA (e.g., cfDNA) is provided. With respect to the disclosed method, for example, a captured set of DNA may be provided after the capture and / or distribution steps described herein. The captured set may include DNA corresponding to a sequence variable target region set and an epigenetic target region set. In some embodiments, the amount of captured sequence variable target region DNA is greater than the amount of captured epigenetic target region DNA, normalized for differences in the size (footprint size) of the targeted regions.

[0181] Alternatively, a first and second captured set may be provided, each containing DNA corresponding to a sequence-variable target region set and DNA corresponding to an epigenetic target region set, respectively. The first and second captured sets may be combined to provide a combined captured set.

[0182] In a captured set containing DNA corresponding to a sequence variable target region set and an epigenetic target region set, including the combined captured sets discussed above, the DNA corresponding to the sequence variable target region set is present at a higher concentration than the DNA corresponding to the epigenetic target region set, for example, 1.1 to 1.2 times higher, 1.2 to 1.4 times higher, 1.4 to 1.6 times higher, 1.6 to 1.8 times higher, 1.8 to 2.0 times higher, 2.0 to 2.2 times higher, 2.2 to 2.4 times higher, 2.4 to 2.6 times higher, 2.6 to 2.8 times higher, 2.8 to 3.0 times higher, 3.0 to 3.5 times higher, and 3 It may exist at concentrations 0.5-4.0, 4.0-4.5 times higher, 4.5-5.0 times higher, 5.0-5.5 times higher, 5.5-6.0 times higher, 6.0-6.5 times higher, 6.5-7.0 times higher, 7.0-7.5 times higher, 7.5-8.0 times higher, 8.0-8.5 times higher, 8.5-9.0 times higher, 9.0-9.5 times higher, 9.5-10.0 times higher, 10-11 times higher, 11-12 times higher, 12-13 times higher, 13-14 times higher, 14-15 times higher, 15-16 times higher, 16-17 times higher, 17-18 times higher, 18-19 times higher, or 19-20 times higher. The degree of the concentration difference explains the normalization with respect to the footprint size of the target region, as discussed in the definition section. i. Epigenetic target region set

[0183] An epigenetic target region set may include one or more types of target regions that can distinguish DNA from neoplastic (e.g., tumor or cancer) cells from DNA from healthy cells, e.g., non-neoplastic circulating cells. Exemplary types of such regions are discussed in detail herein. An epigenetic target region set may also include one or more control regions, for example, described herein.

[0184] In some embodiments, the epigenetic target region set has a footprint of at least 100kb, for example, at least 200kb, at least 300kb, or at least 400kb. In some embodiments, the epigenetic target region set has a footprint in the range of 100 to 1000kb, for example, 100 to 200kb, 200 to 300kb, 300 to 400kb, 400 to 500kb, 500 to 600kb, 600 to 700kb, 700 to 800kb, 800 to 900kb, and 900 to 1,000kb. In some embodiments, the epigenetic target region set has a footprint of at least 1000kb, at least 2000kb, at least 3000kb, at least 4000kb, at least 5000kb, at least 6000kb, at least 7000kb, at least 8000kb, at least 9000kb, or at least 1Mb. In some embodiments, the epigenetic target region set is 1 Mb to 20 Mb, for example, 1 to 1.2 Mb, 1.2 to 1.4 Mb, 1.4 to 1.6 Mb, 1.6 to 1.8 Mb, 1.8 to 2 Mb, 2 to 2.25 Mb, 2.25 to 2.5 Mb, 2.5 to 2.75 Mb, 2.75 to 3 Mb, 3 to 3.25 Mb, 3.25 to 3.5 Mb, 3.5 to 3.75 Mb, 3.75 to 4 Mb, 4 to 4.2 The footprints are in the ranges of 5Mb, 4.25-4.5Mb, 4.5-4.75Mb, 4.75-5Mb, 5-5.5Mb, 5.5-6Mb, 6-6.5Mb, 6.5-7Mb, 7-7.5Mb, 7.5-8Mb, 8-8.5Mb, 8.5-9Mb, 9-9.5Mb, 9.5-10Mb, 10-12Mb, 12-14Mb, ​​14-16Mb, 16-18Mb, and 18-20Mb. In some embodiments, the epigenetic target region set has a footprint in the ranges of 0.2-0.8 megabases, 0.8-1.5 megabases, 1.5-3 megabases, or 3-8 megabases.

[0185] (a) Highly methylated variable target region In some embodiments, the epigenetic target region set includes one or more hypermethylated variable target regions. Generally, a hypermethylated variable target region refers to a region where an observed increase in methylation level indicates an increased likelihood that the sample (e.g., a cfDNA sample) contains DNA produced by neoplastic cells, such as tumor or cancer cells. For example, hypermethylation of tumor suppressor gene promoters has been repeatedly observed. For example, Kang See et al., Genome Biol. 18:53 (2017) and the references cited therein.

[0186] A comprehensive discussion of methylation-variable target regions in colorectal cancer is provided in Lam et al., Biochim Biophys Acta. 1866:106-20 (2016). These include VIM, SEPT9, ITGA4, OSM4, GATA4, and NDRG4. Table 2A provides an exemplary set of hypermethylation-variable target regions, including genes or parts thereof, based on colorectal cancer (CRC) studies. Many of these genes are likely relevant to cancers other than colorectal cancer as well; for example, TP53 is widely recognized as a crucial tumor suppressor, and inactivation based on hypermethylation of this gene may be a common tumorigenic mechanism. [Table 2A]

[0187] In some embodiments, the highly methylated variable target region comprises a plurality of genes or a portion thereof listed in Table 2A, for example, at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% of the genes or a portion thereof listed in Table 2A. For example, with respect to each gene locus included as a target region, there may be one or more probes having a hybridization site that binds between the transcription start site and the stop codon (or the last stop codon for genes that are alternatively spliced). In some embodiments, this one or more probes bind within 300 bp, for example, 200 or 100 bp, upstream and / or downstream of the genes or a portion thereof listed in Table 2A.

[0188] Methylation-variable target regions in various types of lung cancer are, for example, Ooki et al., Clin. Cancer Res. 23:7141-52 (2017); Belinksy, Annu. Rev. Physiol. 77:453-74 (2015); Hulbert et al., Clin. Cancer Res. 23:1998-2005 (2017); Shi et al., BMC Genomics 18:901 (2017); Schneider et al., BMC Cancer. 11:102 (2011); Lissa et al., Transl Lung Cancer Res 5(5):492-504 (2016); Skvortsova et al., Br. J. Cancer. 94(10):1492-1495 (2006); Kim et al., Cancer Res. 61:3419-3424 (2001); Furonaka et al., Pathology International 55:303-309 (2005); Gomes et al., Rev. Gate. Pneumol. 20:20-30 (2014); Kim et al., Oncogene. 20:1765-70 (2001); Hopkins-Donaldson et al., Cell Death Differ. 10:356-64 (2003); Kikuchi et al., Clin. Cancer Res. 11:2954-61 (2005); Heller et al., Oncogene 25:959-968 (2006); Licchesi et al., Carcinogenesis. 29:895-904 (2008); Guo et al., Clin. Cancer Res. 10:7917-24 (2004); Palmisano et al., Cancer Res. 63:4620-4625 (2003); and Toyooka et al., Cancer Res. 61:4556-4560, (2001).

[0189] Table 2B provides an exemplary set of hypermethylated variable target regions, including genes or parts thereof, based on lung cancer research. Many of these genes may also be relevant to cancers other than lung cancer; for example, Casp8 (caspase 8) is a key enzyme in programmed cell death, and inactivation based on hypermethylation of this gene may be a common tumorigenic mechanism not limited to lung cancer. In addition, several genes appear in both Table 2A and Table 2B, indicating their generality. [Table 2B-1] [Table 2B-2]

[0190] Any of the above embodiments relating to the target regions identified in Table 2B may be combined with any of the above embodiments relating to the target regions identified in Table 2A. In some embodiments, the highly methylated variable target region comprises at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% of a plurality of genes or a portion thereof listed in Table 2A or Table 2B, for example, a plurality of genes or a portion thereof listed in Table 2A or Table 2B.

[0191] Additional hypermethylation target regions may be obtained, for example, from cancer genome atlases. Kang et al., Genome Biology 18:53 (2017) describe the construction of a probabilistic method called Cancer Locator using hypermethylation target regions from breast, colon, kidney, liver, and lung. In some embodiments, the hypermethylation target regions may be specific to one or more types of cancer. Thus, in some embodiments, the hypermethylation target regions include one, two, three, four, or five subsets of hypermethylation target regions that collectively exhibit hypermethylation in one, two, three, four, or five of breast cancer, colon cancer, kidney cancer, liver cancer, and lung cancer.

[0192] (b) Hypomethylated variable target regions Global hypomethylation is a phenomenon commonly observed in various cancers. For example, see Hon et al., Genome Res. 22:246-258 (2012) (breast cancer); Ehrlich, Epigenomics 1:239-259 (2009) (review article describing findings on hypomethylation in colorectal cancer, ovarian cancer, prostate cancer, leukemia, hepatocellular cancer, and cervical cancer). For example, regions such as repetitive elements, such as LINE1 elements, Alu elements, centromeric tandem repeats, pericentromeric tandem repeats, and satellite DNA, as well as intergenic regions that are normally methylated in healthy cells, may show reduced methylation in tumor cells. Thus, in some embodiments, the set of epigenetic target regions includes hypomethylated variable target regions, and the observed decrease in methylation level indicates an increased likelihood that the sample (e.g., a cfDNA sample) contains DNA produced by neoplastic cells, such as tumor cells or cancer cells.

[0193] In some embodiments, the hypomethylated variable target regions include repetitive elements and / or intergenic regions. In some embodiments, the repetitive elements include one, two, three, four, or five of LINE1 elements, Alu elements, centromeric tandem repeats, pericentromeric tandem repeats, and / or satellite DNA.

[0194] Exemplary specific genomic regions showing cancer-related hypomethylation include, for example, nucleotides 8403565 - 8953708 and 151104701 - 151106035 of human chromosome 1 according to the hg19 human genome build. In some embodiments, the hypomethylated variable target regions overlap or include one or both of these regions.

[0195] (c) CTCF binding regions CTCF is a DNA-binding protein that contributes to chromatin organization and often co-localizes with cohesin. Perturbations of CTCF binding sites have been reported in a variety of different cancers. See, for example, Katainen et al., Nature Genetics, doi:10.1038 / ng.3335; Guo et al., Nat. Commun. 9:1520 (2018), published online on June 8, 2015. CTCF binding results in a recognizable pattern of cfDNA that can be detected by sequencing, for example, through fragment length analysis. Details on sequencing-based fragment length analysis are provided, for example, in Snyder et al., Cell 164:57-68 (2016); WO2018 / 009723; and U.S. Patent Application Publication 20170211143A1, each of which is incorporated herein by reference.

[0196] Thus, perturbations to CTCF binding lead to variations in the fragmentation pattern of cfDNA. Therefore, the CTCF binding site represents one type of fragmentation-variable target region.

[0197] Many known CTCF binding sites exist. For example, CTCFBSDB (CTCF Binding Site Database), available at insulatordb.uthsc.edu / on the internet; Cuddapah et al., Genome Res. 19:24-32 (2009); Martin et al., See Nat. Struct. Mol. Biol. 18:708-14 (2011); Rhee et al., Cell. 147:1408-19 (2011), each of which is incorporated herein by reference. Exemplary CTCF binding sites are nucleotides 56014955-56016161 on chromosome 8 and nucleotides 95359169-95360473 on chromosome 13, according to the hg19 or hg38 human genome construct.

[0198] Therefore, in some embodiments, the epigenetic target region set includes CTCF-binding regions. In some embodiments, the CTCF-binding regions include at least 10, 20, 50, 100, 200, or 500 CTCF-binding regions, or 10-20, 20-50, 50-100, 100-200, 200-500, or 500-1000 CTCF-binding regions, for example, the CTCF-binding regions in the above or in one or more of the papers by Cuddapah et al., Martin et al., or Rhee et al. cited above in the CTCFBSDB.

[0199] In some embodiments, at least a portion of the CTCF site may be methylated or unmethylated, and the methylation status correlates with whether or not the cell is a cancer cell. In some embodiments, the epigenetic target region set includes regions at least 100 bp, at least 200 bp, at least 300 bp, at least 400 bp, at least 500 bp, at least 750 bp, and at least 1000 bp upstream and / or downstream of the CTCF binding site.

[0200] (d) transcription start site Transcription start sites can also undergo perturbations in neoplastic cells. For example, nucleosome organization at various transcription start sites in healthy hematopoietic cells substantially contributes to cfDNA in healthy individuals, but may differ from nucleosome organization at those transcription start sites in neoplastic cells. This results in different cfDNA patterns, which can be detected by sequencing, for example, as generally discussed in Snyder et al., Cell 164:57-68 (2016); WO2018 / 009723; and U.S. Patent Application Publication 20170211143A1.

[0201] Thus, perturbations at the transcription start site also lead to variations in the fragmentation pattern of cfDNA. Therefore, the transcription start site also represents one type of fragmentation-variable target region.

[0202] Human transcription start sites are available from the DBTSS (Database of Human Transcription Start Sites) at dbtss.hgc.jp on the internet and are described in Yamashita et al., Nucleic Acids Res. 34 (Database issue): D86-D89 (2006), which is incorporated herein by reference.

[0203] Therefore, in some embodiments, the epigenetic target region set includes transcription start sites. In some embodiments, the transcription start sites include at least 10, 20, 50, 100, 200, or 500 transcription start sites, or 10-20, 20-50, 50-100, 100-200, 200-500, or 500-1000 transcription start sites, e.g., transcription start sites described in DBTSS. In some embodiments, at least a portion of the transcription start sites may be methylated or unmethylated, and the methylation status correlates with whether or not the cell is a cancer cell. In some embodiments, the epigenetic target region set includes regions at least 100 bp, at least 200 bp, at least 300 bp, at least 400 bp, at least 500 bp, at least 750 bp, and at least 1000 bp upstream and / or downstream of the transcription start site. (e) Local amplification

[0204] Local amplifications are somatic mutations, but they can be detected by sequencing based on read frequency in a manner similar to approaches for detecting certain epigenetic changes, such as changes in methylation. Therefore, regions that may exhibit local amplification in cancer can be included in a set of epigenetic target regions, and such regions may include one or more of AR, BRAF, CCND1, CCND2, CCNE1, CDK4, CDK6, EGFR, ERBB2, FGFR1, FGFR2, KIT, KRAS, MET, MYC, PDGFRA, PIK3CA, and RAF1. For example, in some embodiments, the set of epigenetic target regions includes at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, or 18 of the aforementioned targets.

[0205] (f) Methylation control region Including control regions can be useful to facilitate data validation. In some embodiments, the epigenetic target region set includes a control region that is expected to be methylated or unmethylated in essentially all samples, regardless of whether the DNA originates from cancer cells or normal cells. In some embodiments, the epigenetic target region set includes a control hypomethylated region that is expected to be hypomethylated in essentially all samples. In some embodiments, the epigenetic target region set includes a control hypermethylated region that is expected to be hypermethylated in essentially all samples. ii. Variable-array target region set

[0206] In some embodiments, the sequence-variable target region set includes multiple regions known to undergo somatic mutations in cancer.

[0207] In some embodiments, the sequence-variable target region set targets a set of several different genes or genomic regions ("Panel") selected such that a predetermined proportion of subjects with cancer exhibit gene variants or tumor markers in one or more different genes or genomic regions within the Panel. The Panel may be selected to limit the sequencing region to a fixed number of base pairs. The Panel may be selected to sequence a desired amount of DNA by, for example, adjusting the affinity and / or amount of the probe as described elsewhere herein. The Panel may further be selected to achieve a desired sequence read depth. The Panel may be selected to achieve a desired sequence read depth or sequence read coverage with respect to the amount of base pairs sequenced. The Panel may be selected to achieve theoretical sensitivity, theoretical specificity, and / or theoretical precision with respect to the detection of one or more gene variants in a sample.

[0208] Probes for detecting a panel of regions may include probes for detecting target genomic regions (hotspot regions) as well as nucleosome recognition probes (e.g., KRAS codons 12 and 13), and may be designed to optimize capture based on analysis of cfDNA coverage and fragment size variations influenced by nucleosome binding patterns and GC sequence composition. The regions used herein may also include non-hotspot regions optimized based on nucleosome location and GC model.

