Methods and systems for cell-free nucleic acid treatment

Abstract

Disclosed herein are methods and systems for targeted detection of circulating tumor DNA (ctDNA) molecules. Conditions can be applied to the cell-free nucleic acid to increase its methylation level, producing a hypermethylated cell-free nucleic acid, and then identifying the sequence of the hypermethylated cell-free nucleic acid.

Description

[0001] Cross-references

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 345,803, filed on May 25, 2022, which is incorporated herein by reference in its entirety. Background Art

[0003] Circulating tumor deoxyribonucleic acid (ctDNA) is increasingly showing promise as a non-invasive, tumor-specific biomarker for routine clinical use. ctDNA can be derived from tumor cells that primarily undergo cell death and are released into the circulation of various body fluids, including the blood. In certain cancer patients, the majority of blood-derived cell-free DNA can be derived from healthy (e.g., non-cancerous) tissues. Furthermore, the fraction of ctDNA observed can range from <0.1% to 90% of total cell-free DNA, depending on several factors, including tumor primary site and disease burden. ctDNA provides a non-invasive approach to understanding the molecular landscape of tumors and disease burden. Summary of the invention

[0004] It is recognized herein that there is a need for methods and systems for detecting circulating tumor deoxyribonucleic acid (ctDNA) with greater sensitivity, including for subjects with low abundance of ctDNA.

[0005] One aspect of the present disclosure provides a method for generating a methylated genomic region sequencing copy library, the method comprising: a) providing a cell-free nucleic acid molecule of a subject and providing a sheared genomic nucleic acid molecule, wherein the sheared genomic nucleic acid molecule is separated from the cell-free nucleic acid molecule; b) subjecting the cell-free nucleic acid molecule or a derivative thereof to conditions sufficient to increase the methylation level of the cell-free nucleic acid molecule or its derivative, and subjecting the sheared genomic nucleic acid molecule or its derivative to conditions sufficient to increase the methylation level of the sheared genomic nucleic acid molecule or its derivative, thereby generating a hypermethylated cell-free nucleic acid molecule and a hypermethylated genomic nucleic acid molecule, respectively; c) incubating the hypermethylated cell-free nucleic acid molecule and the hypermethylated genomic nucleic acid molecule to enrich for a first methylated genomic region of the hypermethylated cell-free nucleic acid molecule and a second methylated genomic region of the hypermethylated genomic nucleic acid molecule; and d) combining the first methylated genomic region of the hypermethylated cell-free nucleic acid molecule and the second methylated genomic region of the hypermethylated genomic nucleic acid molecule to generate the methylated genomic region sequencing copy library. In some cases, the cell-free nucleic acid molecule is a cell-free deoxyribonucleic acid (DNA) molecule. In some cases, the cell-free DNA molecules are derived from a biological sample of the subject. In some cases, the biological sample comprises a blood sample or a plasma sample. In some cases, prior to (d), it also includes subjecting the first methylated genomic region of the hypermethylated cell-free nucleic acid molecule and the second methylated genomic region of the hypermethylated genomic nucleic acid molecule to conditions sufficient to amplify the first methylated genomic region of the hypermethylated cell-free nucleic acid molecule and the second methylated genomic region of the hypermethylated genomic nucleic acid molecule. In some cases, the method further includes sequencing the amplified first methylated genomic region of the hypermethylated cell-free nucleic acid molecule to generate a first plurality of sequencing reads, and sequencing the amplified second methylated genomic region of the hypermethylated genomic nucleic acid molecule to generate a second plurality of sequencing reads. In some cases, the sequencing does not include bisulfite sequencing. In some cases, the method further includes obtaining a first input control of the cell-free nucleic acid molecule, wherein the input control comprises the hypermethylated cell-free nucleic acid molecule derived from b). In some cases, the first input control is not subjected to an enrichment step as in c). In some cases, the method further comprises amplifying and sequencing the first input control under sufficient conditions to generate a plurality of first input control sequencing reads. In some cases, the method further comprises obtaining a second input control of the sheared genomic nucleic acid molecules, wherein the second input control comprises the hypermethylated sheared genomic nucleic acid molecules derived from b). In some cases, the second input control is not subjected to an enrichment step as in c).In some cases, the method further comprises amplifying and sequencing the second input control under sufficient conditions to generate a plurality of second input control sequencing reads. In some cases, the sequencing reads of the first methylated genomic region of the highly methylated cell-free nucleic acid molecule are obtained by comparing the first plurality of sequencing reads with the plurality of first input control sequencing reads. In some cases, the sequencing reads of the second methylated genomic region of the highly methylated cell-free nucleic acid molecule are obtained by comparing the second plurality of sequencing reads with the plurality of second input control sequencing reads. In some cases, the method further comprises generating sequencing reads of enriched methylated DNA regions from a cell-free DNA sample of an object suffering from or suspected of having a disease or condition, and generating sequencing reads of enriched methylated DNA regions from a cell-free DNA sample of a healthy object. In some cases, the method further comprises generating a first plurality of differentially methylated regions (DMRs) mapped to a sequencing copy library of the methylated genomic region. In some cases, the method further comprises generating a second plurality of differentially methylated regions (DMRs) mapped to a whole genome sequencing copy library. In some cases, the first plurality of DMRs or the second plurality of DMRs comprise hypermethylation or hypomethylation. In some cases, the first plurality of DMRs are greater than the second plurality of DMRs. In some cases, when the first plurality of DMRs are used to classify diseased samples and healthy samples, the diseased samples are better separated from the healthy samples than when the second plurality of DMRs are used. In some cases, the separation of the diseased samples and the healthy samples is shown by a gene heat map. In some cases, the separation of the diseased samples and the healthy samples is shown by a uniform manifold approximation and projection (UMAP). In some cases, the running time for classifying the diseased samples and the healthy samples is reduced compared to using the second plurality of DMRs. In some cases, the disease or condition is cancer or a tumor. In some cases, the cancer is breast cancer, bladder cancer, colorectal cancer, endometrial cancer, prostate cancer, kidney cancer, pancreatic cancer or lung cancer. In some cases, the cancer is advanced cancer. In some cases, the cancer is early stage cancer. In some cases, b) includes using a conjugate that binds to one or more methylated nucleotides of a methylated region of the cell-free nucleic acid molecule or the sheared genomic nucleic acid molecule. In some cases, the conjugate includes a protein comprising a methyl-CpG-binding domain. In some cases, the protein is an MBD2 protein. In some cases, the conjugate comprises an antibody. In some cases, the antibody is a 5-mC antibody. In some cases, the antibody is a 5-hydroxymethylcytosine antibody. In some cases, the conjugate exhibits a reduced level of non-specific binding to non-methylated nucleotides of the cell-free nucleic acid molecule or the sheared genomic nucleic acid molecule.In some cases, before c), the method further comprises mixing the hypermethylated cell-free nucleic acid molecules or the hypermethylated genomic nucleic acid molecules with filler deoxyribonucleic acid (DNA) molecules. In some cases, the filler DNA is used in step b) to enrich for multiple methylated regions in the cell-free nucleic acid molecules or their derivatives or the sheared genomic nucleic acid molecules or their derivatives. In some cases, the filler DNA has a length of about 50 base pairs (bp) to about 800 base pairs. In some cases, the length is about 100bp to about 600bp. In some cases, the length is about 200bp to about 600bp. In some cases, the sequencing includes peak calling to identify the enriched portion of the first multiple sequencing reads or the second multiple sequencing reads. In some cases, narrow peak calling is used to identify hypermethylated regions. In some cases, wide peak calling is used to identify hypermethylated regions.

[0006] Another aspect of the present disclosure provides a method of cell-free nucleic acid processing, comprising: a) providing a cell-free nucleic acid molecule of a subject; b) subjecting the cell-free nucleic acid molecule or a derivative thereof to conditions sufficient to increase the methylation level of the cell-free nucleic acid molecule or the derivative thereof, thereby producing a hypermethylated cell-free nucleic acid molecule; and c) identifying the sequence of the hypermethylated cell-free nucleic acid molecule or the derivative thereof.

[0007] In some cases, the cell-free nucleic acid molecules in (a) are obtained from the blood of the subject. In some cases, the blood comprises plasma, and wherein the cell-free nucleic acid molecules in (a) are obtained from the plasma.

[0008] In some cases, the identifying of (c) comprises sequencing the hypermethylated cell-free nucleic acid molecule. In some cases, detecting the methylation pattern comprises identifying highly methylated regions. The sequencing comprises detecting the methylation pattern.

[0009] In some cases, the method further comprises immunoprecipitating the hypermethylated cell-free nucleic acid molecules prior to sequencing. In some cases, the sequencing comprises sequencing the immunoprecipitated hypermethylated cell-free nucleic acid molecules. In some cases, the immunoprecipitation comprises using a methylated nucleic acid binder to bind the hypermethylated cell-free nucleic acid molecules. In some cases, the methylated nucleic acid binder comprises an antibody against methylated nucleic acids. In some cases, the antibody against methylated nucleic acids comprises an anti-5-methylcytosine (5-mC) antibody. In some cases, the sequencing comprises subjecting the cell-free nucleic acid molecules or derivatives thereof to a cfMeDIP-seq protocol.

[0010] In some cases, the cell-free nucleic acid molecule or derivative thereof is subjected to in vitro conditions sufficient to increase the methylation level of the cell-free nucleic acid molecule. In some cases, the conditions sufficient to increase the methylation level of the cell-free nucleic acid molecule include an enzyme. In some cases, the enzyme includes a CpG methyltransferase. In some cases, the cell-free nucleic acid molecule in (a) is obtained from a body fluid, the body fluid including at least one of urine, amniotic fluid, cerebrospinal fluid, prostatic fluid, tears, or saliva.

[0011] In some cases, the method further comprises: a) providing a sheared genomic nucleic acid molecule, wherein the sheared genomic nucleic acid molecule is separated from the cell-free nucleic acid molecule; b) subjecting the sheared genomic nucleic acid molecule or a derivative thereof to conditions sufficient to increase the methylation level of the sheared genomic nucleic acid molecule or a derivative thereof, thereby producing a hypermethylated genomic nucleic acid molecule; and c) identifying the sequence of the hypermethylated genomic nucleic acid molecule or a derivative thereof.

[0012] In some cases, the method further comprises obtaining the sheared genomic nucleic acid molecule from the subject prior to (a). In some cases, the sheared genomic nucleic acid sample is not from the subject. In some cases, the sheared genomic nucleic acid molecule comprises a sequence length similar to that of cell-free nucleic acid. In some cases, the identification in c) comprises sequencing the hypermethylated genomic nucleic acid molecule or a derivative thereof.

[0013] In some cases, the method further comprises using the sequencing to identify the methylation pattern of the hypermethylated genomic nucleic acid molecule or its derivative. In some cases, the method further comprises using the sequencing to identify the highly methylated region of the hypermethylated genomic nucleic acid molecule, wherein the highly methylated region is a region that is at least 50% methylated.

[0014] In some cases, the method further comprises processing the highly methylated region identified in the highly methylated cell-free nucleic acid and the highly methylated region identified in the highly methylated genomic nucleic acid, and identifying overlapping highly methylated region windows. In some cases, the overlapping highly methylated region windows comprise overlapping highly methylated sequences with a length of about 100 base pairs (bp) to 500bp. In some cases, the overlapping highly methylated region windows comprise overlapping highly methylated sequences with a length of about 250bp to 350bp. In some cases, a plurality of identified overlapping highly methylated region windows comprise about 10% to about 20% of the whole genome together. In some cases, the window comprises about 500,000 to 2.5 million highly methylated regions.

[0015] Another aspect of the present disclosure provides a method for identifying differentially methylated regions (DMRs), comprising analyzing and comparing the methylation patterns of multiple overlapping sequence read windows between a diseased sample and a control sample, and identifying high or low methylation regions in the diseased sample as differentially methylated regions (DMRs) compared to the control sample.

[0016] In some cases, analyzing the methylation patterns of the multiple overlapping sequence read windows includes immunoprecipitating the cell-free nucleic acid in the diseased sample, and then analyzing the methylation pattern of the cell-free nucleic acid of the immunoprecipitated. In some cases, the method identifies DMRs that are not found using the whole genome as background. In some cases, the method produces a greater number of DMRs than using the whole genome as background. In some cases, the method produces at least twice the number of DMRs compared to using the whole genome as background. In some cases, the method can identify DMRs faster than using the whole genome as background.

[0017] Another aspect of the present disclosure provides a method of analyzing a sample, comprising analyzing the differentially methylated region (DMR) identified in the diseased sample, wherein the method identifies the sample as a diseased sample or a normal sample.

[0018] In some cases, the method provides better differentiation than using whole genome DMRs. In some cases, the method provides differentiation between cancer samples and normal samples. In some cases, the method provides differentiation between different types of cancer.

[0019] In some cases, the method further comprises providing a therapeutic intervention to the subject upon identifying the sequence. In some cases, providing the therapeutic intervention comprises providing therapy to the subject. In some cases, providing the therapeutic intervention comprises providing a report identifying the sequence of the hypermethylated cell-free nucleic acid molecule or derivative thereof. In some cases, providing the therapeutic intervention comprises providing a report providing a treatment recommendation to the subject for treating a disease associated with the hypermethylated cell-free nucleic acid molecule or derivative thereof. In some cases, providing the therapeutic intervention comprises treating the subject for a disease associated with the hypermethylated cell-free nucleic acid molecule or derivative thereof. In some cases, the disease is cancer.

[0020] Another aspect of the present disclosure provides a non-transitory computer-readable medium comprising machine-executable code that, when executed by one or more computer processors, implements any of the methods described above or elsewhere herein.

[0021] Another aspect of the present disclosure provides a system comprising one or more computer processors and a computer memory coupled thereto. The computer memory includes machine executable code that, when executed by the one or more computer processors, implements any method described above or elsewhere herein.

[0022] Other aspects and advantages of the present disclosure will be readily apparent to those skilled in the art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be appreciated, the present disclosure is capable of realizing other and different embodiments, and its several details can be modified in various obvious aspects, all without departing from the present disclosure. Therefore, the drawings and descriptions should be regarded as illustrative, rather than restrictive.

[0023] Incorporation by reference

[0024] All publications, patents and patent applications mentioned in this specification are hereby incorporated by reference to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference herein. If the publications and patents or patent applications incorporated by reference conflict with the disclosure in this specification, this specification is intended to supersede and / or take precedence over any such conflicting material. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The novel features of the present invention are set forth in detail in the appended claims. The features and advantages of the present invention will be better understood by referring to the following detailed description and the accompanying drawings, which set forth illustrative embodiments utilizing the principles of the present invention, wherein:

[0026] Figure 1 A diagram illustrating a process for collecting flow-through of unmethylated / hypomethylated deoxyribonucleic acid (DNA) fragments is shown.

[0027] Figure 2 A graph showing the results of cfMeDIP-seq analysis in cancer tissues versus healthy tissues is shown. The volcano plot shows the fold change of normalized read counts on the x-axis and the negative log10 of the q-value on the y-axis, using all 300-bp windows in the "new background" (also called background region). DMRs are in gray.

[0028] Figure 3 Figure 2 shows the results of cfMeDIP-seq analysis in cancer tissues versus healthy tissues. The volcano plot shows the fold change of normalized read counts on the x-axis and the negative log10 of the q-value on the y-axis, using all 300-bp genome-wide windows. DMRs are in grey.

[0029] Figure 4 The ability of the identified DMRs to separate cancer samples from control samples is shown. The heat map shows the Z-scores of RPKM obtained from the MEDIPS software. DMRs from background regions are compared to controls, shown in blue in the first annotation row above the heat map. The control clusters are clearly distinguished from the various cancer samples. The standard Ward with Euclidean distance algorithm was used.

[0030] Figure 5 The ability of the identified DMRs to separate cancer samples from control samples is shown. The heat map shows the Z-scores of RPKM obtained from the MEDIPS software. The whole genome DMRs were compared to the controls, shown in blue in the first annotation row above the heat map. Here, some cancer cases are clustered with healthy controls. The standard Ward with Euclidean distance algorithm was used.

[0031] Figure 6 The enhanced ability of DMRs from background regions to distinguish cancer and control samples is shown in a plot generated by the dimensionality reduction software UMAP.

[0032] Figure 7 The reduced ability of genome-wide DMRs to distinguish between cancer and control samples is shown in a plot generated by the dimensionality reduction software UMAP.

[0033] Figure 8 It is shown that the runtime for acquiring DMRs in background regions is almost an order of magnitude lower than that for acquiring whole genome DMRs.

[0034] Fig. 9 A Venn diagram is shown, where high DMRs generated from the whole-genome background are almost entirely present in the list of DMRs generated using background regions, which found more interesting DMRs not found in the whole-genome analysis.

[0035] Fig.10 A computer system is shown that is programmed or otherwise configured to implement the methods provided herein.

[0036] Fig.11 De novo peak calling for fully methylated samples and consensus peak annotation for a region in human chromosome 4 (mu-merge) are shown.

[0037] Fig.12 De novo peak calling for fully methylated samples and consensus peak annotation for a region in human chromosome 17 (mu-merge) are shown.

[0038] Fig.13 Shown are histograms of counts over characteristic regions of interest (ROIs) determined from fully methylated samples.

[0039] Fig.14 Describes the implementation of the workflow. DETAILED DESCRIPTION

[0040] The present disclosure provides methods and systems for processing and analyzing nucleic acids present in biological samples by generating libraries of methylated genomic regions, which can be used to determine the risk or likelihood of a subject having a cancer or tumor with high sensitivity and / or high specificity. The methods and systems provided herein can include creating highly methylated cell-free nucleic acid molecules and highly methylated sheared genomic nucleic acid molecules, which can be processed to distinguish, for example, cancer tissues from non-cancer tissues in circulating free DNA (cfDNA).

[0041] For example, the use and analysis of hypermethylated and / or differentially methylated nucleic acids in a specific range rather than the entire genome can allow for highly sensitive and highly specific detection and / or characterization of circulating tumor DNA (ctDNA) in a fluid sample (e.g., a blood sample) obtained from a subject. In some cases, the use and analysis of hypermethylated and / or differentially methylated nucleic acids can allow for increased sensitivity, specificity, and / or efficiency in determining the risk that a subject has or may have a tumor or cancer.

[0042] definition

[0043] Although various embodiments of the present invention are shown and described herein, it will be apparent to those skilled in the art that these embodiments are provided by way of example only. Those skilled in the art may make many changes, modifications and substitutions without departing from the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed.

[0044] The term "subject" as used herein generally refers to any member of the animal kingdom. A subject may be a human. A subject may be an individual who exhibits a disease (e.g., cancer) or an individual who does not exhibit a disease. A subject may be considered to be at risk for a disease (e.g., cancer). A subject may have symptoms of a disease or be asymptomatic. A subject may be a patient. A subject may be a patient receiving medical care for a disease or condition (e.g., cancer).

[0045] The term "genome" as used herein generally refers to genomic information from an object, which may be, for example, at least a portion or all of the genetic information of the object. The genome may be encoded in DNA or RNA. The genome may include coding regions (e.g., coding regions encoding proteins) and non-coding regions. The genome may include the sequences of all chromosomes in an organism. For example, a human genome generally has 46 chromosomes in total. The sequences of all these chromosomes may constitute a human genome.

[0046] The term "methylation group" used herein generally refers to the amount and / or DNA methylation level of DNA methylation measured at multiple sites or loci in a genome. DNA methylation is the process of adding methyl groups to DNA molecules. DNA methylation can be used to regulate (e.g., inhibit) gene transcription. The methylation group can correspond to the entire genome (whole genome methylation), the major part of the genome, or the relatively small part of the genome. The term "methylation group" used herein can also refer to a methylation modification (e.g., on nucleic acid) set in an organism, in a cell, or in a sample. The methylation group can depend on the method for methylation measurement. For example, when using the antibody 5mC, the methylation group can represent all information of DNA methylation on genomic cytosine.

[0047] As used herein, the term "nucleic acid" generally refers to a polynucleotide comprising two or more nucleotides, i.e., a polymeric form of nucleotides of various lengths (e.g., at least 10, 20, 30, 40, 50, 100, 200, 300, 400, 500, 1000, 10000 or more nucleotides in length), which may be deoxyribonucleotides (dNTPs) or ribonucleotides (rNTPs), or analogs thereof. Non-limiting examples of nucleic acids include deoxyribonucleic acid (DNA), ribonucleic acid (RNA), coding or non-coding regions of a gene or gene fragment, a locus defined by linkage analysis (locus), exons, introns, messenger RNA (mRNA), transfer RNA, ribosomal RNA, short interfering RNA (siRNA), short hairpin RNA (shRNA), micro RNA (miRNA), ribozymes, cDNA, recombinant nucleic acids, branched nucleic acids, plasmids, vectors, isolated DNA of any sequence, isolated RNA of any sequence, nucleic acid probes, and primers. The nucleic acid may comprise one or more modified nucleotides, such as methylated nucleotides and nucleotide analogs. Modifications to the nucleotide structure, if any, may be performed before or after nucleic acid assembly. The nucleotide sequence of a nucleic acid may be interrupted by non-nucleotide components. The nucleic acid may be further modified after polymerization, for example by conjugation or binding to a reporter. A "variant" nucleic acid is a polynucleotide having the same nucleotide sequence as its original nucleic acid except for having at least one modified (e.g., deleted, inserted, or substituted, respectively) nucleotide. A variant may have a nucleotide sequence that is at least about 80%, 90%, 95%, or 99% identical to the nucleotide sequence of the original nucleic acid.

[0048] Cell-free methylated DNA is generally DNA of one or more nucleic acid molecules that can circulate freely in the bloodstream. In some cases, cell-free methylated DNA can be methylated in various regions of DNA. Samples (e.g., plasma samples) can be collected to analyze cell-free methylated DNA. Studies have shown that most of the circulating nucleic acids in the blood come from necrotic or apoptotic cells, and the levels of nucleic acids from apoptosis are greatly increased in diseases such as cancer. Especially cancer, in which circulating DNA has the hallmark features of the disease, including mutations in oncogenes, microsatellite mutations, and viral genomic sequences of certain cancers, DNA or RNA in plasma has been increasingly studied as a potential biomarker for the disease. For example, compared with the standard biomarker carcinoembryonic antigen used in the clinic, quantitative determination of low levels of circulating tumor DNA in total circulating DNA can be used as a better marker for detecting recurrence of colorectal cancer. Cell-free DNA (e.g., circulating cfDNA) may include circulating tumor DNA (ctDNA).

[0049] As used herein, "sequencing," also referred to as "genomic sequencing," is the process used to determine the order of the chemical building blocks (e.g., adenine, cytosine, guanine, thymine, uracil) that make up nucleic acid molecules (e.g., DNA, RNA, cDNA).

[0050] As used herein, "library preparation" generally includes chain end repair, A-tailing, adapter ligation, or any other preparation of cell-free DNA to allow the DNA to be subsequently sequenced. Library preparation can allow a nucleic acid sample (e.g., DNA, cDNA) to adhere to a sequencing device (e.g., a flow cell, beads). Non-limiting examples of library preparation include ligation-based library preparation and tagging-based library preparation. Library preparation can result in the creation of a sequencing library, i.e., a pool of nucleic acid (e.g., DNA) fragments with adapters attached. The type of adapter attached during library preparation can depend on the sequencing platform / device used.

[0051] The output of sequencing can be a "sequencing read". As used herein, a "sequencing read" is an inferred base pair sequence or base pair probability corresponding to all or part of a nucleic acid fragment (e.g., a DNA fragment). The length of the sequencing read may depend on the sequencing platform / device used. The length of the sequencing read can be about 10, about 20, about 30, about 40, about 50, about 60, about 70, about 80, about 90, about 100, about 150, about 200, about 250, about 300, about 350, about 400, about 450, about 500, about 550, about 600, about 650, about 700, about 750, about 800, about 850, about 900, about 950, about 1000, about 1100, about 1200, about 1300, about 1400, about 1500, about 1600, about 1700, about 1800, about 1900, about 2000 or more base pairs in length.

[0052] As used herein, "sequencing depth" refers to the ratio of the total number of bases obtained by sequencing to the size of the genome. Sequencing depth can also refer to the average number of measurements of each base in the genome during sequencing. Factors that can determine sequencing depth can include the error rate of the sequencing method, the assembly algorithm used in the sequencing process, the complexity of the repeat sequences of the nucleic acid molecule, region or genome being sequenced, and the length of the sequencing reads.

[0053] As used herein, "supplemental processed DNA" (eg, "filler DNA") can generally be non-coding DNA, or it can consist of an amplicon.

[0054] In some embodiments, the fragment length metric is fragment length. In some embodiments, the subject's cell-free methylated DNA is limited to fragments having a length of <170 bp, <165 bp, <160 bp, <155 bp, <150 bp, <145 bp, <140 bp, <135 bp, <130 bp, <125 bp, <120 bp, <115 bp, <110 bp, <105 bp, or <100 bp. In other embodiments, the subject's cell-free methylated DNA is limited to fragments having a length of about 100-about 150 bp, 110-140 bp, or 120-130 bp.

[0055] In some embodiments, the fragment length metric is a fragment length distribution of the subject's cell-free methylated DNA. In some embodiments, the subject's cell-free methylated DNA is limited to fragments within the bottom 50th, 45th, 40th, 35th, 30th, 25th, 20th, 15th, or 10th percentile based on length.

[0056] Whenever the term "at least", "greater than", or "greater than or equal to" precedes the first value in a series of two or more values, the term "at least", "greater than", or "greater than or equal to" applies to each value in the series. For example, greater than or equal to 1, 2, or 3 is equivalent to greater than or equal to 1, greater than or equal to 2, or greater than or equal to 3.

[0057] Whenever the term "not greater than," "less than," or "less than or equal to" precedes the first value in a series of two or more values, the term "not greater than," "less than," or "less than or equal to" applies to each value in the series. For example, less than or equal to 3, 2, or 1 is equivalent to less than or equal to 3, less than or equal to 2, or less than or equal to 1.