[0209] Examples of lists of target genomic locations can be found in Tables 3 and 4. In some embodiments, the set of sequence-variable target regions used in the methods of the present disclosure includes at least five, at least ten, at least fifteen, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, at least 60, at least 65, or 70 of the genes in Table 3. In some embodiments, the set of sequence-variable target regions used in the methods of the present disclosure includes at least five, at least ten, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, at least 60, at least 65, or 70 of the SNVs in Table 3. In some embodiments, the sequence-variable target region set used in the methods of the present disclosure includes at least one, at least two, at least three, at least four, at least five, or six fusions from Table 3. In some embodiments, the sequence-variable target region set used in the methods of the present disclosure includes at least a portion of at least one, at least two, or three indels from Table 3. In some embodiments, the sequence-variable target region set used in the methods of the present disclosure includes at least a portion of at least five, at least ten, at least fifteen, at least twenty, at least twenty-five, at least thirty, at least thirty-five, at least forty, at least forty-five, at least fifty, at least fifty-five, at least sixty, at least sixty-five, at least seventy, or seventy-three genes from Table 4. In some embodiments, the sequence-variable target region set used in the method of the present disclosure includes at least 5, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, at least 60, at least 65, at least 70, or 73 SNVs from Table 4.In some embodiments, the set of sequence-variable target regions used in the methods of the present disclosure includes at least one, at least two, at least three, at least four, at least five, or six fusions from Table 4. In some embodiments, the set of sequence-variable target regions used in the methods of the present disclosure includes at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, or eighteen indels from Table 4. Each of these target genomic locations may be identified as a skeletal region or hotspot region with respect to a given panel. An example of a list of target hotspot genomic locations can be found in Table 5. In some embodiments, the set of sequence-variable target regions used in the methods of this disclosure includes at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, or at least twenty of the genes listed in Table 5. Each hotspot genomic region is described with several features, including the associated gene, the chromosome in which it resides, the genomic start and end positions representing the locus, the base pair length of the locus, the exons covered by the gene, and several important features that the given genomic region of interest may attempt to capture (e.g., the type of mutation). [Table 3] [Table 4-1] [Table 4-2] [Table 5-1] [Table 5-2]

[0210] In addition, or separately, suitable target region sets are available from the literature. For example, Gale et al., PLoS One 13: e0194630 (2018), incorporated herein by reference, describes a panel of 35 cancer-related gene targets that can be used as part or all of a sequence-variable target region set. These 35 targets are AKT1, ALK, BRAF, CCND1, CDK2A, CTNNB1, EGFR, ERBB2, ESR1, FGFR1, FGFR2, FGFR3, FOXL2, GATA3, GNA11, GNAQ, GNAS, HRAS, IDH1, IDH2, KIT, KRAS, MED12, MET, MYC, NFE2L2, NRAS, PDGFRA, PIK3CA, PPP2R1A, PTEN, RET, STK11, TP53, and U2AF1.

[0211] In some embodiments, the sequence-variable target region set includes target regions from at least 10, 20, 30, or 35 cancer-related genes, e.g., the cancer-related genes mentioned above. In some embodiments, the sequence-variable target region set has a footprint of at least 10kb, at least 20kb, at least 30kb, at least 40kb, at least 50kb, at least 75kb, at least 100kb, at least 200kb, at least 300kb, or at least 400kb. In some embodiments, the sequence-variable target region set has a footprint in the range of 100 to 1000kb, e.g., 100 to 200kb, 200 to 300kb, 300 to 400kb, 400 to 500kb, 500 to 600kb, 600 to 700kb, 700 to 800kb, 800 to 900kb, and 900 to 1000kb. In some embodiments, the sequence-variable target region set has a footprint of at least 1000kb, at least 2000kb, at least 3000kb, at least 4000kb, at least 5000kb, at least 6000kb, at least 7000kb, at least 8000kb, at least 9000kb, or at least 1Mb. In some embodiments, the sequence-variable target region set has a footprint of 1Mb to 10Mb, for example, 1 to 1.2Mb, 1.2 to 1.4Mb, 1.4 to 1.6Mb, 1.6 to 1.8Mb, 1.8 to 2Mb, 2 to 2.25Mb, 2.25 to 2.5Mb, 2.5 to 2.75Mb, 2.75 to 3Mb, 3 to 3.25Mb, 3.25 to 3.5Mb, 3.5 to 3.75Mb. b. The footprints are in the ranges of 3.75-4Mb, 4-4.25Mb, 4.25-4.5Mb, 4.5-4.75Mb, 4.75-5Mb, 5-5.5Mb, 5.5-6Mb, 6-6.5Mb, 6.5-7Mb, 7-7.5Mb, 7.5-8Mb, 8-8.5Mb, 8.5-9Mb, 9-9.5Mb, and 9.5-10Mb. In some embodiments, the sequence variable target region set has a footprint in the ranges of 10-30 kilobases, 30-60 kilobases, 60 kilobases-1 megabase, or 1-2 megabases. 3. Subject; Sample type / Source

[0212] In some embodiments, DNA (e.g., cfDNA or DNA obtained from a tissue sample) is obtained from a subject having cancer. In some embodiments, DNA (e.g., cfDNA or DNA obtained from a tissue sample) is obtained from a subject suspected of having cancer. In some embodiments, DNA (e.g., cfDNA or DNA obtained from a tissue sample) is obtained from a subject having a tumor. In some embodiments, DNA (e.g., cfDNA or DNA obtained from a tissue sample) is obtained from a subject suspected of having a tumor. In some embodiments, DNA (e.g., cfDNA or DNA obtained from a tissue sample) is obtained from a subject having a neoplasm. In some embodiments, DNA (e.g., cfDNA or DNA obtained from a tissue sample) is obtained from a subject suspected of having a neoplasm. In some embodiments, DNA (e.g., cfDNA or DNA obtained from a tissue sample) is obtained from a subject in remission from a tumor, cancer, or neoplasm (e.g., after chemotherapy, surgical resection, radiation, or a combination thereof). In any of the embodiments described above, the cancer, tumor, or neoplasm, or suspected cancer, tumor, or neoplasm, may be of the lung, colon, rectum, kidney, breast, prostate, or liver. In some embodiments, the cancer, tumor, or neoplasm, or suspected cancer, tumor, or neoplasm, is of the lung. In some embodiments, the cancer, tumor, or neoplasm, or suspected cancer, tumor, or neoplasm, is of the colon or rectum. In some embodiments, the cancer, tumor, or neoplasm, or suspected cancer, tumor, or neoplasm, is of the breast. In some embodiments, the cancer, tumor, or neoplasm, or suspected cancer, tumor, or neoplasm, is of the prostate. In any of the embodiments described above, the subject may be a human subject.

[0213] In some embodiments, the subject has been previously diagnosed with cancer, for example, any of the cancers described above or elsewhere in this specification. Such subject may have previously undergone one or more prior cancer treatments, such as surgery, chemotherapy, radiation and / or immunotherapy. In some embodiments, the sample (e.g., cfDNA or DNA obtained from a tissue sample) is obtained from a subject that has been previously diagnosed and treated at one or more pre-selected time points after one or more prior cancer treatments.

[0214] Samples obtained from a subject (e.g., cfDNA or DNA obtained from a tissue sample) may be sequenced to provide a set of sequence information, which may include sequencing captured DNA molecules of a set of sequence-variable target regions to a higher sequencing depth than captured DNA molecules of an epigenetic target region set, as described in detail elsewhere herein. 4. Collection of target-specific probes

[0215] In some embodiments, the collection of target-specific probes used in the methods disclosed herein includes target-binding probes specific to a set of sequence-variable target regions and target-binding probes specific to a set of epigenetic target regions. In some embodiments, the capture yield of target-binding probes specific to a set of sequence-variable target regions is higher (e.g., at least twice as high) than the capture yield of target-binding probes specific to a set of epigenetic target regions. In some embodiments, the collection of target-specific probes is configured to have a capture yield specific to a set of sequence-variable target regions that is higher (e.g., at least twice as high) than its capture yield specific to a set of epigenetic target regions.

[0216] In some embodiments, the capture yield of target-binding probes specific to sequence-variable target region sets is at least 1.25, 1.5, 1.75, 2, 2.25, 2.5, 2.75, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 times higher than the capture yield of target-binding probes specific to epigenetic target region sets. In some embodiments, the capture yield of target-binding probes specific to sequence-variable target region sets is 1.25–1.5, 1.5–1.75, 1.75–2, 2–2.25, 2.25–2.5, 2.5–2.75, 2.75–3, 3–3.5, 3.5–4, 4–4.5, 4.5–5, 5–5.5, 5.5–6, 6–7, 7–8, 8–9, 9–10, 10–11, 11–12, 13–14, or 14–15 times higher than the capture yield of target-binding probes specific to epigenetic target region sets. In some embodiments, the capture yield of target-binding probes specific to sequence-variable target region sets is 5 to 10 times higher than the capture yield of target-binding probes specific to epigenetic target region sets.

[0217] In some embodiments, the collection of target-specific probes is configured to have a capture yield specific to sequence-variable target region sets that is at least 1.25, 1.5, 1.75, 2, 2.25, 2.5, 2.75, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 times higher than its capture yield for epigenetic target region sets. In some embodiments, the collection of target-specific probes is configured to have a capture yield specific to sequence-variable target region sets that is 1.25–1.5, 1.5–1.75, 1.75–2, 2–2.25, 2.25–2.5, 2.5–2.75, 2.75–3, 3–3.5, 3.5–4, 4–4.5, 4.5–5, 5–5.5, 5.5–6, 6–7, 7–8, 8–9, 9–10, 10–11, 11–12, 13–14, or 14–15 times higher than its capture yield specific to epigenetic target region sets. In some embodiments, the collection of target-specific probes is configured to have a capture yield specific to sequence-variable target region sets that is at least 5 times higher than the capture yield of target-binding probes specific to epigenetic target region sets. In some embodiments, the collection of target-specific probes is configured to have a capture yield specific to sequence-variable target region sets that is 5 to 10 times higher than the capture yield of target-binding probes specific to epigenetic target region sets.

[0218] Probe collections can be configured to provide higher capture yields for sequence-variable target region sets in various ways, including by concentration, different lengths and / or chemistry (e.g., affecting affinity), and combinations thereof. Affinity can be modulated by adjusting probe length and / or by including nucleotide modifications as discussed below.

[0219] In some embodiments, target-specific probes specific to sequence-variable target region sets are present at higher concentrations than target-binding probes specific to epigenetic target region sets. In some embodiments, the concentration of target-binding probes specific to sequence-variable target region sets is at least 1.25, 1.5, 1.75, 2, 2.25, 2.5, 2.75, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 times higher than the concentration of target-binding probes specific to epigenetic target region sets. In some embodiments, the concentration of target-binding probes specific to sequence-variable target region sets is 1.25–1.5, 1.5–1.75, 1.75–2, 2–2.25, 2.25–2.5, 2.5–2.75, 2.75–3, 3–3.5, 3.5–4, 4–4.5, 4.5–5, 5–5.5, 5.5–6, 6–7, 7–8, 8–9, 9–10, 10–11, 11–12, 13–14, or 14–15 times higher than the concentration of target-binding probes specific to epigenetic target region sets. In such embodiments, concentration may refer to the average concentration of mass per volume of individual probes in each set. In some embodiments, the concentration of target-binding probes specific to sequence-variable target region sets is at least 5 times higher than the concentration of target-binding probes specific to epigenetic target region sets. In some embodiments, the concentration of target-binding probes specific to sequence-variable target region sets is 5 to 10 times higher than the concentration of target-binding probes specific to epigenetic target region sets.

[0220] In some embodiments, target-specific probes specific to sequence-variable target region sets have higher affinity for those targets than target-binding probes specific to epigenetic target region sets. Affinity can be modulated in any way known to those skilled in the art, including by using different probe chemistry. For example, certain nucleotide modifications, such as cytosine 5-methylation (in the context of a particular sequence), modifications providing a heteroatom at the 2' sugar position, and LNA nucleotides can increase the stability of double-stranded nucleic acids, and oligonucleotides having such modifications have relatively high affinity for their complementary sequences. See, for example, Severin et al., Nucleic Acids Res. 39: See 8740-8751 (2011); Freier et al., Nucleic Acids Res. 25: 4429-4443 (1997); U.S. Patent No. 9,738,894. Also, for longer sequence lengths, Generally, they provide increased affinity. Other nucleotide modifications, such as the substitution of guanine with hypoxanthine in nucleic acid bases, reduce affinity by reducing the amount of hydrogen bonding between the oligonucleotide and its complementary sequence. In some embodiments, target-specific probes specific to sequence-variable target region sets have modifications that increase their affinity for those targets. In some embodiments, or in addition, target-specific probes specific to epigenetic target region sets have modifications that decrease their affinity for those targets. In some embodiments, target-specific probes specific to sequence-variable target region sets have a longer average length and / or a higher average melting temperature than target-specific probes specific to epigenetic target region sets. These embodiments can be combined with each other and / or with differences in concentration, as considered above, to achieve a desired multiplier difference in capture yield, e.g., any multiplier difference or range described above.

[0221] In some embodiments, the target-specific probe includes a capture portion. The capture portion may be any of the capture portions described herein, for example, biotin. In some embodiments, the target-specific probe is bonded to a solid support, for example, covalently or non-covalently through interactions of bond pairs of the capture portion. In some embodiments, the solid support is a bead, such as a magnetic bead.

[0222] In some embodiments, target-specific probes specific to a sequence-variable target region set and / or target-specific probes specific to an epigenetic target region set are probes that include selected capture portions and sequences to tile across a panel of regions such as genes, as discussed above.

[0223] In some embodiments, the target-specific probe is provided in a single composition. The single composition may be a solution (liquid or frozen). Alternatively, the composition may be a lyophilized product.

[0224] Alternatively, target-specific probes may be provided as multiple compositions, for example, a first composition containing probes specific to a set of epigenetic target regions and a second composition containing probes specific to a set of sequence-variable target regions. These probes may be mixed in appropriate ratios to provide combined probe compositions having any of the aforementioned multiple differences in concentration and / or capture yield. Alternatively, they may be used in separate capture procedures (e.g., in aliquots of a sample or sequentially in the same sample) to provide the first and second compositions containing captured epigenetic target regions and sequence-variable target regions, respectively. a. Probes specific to epigenetic target regions

[0225] Probes to epigenetic target region sets may include probes specific to one or more types of target regions that can distinguish DNA from neoplastic (e.g., tumor or cancer) cells from DNA from healthy cells, e.g., non-neoplastic circulating cells. Exemplary types of such regions are discussed in detail herein, for example, in the section above relating to captured sets. Probes to epigenetic target region sets may also include probes to one or more control regions, for example, as described herein.

[0226] In some embodiments, the probes for the epigenetic target region probe set have a footprint of at least 100kb, for example, at least 200kb, at least 300kb, or at least 400kb. In some embodiments, the probes for the epigenetic target region set have a footprint in the range of 100 to 1000kb, for example, 100 to 200kb, 200 to 300kb, 300 to 400kb, 400 to 500kb, 500 to 600kb, 600 to 700kb, 700 to 800kb, 800 to 900kb, and 900 to 1,000kb. In some embodiments, the probe for the epigenetic target region set has a footprint of at least 1000kb, at least 2000kb, at least 3000kb, at least 4000kb, at least 5000kb, at least 6000kb, at least 7000kb, at least 8000kb, at least 9000kb, or at least 1Mb. In some embodiments, probes for epigenetic target region sets range from 1 Mb to 20 Mb, for example, 1 to 1.2 Mb, 1.2 to 1.4 Mb, 1.4 to 1.6 Mb, 1.6 to 1.8 Mb, 1.8 to 2 Mb, 2 to 2.25 Mb, 2.25 to 2.5 Mb, 2.5 to 2.75 Mb, 2.75 to 3 Mb, 3 to 3.25 Mb, 3.25 to 3.5 Mb, 3.5 to 3.75 Mb, 3.75 to 4 Mb, and 4. It has a footprint in the range of ~4.25Mb, 4.25~4.5Mb, 4.5~4.75Mb, 4.75~5Mb, 5~5.5Mb, 5.5~6Mb, 6~6.5Mb, 6.5~7Mb, 7~7.5Mb, 7.5~8Mb, 8~8.5Mb, 8.5~9Mb, 9~9.5Mb, 9.5~10Mb, 10~12Mb, 12~14Mb, ​​14~16Mb, 16~18Mb, and 18~20Mb. i. Highly methylated variable target regions

[0227] In some embodiments, the probes for a set of epigenetic target regions include probes specific to one or more highly methylated variable target regions. The highly methylated variable target regions may be any of the target regions described above. For example, in some embodiments, the probes specific to highly methylated variable target regions include probes specific to at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% of the loci listed in Table 1, e.g., the loci listed in Table 1. In some embodiments, the probes specific to highly methylated variable target regions include probes specific to at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% of the loci listed in Table 2, e.g., the loci listed in Table 2. In some embodiments, probes specific to highly methylated variable target regions include probes specific to at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% of the loci listed in Table 1 or Table 2. In some embodiments, for each locus included as a target region, there may be one or more probes having a hybridization site that binds between the transcription start site and the stop codon (or the last stop codon for genes that are alternatively spliced). In some embodiments, one or more probes bind within 300 bp, for example, 200 or 100 bp, of the described locations. In some embodiments, the probes have hybridization sites that overlap with the locations described above. In some embodiments, the hypermethylation target region-specific probes include probes specific to one, two, three, four, or five subsets of hypermethylation target regions that collectively exhibit hypermethylation in one, two, three, four, or five of breast cancer, colon cancer, kidney cancer, liver cancer, and lung cancer. ii. Low-methylation variable target regions

[0228] In some embodiments, a probe for a set of epigenetic target regions includes a probe specific to one or more hypomethylated variable target regions. The hypomethylated variable target region may be any of the target regions described above. For example, a probe specific to one or more hypomethylated variable target regions may include probes for repeating elements, such as LINE1 elements, Alu elements, centromere tandem repeats, paracentromere tandem repeats, and satellite DNA, where intergenetic regions that are normally methylated in healthy cells may show reduced methylation in tumor cells.

[0229] In some embodiments, probes specific to low-methylation variable target regions include probes specific to repeat elements and / or intergenetic regions. In some embodiments, probes specific to repeat elements include probes specific to one, two, three, four, or five of the following: LINE1 elements, Alu elements, centromere tandem repeats, paracentromere tandem repeats, and / or satellite DNA.