[0058] Methods for comparing DNA methylation

[0059] Cell-free nucleic acids, such as cell-free DNA (cfDNA), which can be present in biological samples (e.g., blood, urine, saliva, cerebrospinal fluid (CSF), etc.) that can be collected non-invasively, can be a heterogeneous population including cfDNA derived from healthy tissue and cfDNA derived from tumor or cancer cells (e.g., ctDNA). Cancer development can be associated with a focal increase in 5'methylcytosine (5mC), for example, at cytosine-phosphate-guanine (CpG) islands and CpG island shores. Cancer development can also be associated with global (e.g., genome-wide) cytosine demethylation (e.g., global loss of 5mC). In some cases, ctDNA can be distinguished from cfDNA molecules derived from healthy tissues (e.g., non-tumor and / or non-cancerous tissues) by the methylation level of the nucleic acid molecule (e.g., the percentage of methylated nucleotide residues). In some cases, nucleic acid molecules of tumor tissue and / or cancer tissue or nucleic acid molecules derived from tumor tissue and / or cancer tissue can be hypomethylated (e.g., can include a lower level of methylation, e.g., wherein there are fewer methylated nucleotide residues and / or a lower percentage of methylated nucleotide residues) compared to nucleic acid molecules of healthy tissue or nucleic acid molecules derived from healthy tissue (e.g., nucleic acid molecules of healthy tissue or nucleic acid molecules derived from healthy tissue consisting of or containing nucleotide sequences corresponding to the same region of the genome of the subject). For example, tumor-derived nucleic acid molecules (e.g., ctDNA molecules) can include one or more regions with fewer methylated nucleotide residues than nucleic acid molecules (e.g., cfDNA molecules) derived from healthy tissue (e.g., non-tumor and / or non-cancer tissue) in the same biological sample. In some cases, nucleic acid molecules of tumor tissue and / or cancer tissue or nucleic acid molecules derived from tumor tissue and / or cancer tissue can be hypermethylated (e.g., can include a higher level of methylation, e.g., wherein there are more methylated nucleotide residues and / or a greater percentage of methylated nucleotide residues) compared to nucleic acid molecules of healthy tissue or nucleic acid molecules derived from healthy tissue (e.g., nucleic acid molecules of healthy tissue consisting of or comprising nucleotide sequences corresponding to the same region of the subject genome or nucleic acid molecules derived from the healthy tissue). For example, compared to nucleic acid molecules (e.g., cfDNA molecules) from healthy tissue (e.g., non-tumor and / or non-cancer tissue) in the same biological sample, tumor-derived nucleic acid molecules (e.g., ctDNA molecules) can include one or more regions with more methylated nucleotide residues.In some cases, all or part of the fraction of tumor origin of multiple cell-free DNA molecules (e.g., ctDNA) can be distinguished from cfDNA molecules derived from healthy tissues by one or more biophysical properties (e.g., the length of cfDNA molecules or the presence of stereotyped 5' and 3' terminal sequence motifs) and / or one or more fragmentomics patterns. For example, ctDNA molecules can have shorter nucleic acid lengths than cfDNA molecules derived from healthy tissues. In some cases, ctDNA molecules can include stereotyped 5' and 3' terminal motifs. In some cases, one or more of these distinguishing features can be used to deplete cfDNA derived from healthy tissues in nucleic acid molecule populations and / or enrich ctDNA in nucleic acid molecule populations. In some cases, ctDNA can have shorter fragment lengths compared to cfDNA derived from healthy tissues.

[0060] Nucleic acid molecules (e.g., ctDNA) derived from tumor or cancer cells or tissues may be present in a biological sample (and / or a nucleic acid population derived from a biological sample) in much lower amounts than nucleic acid molecules (e.g., cfDNA) derived from healthy tissues. It is difficult to detect or sequence (e.g., determine the sequence identity of) ctDNA present in a biological sample or in a plurality of nucleic acid molecules (e.g., cfDNA) derived from a biological sample, for example, because they are present in a sample in lower amounts relative to cfDNA derived from healthy tissues (e.g., this may require the use of a larger amount of potentially scarce biological samples and / or may require a much higher sequencing depth, if possible).

[0061] Depletion (e.g., removal) of all or a portion of a population of methylated DNA molecules (e.g., molecules having increased nucleotide methylation levels in the entire or a subset of a genomic region represented by a plurality of nucleic acid molecules of a biological sample) from a plurality of nucleic acid molecules (e.g., comprising a plurality of cell-free nucleic acid molecules of a biological sample or amplicons thereof) can produce a remaining population of a plurality of nucleic acids of a biological sample that can be used to determine the presence and / or sequence identity of ctDNA molecules in a biological sample. Depletion and / or removal can be performed by pulling down methylated DNA molecules using a binding agent that is specific for the methylated DNA molecules. The pull-downs are typically collected, and the flow-through containing unmethylated and / or hypomethylated DNA molecules is discarded. The present disclosure provides for the first time a method and system for collecting such a flow-through containing unmethylated and / or hypomethylated DNA molecules, and using methylated and / or hypomethylated DNA molecules or derivatives thereof to produce a sequencing library.

[0062] In some cases, global or whole genome methylation technology can provide information of whole genome methylation, and this information can distinguish healthy tissue and cancer tissue.In some cases, multiple nucleic acids (for example, cfDNA molecules or its amplicon derived from biological samples) in one or more specific regions of the genomic sequence of nucleic acid molecules (for example, CpG islands, CpG island shores or genome repetitive sequences, such as long interspersed nuclear elements (LINE), short interspersed nuclear elements (SINE) or LTR (long terminal repeats)) methylated nucleic acid molecules can be sufficient to achieve the sensitivity and / or specificity of increase in the determination of the presence or absence of ctDNA molecules or sequence identity for determining the multiple.In some cases, the whole genome includes all genomic regions.Alternatively, the whole genome can include all hypomethylation and / or all hypermethylation genomic regions.In some cases, the whole genome file includes all genomic regions of all chromosomes (for example, all human chromosomes) of the sample.Alternatively, the whole genome file can include all genomic regions of autosomes (for example, human chromosomes 1-22).

[0063] Alternatively, a global or whole genome subset can be used to provide specific information about methylation at a specified site. A specified site can include a background genomic region set that, when methylated, can provide additional information to distinguish differentially methylated regions to distinguish cancer samples from control samples. The background genomic region set can be experimentally validated.

[0064] For example, a subset of the whole genome DNA can be obtained using sheared DNA. Sheared DNA, also referred to as "cfDNA mimics," can include a subset of the whole genome DNA. In some cases, sheared DNA includes randomly cut DNA. Alternatively, in some cases, sheared DNA includes DNA cut at a specified position on the genome. In some cases, sheared DNA includes DNA fragmented to a desired fragment range. Physical shearing can be performed using, for example, probe sonication or atomization. The length of the sheared genomic nucleic acid molecule (e.g., sheared DNA) can be about 20, about 30, about 40, about 50, about 60, about 70, about 80, about 90, about 100, about 150, about 200, about 250, about 300, about 350, about 400, about 450, about 500, about 550, about 600, about 650, about 700, about 750, about 800, about 850, about 900, about 950, about 1000 or more base pairs.

[0065] like Fig.14As shown, nucleic acids (e.g., DNA, cell-free DNA (cfDNA)) obtained from an object can be further processed to increase methylation levels. These nucleic acid samples can be described as natural cfDNA samples that are different from cfDNA mimics. Natural cfDNA samples can be closer to the DNA fragmentation patterns observed in clinical samples. Natural cfDNA samples can be obtained as biobank plasma samples corresponding to cancer patients and / or normal or healthy control subjects. In some cases, nucleic acids (e.g., DNA, cfDNA) can be partially methylated. In some cases, nucleic acids (e.g., DNA, cfDNA) can be completely methylated. In some cases, in vitro methylation is performed using enzymes intended to change the methylation level of nucleic acid fragments to increase the methylation level. In some embodiments, the enzyme is a CpG methyltransferase. In some cases, in vitro methylation can result in partial methylation. In some cases, in vitro methylation can result in complete methylation. In some cases, in vitro methylation can result in high methylation. In some cases, methylated DNA binders (e.g., 5mC antibodies) are used to obtain or enrich for high methylated nucleic acids. Highly methylated nucleic acids can be further incubated and amplified.

[0066] Fig.14 It is also shown that the shearing genomic DNA fragments are treated to increase the methylation level. In some cases, the shearing genomic DNA fragments can be partially methylated. In some cases, the shearing DNA can be completely methylated. In some cases, the methylation level of the sheared DNA is increased by in vitro methylation using an enzyme intended to change the methylation level of the nucleic acid fragment. In some embodiments, the enzyme is a CpG methyltransferase. In some cases, in vitro methylation can result in partial methylation. In some cases, in vitro methylation can result in complete methylation. In some cases, in vitro methylation can result in high methylation. In some cases, methylated DNA binding substances (e.g., 5mC antibodies) are used to obtain or enrich for high methylation shearing nucleic acids. High methylation shearing nucleic acids can be further incubated and amplified.

[0067] In some cases, the processing of nucleic acid samples occurs before the processing of shearing DNA samples. Alternatively, the processing of nucleic acid samples can occur simultaneously with the processing of shearing DNA samples. Alternatively, the processing of nucleic acid samples can occur after the processing of shearing DNA samples.

[0068] Additionally, alternative control samples can be used to verify whether the enzyme used to increase the methylation level of the DNA sample is actually working. The alternative control sample can be processed in the same manner as the natural cfDNA sample described above. The alternative control sample can be used to ensure that the commonly methylated regions can be pulled down during the enrichment and processing steps.

[0069] In some cases, before MeDIP enrichment, natural cfDNA samples are divided into two natural cfDNA treatment groups. The first natural cfDNA treatment group can be enriched as described above. The second natural cfDNA treatment group can be used as a first input control to obtain different methylation region sets from the first natural cfDNA treatment group of enrichment. In some cases, the second natural cfDNA treatment group producing the first input control can be about 1%, about 2%, about 3%, about 4%, about 5%, about 6%, about 7%, about 8%, about 9%, about 10%, about 11%, about 12%, about 13%, about 14%, about 15%, about 16%, about 17%, about 18%, about 19%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, about 50% or more of the original natural cfDNA sample.

[0070] In some cases, before MeDIP enrichment, the shearing DNA sample is divided into two treatment groups. The first shearing DNA treatment group can be enriched as described above. The second shearing DNA treatment group can be used as the second input control, so that different methylation region sets are obtained from the first shearing DNA treatment group of enrichment. In some cases, the second shearing DNA treatment group producing the second input control can be about 1%, about 2%, about 3%, about 4%, about 5%, about 6%, about 7%, about 8%, about 9%, about 10%, about 11%, about 12%, about 13%, about 14%, about 15%, about 16%, about 17%, about 18%, about 19%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, about 50% or more of the original shearing DNA sample.

[0071] Following the incubation step with the CpG methyltransferase, one of the two native cfDNA treatment groups can be prepared for cfMeDIP-seq, as Figure 1 As shown. For example, one of the two natural cfDNA treatment groups can be supplemented with control DNA, end-paired, A-tailed, adapter-ligated, and / or otherwise prepared to allow sequencing. The group can be further immunoprecipitated by pulling down the high methylation region with a CpG methylation binder. The filler DNA disclosed herein can also be added to the group in the natural cfDNA treatment group. The high methylation regions of this group of natural cfDNA are enriched. The other group is used as an input control and is not enriched in high methylation regions. The natural cfDNA enriched in high methylation regions can be subjected to cfMeDIP-seq.

[0072] Following the incubation step with the CpG methyltransferase, one of the two sheared cfDNA treatment groups can be prepared for cfMeDIP-seq, as Figure 1As shown. For example, one of the two sheared cfDNA treatment groups can be supplemented with control DNA, end-paired, A-tailed, adapter-ligated, and / or otherwise prepared to allow sequencing. The group can be further immunoprecipitated by pulling down the hypermethylated regions with a CpG methylation binder. The filler DNA disclosed herein can also be added to the group in the sheared cfDNA treatment group. The hypermethylated regions of the sheared cfDNA of this group are enriched. The other group serves as an input control and is not enriched in hypermethylated regions. The sheared cfDNA enriched in hypermethylated regions can be subjected to cfMeDIP-seq.

[0073] In some cases, the first and / or second input controls are processed by incubation and amplification. Alternatively, in some cases, the first and / or second input controls are not processed by incubation and amplification.

[0074] Hypermethylated nucleic acids and / or input controls can be sequenced (e.g., using cfMeDIP-seq) or prepared for sequencing (e.g., creating sequencing reads). Sequencing can be performed before the hypermethylated DNA sample sequence and the hypermethylated sheared DNA sequence are combined. Alternatively, sequencing can be performed after the hypermethylated DNA sample sequence and the hypermethylated sheared DNA sequence are combined.

[0075] The sequencing results and / or sequencing reads can be compared. Data analysis is performed on the sequencing results and / or sequencing reads of the natural cfDNA sample, the first input control, the sheared DNA sample, and the second input control to generate high methylation signal peaks (corresponding to high methylation regions). The high methylation signal peaks of each enrichment group are standardized according to the methylation signal generated by its input control. The different methylation signal peaks (i.e., different sets of genomic regions) between the enriched natural cfDNA and its input control are sorted with the different methylation signal peaks (i.e., different sets of genomic regions) between the enriched cfDNA mimics and its input control to form a sequencing read library. The sequencing read library can be used as a new background spectrum (i.e., containing about 1,218,413 300bp genomic windows) instead of a whole genome spectrum (i.e., containing about 9,583,339 300bp genomic windows) to further process other sequence reads obtained from cfDNA samples of cancer patients, patients at risk of cancer, patients suspected of having cancer, cancer patients before and after receiving treatment intended to cure cancer, and healthy control subjects. Background profiles can be used to compare healthy and cancer cell-free nucleic acid samples to determine disease state.Using background profiles can allow identification of other differentially methylated regions between healthy and cancer cell-free nucleic acid samples.

[0076] In some cases, a nucleic acid sample (e.g., a cell-free nucleic acid sample) can be obtained from an object. The nucleic acid sample can be derived from a sample obtained from an object. Non-limiting examples of samples that can be obtained from an object may include blood, urine, saliva, cerebrospinal fluid, ascites or peritoneal fluid, pleural effusion, pericardial effusion, synovial fluid, gastric juice, and breast milk. The object may be healthy, or may suffer from or be suspected of having a disease or condition. The nucleic acid sample from an object may be processed in the same manner as the above-mentioned cfDNA sample. In some cases, the nucleic acid sample from an object may be sequenced to generate sequencing reads of enriched methylated DNA regions. In some cases, sequencing reads of enriched methylated DNA regions derived from samples from an object may be used to generate a first plurality of differentially methylated regions (DMRs). The first plurality of DMRs may be mapped to background regions. Alternatively or additionally, sequencing reads of enriched methylated DNA regions derived from samples from an object may be used to generate a second plurality of differentially methylated regions (DMRs). The second plurality of DMRs may be mapped to the whole genome. DMRs may include hypermethylation or hypomethylation. The first plurality of DMRs may be greater than the second plurality of DMRs. Alternatively, the second plurality of DMRs may be greater than the first plurality of DMRs. Alternatively, the first plurality of DMRs and the second plurality of DMRs may include the same number of DMRs. In some cases, classifying a sample as diseased or healthy using the first plurality of DMRs includes increasing the separation between disease and healthy samples, which would occur if the sample was classified as diseased or healthy using the second plurality of DMRs.

[0077] In some cases, background regions can be used to distinguish between healthy and cancer cell-free nucleic acid samples. In some cases, background regions can be used to identify DMRs from healthy and / or cancer cell-free nucleic acid samples. In some cases, DMRs identified by using background regions include DMRs identified by using whole genome background. In some cases, DMRs identified by using background regions include a subset of DMRs identified by using whole genome background. In some cases, more DMRs are identified by using DMRs identified by using background regions than by using whole genome background.

[0078] In some cases, use of background regions can identify at least about 100, at least about 150, at least about 200, at least about 250, at least about 300, at least about 350, at least about 400, at least about 450, at least about 500, at least about 550, at least about 600, at least about 650, at least about 700, at least about 750, at least about 800, at least about 850, at least about 900, at least about 1000, or more DMRs as compared to use of the whole genome context. In some cases, use of the background regions can identify at least about 1 times, at least about 2 times, at least about 3 times, at least about 4 times, at least about 5 times, at least about 6 times, at least about 7 times, at least about 8 times, at least about 9 times, at least about 10 times, at least about 15 times, at least about 20 times, at least about 25 times, at least about 30 times, at least about 35 times, at least about 40 times, at least about 45 times, at least about 50 times, at least about 100 times, at least about 150 times, at least about 200 times, at least about 250 times, at least about 300 times, at least about 350 times, at least about 400 times, at least about 450 times, at least about 500 times, or more DMRs as compared to use of the whole genome context.

[0079] In some cases, using the background regions reduces the run time for identifying DMRs by at least about 5 minutes, at least about 10 minutes, at least about 15 minutes, at least about 20 minutes, at least about 25 minutes, at least about 30 minutes, at least about 35 minutes, at least about 40 minutes, at least about 45 minutes, at least about 50 minutes, at least about 55 minutes, at least about 1 hour, at least about 1.5 hours, at least about 2 hours, at least about 2.5 hours, at least about 3 hours, at least about 3.5 hours, at least about 4 hours, at least about 4.5 hours, at least about 5 hours, at least about 5.5 hours, at least about 6 hours, at least about 6.5 hours, at least about 7 hours, at least about 7.5 hours, at least about 8 hours, at least about 8.5 hours, at least about 9 hours, at least about 9.5 hours, at least about 10 hours, or more, compared to using the whole genome background to identify DMRs.

[0080] In some cases, using the background regions reduces the run time for identifying DMRs by about 1-fold, at least about 2-fold, at least about 3-fold, at least about 4-fold, at least about 5-fold, at least about 6-fold, at least about 7-fold, at least about 8-fold, at least about 9-fold, at least about 10-fold, at least about 15-fold, at least about 20-fold, at least about 25-fold, at least about 30-fold, at least about 35-fold, at least about 40-fold, at least about 45-fold, at least about 50-fold, or more, compared to using the whole genome background to identify DMRs.

[0081] In some cases, hypermethylation (the process of adding methyl groups to nucleic acid molecules such as DNA) can identify regions most likely to be depleted using methylated DNA-specific binders (e.g., 5mC antibodies). This identification can create a validated set of genomic regions that can more accurately distinguish between cancer and healthy samples in cfDNA. Hypermethylated segments of DNA can be methylated at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, or more on all bases. Hypermethylated segments of DNA can be tightly packed, resulting in gene silencing.

[0082] Depletion of all or a portion of methylated nucleic acid molecules in a plurality of nucleic acid molecules of a biological sample can include contacting the methylated nucleic acid molecules with a binder (e.g., an affinity molecule specific for methylated nucleotide residues, such as an antibody or protein). For example, creation of a depleted sequencing library can include contacting a plurality of nucleic acid molecules (e.g., cfDNA molecules) or amplicons thereof with a binder that is selective for methylated regions of nucleic acid molecules (e.g., a methylcytosine binder (MBD), such as an MBD-Fc fusion protein). In some cases, the binder can be specific for one or more methylated nucleotide species (e.g., 5-methylcytosine (5mC)), for example, as Figure 1 As shown. Cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq), a genome-wide molecular profiling technique, can be enriched for methylated cfDNA fragments by using a binder, such as an anti-5-methylcytosine (anti-5mC) antibody or a methyl-CpG-binding domain (MBD) protein (e.g., MBD-Fc fusion protein). As described herein, cfMeDIP-seq can include a portion of a method and system for depleting methylated DNA fragments in a cfDNA sample, leaving behind hypomethylated or unmethylated cfDNA fragments, such as ctDNA. Therefore, the identification of hypomethylated or unmethylated cell-free DNA in a clinical sample can be used to determine the presence of a tumor or cancer in a subject.

[0083] In some cases, depletion of a plurality of nucleic acid molecules (e.g., in creating a depleted sequencing library and / or determining the presence or sequence identity of nucleic acid molecules) can include removing one or more nucleic acid molecules having a methylation level above a threshold methylation level (e.g., wherein the one or more nucleic acid molecules removed are hypermethylated, e.g., relative to one or more nucleic acid molecules that were not removed during depletion). In some cases, the methylation level of a particular nucleic acid fragment (e.g., a DNA fragment) can be considered to have reached a threshold methylation level when a binder with sufficient specificity for methylated cytosine is able to bind to the particular nucleic acid fragment with or without the use of filler DNA described herein. In some cases, the methylation level of a particular nucleic acid fragment (e.g., a DNA fragment) can be considered to be below a threshold methylation level when a binder with sufficient specificity for methylated cytosine is unable to bind to the particular nucleic acid fragment with or without the use of filler DNA described herein. In some cases, depletion of a plurality of nucleic acid molecules (e.g., in creating a depleted sequencing library and / or determining the presence or sequence of a nucleic acid molecule) produces (e.g., provides) a remainder population of the plurality of nucleic acid molecules, wherein the remainder of the plurality of nucleic acid molecules includes (or, in some cases, consists of) nucleic acid molecules having a methylation level below a threshold methylation level (e.g., wherein the remainder population is hypomethylated / unmethylated relative to one or more nucleic acid molecules removed from the plurality of nucleic acid molecules during depletion). The methylation level can be calculated as a percentage of highly methylated nucleic acid fragments compared to all nucleic acid fragments contained in the sample. In some cases, the threshold methylation level can be 0.1% to 1%, 1% to 5%, 5% to 10%, 10% to 15%, 15% to 20%, 20% to 25%, 25% to 30%, 30% to 35%, 35% to 40%, 40% to 45%, 45% to 50%, 50% to 55%, 55% to 60%, 65% to 70%, 70% to 75%, 75% to 80%, 80% to 85%, 85% to 90%, 95% to 100%, at least 1%, at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 40%, at least 45%, at least 50%, at least 50%, at least 55%, at least 60%, at least 65% to 70%, at least 70% to 75%, at least 75% to 80%, at least 80% to 85%, at least 85% to 90%, at least 100%, at least 1%, 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%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at most 1%, at most 5%, at most 10%, at most 15%, at most 20%, at most 25%, at most 30%, at most 35%, at most 40%, at most 45%, at most 50%, at most 55%, at most 60%, at most 65%, at most 70%, at most 75%, at most 80%, at most 85%, at least 90%, at least 95%, at most 1%, at most 5%, at most 10%, at most 15%, at most 20%, at most 25%, at most 30%, at most 35%, at most 40%, at most 45%, at most 50%, at most 55%, at most 60%, at most 65%, at most 70%, at most 75%, at most 80%, at most 85%, at most 90%, at most 95% or at most 100%.

[0084] In some cases, the creation of a global or whole genome subset defines the context for methylation determination. The context can include multiple genomic windows or window regions that can be defined by their specificity for a binder. Alternatively or additionally, the context can include multiple genomic windows or window regions that are determined by specific methylation patterns. The context can include 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 15, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 200, at least 300, at least 400, at least 500, at least 600, at least 700, at least 800, at least 900, at least 1,000, at least 5,000, at least 10,000, at least 50,000 or more genomic windows or window regions.

[0085] The length of a genomic window or window region can be at least about 10, at least about 20, at least about 30, at least about 40, at least about 50, at least about 60, at least about 70, at least about 80, at least about 90, at least about 100, at least about 150, at least about 200, at least about 250, at least about 300, at least about 350, at least about 400, at least about 450, at least about 500, at least about 550, at least about 600, at least about 650, at least about 700, at least about 750, at least about 800, at least about 800, at least about 900, at least about 1000, at least about 1500, at least about 2000, at least about 2500, at least about 3000, at least about 3500, at least about 4000, at least about 4500, at least about 5000, at least about 5500, at least about 6000, at least about 6500, at least about 7000, at least about 7500 In some cases, the genomic window is about 300bp in length. In some cases, the genomic windows are adjacent to each other in the genome. Alternatively or additionally, the genomic window can be a non-adjacent region on the genome.

[0086] In some cases, a first plurality of nucleic acid molecules (e.g., comprising nucleic acid molecules from a biological sample of a subject, such as cfDNA) can be combined (e.g., mixed) with a second plurality of nucleic acid molecules (e.g., where the second plurality of nucleic acid molecules is not from the subject from which the biological sample was obtained), e.g., as Figure 1In some cases, the second plurality of nucleic acid molecules include DNA that is supplemented with treatment (e.g., includes 1 DNA). In some cases, each of the second plurality of nucleic acid molecules is not aligned with the human genome.

[0087] In some cases, a method or system disclosed herein may include, for example, using a sequencer (e.g., Figure 1 In some cases, the remaining part of the nucleic acid molecule population can be purified (e.g., after the library is created) to produce a plurality of purified nucleic acid molecules, for example, before or as part of the process of determining or identifying the sequence of all or a portion of the nucleic acid molecule population that is exhausted. In some cases, all or a portion of a plurality of purified nucleic acid molecules can be amplified (e.g., via polymerase chain reaction), for example, before or as part of the process of determining or identifying the sequence of all or a portion of the nucleic acid molecule population that is exhausted. In some cases, the amplified nucleic acid molecule population or its derivatives (e.g., including all or a portion of the amplicon of a plurality of purified nucleic acid molecules) can be sequenced (e.g., for determining and / or identifying the sequence of nucleic acid molecules). In some cases, sequencing can be achieved using a sequencer, as described herein. In some cases, an array or polymerase chain reaction can be used to identify or determine the sequence of a plurality of nucleic acid molecules (or derivatives thereof) of a biological sample. In some cases, the presence of a tumor-derived nucleic acid molecule can be determined by calculating the sum of reads per kilobase per million (RPKM) for a region of the genome (e.g., all or a portion of the genome, such as only a CpG island or only a CpG shore). In some cases, when a depleted sequencing library (e.g., including a remaining population of nucleic acids) is observed to have a low sum of RPKM in one or more regions of interest (e.g., CpG islands or CpG island shores), e.g., less than 70,000, less than 60,000, less than 50,000, less than 40,000, or less than 30,000, the presence of a tumor-derived nucleic acid molecule can be indicated. Supplemental treated DNA (filler DNA)