[0230] Exemplary probes specific to genomic regions exhibiting cancer-related hypomethylation include probes specific to nucleotides 8403565–8953708 and / or 151104701–151106035 of human chromosome 1. In some embodiments, probes specific to hypomethylation variable target regions include probes specific to regions overlapping with or containing nucleotides 8403565–8953708 and / or 151104701–151106035 of human chromosome 1. iii.CTCF binding region

[0231] In some embodiments, the probes for the epigenetic target region set include probes specific to CTCF binding regions. In some embodiments, the CTCF binding region-specific probes include at least 10, 20, 50, 100, 200, or 500 CTCF binding regions, or 10-20, 20-50, 50-100, 100-200, 200-500, or 500-1000 CTCF binding regions, for example, those described above or in CTCFBSDB or cited above by Cuddapah et al., Martin et al., or Rhee A probe specific to the CTCF binding region in one or more et al. papers, etc. In some embodiments, probes for a set of epigenetic target regions include regions at least 100 bp, at least 200 bp, at least 300 bp, at least 400 bp, at least 500 bp, at least 750 bp, or at least 1000 bp upstream and downstream of the CTCF binding site. iv. Transcription initiation site

[0232] In some embodiments, the probes for the epigenetic target region set include probes specific to transcription start sites. In some embodiments, the transcription start site specific probes include probes specific to at least 10, 20, 50, 100, 200, or 500 transcription start sites, or 10-20, 20-50, 50-100, 100-200, 200-500, or 500-1000 transcription start sites, such as transcription start sites described in DBTSS. In some embodiments, the probes for the epigenetic target region set include probes for sequences at least 100 bp, at least 200 bp, at least 300 bp, at least 400 bp, at least 500 bp, at least 750 bp, or at least 1000 bp upstream and downstream of the transcription start site. v. Local amplification

[0233] As mentioned above, local amplifications are somatic mutations, but they can be detected by sequencing based on read frequency in a manner similar to approaches for detecting certain epigenetic changes, such as changes in methylation. Therefore, as discussed above, regions that may exhibit local amplification in cancer can be included in the epigenetic target region set. In some embodiments, the probes specific to the epigenetic target region set include probes specific to local amplification. In some embodiments, the probes specific to local amplification include probes specific to one or more of AR, BRAF, CCND1, CCND2, CCNE1, CDK4, CDK6, EGFR, ERBB2, FGFR1, FGFR2, KIT, KRAS, MET, MYC, PDGFRA, PIK3CA, and RAF1. For example, in some embodiments, a probe specific to local amplification includes a probe specific to at least one or more of the aforementioned targets, namely 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, or 18. vi. contrast region

[0234] Including a control region can be useful to facilitate data validation. In some embodiments, a probe specific to a set of epigenetic target regions includes a probe specific to a control methylation region that is expected to be methylated in essentially all samples. In some embodiments, a probe specific to a set of epigenetic target regions includes a probe specific to a control hypomethylation region that is expected to be hypomethylated in essentially all samples. b. Probes specific to sequence-variable target regions

[0235] Probes for sequences-variable target region sets may include probes specific to multiple regions known to undergo somatic mutations in cancer. Probes may be specific to any sequence-variable target region set described herein. Exemplary sequences-variable target region sets are discussed in detail herein, for example, in the section above relating to captured sets.

[0236] In some embodiments, the sequence-variable target region probe set has a footprint of at least 10kb, for example, at least 20kb, at least 30kb, or at least 40kb. In some embodiments, the sequence-variable target region probe set has a footprint in the range of 10 to 100kb, for example, 10 to 20kb, 20 to 30kb, 30 to 40kb, 40 to 50kb, 50 to 60kb, 60 to 70kb, 70 to 80kb, 80 to 90kb, and 90 to 100kb. In some embodiments, the sequence-variable target region probe set has a footprint of at least 10kb, at least 20kb, at least 30kb, at least 40kb, at least 50kb, at least 75kb, at least 100kb, at least 200kb, at least 300kb, or at least 400kb. In some embodiments, the sequence-variable target region probe set has a footprint in the range of 100-1000kb, for example, 100-200kb, 200-300kb, 300-400kb, 400-500kb, 500-600kb, 600-700kb, 700-800kb, 800-900kb, and 900-1000kb. In some embodiments, the sequence-variable target region probe set has a footprint of at least 1000kb, at least 2000kb, at least 3000kb, at least 4000kb, at least 5000kb, at least 6000kb, at least 7000kb, at least 8000kb, at least 9000kb, or at least 1Mb. In some embodiments, the array-variable target region probe set is 1 Mb to 10 Mb, for example, 1 to 1.2 Mb, 1.2 to 1.4 Mb, 1.4 to 1.6 Mb, 1.6 to 1.8 Mb, 1.8 to 2 Mb, 2 to 2.25 Mb, 2.25 to 2.5 Mb, 2.5 to 2.75 Mb, 2.75 to 3 Mb, 3 to 3.25 Mb, 3.25 to 3.5 Mb, 3.5 to 3.7 It has a footprint in the range of 5Mb, 3.75-4Mb, 4-4.25Mb, 4.25-4.5Mb, 4.5-4.75Mb, 4.75-5Mb, 5-5.5Mb, 5.5-6Mb, 6-6.5Mb, 6.5-7Mb, 7-7.5Mb, 7.5-8Mb, 8-8.5Mb, 8.5-9Mb, 9-9.5Mb, and 9.5-10Mb.

[0237] In some embodiments, probes specific to a set of sequence-variable target regions include probes specific to at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, or 70 genes in Table 3. In some embodiments, probes specific to a set of sequence-variable target regions include probes specific to at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, or 70 SNVs in Table 3. In some embodiments, probes specific to a set of sequence-variable target regions include probes specific to at least 1, 2, 3, 4, 5, or 6 fusions in Table 3. In some embodiments, probes specific to a set of sequence-variable target regions include probes specific to at least some of the indels in Table 3, at least one, at least two, or three. In some embodiments, probes specific to a set of sequence-variable target regions include probes specific to at least some of the genes in Table 4, at least five, at least ten, at least fifteen, at least twenty, at least twenty-five, at least thirty, at least thirty-five, at least forty, at least forty-five, at least fifty, at least fifty-five, at least sixty, at least sixty-five, at least seventy-five, or seventy-three. In some embodiments, probes specific to a set of sequence-variable target regions include probes specific to at least five, at least ten, at least fifteen, at least twenty, at least twenty-five, at least thirty, at least thirty-five, at least forty or seventy-five.In some embodiments, probes specific to a sequence-variable target region set include probes specific to at least one, at least two, at least three, at least four, at least five, or six fusions in Table 4. In some embodiments, probes specific to a sequence-variable target region set include probes specific to at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, or eighteen indels in Table 4. In some embodiments, the probes specific to the sequence variable target region set include probes specific to at least some of the genes listed in Table 5: at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, or at least 20.

[0238] In some embodiments, probes specific to a sequence-variable target region set include probes specific to target regions from at least 10, 20, 30, or 35 cancer-related genes, such as AKT1, ALK, BRAF, CCND1, CDK2A, CTNNB1, EGFR, ERBB2, ESR1, FGFR1, FGFR2, FGFR3, FOXL2, GATA3, GNA11, GNAQ, GNAS, HRAS, IDH1, IDH2, KIT, KRAS, MED12, MET, MYC, NFE2L2, NRAS, PDGFRA, PIK3CA, PPP2R1A, PTEN, RET, STK11, TP53, and U2AF1. c. Probe composition

[0239] In some embodiments, a single composition is used that includes probes for a set of array-variable target regions and probes for a set of epigenetic target regions. The probes can be provided in such a composition at any of the concentration ratios described herein.

[0240] In some embodiments, a first composition is provided that includes probes for a set of epigenetic target regions and a second composition is provided that includes probes for a set of array-variable target regions. The ratio of the concentration of the probes in the first composition to the concentration of the probes in the second composition can be any of the ratios described herein. 5. Composition comprising captured cfDNA

[0241] In some embodiments, a composition comprising captured cfDNA is generated and / or used in the methods disclosed herein. The captured cfDNA can have any of the characteristics described herein for the captured set, including, for example, a concentration of DNA corresponding to a set of array-variable target regions that is higher than the concentration of DNA corresponding to a set of epigenetic target regions (normalized for the size of the footprint discussed above). In some embodiments, the captured set of cfDNA includes sequence tags, which can be added to the cfDNA as described herein. Generally, including sequence tags results in cfDNA molecules that are different from their naturally occurring untagged forms.

[0242] Such a composition may further include a probe set or sequencing primer as described herein, each of which may be different from a naturally occurring nucleic acid molecule. For example, the probe set described herein may include a capture moiety and the sequencing primer may include a label that does not occur naturally. 6. Exemplary method for molecular tag identification of MBD bead-sorted libraries

[0243] An exemplary method for molecular tag identification of MBD bead distribution libraries using NGS is as follows: i) Physical distribution of the extracted DNA sample (e.g., plasma DNA extracted from a human sample subjected to target capture as described herein, if applicable) using a methyl-binding domain protein-bead purification kit, and saving all elutes from the process for downstream processing. ii) Parallel application of differential molecular tags and NGS-enabled adapter sequences to each fraction. For example, a highly methylated fraction, a residually methylated ("washed") fraction, and a lowly methylated fraction are ligated to an NGS-adapter having a molecular tag. iii) Recombine all molecularly tagged fractions and then amplify them using adapter-specific DNA primer sequences. iv) Capture / hybridization of the combined and amplified total library to target the target genomic region (e.g., cancer-specific gene variants and differentially methylated regions). v) Add sample tags and re-amplify the captured DNA library. Different samples are pooled and assayed multiple times using an NGS instrument. vi) Bioinformatics analysis of NGS data in which molecular tags are used to uniquely identify molecules, and deconvolution of samples into differentially MBD-distributed molecules. This analysis may yield information on 5-methylcytosine relative to genomic regions simultaneously with standard gene sequencing / variant detection. As discussed in detail elsewhere herein, the analysis involves sequencing from first and second sets of sequence reads, where (i) the step of calling C-to-T or G-to-A transition mutations against a reference sequence based on the sequence of reads or molecules in the first set requires observation of transition mutations in a greater number of reads than the step of calling C-to-T or G-to-A transition mutations against a reference sequence based on the sequence of reads or molecules in the second set, or (ii) C-to-T or G-to-A transition mutations are not called against a reference sequence based on the sequence of reads or molecules in the first set.

[0244] The exemplary methods described above may further include any features of suitability of the methods disclosed herein that are described elsewhere in this specification. 7. Exemplary Workflow

[0245] Exemplary workflows for distribution and library preparation are provided herein. In some embodiments, some or all features of the distribution and library preparation workflows may be used in combination. The above-described exemplary workflows may further include any features of the methods of this disclosure that are described elsewhere herein. a. Distributing steps

[0246] In some embodiments, sample DNA (e.g., between 1 ng and 300 ng) is mixed with an appropriate amount of methyl-binding domain (MBD) buffer (the amount of MBD buffer depends on the amount of DNA used) and magnetic beads conjugated with MBD protein, and incubated overnight. Methylated DNA (highly methylated DNA) binds to the MBD protein on the magnetic beads during this incubation. Unmethylated (lowly methylated DNA) or lightly methylated DNA (moderately methylated) is washed away from the beads with a buffer containing increasing salt concentrations. For example, one, two, or more fractions containing unmethylated, lowly methylated, and / or moderately methylated DNA may be obtained from such washes. Finally, highly methylated DNA (highly methylated DNA) is eluted from the MBD protein using a buffer with a high salt concentration. In some embodiments, these washes result in three fractions of DNA with increasing methylation levels (lowly methylated fraction, moderately methylated fraction, and highly methylated fraction).

[0247] In some embodiments, the three fractions of DNA are desalted and concentrated in preparation for the enzymatic step of library preparation. b. Library preparation

[0248] In some embodiments (for example, after enriching the DNA in the fraction), the distributed DNA is made ligable, for example, by extending the terminal overhangs of the DNA molecules, by adding adenosine residues to the 3' end of the fragments, and by phosphorylating the 5' end of each DNA fragment. DNA ligase and adapters are added to ligate each distributed DNA molecule with the adapters at each end. These adapters contain fraction tags (e.g., non-random, non-unique barcodes) that are distinguishable from fraction tags in adapters used in other fractions. After ligation, the three fractions are pooled together and amplified (e.g., by PCR, using primers specific to the adapters).

[0249] After PCR, the amplified DNA may be washed and concentrated before capture. The amplified DNA is contacted with a collection of probes described herein (e.g., biotinylated RNA probes, ssDNA probes, or dsDNA probes) that target a specific region of interest. The mixture is incubated, for example, overnight in a salt buffer. The probes are captured (e.g., using streptavidin magnetic beads) and separated from the uncaptured amplified DNA by a series of salt washes, thereby providing a set of captured DNA. After capture, the DNA in the captured set is amplified by PCR. In some embodiments, the PCR primers contain sample tags, thereby incorporating the sample tags into the DNA molecules. In some embodiments, DNA from different samples is pooled together and then multiplexed using, for example, an Illumina NovaSeq sequencer. III. General characteristics of this method 1. Sample

[0250] The sample may be any biological sample isolated from the subject. The sample may be a body sample. The sample may include body tissues, such as known or suspected solid tumors, whole blood, platelets, serum, plasma, feces, red blood cells, white blood cells or leucocytes, endothelial cells, tissue biopsy material, cerebrospinal fluid, synovial fluid, lymph, ascites, interstitial or extracellular fluids, fluids in the intercellular space including gingival crevicular exudate, bone marrow, pleural fluid, cerebrospinal fluid, saliva, mucus, sputum, semen, sweat, and urine. The sample is preferably a body fluid, in particular blood and its fractions, and urine. The sample may be in the form originally isolated from the subject, or may have been further processed to remove or add components such as cells, or to enrich one component with another. Therefore, preferred body fluids for analysis are plasma or serum containing cell-free nucleic acids. The sample can be isolated or obtained from the subject and transported to the sample analysis site. Samples can be stored and transported at a desired temperature, e.g., room temperature, 4°C, -20°C, and / or -80°C. Samples can be isolated or obtained from the subject at the site of sample analysis. The subject may be a human, mammal, animal, companion animal, service animal, or pet. The subject may have cancer. The subject may not have cancer or detectable cancer symptoms. The subject may have been treated with one or more cancer treatments, e.g., one or more of chemotherapy, antibodies, vaccines, or biological agents. The subject may be in remission. The subject may or may not have been diagnosed with susceptibility to cancer or any cancer-related gene mutation / disorder.

[0251] The volume of plasma depends on the desired read depth of the region being sequenced. Exemplary volumes are 0.4–40 ml, 5–20 ml, and 10–20 ml. For example, the volume could be 0.5 mL, 1 mL, 5 mL, 10 mL, 20 mL, 30 mL, or 40 mL. The volume of plasma sampled could be 5–20 mL.

[0252] In some embodiments, the sample may be a DNA sample obtained from tissue. In such embodiments, the DNA obtained from the tissue sample can be fragmented by enzymatic means (e.g., fragmentase) or mechanical means (e.g., sonication).

[0253] The sample may contain various amounts of nucleic acids, including genome equivalents. For example, a sample of approximately 30 ng of DNA may contain approximately 10,000 (10 4 It may contain ) haploid human genome equivalents, and in the case of cfDNA, approximately 200 billion (2 × 10⁻¹⁶). 11 It may contain ) individual polynucleotide molecules. Similarly, a sample of approximately 100 ng of DNA may contain approximately 30,000 haploid human genome equivalents, and in the case of cfDNA, it may contain approximately 600 billion individual molecules.

[0254] The sample may contain nucleic acids from different sources, for example, from the same target cells and cell-free sources, or from different target cells and cell-free sources. The sample may contain nucleic acids with mutations. For example, the sample may contain DNA with germline mutations and / or somatic mutations. Germline mutations refer to mutations present in the germline DNA of the target. Somatic mutations refer to mutations originating from somatic cells of the target, for example, cancer cells. The sample may contain DNA with cancer-related mutations (e.g., cancer-related somatic mutations). The sample may contain epigenetic variants (i.e., chemical or protein modifications), where the epigenetic variant is related to the presence of gene variants such as cancer-related mutations. In some embodiments, the sample contains epigenetic variants related to the presence of gene variants, where the sample does not contain gene variants.

[0255] Exemplary amounts of cell-free nucleic acids in the sample before amplification range from approximately 1 fg to approximately 1 μg, for example, 1 pg to 200 ng, 1 ng to 100 ng, and 10 ng to 1000 ng. For example, the amount may be up to approximately 600 ng, up to approximately 500 ng, up to approximately 400 ng, up to approximately 300 ng, up to approximately 200 ng, up to approximately 100 ng, up to approximately 50 ng, or up to approximately 20 ng of cell-free nucleic acid molecules. The amount may be at least 1 fg, at least 10 fg, at least 100 fg, at least 1 pg, at least 10 pg, at least 100 pg, at least 1 ng, at least 10 ng, at least 100 ng, at least 150 ng, or at least 200 ng of cell-free nucleic acid molecules. The amount can be up to 1 femtogram (fg), 10 fg, 100 fg, 1 picogram (pg), 10 pg, 100 pg, 1 ng, 10 ng, 100 ng, 150 ng, 200 ng, 250 ng, or 300 ng of cell-free nucleic acid molecules. The method may include a step of obtaining 1 femtogram (fg) to 200 ng. In some embodiments, the amount of DNA used may be between 1 fg and 1 μg.