[0088] In some cases, supplemental processed DNA (e.g., filler DNA) can be added to a first plurality of nucleic acids (e.g., a plurality of nucleic acids from a biological sample, which may include cfDNA from healthy tissue and / or cfDNA from tumor tissue, such as ctDNA), e.g., as Figure 1As shown. In some cases, the addition of the DNA (e.g., the second plurality of nucleic acid molecules) to the first plurality of nucleic acid molecules can increase the specificity and / or sensitivity of the methods or systems described herein, for example, with respect to detecting and / or identifying the nucleic acid sequences of the first plurality of nucleic acid molecules. In some cases, the addition of the DNA (e.g., the second plurality of nucleic acid molecules) to the first plurality of nucleic acid molecules can increase the exhaustion rate of the methylated regions of the nucleic acid sequences, for example, in the practice of some embodiments of the methods and systems described herein. In some cases, the addition of the DNA (e.g., the second plurality of nucleic acid molecules) to the first plurality of nucleic acid molecules (e.g., including the cfDNA of a biological sample) can increase the selectivity of the binder to one or more (e.g., multiple) methylated regions of the first plurality of nucleic acid molecules. In some cases, the DNA (e.g., the second plurality of nucleic acid molecules) to be supplemented can be added to the first plurality of nucleic acid molecules, and the amount added is sufficient to make the combined mixture of nucleic acid molecules reach the desired total mass. In some cases, the desired total mass for use in the methods or systems described herein may be 20 ng to 30 ng, 30 ng to 40 ng, 40 ng to 50 ng, 50 ng to 60 ng, 60 ng to 70 ng, 70 ng to 80 ng, 80 ng to 90 ng, 90 ng to 100 ng, 100 ng to 110 ng, 110 ng to 120 ng, 120 ng to 130 ng, 130 ng to 140 ng, 140 ng to 150 ng, 150 ng to 160 ng, 160 ng to 170 ng, 170 ng to 180 ng, 180 ng to 190 ng, 190 ng to 200 ng, greater than 200 ng, or less than 20 ng. In some cases, an amount of 1 ng to 5 ng, 5 ng to 10 ng, 10 ng to 20 ng, 20 ng to 30 ng, 30 ng to 40 ng, 40 ng to 50 ng, 50 ng to 60 ng, 60 ng to 70 ng, 70 ng to 80 ng, 80 ng to 90 ng, 90 ng to 100 ng, 100 ng to 110 ng, 110 ng to 120 ng, 120 ng to 130 ng, 130 ng to 140 ng, 140 ng to 150 ng, 150 ng to 160 ng, 160 ng to 170 ng, 170 ng to 180 ng, 180 ng to 190 ng, 190 ng to 200 ng, greater than 200 ng, less than 20 ng, less than 10 ng, or less than 5 ng of the supplemental treated DNA can be added to the first plurality of nucleic acid molecules (e.g., to bring the total mixture of the supplemental treated DNA and the first plurality of nucleic acid molecules to a desired total mass).In some embodiments, the present disclosure includes methods and systems for filling a sample with a certain amount of supplementary treated DNA (e.g., filler DNA) to generate a mixture sample, wherein the mixture sample includes at least about 50ng, 55ng, 60ng, 65ng, 70ng, 75ng, 80ng, 85ng, 90ng, 95ng, 100ng, 120ng, 140ng, 160ng, 180ng, 200ng or any amount of nucleic acid mixture between these numbers. In some embodiments, the supplementary treated DNA contains at least about 5%, 10%, 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 100% methylated supplementary treated DNA, wherein the remainder is unmethylated supplementary treated DNA, and in some cases between 5% and 50%, or between 10%-40%, or between 15%-30% methylated supplementary treated DNA. In some embodiments, the mixture sample comprises an amount of about 20 ng to about 100 ng, about 30 ng to about 100 ng, or about 50 ng to about 100 ng of the supplemented treated DNA. In some embodiments, the cell-free DNA from the sample and the first amount of the supplemented treated DNA together comprise at least 50 ng of total DNA. In some embodiments, the cfDNA from the sample and the first amount of the supplemented treated DNA together constitute at least 100 ng of total DNA.

[0089] In some cases, the DNA of complementary treatment can be produced by fragmentation (for example, by ultrasonic treatment). In some embodiments, the DNA of complementary treatment can be about 50bp to about 800bp long, about 100bp to about 600bp long or about 200bp to about 600bp long. In some embodiments, the DNA of complementary treatment is double-stranded. The DNA of complementary treatment can be double-stranded DNA. For example, the DNA of complementary treatment can be useless DNA. The DNA of complementary treatment can also be endogenous or exogenous DNA. For example, the DNA of complementary treatment is non-human DNA, such as λDNA. As used herein, "λDNA" generally refers to enterobacteriaceae phage λDNA. In some embodiments, the DNA of complementary treatment is not compared with human DNA substantially. In some embodiments, the complementary DNA can be highly methylated.

[0090] sample

[0091] Sample can be any biological sample separated from object.For example, sample can include but not limited to body fluid, whole blood, platelet, serum, plasma, feces, leukocyte or leukocyte, endothelial cell, tissue biopsy, synovial fluid, lymphatic fluid, ascites, interstitial or extracellular fluid, fluid in the space between cells, including gingival sulcus fluid, bone marrow, cerebrospinal fluid, saliva, mucus, sputum, semen, sweat, urine, fluid from nasal brush, fluid from Pap smear or any other body fluid.Body fluid can include saliva, blood or serum.Sample can also be tumor sample, which can be obtained from object by various methods (including but not limited to venipuncture, excretion, ejaculation, massage, biopsy, needle aspiration, lavage, scraping, surgical incision or intervention or other methods).Sample can be acellular sample (for example, substantially free of cells).DNA sample can be denatured, for example, using enough heat.

[0092] The sample may be taken from a subject suffering from a disease or condition. The sample may be taken from a subject suspected of suffering from a disease or condition. In some embodiments, the sample may be obtained before and / or after treating a subject suffering from a disease or condition. The sample may be obtained from a subject during a treatment or treatment regimen. Multiple samples may be obtained from a subject to monitor the effects of treatment over time. The disease or condition may be cancer. The cancer may be advanced cancer or early stage cancer.Specific illustrative examples of cancer types include cancer types suitable for detection using the methods according to the present disclosure, including acute lymphoblastic leukemia, acute myeloid leukemia, adrenocortical carcinoma, AIDS-related cancer, AIDS-related lymphoma, anal cancer, appendix cancer, astrocytoma, basal cell carcinoma, bile duct cancer, bladder cancer, bone cancer, brain tumors, such as cerebellar astrocytoma, brain astrocytoma / malignant glioma, ependymoma, medulloblastoma, supratentorial primitive neuroectodermal tumor, visual pathway and hypothalamic glioma, breast cancer, bronchial adenoma, Burkitt's lymphoma, carcinoma of unknown primary, central nervous system lymphoma, cerebellar astrocytoma, cervical cancer, childhood cancer, chronic lymphocytic leukemia, chronic myeloid leukemia, chronic myeloproliferative disorder, colon cancer, cutaneous T-cell lymphoma, desmoplastic small round cell tumor, uterine Endometrial cancer, ependymoma, esophageal cancer, Ewing sarcoma, germ cell tumors, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal tumors, gliomas, hairy cell leukemia, head and neck cancer, heart cancer, hepatocellular (liver) cancer, Hodgkin lymphoma, hypopharyngeal cancer, intraocular melanoma, pancreatic islet cell carcinoma, Kaposi sarcoma, kidney cancer, laryngeal cancer, lip and oral cancer, liposarcoma, liver cancer, lung cancer, such as non-small cell and small cell lung cancer, lymphoma, leukemia, macroglobulinemia, bone / osteosarcoma malignant fibrous histiocytoma, medulloblastoma, melanoma, mesothelioma, occult primary metastatic squamous neck cancer, oral cancer (mouth cancer), multiple endocrine neoplasms syndrome, myelodysplastic syndrome, myeloid leukemia, nasal cancer and paranasal sinus cancer, nasopharyngeal carcinoma, neuroblastoma, non-Hodgkin's lymphoma, non-small cell lung cancer, oral cancer (oral cancer), oropharyngeal cancer, osteosarcoma / malignant fibrous histiocytoma of bone, ovarian cancer, ovarian epithelial cancer, ovarian germ cell tumor, pancreatic cancer, islet cell pancreatic cancer, paranasal sinus and nasal cavity cancer, parathyroid cancer, penile cancer, pharyngeal cancer, pheochromocytoma, pineal astrocytoma, pineal germ cell tumor, pituitary adenoma, pleuropulmonary blastoma, plasma cell tumor, primary central nervous system lymphoma, prostate cancer, rectal cancer, renal cell carcinoma, renal pelvis and ureteral transitional cell carcinoma, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma, skin cancer, Merkel cell skin carcinoma, small intestine cancer, soft tissue sarcoma, squamous cell carcinoma, gastric cancer, T-cell lymphoma, laryngeal cancer, thymoma, thymic carcinoma, thyroid cancer, trophoblastic tumor (pregnancy), cancer of unknown primary site, urethral cancer, uterine sarcoma, vaginal cancer, vulvar cancer, Waldenstrom's macroglobulinemia, and Wilms' tumor. In embodiments, the cancer is head and neck squamous cell carcinoma.

[0093] Samples can be taken from healthy individuals. In some cases, samples can be obtained longitudinally from the same individual. In some cases, the samples obtained longitudinally can be analyzed for the purpose of monitoring the early detection of individual health and health problems. In some embodiments, samples can be collected in a home environment or a point of care environment, and then transported by mail delivery, courier delivery or other transportation methods before analysis. For example, a home user can collect a blood spot sample by finger puncture, and the blood spot sample can be dried and then delivered by mail before analysis. In some cases, the sample obtained longitudinally can be used to monitor the response to the stimulation of expected impact health, motor performance or cognitive performance. Non-limiting examples include the response to drug therapy, diet or exercise regimen.

[0094] In some embodiments, the disclosure provides a system, method or kit comprising or using one or more biological samples. One or more samples used herein may include any substance containing or inferred to contain nucleic acid. The sample may include a biological sample obtained from a subject. In some embodiments, the biological sample is a liquid sample.

[0095] In some embodiments, the sample comprises less than about 100 ng, 90 ng, 80 ng, 75 ng, 70 ng, 60 ng, 50 ng, 40 ng, 30 ng, 20 ng, 10 ng, 5 ng, 1 ng, or any amount of cell-free nucleic acid molecules in between. Further, in some embodiments, the sample comprises less than about 1 pg, less than about 5 pg, less than about 10 pg, less than about 20 pg, less than about 30 pg, less than about 40 pg, less than about 50 pg, less than about 100 pg, less than about 200 pg, less than about 500 pg, less than about 1 ng, less than about 5 ng, less than about 10 ng, less than about 20 ng, less than about 30 ng, less than about 40 ng, less than about 50 ng, less than about 100 ng, less than about 200 ng, less than about 500 ng, less than about 1000 ng, or any amount of cell-free nucleic acid molecules in between.

[0096] In some cases, creating or providing a plurality of nucleic acid molecules from a biological sample can include performing one or more of end repair, A-tailing, and adaptor ligation on the plurality of nucleic acid molecules (eg, after purification from the biological sample).

[0097] In some embodiments, a sample can be collected and sequenced at a first time point, and then another sample can be collected and sequenced at a subsequent time point. This method can be used, for example, for longitudinal monitoring purposes to track the development or progression of a disease. In some embodiments, the progression of a disease can be tracked before, after, or during treatment to determine the effectiveness of treatment. For example, the methods described herein can be performed on a subject before and after drug treatment to measure the progression or regression of a disease in response to drug treatment.

[0098] After obtaining a sample from an object, the sample can be processed to generate a data set indicating a disease or condition of the object. For example, the presence, absence or quantitative assessment of a cell-free nucleic acid molecule (e.g., ctDNA molecule) of a sample at a set of cancer-related genomic loci or microbiome-related loci can indicate the cancer of the object. Processing a sample obtained from an object may include (i) subjecting the sample to conditions sufficient to separate, enrich or extract a plurality of cell-free nucleic acid molecules, and (ii) determining a plurality of cell-free nuclear nucleic acid molecules to generate a data set (e.g., nucleic acid sequence). In some embodiments, a plurality of cell-free nucleic acid molecules are extracted from a sample and sequenced to generate a plurality of sequencing reads.

[0099] In some embodiments, the cell-free nucleic acid molecule may include cell-free ribonucleic acid (cfRNA) or cell-free deoxyribonucleic acid (cfDNA). Cell-free nucleic acid molecules (e.g., cfRNA or cfDNA) can be extracted from a sample by multiple methods. Cell-free nucleic acid molecules can be enriched by multiple probes, which are configured to enrich nucleic acid (e.g., RNA or DNA) molecules corresponding to a group of cancer-related genomic loci. The probe can have sequence complementarity with a nucleic acid sequence from one or more genomic loci of the group of cancer-related genomic loci. The set of cancer-associated genomic loci can include 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, at least 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 55, at least about 60, at least about 65, at least about 70, at least about 75, at least about 80, at least about 85, at least about 90, at least about 95, at least about 100 or more different cancer-associated genomic loci. The probe can be a nucleic acid molecule (e.g., RNA or DNA) having sequence complementarity with a nucleic acid sequence (e.g., RNA or DNA) of one or more genomic loci (e.g., cancer-associated genomic loci). These nucleic acid molecules can be primers or enrichment sequences. Using probes that are selective for one or more genomic loci (e.g., cancer-associated genomic loci or microbiome-associated loci) to assay a sample can include using array hybridization, polymerase chain reaction (PCR), or nucleic acid sequencing (e.g., RNA sequencing or DNA sequencing).

[0100] Certain methods for capturing cell-free methylated DNA are described in WO 2017 / 190215 and WO 2019 / 010564, both of which are incorporated herein by reference in their entirety and for all purposes.

[0101] Nucleic acid molecule sequencing

[0102] Various assays can be used in the methods of the present disclosure, such as library preparation (which may include polymerase chain reaction (PCR)) followed by sequencing (e.g., next generation sequencing, Sanger sequencing, etc.). Next generation sequencing (NGS) technology, also known as high throughput sequencing, can include various sequencing technologies, including: Illumina (Solexa) sequencing, Roche 454 sequencing, Iontorrent: proton / PGM sequencing, SOLiD sequencing, long read sequencing (Oxford Nanopore and Pactbio). NGS allows faster and cheaper sequencing of DNA and RNA than previously used Sanger sequencing. In some embodiments, the sequencing is optimized for short read sequencing.

[0103] The sequencing library of high methylation can improve the specificity, sensitivity and / or efficiency of the method and system for processing nucleic acid.For example, the sequencing library of high methylation can improve the specificity, sensitivity and / or efficiency of the determination of the existence and / or sequence identity of the nucleic acid sequence.The sequencing library of high methylation can include multiple nucleic acids and / or its fragment.In some cases, the sequencing library of high methylation (for example, " the library of exhaustion " or " the library of methylation exhaustion ") can include multiple nucleic acid molecules (for example, nucleic acid colony and / or its fragment).Multiple nucleic acid molecules can include all or part of the first multiple nucleic acid molecules, for example, wherein the first multiple nucleotide molecules include one or more nucleic acid molecules containing methylated nucleic acid residues and one or more nucleic acid molecules not containing methylated nucleic acid residues.In some cases, methylated nucleic acid can include one or more methylated nucleic acid residues.For example, methylated nucleic acid can include one or more methylated cytosines (for example, one or more 5-methylcytosines (5mC) and / or one or more 5-hydroxymethylcytosines (5hmC)). A plurality of nucleic acid molecules (e.g., a plurality of nucleic acid molecules derived from a biological sample) can be depleted of methylated nucleic acid molecules by using, for example, a conjugate as described herein to form a depleted sequencing library. In some cases, before using a conjugate to create a depleted sequencing library, a first plurality of nucleic acid molecules (e.g., including a plurality of cfDNA molecules derived from a biological sample) can be mixed with a second plurality of nucleic acid molecules (e.g., DNA containing a supplementary treatment). In some cases, a sequencing library depleted of methylated nucleic acids can completely deplete methylated nucleic acid molecules. For example, a sequencing library may not contain (0%) methylated nucleic acid residues (e.g., a sequencing library not containing methylated cytosine residues). In some cases, a sequencing library depleted of methylated nucleic acids can partially deplete methylated nucleic acid molecules. In some cases, a sequencing library depleted of methylated nucleic acids can deplete nucleic acids having methylated nucleotides in one or more specific regions (e.g., CpG islands or CpG island shores) of a genomic sequence.

[0104] A plurality of nucleic acid molecules (e.g., a plurality of nucleic acid molecules derived from a biological sample) can be hypermethylated and enriched using a conjugate, for example, as described herein, to form a hypermethylated sequencing library that can be used as a background, rather than a full genome background, for analysis of cfDNA. In some cases, DNA can be hypermethylated before using a conjugate to create a sequencing library with background. Background sequencing libraries can include a collection of background genomic regions enriched by a conjugate.

[0105] The present disclosure provides methods and techniques for determining the sequence of nucleotide bases in one or more polynucleotides. A polynucleotide can be, for example, a nucleic acid molecule such as deoxyribonucleic acid (DNA) or ribonucleic acid (RNA), including variants or derivatives thereof (e.g., single-stranded DNA). Sequencing can be performed by various currently available systems, such as, but not limited to, Pacific Biosciences or Life Technologies Sequencing system. Further, any sequencing method providing fragment length, such as paired end sequencing, can be utilized. Alternatively or additionally, sequencing can be performed using nucleic acid amplification, polymerase chain reaction (PCR) (e.g., digital PCR, quantitative PCR or real-time PCR) or isothermal amplification. Such a system can provide a plurality of original genetic data corresponding to the genetic information of an object (e.g., a person), as generated by a sample provided by the system from an object. In some instances, such a system provides sequencing reading (also referred to herein as "reading"). Reading can include a nucleic acid base string corresponding to the sequence of a nucleic acid molecule that has been sequenced. In some cases, the systems and methods provided herein can be used together with proteomics information.

[0106] In some embodiments, sequencing reads are obtained via a next generation sequencing method or a next generation sequence method. In some embodiments, the sequencing method includes cfMeDIP sequencing, for example, including steps or systems as described by Shen et al. ("Sensitive tumor detection and classification using plasma cell-free DNA methylomes," (2018) Nature), which is incorporated herein in its entirety. In some embodiments, sequencing can be performed using methyl-CpG-binding domain sequencing (MBD-seq). In some cases, MBD-seq may include capturing (e.g., via a binder, such as an antibody specific to a class of methylated nucleotides) double-stranded methylated DNA fragments for sequencing a methylation-enriched DNA fragment library. In some embodiments, the sequencing method includes personalized cancer profiling (CAPP-Seq) via deep sequencing, which is a next generation sequencing-based method for quantifying circulating DNA (ctDNA) in cancer. In some embodiments, the sequencing method includes chromatin immunoprecipitation sequencing (ChIP-Seq). This method can be generalized for any cancer type known to have recurrent mutations and can detect one mutant DNA molecule in 10,000 healthy DNA molecules. In some embodiments, sequencing includes bisulfite sequencing. Alternatively, in some embodiments, sequencing does not include bisulfite sequencing.

[0107] Sequencing can include analysis of the results of sequencing methods. In some embodiments, sequencing analysis can include model-based analysis using ChIP-Seq (MACS) software. The MACS algorithm captures the impact of genomic complexity to assess the significance of enriched regions. Sequencing analysis can include identifying wide peaks (wide peak calls) or identifying narrow peaks (narrow peak calls). In some cases, narrow peak calls can be used to identify hypermethylated regions. The MACS algorithm can generate peak annotations that mark regions of interest by determining significantly different signals between two samples (e.g., between a sample and a background region). In some cases, wide peak calls can be used to identify hypermethylated regions. In some cases, narrow peak calls and wide peak calls will identify the same hypermethylated regions. In some cases, narrow peak calls and wide peak calls will identify different hypermethylated regions. The supplementary processing of peak annotations can merge regions of interest in multiple samples to produce higher resolution and more accurate results. More accurate results can include regions that are rarely read in the sample but can be used to distinguish between healthy and disease samples. In some cases, sequencing analysis can be illustrated using a gene heat map. Alternatively or additionally, sequencing analysis can be illustrated using uniform manifold approximation and projection (UMAP).

[0108] In some cases, a sample or a portion thereof (e.g., a plurality of nucleic acids of a sample) can be subjected to library preparation prior to sequencing. Briefly, after end repair and A-tailing, the sample is ligated to nucleic acid adapters and digested using enzymes.

[0109] In some embodiments, sequencing includes modification of a nucleic acid molecule or a fragment thereof, for example, by attaching a barcode, a unique molecular identifier (UMI), or another tag to the nucleic acid molecule or a fragment thereof. Attaching a barcode, UMI, or tag to one end of a nucleic acid molecule or a fragment thereof can facilitate analysis of the nucleic acid molecule or fragment thereof after sequencing. In some embodiments, the barcode is a unique barcode (e.g., UMI). In some embodiments, the barcode is non-unique, and the barcode sequence can be used in conjunction with endogenous sequence information, such as the start and end sequences of the target nucleic acid (e.g., the target nucleic acid is sandwiched on both sides by the barcode and the barcode sequence, combined with the sequence at the beginning and end of the target nucleic acid to form a uniquely labeled molecule). The barcode, UMI, or tag can be a known sequence for associating a polynucleotide or a fragment thereof with an input or target nucleic acid molecule or a fragment thereof. The barcode, UMI, or tag can include natural nucleotides or non-natural (e.g., modified) nucleotides (e.g., as described herein). The barcode sequence can be included in an adapter sequence so that the barcode sequence can be included in the sequencing read. The length of the barcode sequence may include at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or more nucleotides. In some cases, a barcode sequence may be of sufficient length and may be sufficiently different from another barcode sequence to allow identification of a sample based on the barcode sequence associated therewith. A barcode sequence or combination of barcode sequences may be used to tag and subsequently identify "original" nucleic acid molecules or fragments thereof (e.g., nucleic acid molecules or fragments thereof present in a sample from a subject). In some cases a barcode sequence or combination of barcode sequences is used in conjunction with endogenous sequence information to identify the original nucleic acid molecule or fragment thereof. For example, a barcode sequence or combination of barcode sequences may be used with an endogenous sequence adjacent to a barcode, UMI, or tag (e.g., the beginning and end of an endogenous sequence).

[0110] As described herein, the prepared library can be combined with filler nucleic acid (e.g., filler λDNA) to minimize the effect of low abundance ctDNA in the prepared library and generate a mixed sample. In some embodiments, when the disease and / or condition is a local (non-metastatic) cancer, the amount of ctDNA is low and cannot be easily and accurately measured and quantified. The mixed sample can be made to at least about 50ng, 80ng, 100ng, 120ng, 150ng or 200ng and further enriched.

[0111] Processing nucleic acid molecules or their fragments may include performing nucleic acid amplification. For example, any type of nucleic acid amplification reaction may be used to amplify a target nucleic acid molecule or its fragment and generate an amplified product. Non-limiting examples of nucleic acid amplification methods include reverse transcription, primer extension, polymerase chain reaction (PCR), ligase chain reaction, asymmetric amplification, rolling circle amplification, and multiple displacement amplification (MDA). Examples of PCR include, but are not limited to, quantitative PCR, real-time PCR, digital PCR, emulsion PCR, hot start PCR, multiplex PCR, asymmetric PCR, nested PCR, and assembly PCR. Nucleic acid amplification may involve one or more reagents, such as one or more primers, probes, polymerases, buffers, enzymes, and deoxyribonucleotides. Nucleic acid amplification may be isothermal, or may include thermal cycling. And / or have a length of endogenous sequence.

[0112] Conjugate

[0113] Binding substance can be used to deplete nucleic acid molecule colonies (e.g., multiple nucleic acid molecules derived from biological samples). In some cases, binding substance can be used to deplete multiple nucleic acid molecules of one or more nucleic acid molecules whose methylation level is equal to or higher than the threshold methylation level (e.g., by binding to one or more methylated nucleotides of one or more nucleic acid molecules). Binding substance can be used to enrich nucleic acid molecule colonies (e.g., multiple nucleic acids derived from biological samples). In some cases, binding substance can show reduced non-specific binding levels with non-methylated nucleotides or non-methylated genomic regions.

[0114] In some cases, the binder can be specific for one or more methylated nucleotide species (e.g., 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC), 4-methylcytosine (4mC), or 6-methyladenine (6mA). In some cases, the binder can be selected from anti-5-methylcytosine antibodies or derivatives thereof, anti-5-carboxycytosine antibodies or derivatives thereof, anti-5-formylcytosine antibodies or derivatives thereof, anti-5-hydroxymethylcytosine antibodies or derivatives thereof, anti-3-methylcytosine antibodies or derivatives thereof, and any combination thereof. In some cases, the binder can be an anti-5-methylcytosine antibody or derivatives thereof. In some embodiments, the binder is a protein comprising a methyl CpG binding domain. One such exemplary protein is the MBD2 protein. As used herein, "methyl CpG binding domain (MBD)" refers to certain domains of proteins and enzymes that are approximately 70 residues long and bind to DNA containing one or more symmetrically methylated CpGs. MBD of MeCP2, MBD1, MBD2, MBD4 and BAZ2 mediate binding to DNA and, in the case of MeCP2, MBD1 and MBD2, preferentially to methylated CpG. The human proteins MECP2, MBD1, MBD2, MBD3 and MBD4 comprise a family of nuclear proteins associated with the presence of a methyl-CpG binding domain (MBD) in each. Each of these proteins, except MBD3, is able to bind specifically to methylated DNA.

[0115] In other embodiments, the binding agent is an antibody, and capturing the cell-free methylated DNA includes immunoprecipitating the cell-free methylated DNA using the antibody. As used herein, "immunoprecipitation" refers to a technique in which an antigen (such as a polypeptide and a nucleotide) is precipitated from a solution using an antibody that specifically binds to a specific antigen. This process can be used to separate and concentrate specific proteins or DNA from a sample and requires that the antibody be coupled to a solid substrate at a certain point in the process. The solid substrate includes, for example, beads, such as magnetic beads. Other types of beads and solid substrates can be used.

[0116] For example, a 5-mC antibody (e.g., wherein the 5-mC antibody specifically binds to 5-methylcytosine) can be used as a binder. For the immunoprecipitation process, in some embodiments, at least 0.05 μg of the antibody is added to the sample; alternatively, at least 0.16 μg of the antibody is added to the sample. In some cases, 0.05 μg to 0.80 μg, 0.16 μg to 0.80 μg, 0.40 μg to 0.80 μg, 0.16 μg to 0.40 μg, 0.10 μg to 0.80 μg, 0.20 μg to 0.60 μg, 0.30 μg to 0.50 μg, or 0.40 μg to 0.50 μg of the antibody can be used. In order to confirm the immunoprecipitation reaction, in some embodiments, the method described herein also includes a step of adding a second amount of control DNA to the sample.

[0117] Methylation profile

[0118] The present disclosure provides methods and systems for generating methylation profiles of objects suffering from diseases and / or conditions or suspected of suffering from such diseases and / or conditions, wherein the methylation profile can be used to determine whether the object suffers from the disease and / or condition or is at risk of suffering from the disease and / or condition. In some cases, the methylation profile may include analysis (e.g., including sequencing) of multiple nucleic acids (e.g., multiple nucleic acid molecules of a sequencing library exhausted, as described herein). In some cases, the methylation profile may include the detection of methylated nucleotides and / or the quantification of methylated nucleotide counts. In some cases, the methylation profile may include the determination of methylation signals, for example, in a nucleic acid population of a sequencing library exhausted, as described herein. In some cases, the methylation profile is compared with a full genome background profile. In some cases, the methylation profile is compared with a background profile created using high methylation cfDNA.