[0256] Cell-free nucleic acids are nucleic acids that are not contained within cells nor bound to cells in some other manner, or, put another way, nucleic acids that remain in a sample after the removal of intact cells. Cell-free nucleic acids include DNA, RNA, and their hybrids, including genomic DNA, mitochondrial DNA, siRNA, miRNA, circular RNA (cRNA), tRNA, rRNA, small nucleolar RNA (snoRNA), Piwi-interacting RNA (piRNA), long non-coding RNA (long ncRNA), or fragments of any of these. Cell-free nucleic acids can be double-stranded, single-stranded, or their hybrids. Cell-free nucleic acids can be released into body fluids via secretion or cell death processes such as necrosis and apoptosis. Some cell-free nucleic acids, such as circulating tumor DNA (ctDNA), are released from cancer cells into body fluids. Others are released from healthy cells. In some embodiments, cfDNA is cell-free fetal DNA (cffDNA). In some embodiments, cell-free nucleic acids are produced by tumor cells. In some embodiments, cell-free nucleic acids are produced by a mixture of tumor and non-tumor cells.

[0257] Cell-free nucleic acids have an exemplary size distribution of about 100 to 500 nucleotides, with molecules of 110 to about 230 nucleotides corresponding to about 90% of the molecules, the most frequent value being about 168 nucleotides, and a second minor peak in the range between 240 and 440 nucleotides.

[0258] In some embodiments, the DNA in a sample consists essentially of cell-free DNA. This means that all or nearly all of the DNA in the sample, e.g., at least 90% of the DNA by weight or molar fraction, is cfDNA. In some embodiments, at least 95%, 97%, 98%, 99%, 99.5%, or 99.9% of the DNA in the sample by weight or molar fraction is cfDNA. In some embodiments, the DNA in the sample consists of cell-free DNA.

[0259] Cell-free nucleic acids can be isolated from body fluids via fractionation or partitioning steps, where the cell-free nucleic acids found in the solution are separated from intact cells and other insoluble components of the body fluid. Partitioning may involve techniques such as centrifugation or filtration. Alternatively, cells in the body fluid can be lysed, and the cell-free and cellular nucleic acids can be processed together. Generally, after buffer addition and washing steps, the nucleic acids can be precipitated with alcohol. Further cleansing steps, such as silica columns, can be used to remove impurities or salts. In certain aspects of the procedure, for example to optimize yield, non-specific bulk carrier nucleic acids, such as C1 DNA, DNA, or proteins for bisulfite sequencing, hybridization, and / or ligation, can be added throughout the reaction.

[0260] After such processing, the sample may contain various forms of nucleic acids, including double-stranded DNA, single-stranded DNA, and single-stranded RNA. In some embodiments, single-stranded DNA and RNA can be converted to double-stranded forms so that they can be included in subsequent processing and analysis steps.

[0261] Double-stranded DNA molecules in a sample and single-stranded nucleic acid molecules converted to double-stranded DNA molecules can be ligated to an adapter at either one or both ends. Typically, double-stranded molecules are blunt-ended by treating them with a polymerase having 5'-3' polymerase and 3'-5' exonuclease (or proofreading function) in the presence of all four standard nucleotides. Klenow large fragment and T4 polymerase are examples of suitable polymerases. Blunt-ended DNA molecules can be ligated to at least partially double-stranded adapters (e.g., Y-shaped or bell-shaped adapters). Alternatively, complementary nucleotides can be added to the blunt ends of the sample nucleic acid and adapter to facilitate ligation. Both blunt-end and sticky-end ligation are intended herein. In blunt-end ligation, both the nucleic acid molecule and the adapter tag have blunt ends. In sticky-end ligation, typically the nucleic acid molecule has an "A" overhang and the adapter has a "T" overhang. 2. Tags

[0262] A tag containing a barcode can be incorporated into the adapter or otherwise attached. Among other methods, the tag can be incorporated by ligation or overlapping extension PCR. a. Molecular tagging strategies

[0263] Molecular tagging refers to the practice of tagging molecules that enable the identification of the molecule from which a sequence read originates. Tagging strategies can be divided into unique tagging and non-unique tagging strategies. In unique tagging, all or substantially all molecules in a sample have different tags, and therefore, reads can be assigned to the original molecule based solely on the tag information. Tags used in such a method are sometimes referred to as "unique tags." In non-unique tagging, different molecules in the same sample may have the same tag, and therefore, other information is used in addition to the tag information to assign sequence reads to the original molecule. Such information may include start and end coordinates, coordinates to which the molecule is mapped, or start or end coordinates alone. Tags used in such a method are sometimes referred to as "non-unique tags." Therefore, it is not necessary to tag every molecule in a sample uniquely. It is sufficient to tag molecules that fall within a range of identifiable classes in the sample uniquely. Thus, molecules of different identifiable families may have the same tag without losing information about the identity of the tagged molecule.

[0264] In certain embodiments of non-unique tagging, the number of different tags used may be sufficient to ensure that all molecules in a particular group have different tags (e.g., at least 99%, at least 99.9%, at least 99.99%, or at least 99.999%). When using barcodes as tags, and attaching them to both ends of a molecule, for example, randomly, it should be noted that combinations of barcodes may also constitute a tag. This number then depends on the number of molecules that belong to a class. For example, a class could be all molecules that map to the same start-stop position in a reference genome. A class could be all molecules that map to a particular locus, e.g., a particular base or region (e.g., up to 100 bases or a gene or exon of a gene). In certain embodiments, the number of different tags used to uniquely identify the number z of molecules in a class is 2 * z, 3 * z, 4* z, 5 * z, 6 * z, 7 * z, 8 * z, 9 * z, 10 * z, 11 * z, 12 * z, 13 * z, 14 * z, 15 * z, 16 * z, 17 * z, 18 * z, 19 * z, 20 * z or 100 * any of z (e.g., lower limit) and 100,000 * z, 10,000 * z, 1000 * z or 100 * It may be between any of z (e.g., upper limit) and.

[0265] For example, in a sample of about 3 ng to 30 ng of human cell-free DNA, approximately 10 3 ~10 4 molecules are mapped to a specific nucleotide coordinate, and it is expected that about 3 to 10 molecules share the same termination coordinate with an arbitrary start coordinate. Therefore, to tag all such molecules uniquely, about 50 to about 50,000 different tags (e.g., about 6 to 220 barcode combinations) may be sufficient. For 10 3 ~10 4 To tag all the molecules mapped to one nucleotide coordinate uniquely, about 1 million to about 20 million different tags are required.

[0266] Generally, the assignment of unique or non-unique tag barcodes in a reaction follows the methods and systems described in U.S. Patent Application Nos. 20010053519, 20030152490, 20110160078, as well as U.S. Patent Nos. 6,582,908, 7,537,898, and 9,598,731. The tags can be linked to the sample nucleic acid randomly or non-randomly.

[0267] In some embodiments, tagged nucleic acids are loaded into a microwell plate and then sequenced. The microwell plate may have 96, 384, or 1536 microwells. In some cases, they are introduced in a predicted ratio of unique tags to microwells. For example, unique tags can be loaded so that approximately 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 500, 1000, 500, 1000, 5000, 10000, 100000, 50000, 50000, 50000, 500000, 1000000, 10000000, 5000000, 10 In some cases, unique tags can be loaded such that approximately fewer than 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 500, 1000, 5000, 10000, 10000, 50000, 100000, 500000, 100000, 100000, 1000000, 1000000, 1000000, 1000000, 10000000, 10000000, 10000000, 5000000, or 1000000000 unique tags are loaded per genome sample.In some cases, the average number of unique tags loaded per sample genome is less than approximately 1, less than 2, less than 3, less than 4, less than 5, less than 6, less than 7, less than 8, less than 9, less than 10, less than 20, less than 50, less than 100, less than 500, less than 1,000, less than 5,000, less than 10,000, less than 50,000, less than 50,000, less than 50,000, less than 100,000, less than 500,000, less than 1,000,000, less than 10,000,000, less than 50,000,000, or less than 1,000,000,000, or more than approximately 1 and more than approximately 2. The unique tags are more than approximately 3, more than approximately 4, more than approximately 5, more than approximately 6, more than approximately 7, more than approximately 8, more than approximately 9, more than approximately 10, more than approximately 20, more than approximately 50, more than approximately 100, more than approximately 500, more than approximately 1000, more than approximately 5000, more than approximately 10000, more than approximately 50000, more than approximately 50000, more than approximately 50000, more than approximately 100000, more than approximately 100000, more than approximately 1000000, more than approximately 1000000, more than approximately 1000000, more than approximately 5000000, more than approximately 10000000, more than approximately 50000000, or more than approximately 1000000000.

[0268] A preferred format uses 20 to 50 different tags (e.g., barcodes) ligated to both ends of the target nucleic acid. For example, 35 different tags (e.g., barcodes) ligated to both ends of the target molecule create 35 × 35 permutations, which is equivalent to 1225 tag combinations for 35 tags. The number of such tags is sufficient to ensure that different molecules with the same start and end points have a high probability (e.g., at least 94%, 99.5%, 99.99%, 99.999%) of receiving different tag combinations. Other barcode combinations include any number between 10 and 500, e.g., approximately 15 × 15, approximately 35 × 35, approximately 75 × 75, approximately 100 × 100, approximately 250 × 250, and approximately 500 × 500.

[0269] In some cases, the unique tag may be a predetermined, random, or semi-random sequence oligonucleotide. In other cases, multiple barcodes may be used, and therefore the barcodes within the multiple are not necessarily unique to one another. In this example, barcodes can be ligated to individual molecules, and thus the combination of the barcode and the sequence to which it is ligated creates a unique sequence that can be tracked individually. As described herein, it becomes possible to assign a unique identity to a particular molecule by detecting a non-unique barcode in combination with sequence data of the start and end portions of the sequence read. The length or number of base pairs of individual sequence reads can also be used to assign a unique identity to such molecules. As described herein, a fragment from a single strand of nucleic acid to which a unique identity has been assigned may enable the subsequent identification of the fragment from the parent strand. 3. Amplification

[0270] The adapter can amplify the adjacent sample nucleic acid by PCR and other amplification methods. Amplification is typically primed by a primer that binds to the primer binding site in the adapter adjacent to the DNA molecule to be amplified. The amplification method may involve denaturation, annealing, and extension cycles resulting from thermal cycling, or it may be constant temperature, as in transcription-mediated amplification. Other amplification methods include ligase chain reaction, strand displacement amplification, nucleic acid sequence-based amplification, and sequence-based self-persistent replication.

[0271] In this method, it is preferable to perform dsDNA ligation using T-tailed and C-tailed adapters, which results in amplification of at least 50%, 60%, 70%, or 80% of the double-stranded nucleic acid before ligation to the adapter. In this method, it is preferable that the amount or number of molecules amplified increases by at least 10%, 15%, or 20% compared to a control method performed with a T-tailed adapter alone. 4. Bait set; capture part; enrichment

[0272] As described above, nucleic acids in a sample can be subjected to a capture step, where molecules having a target sequence are captured for subsequent analysis. Target capture may involve the use of a bait set containing oligonucleotide baits labeled with a capture moiety, such as biotin or other examples mentioned below. The probe may have a sequence selected for tiling across a panel of regions, such as genes. In some embodiments, the bait set may have higher and lower capture yields for sets of target regions, such as sequence-variable target region sets and epigenetic target region sets, respectively, as discussed elsewhere in this specification. In some embodiments, the bait (i.e., probe) may be RNA, ssDNA, or dsDNA. The bait set is combined with the sample under conditions that allow hybridization of the target molecule having the bait. The captured molecule is then isolated using the capture moiety, for example, by bead-based streptavidin for the biotin capture moiety. Such methods are further described, for example, in U.S. Patent No. 9,850,523 issued December 26, 2017, which is incorporated herein by reference.

[0273] The capture portion includes, but is not limited to, biotin, avidin, streptavidin, nucleic acids containing a specific nucleotide sequence, haptens recognized by antibodies, and magnetically attractable particles. The extraction portion may be a member of a binding pair such as biotin / streptavidin or hapten / antibody. In some embodiments, the capture portion attached to the analyte is captured by its binding pair attached to an isolateable portion, such as magnetically attractable particles or larger particles that can be settled by centrifugation. The capture portion may be any type of molecule that enables affinity separation of nucleic acids having the capture portion from nucleic acids lacking the capture portion. Exemplary capture portions are biotin that enables affinity separation by binding to streptavidin linked or attachable to a solid phase, or oligonucleotides that enable affinity separation by binding to complementary oligonucleotides linked or attachable to a solid phase. 5. Sequencing

[0274] If necessary, the sample nucleic acids adjacent to the adapter are generally subjected to sequencing with or without prior amplification. Sequencing methods or commercially available formats used as needed include, for example, Sanger sequencing, high-throughput sequencing, pyrosequencing, synthesis sequencing, single-molecule sequencing, nanopore-based sequencing, semiconductor sequencing, ligation sequencing, hybridization sequencing, RNA-Seq (Illumina), Digital Gene Expression (Helicos), next-generation sequencing (NGS), synthesis single-molecule sequencing (SMSS) (Helicos), large-scale parallel sequencing, Clonal Single Molecule Array (Solexa), shotgun sequencing, Ion Torrent, Oxford Nanopore, Roche Genia, Maxam-Gilbert sequencing, primer walking, and sequencing using PacBio, SOLiD, Ion Torrent, or the Nanopore platform. The principles described herein, which include calling C-to-T or G-to-A transition mutations detected from reads or sequences of molecules derived from a highly methylated fraction with greater rigor, can be applied by those skilled in the art to sequencing methods that directly detect methylation, such as sequencing using Oxford Nanopore or PacBio. Sequencing reactions can be carried out in various sample processing units, which may include multiple lanes, multiple channels, multiple wells, or other means for processing multiple sets of samples substantially simultaneously. Sample processing units may also include multiple sample chambers capable of processing multiple trials simultaneously.

[0275] Sequencing reactions may be performed on one or more nucleic acid fragment types or regions containing markers for cancer or other diseases. Sequencing reactions may also be performed on any nucleic acid fragment present in the sample. Sequencing reactions may be performed on at least about 5%, 10%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 99.9%, or 100% of the genome. In other cases, sequencing reactions may be performed on less than about 5%, less than about 10%, less than about 15%, less than about 20%, less than about 25%, less than about 30%, less than about 40%, less than about 50%, less than about 60%, less than about 70%, less than about 80%, less than about 90%, less than about 95%, less than about 99%, less than about 99.9%, or less than 100% of the genome.

[0276] Simultaneous sequencing reactions may be carried out using multiple sequencing techniques. In some embodiments, cell-free polynucleotides are sequenced by at least about 1,000, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, 10,000, 50,000, or 100,000 sequencing reactions. In other embodiments, cell-free polynucleotides are sequenced by less than about 1,000, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, 10,000, 50,000, or 100,000 sequencing reactions. Sequencing reactions are typically carried out sequentially or simultaneously. Subsequent data analysis is generally performed on all or part of the sequencing reactions. In some embodiments, data analysis is performed for at least approximately 1,000, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, 10,000, 50,000, or 100,000 sequencing reactions. In other embodiments, data analysis may be performed for fewer than approximately 1,000, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, 10,000, 50,000, or 100,000 sequencing reactions. An example of read depth is approximately 1,000 to 50,000 reads per locus (e.g., base position). a. Differential sequencing depth

[0277] In some embodiments, nucleic acids corresponding to sequence-variable target region sets are sequenced to a higher sequencing depth than nucleic acids corresponding to epigenetic target region sets. For example, the sequencing depth for nucleic acids corresponding to sequence variant target region sets is at least 1.25, 1.5, 1.75, 2, 2.25, 2.5, 2.75, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 times, or 1.25 times, compared to the sequencing depth for nucleic acids corresponding to epigenetic target region sets. The sequencing depth can be ~1.5 times, 1.5 times to 1.75 times, 1.75 times to 2 times, 2 times to 2.25 times, 2.25 times to 2.5 times, 2.5 times to 2.75 times, 2.75 times to 3 times, 3 times to 3.5 times, 3.5 times to 4 times, 4 times to 4.5 times, 4.5 times to 5 times, 5 times to 5.5 times, 5.5 times to 6 times, 6 times to 7 times, 7 times to 8 times, 8 times to 9 times, 9 times to 10 times, 10 times to 11 times, 11 times to 12 times, 13 times to 14 times, 14 times to 15 times, or 15 times to 100 times. In some embodiments, the sequencing depth is at least 2 times. In some embodiments, the sequencing depth is at least 5 times. In some embodiments, the sequencing depth is at least 10 times. In some embodiments, the sequencing depth is 4 times to 10 times. In some embodiments, the sequencing depth is 4 to 100 times. Each of these embodiments refers to the extent to which the nucleic acids corresponding to the sequence variable target region set are sequenced to a higher sequencing depth than the nucleic acids corresponding to the epigenetic target region set.

[0278] In some embodiments, captured cfDNA corresponding to a sequence variable target region set and captured cfDNA corresponding to an epigenetic target region set are simultaneously sequenced, for example, in the same sequencing cell (e.g., a flow cell of an Illumina sequencer) and / or in the same composition, which may be a pooled composition resulting from the recombination of separately captured sets or a composition obtained by capturing cfDNA corresponding to a sequence variable target region set and captured cfDNA corresponding to an epigenetic target region set in the same container. b. Preparation for sequencing

[0279] In some embodiments, a population of nucleic acids for sequencing is prepared by enzymatically forming blunt ends on double-stranded nucleic acids having single-stranded overhangs at one or both ends. In these embodiments, the population is typically treated with an enzyme having 5'-3' DNA polymerase activity and 3'-5' exonuclease activity in the presence of nucleotides (e.g., A, C, G, and T or U). Examples of enzymes or catalytic fragments that may be used as needed include Klenow large fragments and T4 polymerase. For the 5' overhang, the enzyme typically extends the retracted 3' end over the opposing strand until it overlaps with the 5' end to produce a blunt end. For the 3' overhang, the enzyme generally digests from the 3' end to the 5' end of the opposing strand, and sometimes beyond. If this digestion proceeds beyond the 5' end of the opposing strand, the gap can be filled in by an enzyme having the same polymerase activity used for the 5' overhang. The formation of blunt ends in double-stranded nucleic acids facilitates, for example, adapter binding and subsequent amplification.