[0119] Genomic mutation spectrum

[0120] The present disclosure provides methods and systems for generating a mutation profile of a subject having a disease and / or condition or suspected of having such a disease and / or condition, wherein the methylation profile can be used to determine whether the subject has the disease and / or condition or is at risk of having the disease and / or condition. The samples disclosed herein can be subjected to library preparation and next generation deep sequencing, for example, to a depth of 1 million (M) to 60M single reads, 10M to 60M single reads, 10M to 100M single reads, 40M to 60M single reads, 40M to 100M single reads, 60M to 100M single reads, 60M to 200M single reads, 1M to 10M single reads, 1M to 40M single reads, 1M single reads to 100M single reads, 1M single reads to 200M single reads, at least 1M single reads, at least 10M single reads, at least 40M single reads, at least 60M single reads, at least 100M single reads, or at least 200M single reads. In some cases, sequencing can be performed at a lower sequencing depth (e.g., 10M single reads, 20M single reads, 30M single reads, 40M single reads, 1M single reads to 10M single reads, 10M single reads to 20M single reads, 20M single reads to 30M single reads, 30M single reads to 40M single reads, up to 10M single reads, up to 20M single reads, up to 30M single reads, or up to 40M single reads). In some cases, the samples disclosed herein can be between 0.1X and 100X, 0.1X and 60X, 0.1X and 40X, 0.1X and 30X, 0.1X and 20X, 0.1X and 10X, 0.1X and 5.0X, 0.5X and 100X, 0.5X and 60X, 0.5X and 40X, 0.5X and 30X, 0.5X and 20X, 0.5X and 10X, 0.5X and 5.0X, 1.0X and 100X, 1.0X and 60X, 1.0X and 40X, 1.0X and 30X, 1.0X and 20X, 1.0X and 10X, 1.0X and 5.0X, at least 0.1X, at least The depth of sequencing may be at least 0.5X, at least 1.0X, at least 2.0X, at least 3.0X, at least 4.0X, at least 5.0X, at least 10.0X, at least 20.0X, at least 30.0X, at least 40.0X, at least 50.0X, at least 60.0X, at least 100X, at least 200X, at most 0.1X, at most 0.5X, at most 1.0X, at most 2.0X, at most 3.0X, at most 4.0X, at most 5.0X, at most 10.0X, at most 20.0X, at most 30.0X, at most 40.0X, at most 50.0X, at most 60.0X, at most 100X, or at most 200X. Multiple sequencing reads are generated and analyzed. In some embodiments, deep sequencing can be configured to maximize the identification of genomic mutations associated with diseases and / or conditions.

[0121] In some embodiments, the relative measurement of ctDNA abundance is calculated by average mutant allele fraction (MAF). In some embodiments, the average MAF of the mutation identified in the object and included in his / her mutation spectrum is at least about 0.01% to at least about 10% range. In some cases, the MAF of the ctDNA score of the sample can be at least 0.01%, 0.02%, 0.03%, 0.04%, 0.05%, 0.06%, 0.07%, 0.08%, 0.09%, 0.1%, 0.15%, 0.2%, 0.5%, 1%, 1.5%, 2%, 2.5%, 3%, 3.5%, 4%, 4.5%, 5%, 5.5%, 6%, 6.5%, 7%, 7.5%, 8%, 8.5%, 9%, 9.5%, 10%, or any percentage therebetween.

[0122] In some embodiments, the mutation spectrum of the object generated can be generated from the sequencing results. In some embodiments, the mutation spectrum includes genetic polymorphisms, such as missense variants, nonsense variants, deletion variants, insertion variants, repeat variants, inversion variants, frameshift variants or repeat sequence amplification variants. In some embodiments, the mutation spectrum may include mutation variants of scores of cell-free nucleic acid molecules derived from a specific size range. The present disclosure provides methods and systems for generating mutation spectrums of objects suffering from diseases and / or conditions or suspected of suffering from such diseases and / or conditions, wherein methylation profiles may be used to determine whether the object suffers from the disease and / or condition or whether it is in the risk of suffering from a disease and / or condition. Generating a genomic mutation spectrum may include performing library preparation and next generation deep sequencing (e.g., MeDIP-seq) to multiple nucleic acid molecules. Multiple sequencing reads may be generated and analyzed, and in some cases, deep sequencing may be configured to maximize identification of genomic mutations associated with diseases and / or conditions. For example, a group of classical cancer driver genes may be included in the selector for sequencing result analysis. In some embodiments, including genes with no known driver effects in a particular cancer type in the analysis of sequencing data can increase the sensitivity of ctDNA detection.

[0123] In some embodiments, the relative measurement of ctDNA abundance is calculated by average mutant allele fraction (MAF). In some embodiments, the average MAF of the mutation identified in the object and included in his / her mutation spectrum is at least about 0.01% to at least about 10%. The ctDNA score of the sample disclosed herein is about at least 0.01%, 0.02%, 0.03%, 0.04%, 0.05%, 0.06%, 0.07%, 0.08%, 0.09%, 0.1%, 0.15%, 0.2%, 0.5%, 1%, 1.5%, 2%, 2.5%, 3%, 3.5%, 4%, 4.5%, 5%, 5.5%, 6%, 6.5%, 7%, 7.5%, 8%, 8.5%, 9%, 9.5%, 10% or any percentage therebetween.

[0124] In some embodiments, the generated mutation spectrum of the subject does not include mutation variants derived from a source of cell-free nucleic acid molecules of the biological sample. In some embodiments, the mutation spectrum includes genetic polymorphisms such as missense variants, nonsense variants, deletion variants, insertion variants, duplication variants, inversion variants, frameshift variants, or repeat expansion variants. In some embodiments, the mutation spectrum can include mutation variants derived from a fraction of cell-free nucleic acid molecules of a specific size range.

[0125] Fragment length spectrum

[0126] In some embodiments, the length of the ctDNA fragments is shorter than that of the cell-free nucleic acid molecules derived from healthy subjects. In some embodiments, the length of the ctDNA comprising at least one mutation is shorter than that of the cell-free nucleic acid molecules comprising the corresponding reference allele.

[0127] In some embodiments, sequencing does not utilize bisulfite sequencing because it causes degradation of ctDNA fragments and prevents the preservation of ctDNA length distribution. In some embodiments, the fragment length of multiple nucleic acids disclosed herein (e.g., comprising a mixture cfDNA molecule derived from a tumor or cancer tissue and a healthy tissue, comprising only cfDNA molecules derived from healthy tissue and / or comprising only ctDNA) can be 1 to about 800 base pairs (bp), about 50bp to about 800bp, about 100bp to about 200bp, about 120bp to about 150bp, about 60 to about 500bp, about 80 to about 300bp, 90 to about 250bp, 80 to 170bp, or about 100 to about 150bp. In some embodiments, the fragment length of multiple nucleic acids disclosed herein (e.g., comprising a mixture of cfDNA molecules derived from tumor or cancer tissue and healthy tissue, comprising only cfDNA molecules derived from healthy tissue and / or comprising only ctDNA) can be at least 800 base pairs (bp), at least 700 base pairs (bp), at least 600 base pairs (bp), at least 500 base pairs (bp), at least 400 base pairs (bp), at least 300 base pairs (bp), at least 200 base pairs (bp), at least 150 base pairs (bp), at least 100 base pairs (bp), or at least 50 base pairs. In some embodiments, the fragment length of multiple nucleic acids disclosed herein (e.g., comprising a mixture cfDNA molecules derived from tumor or cancer tissue and healthy tissue, comprising only cfDNA molecules derived from healthy tissue and / or comprising only ctDNA) can be at most 800 base pairs (bp), at most 700 base pairs (bp), at most 600 base pairs (bp), at most 500 base pairs (bp), at most 400 base pairs (bp), at most 300 base pairs (bp), at most 200 base pairs (bp), at most 150 base pairs (bp), at most 100 base pairs (bp) or at most 50 base pairs. In some embodiments, the present disclosure provides enrichment of cell-free nucleic acid samples based on the selection of cell-free molecules of a certain size. In some embodiments, multimodal analysis includes utilizing the mutation spectrum and fragment length spectrum described herein by selectively including multiple nucleic acid molecules in the mutation spectrum based on their fragment length. In some embodiments, the multimodal analysis comprises utilizing the methylation profile and the fragment length profile described herein by selectively including a plurality of nucleic acid molecules in the methylation profile based on their fragment lengths. In some embodiments, the multimodal analysis comprises utilizing the mutation profile, the methylation profile, and the fragment length profile together by selectively including a plurality of nucleic acid molecules in the mutation profile based on their fragment lengths and by selectively including a plurality of nucleic acid molecules in the methylation profile based on their fragment lengths, respectively.

[0128] Tumor Detection and Prognosis

[0129] The present disclosure provides methods and systems for determining whether a subject has a disease or is at risk of having a disease, wherein the methods and systems include sequencing a plurality of nucleic acid molecules derived from a cell-free nucleic acid sample obtained from a subject to generate at least one of (i) a methylation profile, (ii) a mutation profile, and (iii) a fragment length profile; and processing the at least one profile to determine whether the subject has the disease or is at risk of the disease with a sensitivity of at least 80% or a specificity of at least about 90%, wherein the cell-free nucleic acid sample contains less than 30 ng / ml of the plurality of nucleic acid molecules. In some embodiments, the sensitivity is at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any percentage between these numbers. In some embodiments, the specificity is at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any percentage in between these numbers.

[0130] In some embodiments, the methods and systems may include sequencing a plurality of nucleic acid molecules derived from a cell-free nucleic acid sample obtained from the subject to generate at least two of (i) a methylation profile, (ii) a mutation profile, and (iii) a fragment length profile. The method provides a sensitivity of at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any percentage between these numbers. In some embodiments, the sensitivity when using two profiles is increased by at least about 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, or any percentage between these numbers compared to the sensitivity when using one profile. In some embodiments, the sensitivity when using three spectra is increased by at least about 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, or any percentage between these numbers compared to the sensitivity when using two spectra.

[0131] Further, the method provides a specificity of at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9% or any percentage between these numbers. In some embodiments, the specificity when using two spectra is increased by at least about 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10% or any percentage between these numbers compared to the specificity when using one spectrum. In some embodiments, the specificity when using three spectra is increased by at least about 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10% or any percentage between these numbers compared to the specificity when using two spectra.

[0132] The present disclosure provides methods and systems for processing a cell-free nucleic acid sample of a subject to determine whether the subject has a disease or is at risk of having a disease, the methods and systems comprising providing the cell-free nucleic acid sample comprising a plurality of nucleic acid molecules; sequencing the plurality of nucleic acid molecules or derivatives thereof to generate a plurality of sequencing reads; computer processing the plurality of sequencing reads to identify (i) methylation profiles, (ii) mutation profiles, and (iii) fragment length profiles of the plurality of nucleic acid molecules; and determining whether the subject has the disease or is at risk of having the disease using at least the methylation profiles, the mutation profiles, and the fragment length profiles. In some embodiments, the method provides a sensitivity of at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any percentage in between these numbers. The method provides a specificity of at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any percentage in between these numbers.

[0133] The present disclosure provides methods and systems for determining the tissue origin of a tumor, including identifying a nucleotide sequence specific to a specific cancer (e.g., breast cancer, colon cancer, prostate cancer, HSNCC or lung cancer), wherein the fraction of cell-free nucleic acid molecules is from the sequence. In some embodiments, the fraction of cell-free nucleic acid molecules is derived from ctDNA. In some embodiments, the sensitivity provided by the method is at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9% or any percentage between these numbers. The methods provide a specificity of at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any percentage in between these numbers.

[0134] The present disclosure describes methods and systems for providing a prognosis to an object after receiving treatment for a disease and / or condition. For example, treatment includes surgical resection of a tumor, chemotherapy designed for a specific type of cancer, radiotherapy, or immunotherapy (e.g., TCR, CAR, etc.). In some embodiments, the method or system includes sequencing a plurality of nucleic acid molecules derived from a cell-free nucleic acid sample obtained from the object to generate at least one spectrum of (i) a methylation spectrum, (ii) a mutation spectrum, and (iii) a fragment length spectrum; and monitoring or detecting minimal residual disease (MRD) based on at least one spectrum.

[0135] Once a subject is accurately diagnosed and receives treatment to treat cancer, such as surgical resection, chemotherapy, radiation therapy, etc., it may be important to monitor the effectiveness of the treatment and predict the patient's survival rate. Further, it may be important to detect minimal residual disease of cancer cells.

[0136] In some embodiments, the method further comprises the step of adding a second amount of control DNA to the sample to confirm the immunoprecipitation reaction.

[0137] As used herein, a "control" may include both positive and negative controls, or include at least a positive control.

[0138] In some embodiments, the method further comprises the step of adding a second amount of control DNA to the sample to confirm capture of the cell-free methylated DNA.

[0139] In some embodiments, identifying the presence of DNA from a cancer cell further comprises identifying the tissue from which the cancer cell originated.

[0140] In some cases, tumor tissue sampling may be challenging or have significant risks, in which case it may be desirable to diagnose and / or type cancer without the need for tumor tissue sampling. For example, lung tumor tissue sampling may require invasive procedures such as mediastinoscopy, thoracotomy, or percutaneous needle biopsy; these procedures may result in the need for hospitalization, chest tubes, mechanical ventilation, antibiotics, or other medical interventions. Some individuals may not undergo the invasive procedures required for tumor tissue sampling due to medical comorbidities or preferences. In some cases, the actual process of tumor tissue acquisition may depend on the suspected cancer subtype. In other cases, cancer subtypes may evolve over time in the same individual; serial assessments using invasive tumor tissue sampling procedures are often impractical and not well tolerated by patients. Therefore, non-invasive cancer typing via blood testing may have many advantageous applications in clinical oncology practice.

[0141] Therefore, in some embodiments, identifying the cancer cell source tissue also includes identifying cancer subtypes. In some cases, cancer subtypes are based on staging (e.g., early cancer treated with surgery and advanced cancer treated with chemotherapy), histology (e.g., small cell carcinoma and adenocarcinoma and squamous cell carcinoma in lung cancer), gene expression patterns or transcription factor activity (e.g., ER status in breast cancer), copy number abnormalities (e.g., HER2 status in breast cancer), specific rearrangements (e.g., FLT3 in AML), specific gene point mutation status (e.g., IDH gene point mutation) and DNA methylation patterns (e.g., MGMT gene promoter methylation in brain cancer) to distinguish cancer.