[0280] In some embodiments, the nucleic acid population is subjected to further processing, such as conversion from single-stranded nucleic acids to double-stranded nucleic acids and / or conversion from RNA to DNA (e.g., complementary DNA, i.e., cDNA). These forms of nucleic acids are also ligated to adapters and amplified as needed.

[0281] Sequenced nucleic acids can be produced by sequencing the nucleic acids subjected to the blunt-end formation process described above, with or without prior amplification, and, if necessary, other nucleic acids in the sample. Sequenced nucleic acids may be referred to as nucleic acid sequences (e.g., sequence information), or nucleic acids whose sequences have been determined. Sequencing can be performed to provide sequence data of individual nucleic acid molecules in the sample, directly or indirectly, from the consensus sequences of the amplified products of individual nucleic acid molecules in the sample.

[0282] In some embodiments, double-stranded nucleic acids with single-stranded overhangs in a sample after blunt end formation are ligated at both ends to an adapter containing a barcode, and sequencing determines the nucleic acid sequence and the inline barcode introduced by the adapter. The blunt-ended DNA molecule is ligated to the blunt ends of an adapter that is at least partially double-stranded (e.g., a Y-shaped or bell-shaped adapter), if necessary. Alternatively, complementary nucleotide tails can be attached to the blunt ends of the sample nucleic acid and the adapter to facilitate ligation (e.g., for adherent end ligation).

[0283] Nucleic acid samples are typically brought into contact with a sufficient number of adapters such that the probability of any two copies of the same nucleic acid receiving the same adapter barcode combination from adapters ligated to both ends is low (e.g., less than approximately 1 or 0.1%). Using adapters in this manner allows for the identification of families of nucleic acid sequences that have the same starting and ending points on the reference nucleic acid and are ligated to the same barcode combination. Such families may represent the sequences of the amplified products of the nucleic acids in the sample before amplification. The sequences of family members can be compiled to derive consensus nucleotides or complete consensus sequences of the nucleic acid molecules in the original sample, modified by blunt end formation and adapter ligation. In other words, nucleotides occupying specific positions in the nucleic acid in the sample can be determined to be the consensus of nucleotides occupying corresponding positions in the family member sequences. Families may include sequences of one or both strands of a double-stranded nucleic acid. If family members include sequences of both strands from a double-stranded nucleic acid, the sequence of one strand can be converted to its complement for the purpose of compiling the sequences to derive consensus nucleotides or sequences. Some families contain only single-member sequences. In this case, this sequence can be considered as the nucleic acid sequence in the sample before amplification. Alternatively, families containing only single-member sequences may be excluded from subsequent analysis.

[0284] Nucleotide variations (e.g., SNVs or indels) in sequenced nucleic acids can be determined by comparing the sequenced nucleic acid with a reference sequence. The reference sequence is often a known sequence, such as a known whole or partial genome sequence from a subject (e.g., the whole genome sequence of a human subject). The reference sequence may be, for example, hG19 or hG38. As described above, the sequenced nucleic acid may represent a sequence directly determined for the nucleic acid in the sample, or a consensus of sequences of amplified products of such nucleic acids. The comparison can be performed at one or more designated positions in the reference sequence. A subset of sequenced nucleic acids can be identified, including positions corresponding to designated positions in the reference sequence, assuming each sequence is maximally aligned. Within such a subset, it can be determined whether, if sequenced nucleic acids exist, they contain nucleotide variations at designated positions, or, if necessary, reference nucleotides (e.g., the same as the reference sequence), if any. If the number of sequenced nucleic acids in the subset containing nucleotide variants exceeds a selected threshold, the variant nucleotides can be called at designated positions. The threshold may be a simple number of sequenced nucleic acids, such as at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10, in the subset containing the nucleotide variant, or it may be a ratio of at least 0.5, 1, 2, 3, 4, 5, 10, 15, or 20 of sequenced nucleic acids in the subset containing the nucleotide variant. The comparison can be repeated for any designated position of interest in the reference sequence. Sometimes, the comparison may be performed for designated positions occupying at least approximately 20, 100, 200, or 300 adjacent positions in the reference sequence, for example, approximately 20–500 or approximately 50–300 adjacent positions.

[0285] Further details regarding nucleic acid sequencing, including the formats and applications described herein, can be found, for example, in Levy et al., Annual Review of Genomics and Human Genetics, 17: 95-115 (2016), Liu et al., J. of Biomedicine and Biotechnology, Volume 2012, Article ID 251364:1-11 (2012), Voelkerding et al., Clinical Chem., 55: 641-658 (2009), MacLean et al., each of which is thus incorporated herein by reference in whole: Nature Rev. Microbiol., 7: 287-296 (2009), Astier et al., J Am Chem Soc., 128(5):1705-10 (2006), also provided under U.S. Patent Nos. 6,210,891, 6,258,568, 6,833,246, 7,115,400, 6,969,488, 5,912,148, 6,130,073, 7,169,560, 7,282,337, 7,482,120, 7,501,245, 6,818,395, 6,911,345, 7,501,245, 7,329,492, 7,170,050, 7,302,146, 7,313,308, and 7,476,503. 6.Analysis

[0286] Sequencing can generate multiple sequence reads or reads. A sequence read or read may contain a sequence of nucleotide data less than approximately 150 bases or less than approximately 90 bases in length. In some embodiments, a read may be between approximately 80 and 90 bases in length, for example, about 85 bases. In some embodiments, the method of this disclosure applies to very short reads, for example, less than approximately 50 bases or less than approximately 30 bases in length. Sequence read data may include sequence data and metadata. Sequence read data can be stored in any suitable file format, including, for example, VCF files, FASTA files, or FASTQ files.

[0287] FASTA can refer to a computer program for searching sequence databases, and the name FASTA can also refer to a standard file format. For example, FASTA is described in Pearson & Lipman, 1988, Improved tools for biological sequence comparison, PNAS 85:2444-2448, which is thus incorporated herein by reference in its entirety. It is listed as follows. In FASTA format, an array begins with a single line of description, followed by multiple lines of array data. The description line is distinguished from the array data by the "greater than" (>) sign in the first column. The word following the ">" sign is the array identifier, and the rest of the line is the description (both are optional). There does not need to be a space between the ">" and the first character of the identifier. It is recommended that all lines of text be shorter than 80 characters. The array ends when another line beginning with ">" appears. This indicates the start of another array.

[0288] The FASTQ format is a text-based format for storing both biological sequences (typically nucleotide sequences) and their corresponding quality scores. It is similar to the FASTA format but includes quality scores following the sequence data. For brevity, both sequence characters and quality scores are encoded by a single ASCII character. The FASTQ format is the de facto standard for storing the output of high-throughput sequencing instruments such as Illumina's Genome Analyzer, as described, for example, in Cock et al. ("The Sanger FASTQ file format for sequences with quality scores, and the Solexa / Illumina FASTQ variants," Nucleic Acids Res 38 (6): 1767-1771, 2009), which is thus incorporated herein by reference in its entirety.

[0289] For FASTA and FASTQ files, metadata includes a description line but does not include a row of sequence data. In some embodiments, for FASTQ files, metadata includes a quality score. For FASTA and FASTQ files, sequence data begins after the description line and typically exists using a subset of IUPAC ambiguous codes, which may optionally contain a hyphen ("-"). In some embodiments, sequence data may use the A, T, C, G, and N characters, which may optionally contain a hyphen ("-") or a U (e.g., representing a gap or uracil).

[0290] In some embodiments, at least one master sequence read file and output file are stored as plain text files (using encoding such as ASCII; ISO / IEC 646; EBCDIC; UTF-8; or UTF-16). The computer systems provided by this disclosure may include a text editor program capable of opening plain text files. A text editor program may refer to a computer program capable of displaying the contents of a text file (e.g., a plain text file) on a computer screen, enabling a person to edit the text (e.g., using a monitor, keyboard, and mouse). Examples of text editors include, but are not limited to, Microsoft Word, emacs, pico, vi, BBEdit, and TextWrangler. A text editor program may enable displaying a plain text file on a computer screen and presenting metadata and sequence reads in a human-readable format (e.g., using alphanumeric characters that can be used for printing or handwriting instead of binary encoding).

[0291] While the methods have been discussed with reference to FASTA or FASTQ files, the methods and systems of this disclosure can be used to compress any suitable array file format, including, for example, files in Variant Call Format (VCF) format. A typical VCF file may include a header section and a data section. The header contains any number of metadata lines, each beginning with the character "##", and tab-separated field definition lines beginning with a single "#" character. The field definition lines name eight required fields, and the body section contains lines of data filling the fields defined by the field definition lines. The VCF format is, for example, Danecek et al. ("The Variant Call Format and VCF"), which is thus incorporated herein by reference in its entirety. This is described in "tools," Bioinformatics 27 (15): 2156-2158, 2011). The header section may be treated as metadata to be written to the compressed file, and the data sections may be treated as rows that can be stored in the master file only if each of them is unique.

[0292] Some embodiments provide assembly of sequence reads. In assembly by alignment, for example, the sequence reads are aligned to each other or to a reference sequence. By aligning each read, in turn, to a reference genome, all the reads are positioned in relation to each other and an assembly is generated. Furthermore, aligning or mapping sequence reads to a reference sequence can also be used to identify variant sequences within the sequence reads. Identifying variant sequences in combination with the methods and systems described herein can further assist in the diagnosis or prognosis of a disease or condition, or guide decisions regarding treatment.

[0293] In some embodiments, one or all of the steps are automated. Alternatively, the methods of the Disclosure may be embodied, in whole or in part, in one or more dedicated programs, each written as necessary in a compiled language such as C++, and then compiled and distributed as binaries. The methods of the Disclosure may be implemented, in whole or in part, as modules within an existing sequence analysis platform or by invoking functionality within that platform. In some embodiments, the methods of the Disclosure include several steps, all of which are automatically invoked in response to a single start queue (e.g., one or a combination of human activity, another computer program, or a machine-induced event). Thus, the Disclosure provides a method in which any one or any combination of steps may occur automatically in response to a queue. "Automatically" generally means without any intervening human input, influence, or interaction (e.g., responding only to the original or prior human activity).

[0294] The methods of this disclosure may also include various forms of output, including accurate and sensitive interpretations of the nucleic acid sample of interest. The search output may be provided in the format of a computer file. In some embodiments, the output is a FASTA file, a FASTQ file, or a VCF file. The output may be processed to generate a text file or an XML file containing sequence data, for example, in which the nucleic acid sequence is aligned to the sequence of a reference genome. In other embodiments, processing may yield an output containing coordinates or strings describing one or more mutations of the nucleic acid of interest relative to the reference genome. Alignment strings may include Simple UnGapped Alignment Report (SUGAR), Verbose Useful Labeled Gapped Alignment Report (VULGAR), and Compact Idiosyncratic Gapped Alignment Report (CIGAR) (for example, described in Ning et al., Genome Research 11(10):1725-9, 2001, which is thus incorporated herein by reference in its entirety). These strings can be implemented, for example, using Exonerate sequence alignment software from the European Bioinformatics Institute (Hinxton, UK).

[0295] In some embodiments, a sequence alignment is generated, such as a sequence alignment map (SAM) or binary alignment map (BAM) file containing, for example, a CIGAR string (the SAM format is described, for example, in Li et al., "The Sequence Alignment / Map format and SAMtools," Bioinformatics, 25(16):2078-9, 2009, which is thus incorporated herein by reference in its entirety). In this format, CIGAR presents or includes one gap alignment per row. CIGAR is a compressed pairwise alignment format reported as a CIGAR string. CIGAR strings can be useful for representing long pairwise alignments (e.g., genomes). CIGAR strings can be used in the SAM format to represent read alignments to a reference genome sequence.

[0296] CIGAR strings can follow established motifs. Each character is preceded by a number that gives the base count of the event. The characters used may include M, I, D, N, and S (M=match; I=insertion; D=deletion; N=gap; S=substitution). A CIGAR string defines an array of matches and / or mismatches and deletions (or gaps). For example, the CIGAR string 2MD3M2D2M may indicate that the alignment contains two matches, one deletion (the number 1 is omitted to save some space), three matches, two deletions, and two matches. IV. Computer Systems

[0297] The methods of the present disclosure can be implemented using or with the help of a computer system. For example, such a method is a step of distributing a DNA sample into a plurality of fractions, the plurality of fractions comprising a hypermethylated fraction and a hypomethylated fraction; a step of tagging DNA in the hypermethylated and hypomethylated fractions to produce tagged nucleic acids, the tagged nucleic acids comprising molecular barcodes; a step of obtaining sequence reads of molecules derived from the hypermethylated fraction and sequence reads of molecules derived from the hypomethylated fraction, the sequence reads comprising molecular barcode sequences and sample sequences; a step of grouping the sequence reads into families based on at least one of (a) molecular barcode sequences and (b) genomic positions corresponding to the first and last nucleotides of the sample sequences, the families comprising sequence reads derived from a single DNA molecule in the sample; a step of determining a first set of sequences of molecules derived from the hypermethylated fraction and a second set of sequences of molecules derived from the hypomethylated fraction; and the first and second sets of sequences A step of calling multiple bases based on a set, which may include: (i) a step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequences of molecules in a first set, which requires observation of transition mutations in a greater number of molecules than a step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequences of molecules in a second set; or (ii) a C-to-T or G-to-A transition mutation is not called compared to a reference sequence based on the sequences of molecules in a first set, or a C-to-T or G-to-A transition mutation is called compared to a reference sequence based on the sequences of molecules in a second set without using the sequences of molecules in a first set, or a C-to-T or G-to-A transition mutation is called compared to a reference sequence only if at least one sequence of molecules in the second set contains a C-to-T or G-to-A transition mutation.

[0298] Figure 2 shows a computer system 201 programmed or otherwise configured to implement the method of this disclosure. The computer system 201 can control various embodiments of sample preparation, sequencing, and / or analysis. In some examples, the computer system 201 is configured to perform sample preparation and sample analysis, including nucleic acid sequencing.

[0299] The computer system 201 includes a central processing unit (CPU, also referred to herein as “processor” and “computer processor”) 205, which may be a single-core or multi-core processor, or multiple processors for parallel processing. The computer system 201 also includes memory or memory locations 210 (e.g., random-access memory, read-only memory, flash memory), electronic storage units 215 (e.g., hard disks), communication interfaces 220 for communicating with one or more other systems (e.g., network adapters), and peripherals 225, e.g., cache, other memory, data storage, and / or electronic display adapters. The memory 210, storage units 215, interfaces 220, and peripherals 225 communicate with the CPU 205 through a communication network or bus (solid wire), such as a motherboard. The storage units 215 may be data storage units (or data repositories) for storing data. The computer system 201 can be operably connected to a computer network 230 with the help of the communication interface 220. The computer network 230 may be the Internet, an internet and / or extranet, or an intranet and / or extranet communicating with the Internet. In some cases, the computer network 230 may be a telecommunications and / or data network. The computer network 230 may include one or more computer servers and can enable distributed computing, such as cloud computing. In some cases, with the help of the computer system 201, the computer network 230 may implement a peer-to-peer network, which may allow devices to be connected to the computer system 201 and act as clients or servers.

[0300] The CPU 205 can execute a sequence of machine-readable instructions, which can be embodied in a program or software. These instructions may be stored in a memory location, for example, memory 210. Examples of operations performed by the CPU 205 may include fetching, decoding, executing, and writing back.

[0301] The storage unit 215 can store files, such as drivers, libraries, and saved programs. The storage unit 215 can store user-generated programs and recorded sessions, as well as output associated with programs. The storage unit 215 can store user data, such as user preferences and user programs. In some cases, the computer system 201 may include one or more additional data storage units located outside the computer system 201, such as on a remote server communicating with the computer system 201 via an intranet or the internet. Data may be transferred from one location to another using, for example, a communication network or physical data transfer (e.g., using a hard drive, thumb drive, or other data storage mechanism).

[0302] Computer system 201 can communicate with one or more remote computer systems via network 230. In embodiments, computer system 201 can communicate with a user's (e.g., operator's) remote computer system. Examples of remote computer systems include personal computers (e.g., mobile PCs), slate or tablet PCs (e.g., Apple® iPad®, Samsung® Galaxy Tab), telephones, smartphones (e.g., Apple® iPhone®, Android®-enabled devices, Blackberry®), or personal digital assistants. Users can access computer system 201 via network 230.

[0303] The methods described herein can be implemented by machine-executable code (e.g., a computer processor) stored in an electronic storage location of the computer system 201, such as memory 210 or an electronic storage unit 215. The machine-executable or machine-readable code may be provided in the form of software. During use, the code may be executed by the processor 205. In some cases, the code is retrieved from the storage unit 215 and stored in memory 210 for easy access by the processor 205. In some situations, the electronic storage unit 215 may be excluded, and machine-executable instructions are stored in memory 210.

[0304] In some embodiments, the disclosure includes the steps of: distributing a DNA sample into a plurality of fractions, the plurality of fractions comprising a hypermethylated fraction and a hypomethylated fraction; tagging DNA in the hypermethylated and hypomethylated fractions to produce tagged nucleic acids, the tagged nucleic acids comprising molecular barcodes; obtaining sequence reads of molecules from the hypermethylated fraction and sequence reads of molecules from the hypomethylated fraction, the sequence reads comprising molecular barcode sequences and sample sequences; grouping the sequence reads into families based on at least one of (a) molecular barcode sequences and (b) genomic positions corresponding to the first and last nucleotides of the sample sequences, the families comprising sequence reads derived from a single DNA molecule in the sample; determining a first set of sequences of molecules from the hypermethylated fraction and a second set of sequences of molecules from the hypomethylated fraction; and grouping a plurality based on the first and second sets of sequences The present invention provides a non-transient computer-readable medium that includes a computer-executable instruction for performing at least a portion of a method comprising the steps of: (i) calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on the sequences of a first set of molecules, wherein the step of calling a base requires observation of transition mutations in a greater number of molecules than the step of calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on the sequences of a second set of molecules; or (ii) a C-to-T or G-to-A transition mutation is not called compared to a reference sequence based on the sequences of a first set of molecules, or a C-to-T or G-to-A transition mutation is called compared to a reference sequence based on the sequences of a second set of molecules without using the sequences of a first set of molecules, or a C-to-T or G-to-A transition mutation is called compared to a reference sequence only if at least one sequence of the second set of molecules contains a C-to-T or G-to-A transition mutation.

[0305] The code may be pre-compiled and configured for use in a machine with a processor adapted to run the code, or it may be compiled at runtime. The code may be written and supplied pre-compiled, or in a programming language that can be chosen to allow the code to run while being compiled.

[0306] Embodiments of the systems and methods provided herein, for example, computer system 201, can be embodied during programming. Various embodiments of the art can typically be considered as “products” or “manufactured articles” in the form of related data contained in or embodied in machine-executable code and / or machine-readable media. Machine-executable code can be stored in electronic storage units, such as memory (e.g., read-only memory, random-access memory, flash memory) or hard disks. “Storage” media include any or all of the tangible memory, processor or similar of a computer, or its associated modules, such as various semiconductor memories, tape drives, disk drives and similar, which can provide non-transient storage at any time for software programming.

[0307] All or part of the software may be communicated, at times, over the Internet or other various telecommunication networks. Such communication may enable the loading of software, for example, from one computer or processor to another, or from a management server or host computer to an application server computer platform. Thus, other types of media that may have software elements include light, electricity, and electromagnetic waves, such as those used over various air links, through wired and optical terrestrial communication networks, across physical interfaces between local devices. Physical elements that carry such waves, such as wired or wireless links, optical links, or similar, may also be considered media having software. As used herein, unless limited to non-transient tangible “storage” media, terms such as computer or machine-readable media mean any medium that contributes to providing instructions for execution to a processor.

[0308] Therefore, machine-readable media, such as 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 optical or magnetic disks, such as any storage device, such as a computer, used to implement a database, as shown in the figure. Volatile storage media include dynamic memory, such as the main memory of a computer platform. Tangible transmission media include coaxial cables, copper wires, and optical fibers (including wires, such as buses, in computer systems). Carrier media can take the form of electrical or electromagnetic signals, or acoustic or optical waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Therefore, common forms of computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, DVDs or DVD-ROMs, any other optical media, punch cards, paper tapes, any other physical storage media having a pattern of holes, RAM, ROMs, PROMs and EPROMs, FLASH®-EPROMs, any other memory chips or cartridges, carrier-transmitted data or instructions, cables or links that transport such carriers, or any other media from which a computer can read programming code and / or data. Many of these forms of computer-readable media may be involved in transporting one or more sequences of one or more instructions to a processor for execution.

[0309] The computer system 201 may include, or communicate with, an electronic display containing a user interface (UI) for providing, for example, one or more results of sample analysis. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces.

[0310] Further details regarding computer systems and networks, databases, and computer program products can be found, for example, in Peterson, Computer Networks, each of which is thus incorporated herein by reference. A Systems Approach, Morgan Kaufmann, 5th Ed. (2011), Kurose, Computer Networking: A Top-Down Approach, Pearson, 7 th Ed. (2016), Elmasri, Fundamentals of Database Systems, Addison Wesley, 6th Ed. (2010), Coronel, Database Systems: Design, Implementation, & Management, Cengage Learning, 11 th Ed. (2014), Tucker, Programming Languages, McGraw-Hill Science / Engineering / Math, 2nd Ed. (2006), and Rhoton, Cloud It is also available in Computing Architected: Solution Design Handbook, Recursive Press (2011). V. Application 1. Cancer and other diseases

[0311] This method may be used to diagnose a condition in a subject, particularly the presence of cancer; to characterize the condition (e.g., to stage cancer or determine cancer heterogeneity); to monitor the response to treatment of the condition; and to determine the risk of the condition developing or the prognosis of its subsequent course. This disclosure may also be useful in determining the effectiveness of a particular treatment option. If the treatment is successful, more cancer cells will be killed and DNA will be lost, so a successful treatment option may increase the amount of copy number variation or rare mutations detected in the subject's blood. In other cases, this may not occur. In another case, perhaps a particular treatment option may correlate over time with the genetic profile of the cancer. This correlation may be useful in selecting a treatment.

[0312] Furthermore, if remission of the cancer is observed after treatment, this method can be used to monitor residual disease or disease recurrence.

[0313] In some embodiments, the methods and systems disclosed herein may be used to identify customized or targeted therapies for treating a given disease or condition in a patient based on the classification of nucleic acid variants as having somatic or germline origin. Typically, the disease considered is a certain type of cancer. Non-specific examples of such cancers include biliary tract cancer, bladder cancer, head and neck cancer, transitional cell carcinoma, urothelial carcinoma, brain cancer, glioma, astrocytoma, breast cancer, metaplastic carcinoma, cervical cancer, cervical squamous cell carcinoma, rectal cancer, colorectal cancer, colon cancer, hereditary nonpolyposis colorectal cancer, colorectal adenocarcinoma, gastrointestinal stromal tumor (GIST), endometrial cancer, endometrial stromal sarcoma, esophageal cancer, esophageal squamous cell carcinoma, esophageal adenocarcinoma, ocular melanoma, uveal melanoma, gallbladder cancer, gallbladder adenocarcinoma, renal cell carcinoma, clear cell renal cell carcinoma, transitional cell carcinoma, urothelial carcinoma, Wilms' tumor, leukemia, acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myeloid leukemia (CML), and chronic myelomonocytic leukemia. This includes (CMML), liver cancer, hepatoma, hepatocellular carcinoma, cholangiocarcinoma, hepatoblastoma, lung cancer, non-small cell lung cancer (NSCLC), mesothelioma, B-cell lymphoma, non-Hodgkin lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, T-cell lymphoma, non-Hodgkin lymphoma, progenitor T-lymphoblastic lymphoma / leukemia, peripheral T-cell lymphoma, multiple myeloma, nasopharyngeal carcinoma (NPC), neuroblastoma, oropharyngeal cancer, oral squamous cell carcinoma, osteosarcoma, ovarian cancer, pancreatic cancer, pancreatic ductal adenocarcinoma, pseudopapillary neoplasm, acinar cell carcinoma, prostate cancer, prostate adenocarcinoma, skin cancer, melanoma, malignant melanoma, cutaneous melanoma, small intestine cancer, stomach cancer, gastrointestinal stromal tumor (GIST), uterine cancer, or uterine sarcoma. The type and / or stage of cancer may be detected from genetic variations, including mutations, rare mutations, indels, copy number variations, transversions, translocations, inversions, deletions, aneuploidy, partial aneuploidy, ploidy, chromosomal instability, chromosomal structural alterations, gene fusions, chromosome fusions, gene shortening, gene amplification, gene duplication, chromosomal lesions, DNA lesions, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns, and abnormal changes in nucleic acid 5-methylcytosine.

[0314] Genetic data can also be used to characterize specific forms of cancer. Cancers are often heterogeneous in both composition and staging. Genetic profiling data may make it possible to characterize specific subtypes of cancer, which may be important in the diagnosis or treatment of that particular subtype. This information may provide subjects or practitioners with clues regarding prognosis for specific types of cancer, and may also allow either subjects or practitioners to adapt treatment options as the disease progresses. Some cancers may progress and become more aggressive and genetically unstable. Other cancers may remain benign, inactive, or quiescent. The systems and methods of this disclosure may be useful in determining disease progression.

[0315] Furthermore, the methods of this disclosure may be used to characterize heterogeneity of an abnormal condition in a subject. Such methods may include, for example, generating a gene profile of extracellular polynucleotides derived from the subject, where the gene profile includes multiple data obtained from the analysis of copy number variation and rare mutations. In some embodiments, the abnormal condition is cancer. In some embodiments, the abnormal condition may result in a heterogeneous genomic population. In the example of cancer, it has been found that some tumors contain tumor cells at different stages of cancer. In other examples, the heterogeneity may include a multitude of disease lesions. Again, in the example of cancer, a multitude of tumor lesions may be present, in which case one or more lesions are probably the result of metastasis spreading from the primary site.

[0316] This method can be used to generate or profile a fingerprint or set of data, which is the sum of genetic information derived from different cells in heterogeneous diseases. This set of data may include, individually or in combination, analysis of copy number variation, epigenetic variation, and mutation.

[0317] This method may be used to diagnose, prognose, monitor, or observe cancer or other diseases. In some embodiments, the methods of the present invention do not involve fetal diagnosis, prognosis, or monitoring, and therefore do not involve non-invasive prenatal testing. In other embodiments, these method systems may be used in pregnant subjects to diagnose, prognose, monitor, or observe cancer or other diseases in unborn subjects in which DNA and other polynucleotides can co-circulate with maternal molecules.

[0318] Other genetic diseases, disorders, or conditions that may be evaluated as needed using the methods and systems disclosed herein include, but are not limited to, chondrodysplasia, alpha-1 antitrypsin deficiency, antiphospholipid syndrome, autism, autosomal dominant polycystic kidney disease, Charcot-Marie-Tooth (CMT), feline crying disorder, Crohn's disease, cystic fibrosis, Darkham's disease, Down syndrome, Duane syndrome, Duchenne muscular dystrophy, factor V Leiden embolism, familial hypercholesterolemia, familial Mediterranean fever, fragility X syndrome, and Gorgonian syndrome. This includes Schiech disease, hemochromatosis, hemophilia, holoprosencephalopathy, Huntington's disease, Klinefelter syndrome, Marfan syndrome, myotonic dystrophy, neurofibromatosis, Noonan syndrome, osteogenesis imperfecta, Parkinson's disease, phenylketonuria, Poland anomaly, porphyria, progeria, retinitis pigmentosa, severe combined immunodeficiency (SCID), sickle cell disease, spinal muscular atrophy, Tay-Sachs disease, thalassemia, trimethylaminuria, Turner syndrome, palatocardiafacial syndrome, WAGR syndrome, Wilson's disease, or similar conditions.

[0319] In some embodiments, the method described herein includes the step of using a set of sequence information obtained as described herein to detect the presence or absence of DNA originating from or derived from tumor cells at a pre-selected time point after a previous cancer treatment of a subject previously diagnosed with cancer. The method may further include the step of determining a cancer recurrence score indicating the presence or absence of DNA originating from or derived from tumor cells for the subject of test.

[0320] When determining a cancer recurrence score, the cancer recurrence score may be further used to determine the cancer recurrence state. A cancer recurrence state may indicate a risk of cancer recurrence, for example, when the cancer recurrence score is above a predetermined threshold. A cancer recurrence state may indicate a low or lesser risk of cancer recurrence, for example, when the cancer recurrence score is above a predetermined threshold. In certain embodiments, a cancer recurrence score equal to a predetermined threshold may result in a cancer recurrence state where there is a risk of cancer recurrence, or a low or lesser risk of cancer recurrence.

[0321] In some embodiments, the cancer recurrence score is compared to a predetermined cancer recurrence threshold. If the cancer recurrence score is above the cancer recurrence threshold, the subject is classified as a candidate for further cancer treatment; if the cancer recurrence score is below the cancer recurrence threshold, the subject is classified as not a candidate for treatment. In certain embodiments, a cancer recurrence score equal to the cancer recurrence threshold may result in classification as either a candidate for further cancer treatment or not a candidate for treatment.

[0322] The above method may further include any one or more suitability features described elsewhere in this Specification, including sections on methods for determining the risk of cancer recurrence in a test subject and / or methods for classifying a test subject as a candidate for subsequent cancer treatment. 2. A method for determining the risk of cancer recurrence in study subjects and / or a method for classifying study subjects as candidates for subsequent cancer treatment.

[0323] In some embodiments, the methods provided herein are for determining the risk of cancer recurrence in a test subject. In some embodiments, the methods provided herein are for classifying a test subject as a candidate for subsequent cancer treatment.

[0324] Any such method may include the step of collecting DNA (e.g., originating from or derived from tumor cells) from a test subject diagnosed with cancer at one or more pre-selected time points after one or more previous cancer treatments to the test subject. The subject may be any of the subjects described herein. The DNA may be cfDNA. The DNA may be obtained from a tissue sample.

[0325] One such method may include the step of capturing a set of target regions from DNA derived from a target, wherein the set of target regions includes a sequence-variable target region set and an epigenetic target region set, and a set of captured DNA molecules is produced. The capturing step may be carried out according to any of the embodiments described elsewhere in this specification.

[0326] In any of these methods, the prior cancer treatment may include surgery, administration of therapeutic compositions, and / or chemotherapy.

[0327] One of these methods may involve a step of sequencing the captured DNA molecules, thereby producing a set of sequence information. Captured DNA molecules of a sequence-variable target region set can be sequenced to a higher sequencing depth than captured DNA molecules of an epigenetic target region set.

[0328] Any such method may include the step of using a set of sequence information to detect the presence or absence of DNA originating from or derived from tumor cells at a pre-selected point in time. The detection of the presence or absence of DNA originating from or derived from tumor cells may be carried out according to any of the embodiments described elsewhere in this Spec.

[0329] A method for determining the risk of cancer recurrence in a test subject may include the step of determining a cancer recurrence score for the test subject, indicating the presence or absence, or amount, of DNA originating from or derived from tumor cells. The cancer recurrence score may be further used to determine a cancer recurrence state. A cancer recurrence state may indicate a risk of cancer recurrence, for example, when the cancer recurrence score is above a predetermined threshold. A cancer recurrence state may indicate a low or lesser risk of cancer recurrence, for example, when the cancer recurrence score is above a predetermined threshold. In certain embodiments, a cancer recurrence score equal to a predetermined threshold may result in a cancer recurrence state where there is a risk of cancer recurrence, or a low or lesser risk of cancer recurrence.

[0330] A method for classifying a subject as a candidate for subsequent cancer treatment includes the step of comparing the subject's cancer recurrence score to a predetermined cancer recurrence threshold, classifying the subject as a candidate for subsequent cancer treatment if the cancer recurrence score is above the cancer recurrence threshold, or classifying it as not a candidate for treatment if the cancer recurrence score is below the cancer recurrence threshold. In certain embodiments, a cancer recurrence score equal to the cancer recurrence threshold may result in classification as either a candidate for subsequent cancer treatment or not a candidate for treatment. In some embodiments, the subsequent cancer treatment includes chemotherapy or administration of a therapeutic composition.

[0331] Any such method may include a step of determining the disease-free survival (DFS) period for the study subject based on a cancer recurrence score, for example, the DFS period may be 1 year, 2 years, 3 years, 4 years, 5 years, or 10 years.

[0332] In some embodiments, the sequence information set includes a sequence variable target region sequence, and the step of determining the cancer recurrence score may include the step of determining at least a first subscore indicating the amount of SNVs, insertions / deletions, CNVs, and / or fusions present in the sequence variable target region sequence.

[0333] In some embodiments, the number of mutations in the sequence variable target region selected from 1, 2, 3, 4, or 5 is sufficient to result in a cancer recurrence score in which the first subscore is classified as positive for cancer recurrence. In some embodiments, the number of mutations is selected from 1, 2, or 3.

[0334] In some embodiments, the set of sequence information includes epigenetic target region sequences, and the step of determining the cancer recurrence score includes the step of determining a second subscore indicating the amount of aberrant sequence reads in the epigenetic target region sequences. Aberrant sequence reads may be reads that exhibit an epigenetic state different from the DNA found in the corresponding sample from a healthy subject (e.g., cfDNA found in a blood sample from a healthy subject, or DNA found in a tissue sample from a healthy subject if the tissue sample is of the same type as the tissue obtained from the test subject). Aberrant reads may match cancer-related epigenetic changes, such as methylation of a highly methylated variable target region and / or perturbed fragmentation of a fragmented variable target region, where “perturbed” means different from the DNA found in the corresponding sample from a healthy subject.

[0335] In some embodiments, the second subscore is sufficient to classify a case as positive for cancer recurrence by the percentage of reads corresponding to the hypermethylated variable target region set and / or fragmented variable target region set that indicate that hypermethylation in the hypermethylated variable target region set and / or abnormal fragmentation in the fragmented variable target region set are greater than or equal to a value in the range of 0.001% to 10%. The range may be 0.001% to 1%, 0.005% to 1%, 0.01% to 5%, 0.01% to 2%, or 0.01% to 1%.

[0336] In some embodiments, any such method may include the step of determining the proportion of tumor DNA from the proportion of reads in a set of sequence information that exhibit one or more features indicating origin from tumor cells. This may be done for reads corresponding to some or all of epigenetic target regions, including, for example, one or both of a hypermethylation variable target region and a fragmentation variable target region (hypermethylation of a hypermethylation variable target region and / or abnormal fragmentation of a fragmentation variable target region can be considered to indicate origin from tumor cells). This may be done for reads corresponding to sequence variable target regions, such as reads containing cancer-matching modifications such as SNVs, indels, CNVs, and / or fusions. The proportion of tumor DNA may be determined based on a combination of reads corresponding to epigenetic target regions and reads corresponding to sequence variable target regions.

[0337] The determination of the cancer recurrence score is obtained, at least in part, based on the percentage of tumor DNA, 10 -11 ~1 or 10 -10 A percentage of tumor DNA greater than a threshold in the range of ~1 is sufficient for the cancer recurrence score to be classified as positive for cancer recurrence. In some embodiments, 10 -10 ~10 -9 , 10 -9 ~10 -8 , 10 -8 ~10 -7 , 10 -7 ~10 -6 , 10 -6 ~10 -5 , 10 -5 ~10 -4 , 10 -4 ~10 -3 , 10 -3 ~10 -2 , or 10 -2 ~10 -1 A percentage of tumor DNA greater than or equal to a threshold within a certain range is sufficient for the cancer recurrence score to be classified as positive for cancer recurrence. In some embodiments, at least 10 -7A percentage of tumor DNA greater than a threshold is sufficient for the cancer recurrence score to be classified as positive for cancer recurrence. The determination that the percentage of tumor DNA is greater than a threshold, such as the threshold corresponding to any of the embodiments described above, may be made based on cumulative probability. For example, a sample is considered positive if the cumulative probability that the percentage of tumor DNA is greater than any of the thresholds in the range described above exceeds a probability threshold of at least 0.5, 0.75, 0.9, 0.95, 0.98, 0.99, 0.995, or 0.999. In some embodiments, the probability threshold is at least 0.95, for example, 0.99.

[0338] In some embodiments, the set of sequence information includes a sequence variable target region sequence and an epigenetic target region sequence, and the step of determining the cancer recurrence score includes determining a first subscore indicating the amount of SNVs, insertions / deletions, CNVs and / or fusions present in the sequence variable target region sequence and a second subscore indicating the amount of abnormal sequence reads in the epigenetic target region sequence, and combining the first and second subscores to obtain the cancer recurrence score. When combining the first and second subscores, the combination can be achieved by independently applying thresholds to each subscore (e.g., greater than a predetermined number of mutations in the sequence variable target region (e.g., >1) and greater than a predetermined percentage of abnormal (e.g., tumor) reads in the epigenetic target region), or by training a machine learning classifier to determine the state based on multiple positive and negative training samples.

[0339] In some embodiments, a combined score value falling within the range of -4 to 2 or -3 to 1 is sufficient for the cancer recurrence score to be classified as positive for cancer recurrence.

[0340] In any embodiment in which the cancer recurrence score is classified as positive for cancer recurrence, the subject's cancer recurrence status may be classified as being at risk of cancer recurrence, and / or the subject may be classified as a candidate for subsequent cancer treatment.

[0341] In some embodiments, the cancer is one of the types of cancer described elsewhere in this specification, for example, colorectal cancer.

[0342] 3. Treatment and related administration In certain embodiments, the methods disclosed herein relate to identifying and administering customized therapies to patients given a state of nucleic acid variant originating from somatic cells or germline cells. In some embodiments, essentially any cancer therapy (e.g., surgery, radiotherapy, chemotherapy, and / or similar) may be included as part of these methods. Typically, a customized therapy includes at least one immunotherapy (or immunotherapy agent). Immunotherapy generally means a method of enhancing the immune response to a given type of cancer. In certain embodiments, immunotherapy means a method of enhancing the T-cell response to a tumor or cancer.

[0343] In a particular embodiment, the nucleic acid variant status of a sample from a subject originating from somatic cells or germline cells is compared to a database of comparator results from a reference population to identify customized or targeted therapies for that subject. Typically, the reference population includes patients with the same type of cancer or disease as the subject of study, and / or patients who are receiving or have received the same therapy as the subject of study. If the nucleic acid variant and the comparator results meet certain classification criteria (e.g., substantially or approximately match), a customized or targeted therapy (one or more therapies) may be identified.

[0344] In certain embodiments, the customized therapies described herein are typically administered parenterally (e.g., intravenously or subcutaneously). Pharmaceutical compositions containing immunotherapeutic agents are typically administered intravenously. Certain therapeutic agents are administered orally. However, customized therapies (e.g., immunotherapeutic agents, etc.) may also be administered by means such as buccal, sublingual, rectal, vaginal, urethral, ​​topical, intraocular, intranasal and / or intraauricular, and administration may include tablets, capsules, granules, aqueous suspensions, gels, sprays, suppositories, salves, ointments, or similar.

[0345] Preferred embodiments of the present invention have been shown and described herein, but it will be apparent to those skilled in the art that such embodiments are provided only as examples. The present invention is not intended to be limited by any specific examples provided herein. The present invention is described with reference to the above specification, but the descriptions and explanations of embodiments herein are not meant to be construed as limiting. Many modifications, changes, and substitutions will occur here to those skilled in the art without departing from the present invention. Furthermore, it should be understood that all aspects of the present invention are not limited to any specific depictions, configurations, or relative proportions described herein, which depend on various conditions and variables. It should be understood that various alternative options may be adopted for the embodiments disclosed herein in the practice of the present invention. Accordingly, this disclosure is intended to encompass any such options, modifications, variations, or equivalents. The following claims define the scope of the present invention, and methods and structures within the scope of these claims and their equivalents are intended to be encompassed thereby.

[0346] While the above disclosure is described in some detail as an explanation and example for the purpose of clarity and understanding, it will be apparent to those skilled in the art that various modifications can be made in form and detail without departing from the true scope of this disclosure and that it can be implemented within the scope of the appended claims. For example, all methods, systems, computer-readable media, and / or features, steps, elements, or other aspects of the components can be used in various combinations.

[0347] All patents, patent applications, websites, other publications and documents, accession numbers, and similar items cited herein are incorporated by reference to the same extent as each separate item is specifically and separately indicated to be incorporated by reference, and the whole is incorporated by reference to the same extent. Where an array of different versions relates to an accession number at different times, the version related to the accession number at the effective filing date of this application is meant. The effective filing date means the earlier of the actual filing date or the filing date of the priority application referring to the accession number, where applicable. Similarly, where different versions of a publication, website, or similar item were published at different times, unless otherwise indicated, the version published closest to the effective filing date of this application is meant. [Examples]

[0348] VI. Examples i) Observation of increased frequency of artificial C-to-T and G-to-A transition mutations in cfDNA of the hypermethylated fraction. In this example, we demonstrate that the hypermethylated fraction of cell-free DNA contains a greater number of apparent C-to-T and G-to-A transition mutations than predicted. Samples were collected from 30 self-declared healthy individuals, and each sample was fractionated into at least two fractions, one containing hypermethylated DNA and the other hypomethylated DNA. The fractions (containing both hypermethylated and hypomethylated DNA fractions) were sequenced and analyzed for error rates. Here, the error rate for each molecule satisfies an 80% agreement threshold between bases at the read-level, with at least two sequence reads representing both DNA strands. Figure 3 shows the SNV error rate per base depending on specific nucleotide substitutions, i.e., A to C, A to G, A to T, C to A, C to G, C to T, G to A, G to C, G to T, T to A, T to C, and T to G. The height of the bar represents the mean SNV error rate, and the error bar represents its standard error. Light gray bars represent results from the highly methylated DNA fraction, and dark gray bars represent results from the less methylated DNA fraction. C-to-T substitutions and complementary G-to-A substitutions were the most frequently observed errors. In the highly methylated fraction, C-to-T and G-to-A substitutions increased, indicating a higher rate of chemical damage in highly methylated molecules.

[0349] Table 6 is a 2×2 contingency table showing the number of highly methylated and low-methylated molecules with C-to-T or G-to-A nucleotide substitutions relative to all other substitutions. As described above, substitutions were determined by bases at the read-level that met the 80% agreement threshold, having at least two sequence reads representing both strands. In Table 6, the number of determined substitutions is compared to the calculated predicted substitutions, which were determined by multiplying the row sum by the column sum and dividing by the total sum (n). As determined by the chi-squared test with 1 degree of freedom, there are significantly more molecules with C-to-T or G-to-A substitutions in highly methylated molecules than in low-methylated molecules, more than would be expected by chance (p-value 8.16×10⁻⁶). -196 ). [Table 6] ii) A sequencing method in which the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequence of molecules derived from the high-methylation fraction requires observation of transition mutations in a greater number of reads than the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequence of molecules derived from the low-methylation fraction.

[0350] This embodiment describes an embodiment of a method for mitigating the effect of artificial deamination in the highly methylated fraction on the accuracy of sequencing.

[0351] As described herein, a DNA sample derived from the target (e.g., cfDNA such as human cfDNA) is obtained, and at least two fractions are prepared therefrom, including a highly methylated fraction and a hypomethylated fraction. The fractions (including the highly methylated and hypomethylated fractions) are differentially tagged and then pooled. The target regions of interest (e.g., sequence-variable target regions and epigenetic target regions) are captured using a capture probe and then amplified and sequenced using, for example, next-generation and / or synthetic sequencing techniques.

[0352] Sequence reads are classified based on their tag sequences as originating from either the hypermethylated or hypomethylated fraction, and grouped according to the original sample molecule from which they originate, based on the tag sequence, the genomic position corresponding to the first and last nucleotides of the sample sequence, and / or one or more of the sequences of multiple bases immediately following the 5' tag sequence and immediately following the 3' tag sequence. For each group of reads, the sequence of the molecule from which they originate is determined. Sequences of molecules from the hypomethylated fraction are mapped to the reference genome sequence to identify C-to-T and G-to-A mutations, where the mutations are observed in at least two or three molecular sequences. Sequences from the hypermethylated fraction are mapped to the reference genome sequence to identify C-to-T and G-to-A mutations, where the mutations are observed in at least three, four, or five molecular sequences, and the number of required molecular sequences is greater than the number of reads required to identify C-to-T or G-to-A mutations based on the sequences of molecules from the hypomethylated fraction.

[0353] The results determined in this way show that the number of false-positive C-to-T and G-to-A mutations is less than in control sequencing where the number of molecular sequences required to identify C-to-T or G-to-A mutations based on the sequences of molecules from the low-methylation fraction is equal to the number of molecular sequences required to identify C-to-T or G-to-A mutations based on the sequences of molecules from the low-methylation fraction. iii) Sequencing methods that do not use sequences of molecules derived from the hypermethylated fraction to call C-to-T or G-to-A transition mutations.

[0354] This embodiment describes another embodiment of a method for mitigating the effect of artificial deamination on the accuracy of sequencing in the highly methylated fraction.

[0355] As described herein, a DNA sample derived from the target (e.g., cfDNA such as human cfDNA) is obtained, and at least two fractions are prepared therefrom, including a highly methylated fraction and a hypomethylated fraction. The fractions (including the highly methylated and hypomethylated fractions) are differentially tagged and then pooled. The target regions of interest (e.g., sequence-variable target regions and epigenetic target regions) are captured using a capture probe and then amplified and sequenced using, for example, next-generation and / or synthetic sequencing techniques.

[0356] Sequence reads are classified based on their tag sequences as originating from either the hypermethylated or hypomethylated fraction, and grouped according to the original sample molecule from which they originate, according to one or more of the tag sequences, the genomic coordinates corresponding to the bases immediately preceding and following the 5' and 3' tag sequences, and / or the sequences of multiple bases immediately preceding and following the 5' and 3' tag sequences. For each group of reads, the sequence of the molecule from which they originate is determined. Sequences of molecules from the hypomethylated fraction are mapped to the reference genome sequence to identify C-to-T and G-to-A mutations, where mutations are observed in the sequences of at least two or three molecules. Sequences from the hypermethylated fraction are mapped to the reference genome sequence and are not used for calling C-to-T and G-to-A mutations compared to the reference genome sequence.

[0357] The results determined in this way show that the number of false-positive C-to-T and G-to-A mutations is less than in control sequencing where the number of molecular sequences required to identify C-to-T or G-to-A mutations based on the sequences of molecules from the low-methylation fraction is equal to the number of molecular sequences required to identify C-to-T or G-to-A mutations based on the sequences of molecules from the low-methylation fraction. iv) A sequencing method in which the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on reads from the highly methylated fraction requires observation of transition mutations in a greater number of reads than the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on reads from the low-methylated fraction.

[0358] This embodiment describes another embodiment of a method for mitigating the effect of artificial deamination on the accuracy of sequencing in the highly methylated fraction.

[0359] As described herein, a DNA sample derived from the target (e.g., cfDNA such as human cfDNA) is obtained, and at least two fractions are prepared therefrom, including a highly methylated fraction and a hypomethylated fraction. The fractions (including the highly methylated and hypomethylated fractions) are differentially tagged and then pooled. The target regions of interest (e.g., sequence-variable target regions and epigenetic target regions) are captured using a capture probe and then amplified and sequenced using, for example, next-generation and / or synthetic sequencing techniques.

[0360] Sequence reads are classified based on their tag sequences as originating from either a hypermethylated or hypomethylated fraction. Sequences from the hypomethylated fraction are mapped to a reference genome sequence to identify C-to-T and G-to-A mutations, where the mutations are observed in at least two or three reads. Sequences from the hypermethylated fraction are mapped to a reference genome sequence to identify C-to-T and G-to-A mutations, where the mutations are observed in at least three, four, or five reads, where the required number of reads is greater than the number of reads required to identify C-to-T or G-to-A mutations based on sequences from the hypomethylated fraction.

[0361] The number of false-positive C-to-T and G-to-A mutations contained in the obtained sequences is less than in control sequencing where the number of reads required to identify C-to-T or G-to-A mutations based on sequences from the low-methylation fraction is equal to the number of reads required to identify C-to-T or G-to-A mutations based on sequences from the low-methylation fraction. v) Sequencing methods that do not use reads derived from the highly methylated fraction to call transition mutations from C to T or G to A

[0362] This embodiment describes another embodiment of a method for mitigating the effect of artificial deamination on the accuracy of sequencing in the highly methylated fraction.

[0363] As described herein, a DNA sample derived from the target (e.g., cfDNA such as human cfDNA) is obtained, and at least two fractions are prepared therefrom, including a highly methylated fraction and a hypomethylated fraction. The fractions (including the highly methylated and hypomethylated fractions) are differentially tagged and then pooled. The target regions of interest (e.g., sequence-variable target regions and epigenetic target regions) are captured using a capture probe and then amplified and sequenced using, for example, next-generation and / or synthetic sequencing techniques.

[0364] Sequence reads are classified as originating from either the hypermethylated or hypomethylated fraction based on their tag sequences. Sequences from the hypomethylated fraction are mapped to the reference genome sequence to identify apparent C-to-T and G-to-A mutations, where mutations are observed in at least two or three reads. Sequences from the hypermethylated fraction are mapped to the reference genome sequence and these reads are not used to call C-to-T and G-to-A mutations compared to the reference genome sequence.

[0365] The number of false-positive C-to-T and G-to-A mutations contained in the obtained sequences is less than in control sequencing where the number of reads required to identify C-to-T or G-to-A mutations based on sequences from the low-methylation fraction is equal to the number of reads required to identify C-to-T or G-to-A mutations based on sequences from the low-methylation fraction. vi) Characterization of target region probe sets with different probe concentrations for sequence-variable target region sets and for epigenetic target region sets.

[0366] This embodiment describes the evaluation of the performance of a probe set containing probes for sequence variable target region sets and probes for epigenetic target region sets, as part of an effort to combine epigenetic analysis and genotyping of liquid biopsy cfDNA.

[0367] cfDNA samples were treated by partitioning based on methylation status (thus generating multiple fractions including highly methylated and hypomethylated fractions), end repair, ligation with adapters, and amplification by PCR (e.g., using primers that target the adapters), and then contacted with a target region probe set.

[0368] The treated samples were contacted with a target region probe set containing probes for sequence-variable target regions and probes for epigenetic target regions. The target region probes were in the form of biotinylated oligonucleotides designed to tile the target regions. The probes for sequence-variable target regions had a footprint of approximately 50 kb, and the probes for epigenetic target regions had a target region footprint of approximately 500 kb. The probes for sequence-variable target regions included oligonucleotides targeting selected regions identified in Tables 3-5, while the probes for epigenetic target regions included oligonucleotides targeting selected high-methylation variable target regions, low-methylation variable target regions, CTCF-binding target regions, transcription start site target regions, local amplification target regions, and methylation control regions.

[0369] Next, the isolated and captured cfDNA was prepared for sequencing and sequenced using an Illumina HiSeq or NovaSeq sequencer. The results were analyzed for the diversity (number of unique families of sequence reads) and read family size (number of individual reads within each family) of sequence reads corresponding to probes for variable sequence target regions and probes for epigenetic target regions. The values ​​reported below were obtained using 70 ng of input DNA. 70 ng of input is considered a relatively high amount and represents a challenging condition for maintaining the desired levels of diversity and family size.

[0370] When probes for the sequence-variable target region set and the epigenetic target region set were used in a 1:1 ratio (i.e., the mass-to-volume concentration of individual oligonucleotides in the two sets was equal), the diversity was approximately 5–10% lower than predicted based on the input amount for the sequence-variable target regions. This indicates that the sequencing data did not contain the predicted number of different read families.

[0371] The probe ratios of 2:1 and 5:1 (epigenetic: sequence-variable probe set) resulted in a greater decrease in diversity compared to the theoretical value for sequence-variable target regions.

[0372] The probe ratios of 1:2 or 1:5 (epigenetic: sequence-variable probe set) yielded a high level of diversity for sequence-variable target regions, which was generally close to theoretical values. This indicates that, at these ratios, the presence of epigenetic target regions did not substantially interfere with the generation of the predicted number of distinct read families from sequence-variable target regions.

[0373] With respect to epigenetic target regions, the diversity levels were substantially lower than theoretical values ​​for all ratios. However, this is not considered problematic, given that the analysis of methylation, copy number, and similar factors for epigenetic target regions does not require the same level of high-density and deep sequencing coverage as the determination of the presence or absence of nucleotide substitutions or indels intended for sequence-variable regions.

[0374] To improve accuracy by reducing the frequency of false-positive C-to-T and G-to-A mutations being called based on the sequence of a read or molecule corresponding to the hypermethylated fraction, the sequence or molecule sequence can be determined using sequence reads, as basically described in any one of Examples ii) to v) above, and the mutations can be called. vii) Cancer detection using a combination of epigenetic target region sets and sequence-variable target region sets

[0375] A cohort of cfDNA samples from cancer patients with cancer at different stages I through IVA (a total of seven stages) are processed and sequenced as described in Example vi) above, using probes in a 1:5 ratio (epigenetic:sequence-variable probe set). Sequence-variable target region sequences are analyzed by detecting genomic alterations such as SNVs, insertions, deletions, and fusions that can be called with sufficient support to distinguish actual tumor variants from technical errors. Epigenetic target region sequences are analyzed independently to detect methylated fragments within regions that have been shown to be differentially methylated in cancer compared to blood cells. Finally, the results of both analyses are combined to generate a final tumor presence / absence call to determine whether a profile matching cancer with 95% specificity was shown.

[0376] Cancer detection was 100% sensitive for the stage IIIA and IIIC cohorts using either method alone. Sensitivity increased by approximately 10–30% in all other cohorts, with the exception of one cohort that included analysis of epigenetic target region sequences. The one exception was the stage IIB cohort, where all samples were either true positive or false negative according to both methods.

[0377] Accordingly, the methods and compositions of this disclosure may provide captured cfDNA that can be simultaneously used for sequencing to different sequencing depths and sensitivities of epigenetic target regions and sequence-variable target regions, combining sequence-based cancer detection and epigenetic cancer detection.

[0378] To improve accuracy by reducing the frequency of false positives for C-to-T and G-to-A mutations based on the sequence of the read or molecule corresponding to the hypermethylated fraction, sequencing can be performed using sequence reads, essentially as described in any one of Examples ii) to v) above. In certain embodiments, for example, the following items are provided: (Item 1) A method for analyzing a DNA sample, A step of distributing the DNA sample into a plurality of fractions, wherein the plurality of fractions include a highly methylated fraction and a lowly methylated fraction; A step of tagging the DNA in the high-methylated and low-methylated fractions to produce tagged nucleic acids, wherein the tagged nucleic acids include a molecular barcode; A step of obtaining sequence reads of molecules derived from the high-methylation fraction and sequence reads of molecules derived from the low-methylation fraction, wherein the sequence reads include a molecular barcode sequence and a sample sequence; Based on (a) the molecular barcode sequence and (b) at least one of the genomic positions corresponding to the first and last nucleotides of the sample sequence, the sequence reads are classified into a family. A step of grouping, wherein the family comprises sequence reads derived from a single DNA molecule in the sample; The steps of determining a first set of sequences of molecules derived from the high-methylated fraction and a second set of sequences of molecules derived from the low-methylated fraction; and A step of calling a plurality of bases based on the first and second sets of sequences, (i) The step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequences of the first set of molecules requires observation of the transition mutations in a greater number of molecules than the step of calling C-to-T or G-to-A transition mutations compared to the reference sequence based on the sequences of the second set of molecules; or (ii) A method comprising the step that a C-to-T or G-to-A transition mutation is not called by comparison with a reference sequence based on the sequence of the first set of molecules, or a C-to-T or G-to-A transition mutation is called by comparison with a reference sequence based on the sequence of the second set of molecules without using the sequence of the first set of molecules, or a C-to-T or G-to-A transition mutation is called by comparison with a reference sequence only if at least one sequence of the second set of molecules contains the C-to-T or G-to-A transition mutation. (Item 2) The method of the preceding item, wherein the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequences of the first set of molecules requires observation of the transition mutations in a greater number of molecules than the step of calling C-to-T or G-to-A transition mutations compared to the reference sequence based on the sequences of the second set of molecules. (Item 3) The method according to any one of the preceding items, wherein the step of calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on the sequence of the first set of molecules requires observation of the transition mutation in at least three molecules. (Item 4) The method of the preceding item, wherein the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequences of the first set of molecules requires observation of the transition mutations in at least four molecules. (Item 5) The method of the preceding item, wherein the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequences of the first set of molecules requires observation of the transition mutations in at least five molecules. (Item 6) The method according to any one of the preceding items, wherein the step of calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on the sequences of the second set of molecules requires observation of the transition mutation in at least two molecules. (Item 7) The method of the preceding item, wherein the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequences of the second set of molecules requires observation of the transition mutations in at least three molecules. (Item 8) The method according to any one of the preceding items, wherein the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequences of the first set of molecules requires observation of the transition mutations in at least two more molecules than the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the sequences of the second set of molecules. (Item 9) The method according to any one of the preceding items, wherein a first threshold is used to call a C-to-T or G-to-A transition based on the sequence of molecules in the first set, and a second threshold is used to call a C-to-T or G-to-A transition based on the sequence of molecules in the second set; the first threshold provides a first level of specificity for calling a C-to-T or G-to-A transition; the second threshold provides a second level of specificity for calling a C-to-T or G-to-A transition; and the first level of specificity is approximately equal to the second level of specificity, or the first level of specificity is within 10%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.2%, or 0.1% of the second level of specificity. (Item 10) The method according to the preceding item, wherein the first and second thresholds are specific to transitions from C to T and / or from G to A. (Item 11) The method according to item 9 or 10, wherein the first and second thresholds are determined from at least one or more control samples, and optionally, the at least one or more control samples are derived from individuals not suspected of having cancer. (Item 12) The method according to any one of items 1 to 8, wherein a first group of position-specific background error rates is used for multiple positions for the sequence of the first set of molecules; a second group of position-specific background error rates is used for multiple positions for the sequence of the second set of molecules; the second group comprises position-specific background error rates higher than the corresponding position-specific background error rates of the first group; and the step of calling a C-to-T or G-to-A transition mutation based on the sequence of the first set of molecules requires the observation of the C-to-T or G-to-A transition mutation at a frequency exceeding the corresponding rate derived from the first group of position-specific background error rates. (Item 13) The method of the preceding item, wherein the step of calling a C-to-T or G-to-A transition mutation based on the sequence of molecules in the first set requires the observation of the C-to-T or G-to-A transition mutation at a frequency at least 2, 3, 4, or 5 times higher than the corresponding rate from the first group of the position-specific background error rate. (Item 14) The method of the preceding item, wherein the step of calling a C-to-T or G-to-A transition mutation based on the sequence of molecules in the first set requires the observation of the C-to-T or G-to-A transition mutation at a frequency exceeding the corresponding rate from the first group of the position-specific background error rate, by an amount that matches a confidence level of at least 95%, 98%, 99%, 99.5%, or 99.9%. (Item 15) The method according to any one of items 12 to 14, wherein the first and second groups of position-specific background error rates are determined from a plurality of control samples, and the control samples, if necessary, are derived from individuals not suspected of having cancer. (Item 16) The method according to any one of items 12 to 14, wherein the first and second groups of position-specific background error rates are determined using a plurality of control samples, wherein, if necessary, the control samples are derived from individuals not suspected of having cancer. (Item 17) The method according to any one of items 12 to 14, wherein the first and second groups of location-specific background error rates are determined using medical history data. (Item 18) The first and second groups of location-specific background error rates are, respectively, the high methyl The method according to any one of items 12 to 14, determined using reads and / or sequences of molecules derived from the methylated and low-methylated fractions. (Item 19) A step to obtain sequence reads of molecules derived from the moderate fraction; The step of determining a third set of sequences of molecules derived from the intermediate fraction; and The method according to any one of the preceding items, further comprising the step of calling a plurality of bases based on a third set of the sequence. (Item 20) The method of the preceding item, wherein transition mutations from C to T and from G to A are called based on the sequence of a third set with less strictness than when transition mutations from C to T and from G to A are called based on the sequence of the first set of molecules. (Item 21) The method described in the preceding item, wherein transition mutations from C to T and G to A are called based on the sequences of the third set in the same manner as the transition mutations from C to T and G to A are called based on the sequences of the second set, or with greater strictness than the transition mutations from C to T and G to A are called based on the sequences of the second set. (Item 22) A method for analyzing a DNA sample, The steps of obtaining first and second sets of sequence reads derived from the highly methylated and low methylated fractions of the sample, respectively; and A step of determining the sequences derived from the first and second sets of sequence reads, (i) The step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the first set of reads requires observation of the transition mutations in a greater number of reads than the step of calling C-to-T or G-to-A transition mutations compared to the reference sequence based on the second set of reads; or (ii) A method comprising the step that a C-to-T or G-to-A transition mutation is not called compared to a reference sequence based on reads from the first set, or a C-to-T or G-to-A transition mutation is called compared to a reference sequence based on sequences from the second set of molecules without using sequences from the first set of molecules, or a C-to-T or G-to-A transition mutation is called compared to a reference sequence only if at least one sequence from the second set of molecules contains the C-to-T or G-to-A transition mutation. (Item 23) The method of the preceding item, wherein the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the reads of the first set requires observation of the transition mutations in a greater number of reads than the step of calling C-to-T or G-to-A transition mutations compared to the reference sequence based on the reads of the second set. (Item 24) The method according to either item 22 or 23, wherein the step of calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on the reads of the first set of reads requires observation of the transition mutation in at least three reads. (Item 25) The method of the preceding item, wherein the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the reads of the first set of reads requires observation of the transition mutations in at least four reads. (Item 26) Based on the reads of the first set described above, the C to T or G to A sequences were compared to the reference sequence. The method of the preceding item, wherein the step of calling a transition mutation requires observation of the transition mutation in at least five reads. (Item 27) The method according to any one of items 22 to 26, wherein the step of calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on the reads of the second set of reads requires observation of the transition mutation in at least two reads. (Item 28) The method of the preceding item, wherein the step of calling C-to-T or G-to-A transition mutations compared to a reference sequence based on the reads of the second set of reads requires observation of the transition mutations in at least three reads. (Item 29) The method according to any one of items 22 to 28, wherein the step of calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on the reads of the first set requires observation of the transition mutation in at least two more reads than the step of calling a C-to-T or G-to-A transition mutation compared to a reference sequence based on the reads of the second set. (Item 30) The method of any one of the preceding items, further comprising the step of obtaining a third set of sequence reads derived from a moderate fraction, wherein the sequence is determined from the third set in addition to the first and second sets. (Item 31) The method described in the preceding item, wherein transition mutations from C to T and G to A are called based on a third set of reads with lower strictness than those called based on the first set of reads. (Item 32) The method described in the preceding item, wherein transition mutations from C to T and G to A are called based on a third set of reads, in the same manner that transition mutations from C to T and G to A are called based on a second set of reads. (Item 33) The method according to any one of the preceding items, wherein the DNA in the high-methylation fraction and the DNA in the low-methylation fraction are differentially tagged. (Item 34) The method according to any one of the preceding items, wherein the DNA in the high-methylation fraction and the DNA in the low-methylation fraction are differentially tagged with sequence tags including barcodes. (Item 35) The method according to any one of the preceding items, wherein the high-methylated and low-methylated fractions are prepared by contacting the DNA of the sample with a methyl-binding reagent immobilized on a solid support. (Item 36) The method described in the preceding item, wherein the methyl bonding reagent includes MBD. (Item 37) The method according to item 36, wherein the methyl bonding reagent comprises MeCP. (Item 38) The method according to item 36, wherein the methyl-binding reagent comprises an antibody that binds to a methylated nucleotide, and optionally the methylated nucleotide is methylated cytosine. (Item 39) The method according to any one of items 35 to 38, comprising the step of contacting the DNA of the sample with the methyl-binding reagent immobilized on the solid support to obtain the low-methylated fraction and the high-methylated fraction based on differential binding to the methyl-binding reagent. (Item 40) The method according to any one of items 35 to 39, comprising the step of attaching differential tags to the DNA of the highly methylated fraction and the DNA of the low methylated fraction before sequencing. (Item 41) The method according to any one of the preceding items, wherein the step of determining the sequence includes the step of mapping first and second sets of the sequence reads to a reference sequence to produce mapped sequence reads. (Item 42) The method according to any one of the preceding items, wherein the DNA of the sample or the hypermethylated and hypomethylated fractions includes a region of interest that is enriched or captured. (Item 43) The method according to any one of the preceding items, comprising the steps of enriching the DNA of the sample or the hypermethylated and hypomethylated fractions with respect to a region of interest, or capturing the region of interest from the sample or the hypermethylated and hypomethylated fractions. (Item 44) The method according to the preceding item, wherein the enrichment or capture step includes contacting the DNA with a set of target-specific probes, thereby producing a captured set of DNA molecules. (Item 45) The method according to any one of items 42 to 44, wherein the region of interest includes a sequence-variable target region. (Item 46) The method according to the preceding item, wherein the set of target-specific probes includes target-binding probes specific to a sequence-variable target set. (Item 47) The method described in the previous item, wherein the footprint of the sequence variable target region set is at least 25kB or at least 50kB. (Item 48) The method according to any one of items 42 to 47, wherein the region of interest includes an epigenetic target region. (Item 49) The method described in the preceding item, wherein the set of target-specific probes includes target-binding probes specific to an epigenetic target set. (Item 50) The method according to any one of items 42 to 49, wherein the region of interest includes a set of sequence-variable target regions and a set of epigenetic target regions. (Item 51) The method according to the preceding item, wherein the sequence variable target region set comprises at least 10 regions, and the epigenetic target region set comprises at least 100 regions. (Item 52) The method according to any one of items 50 to 51, wherein the footprint of the epigenetic target region set is at least twice as large as the size of the sequence variable target region set. (Item 53) The method according to the preceding item, wherein the footprint of the epigenetic target region set is at least 10 times larger than the size of the sequence variable target region set. (Item 54) The set of target-specific probes captures cfDNA corresponding to the sequence-variable target set with a larger capture yield than the cfDNA corresponding to the epigenetic target set. The method described in item 52 or 53, which is configured to be used in this way. (Item 55) The method according to any one of items 50 to 54, wherein the sequence variable target region set has a footprint in the range of 10 to 30 kilobases. (Item 56) The method according to any one of items 50 to 54, wherein the sequence variable target region set has a footprint in the range of 30 to 60 kilobases. (Item 57) The method according to any one of items 50 to 54, wherein the sequence variable target region set has a footprint in the range of 60 kilobases to 1 megabase. (Item 58) The method according to any one of items 50 to 54, wherein the sequence variable target region set has a footprint in the range of 1 to 2 megabases. (Item 59) The method according to any one of items 50 to 58, wherein the epigenetic target region set has a footprint in the range of 0.2 to 0.8 megabases. (Item 60) The method according to any one of items 50 to 58, wherein the epigenetic target region set has a footprint in the range of 0.8 to 1.5 megabases. (Item 61) The method according to any one of items 50 to 58, wherein the epigenetic target region set has a footprint in the range of 1.5 to 3 megabases. (Item 62) The method according to any one of items 50 to 58, wherein the set of epigenetic target regions has a footprint in the range of 3 to 8 megabases. (Item 63) The method according to any one of items 50 to 62, wherein the epigenetic target region set includes a set of highly methylated variable target regions. (Item 64) The method according to any one of items 50 to 63, wherein the epigenetic target region set includes a hypomethylated variable target region set. (Item 65) The method according to any one of items 50 to 64, wherein the epigenetic target region set includes a fragmentation variable target region set. (Item 66) The method described in the preceding item, wherein the fragmentation variable target region set includes a transcription start site region. (Item 67) The method according to item 65 or 66, wherein the fragmentation variable target region set includes a CTCF binding region. (Item 68) The method according to any one of items 50 to 67, wherein the captured DNA of the sequence variable target set is sequenced to a higher sequencing depth than the captured DNA of the epigenetic target region set. (Item 69) The method according to the preceding item, wherein the captured DNA of the sequence variable target set is sequenced to a sequencing depth at least 2, 3, or 4 times higher than the captured cfDNA molecule of the epigenetic target region set, or to a sequencing depth 4 to 10 or 4 to 100 times higher. (Item 70) The method according to any one of items 50 to 69, wherein the captured DNA of the sequence variable target set is pooled together with the captured DNA of the epigenetic target region set before sequencing. (Item 71) The method according to any one of items 50 to 70, wherein the captured DNA of the sequence variable target set and the captured DNA of the epigenetic target region set are sequenced in the same sequencing cell. (Item 72) The method according to any one of items 50 to 71, wherein the DNA in the hypermethylated and hypomethylated fractions is amplified before capture. (Item 73) The method according to any one of the preceding items, wherein the sample is obtained from biological tissue or biological fluid. (Item 74) The method according to any one of the preceding items, wherein the sample is obtained from blood. (Item 75) The method according to any one of the preceding items, wherein the DNA of the sample includes cell-free DNA. (Item 76) The method according to any one of the preceding items, wherein the DNA of the sample essentially consists of cell-free DNA. (Item 77) The method according to any one of the preceding items, wherein the sample is derived from a subject having or suspected to have a proliferative disorder or a solid tumor. (Item 78) The method according to any one of the preceding items, wherein the sample is derived from a subject that is or has been treated for a proliferative disorder or a solid tumor. (Item 79) The method according to any one of the preceding items, further comprising the step of determining the likelihood that the subject has a proliferative disorder or a solid tumor based on the sequence determined from the sequence reads. (Item 80) The method according to any one of the preceding three items, wherein the proliferative disorder or solid tumor is cancer.

Claims

[Claim 1] The invention described in the specification.

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