[0142] In some embodiments, the comparison can be made within the whole genome. In other embodiments, the comparison can be limited to a specific regulatory region from the whole genome, such as, but not limited to, long interspersed nuclear elements (LINEs), short interspersed nuclear elements (SINEs), long terminal repeats (LTRs), FANTOM5 enhancers, CpG islands, CpG banks, CpG racks, or any combination of the foregoing. In other embodiments, the comparison can be limited to a background defined by enrichment of high methylation regions from the whole genome.

[0143] In some embodiments, the methods herein are used to detect cancer.

[0144] In some embodiments, the methods herein are used to monitor therapy for cancer.

[0145] Data analysis system and method

[0146] The methods and systems disclosed herein may include algorithms or their uses. One or more algorithms may be used to classify one or more samples from one or more objects. One or more algorithms may be applied to data from one or more samples. The data may include biomarker expression data. In some embodiments, the method or system includes sequencing a plurality of nucleic acid molecules derived from a cell-free nucleic acid sample obtained from the object to generate at least one spectrum in (i) a methylation spectrum, (ii) a mutation spectrum, and (iii) a fragment length spectrum; and monitoring or detecting minimal residual disease (MRD) based on at least one spectrum. The methods disclosed herein may include assigning classification to one or more samples from one or more objects. Assigning classification to samples may include applying algorithms to methylation spectra, mutation spectra, and fragment length spectra. In some cases, at least one spectrum is input to a data analysis system, which includes a training algorithm for classifying samples obtained from an object suffering from a disease or minor injury.

[0147] The data analysis system can be a trained algorithm. The algorithm can include a linear classifier. In some cases, the linear classifier includes one or more of a linear discriminant analysis, a Fisher linear discriminant, a naive Bayes classifier, a logistic regression, a perceptron, a support vector machine, or a combination thereof. The linear classifier can be a support vector machine (SVM) algorithm. The algorithm can include a bidirectional classifier. The bidirectional classifier can include one or more decision trees, random forests, Bayesian networks, support vector machines, neural networks, or logistic regression algorithms.

[0148] The algorithm may include one or more linear discriminant analysis (LDA), basic perceptron, elastic net, logistic regression, (kernel) support vector machine (SVM), diagonal linear discriminant analysis (DLDA), Golub classifier, Parzen-based, (kernel) Fisher discriminant classifier, k nearest neighbor, iterative RELIEF, classification tree, maximum likelihood classifier, random forest, nearest centroid, microarray prediction analysis (PAM), k median clustering, fuzzy C-means clustering, Gaussian mixture model, quantitative response (GR), gradient boosting method (GBM), elastic net logistic regression, logistic regression or a combination thereof. The algorithm may include a diagonal linear discriminant analysis (DLDA) algorithm. The algorithm may include a nearest centroid algorithm. The algorithm may include a random forest algorithm. In some embodiments, for the distinction between pre-eclampsia and non-pre-eclampsia, the performance of logistic regression, random forest and gradient boosting method (GBM) is better than the performance of linear discriminant analysis (LDA), neural network and support vector machine (SVM).

[0149] The present disclosure provides methods and systems for determining whether a subject has a disease or is at risk of having a disease, wherein the methods and systems include sequencing a plurality of nucleic acid molecules derived from a cell-free nucleic acid sample obtained from the subject to generate at least one of (i) a methylation profile, (ii) a mutation profile, and (iii) a fragment length profile; and processing the at least one profile to determine whether the subject has the disease or is at risk of the disease with a sensitivity of at least 80% or a specificity of at least about 90%, wherein the cell-free nucleic acid sample contains less than 30 ng / ml of the plurality of nucleic acid molecules. In some embodiments, the sensitivity is at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any percentage between these numbers. In some embodiments, the specificity is at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any percentage in between these numbers.

[0150] In some embodiments, the methods and systems may include sequencing a plurality of nucleic acid molecules derived from a cell-free nucleic acid sample obtained from the subject to generate at least two profiles of (i) a methylation profile, (ii) a mutation profile, and (iii) a fragment length profile. The method provides a sensitivity of at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any percentage between these numbers. In some embodiments, the sensitivity when using two profiles is increased by at least about 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, or any percentage between these numbers compared to the sensitivity when using one profile. In some embodiments, the sensitivity when using three spectra is increased by at least about 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, or any percentage between these numbers compared to the sensitivity when using two spectra.

[0151] Further, the method provides a specificity of at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9% or any percentage between these numbers. In some embodiments, the specificity when using two spectra is increased by at least about 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10% or any percentage between these numbers compared to the specificity when using one spectrum. In some embodiments, the specificity when using three spectra is increased by at least about 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10% or any percentage between these numbers compared to the specificity when using two spectra.

[0152] The present disclosure provides methods and systems for processing a cell-free nucleic acid sample of a subject to determine whether the subject has a disease or is at risk of having a disease, the methods and systems comprising providing the cell-free nucleic acid sample comprising a plurality of nucleic acid molecules; sequencing the plurality of nucleic acid molecules or derivatives thereof to generate a plurality of sequencing reads; computer processing the plurality of sequencing reads to identify (i) methylation profiles, (ii) mutation profiles, and (iii) fragment length profiles of the plurality of nucleic acid molecules; and determining whether the subject has the disease or is at risk of having the disease using at least the methylation profiles, the mutation profiles, and the fragment length profiles. In some embodiments, the method provides a sensitivity of at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any percentage in between these numbers. The method provides a specificity of at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any percentage in between these numbers.

[0153] The present disclosure describes methods and systems for providing a prognosis to an object after receiving treatment for a disease and / or condition. For example, treatment includes surgical resection of a tumor, chemotherapy designed for a specific type of cancer, radiotherapy, or immunotherapy (e.g., TCR, CAR, etc.). In some embodiments, the method or system includes sequencing a plurality of nucleic acid molecules derived from a cell-free nucleic acid sample obtained from the object to generate at least one spectrum of (i) a methylation spectrum, (ii) a mutation spectrum, and (iii) a fragment length spectrum; and monitoring or detecting minimal residual disease (MRD) based on at least one spectrum.

[0154] Computer Systems

[0155] The present disclosure provides a computer system programmed to implement the method of the present disclosure. Fig.10 A computer system 1001 is shown that is programmed or otherwise configured to identify, analyze, and compare sequences of highly methylated cell-free nucleic acids (e.g., cfDNA). The computer system 1001 can be an electronic device of a user or a computer system remotely located relative to an electronic device. The electronic device can be a mobile electronic device.

[0156] The computer system 1001 includes a central processing unit (CPU, also referred to herein as a "processor" and "computer processor") 1005, which may be a single-core or multi-core processor or multiple processors for parallel processing. The computer system 1001 also includes a memory or memory location 1010 (e.g., random access memory, read-only memory, flash memory), an electronic storage unit 1015 (e.g., a hard disk), a communication interface 1020 (e.g., a network adapter) for communicating with one or more other systems, and peripherals 1025 (such as cache, other memory, data storage, and / or an electronic display adapter). The memory 1010, the storage unit 1015, the interface 1020, and the peripherals 1025 communicate with the CPU 1005 via a communication bus (solid lines) such as a motherboard. The storage unit 1015 may be a data storage unit (or data repository) for storing data. The computer system 1001 may be operably coupled to a computer network ("network") 1030 by means of the communication interface 1020. The network 1030 may be the Internet, the Internet and / or an extranet, or an intranet and / or an extranet that communicates with the Internet. The network 1030 is a telecommunications and / or data network in some cases. The network 1030 may include one or more computer servers that may support distributed computing such as cloud computing. The network 1030 may implement a peer-to-peer network with the aid of the computer system 1001 in some cases, which may enable a device coupled to the computer system 1001 to act as a client or a server.

[0157] The CPU 1005 may execute a sequence of machine-readable instructions that may be embodied in a program or software. The instructions may be stored in a memory location such as the memory 1010. The instructions may be directed to the CPU 1005, which may then program or otherwise configure the CPU 1005 to implement the methods of the present disclosure. Examples of operations performed by the CPU 1005 may include fetching, decoding, executing, and writing back.

[0158] CPU 1005 may be part of a circuit such as an integrated circuit. One or more other components of system 1001 may be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

[0159] Storage unit 1015 may store files such as drivers, libraries, and saved programs. Storage unit 1015 may store user data, such as user preferences and user programs. Computer system 1001 may include one or more additional data storage units in some cases that are external to computer system 1001, such as located on a remote server that communicates with computer system 1001 via an intranet or the Internet.

[0160] Computer system 1001 can communicate with one or more remote computer systems via network 1030. For example, computer system 1001 can communicate with a user's remote computer system. Examples of remote computer systems include personal computers (e.g., portable PCs), tablet computers or tablet PCs (e.g., iPad, Galaxy Tab), phones, smartphones (e.g. iPhone, Android-enabled devices, ) or a personal digital assistant. A user can access the computer system 1001 via the network 1030.

[0161] The methods described herein may be implemented by machine (e.g., computer processor) executable code stored in an electronic storage location of the computer system 1001, such as, for example, the memory 1010 or the electronic storage unit 1015. The machine executable code or machine readable code may be provided in the form of software. During use, the code may be executed by the processor 1005. In some cases, the code may be retrieved from the storage unit 1015 and stored on the memory 1010 for ready access by the processor 1005. In some cases, the electronic storage unit 1015 may not be included, and the machine executable instructions are stored on the memory 1010.

[0162] The code may be precompiled and configured for use by a machine having a processor suitable for executing the code, or may be compiled during runtime. The code may be provided in a programming language that may be selected to enable the code to be executed in a precompiled or as-compiled manner.

[0163] Various aspects of the systems and methods provided herein, such as computer system 1001, can be embodied in programming. Various aspects of the technology can be considered as "products" or "articles", which are generally in the form of machine (or processor) executable code and / or related data carried or embodied on a type of machine-readable medium. The machine executable code can be stored in an electronic storage unit, such as a memory (e.g., read-only memory, random access memory, flash memory) or a hard disk. "Storage" type media can include any or all tangible memories, processors, etc. of a computer, or its associated modules, such as various semiconductor memories, tape drives, disk drives, etc., which can provide non-temporary storage for software programming at any time. All or part of the software can sometimes communicate through the Internet or various other telecommunications networks. For example, such communication can enable software to be loaded from one computer or processor to another computer or processor, such as from a management server or host to a computer platform of an application server. Therefore, another type of medium that can carry software elements includes light waves, radio waves, and electromagnetic waves, such as across physical interfaces between local devices, through wired and optical landline networks, and through various air links. Physical elements that carry such waves, such as wired or wireless links, optical links, etc., can also be considered as media that carry the software. As used herein, unless limited to non-transitory, tangible "storage" media, terms such as computer or machine "readable media" refer to any medium that participates in providing instructions to a processor for execution.

[0164] Thus, machine-readable media such as computer executable code may take a variety of forms, including but not limited to tangible storage media, carrier media, or physical transmission media. Non-volatile storage media include, for example, optical or magnetic disks, such as any storage device in any computer, and the like, such as may be used to implement a database such as shown in the accompanying drawings. Volatile storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables, copper wire, and optical fiber, including the wires that make up a bus within a computer system. Carrier transmission media may take the form of electrical or electromagnetic signals, or acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Thus, common forms of computer readable media include, for example: a floppy disk, a flexible disk, a hard disk, a magnetic tape, any other magnetic medium, a CD-ROM, a DVD or DVD-ROM, any other optical medium, a punched card stock tape, any other physical storage medium having a pattern of holes, a RAM, a ROM, a PROM and an EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave that transmits data or instructions, a cable or link that transmits such a carrier wave, or any other medium from which a computer can read programming code and / or data. Many of these forms of computer readable media may participate in carrying one or more sequences of one or more instructions to a processor for execution.

[0165] Computer system 1001 may include or be in communication with electronic display 1035, which includes user interface (UI) 1040. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces.

[0166] The methods and systems of the present disclosure may be implemented by one or more algorithms. The algorithms may be implemented by software when executed by the central processing unit 1005. For example, the algorithms may identify, sequence, and analyze hypermethylated DNA regions.

[0167] Although preferred embodiments of the present invention have been shown and described herein, 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 the specific examples provided in this specification. Although the present invention has been described with reference to the above description, the description and illustration of the embodiments herein are not intended to be interpreted in a restrictive sense. Those skilled in the art will now think of many variations, changes and substitutions without departing from the present invention. In addition, it should be understood that all aspects of the present invention are not limited to the specific description, configuration or relative proportion described herein, which depends on a variety of conditions and variables. It should be understood that various alternatives of the embodiments of the present invention described herein can be adopted when practicing the present invention. Therefore, it is expected that the present invention should also cover any such alternatives, modifications, variations or equivalents. It is intended that the following claims define the scope of the present invention and thus cover methods and structures and their equivalents within the scope of these claims.

[0168] Alternative Implementation

[0169] By reviewing the numbered embodiments described herein, the present disclosure may be further understood. The various embodiments contemplated herein may include, but are not necessarily limited to, one or more of the following and combinations thereof:

[0170] 1. A method for processing cell-free nucleic acids, comprising: a. providing a subject's cell-free nucleic acid molecules; b. subjecting the cell-free nucleic acid molecules or their derivatives to conditions sufficient to increase the methylation level of the cell-free nucleic acid molecules or their derivatives, thereby producing hypermethylated cell-free nucleic acid molecules; and c. identifying the sequence of the hypermethylated cell-free nucleic acid molecules or their derivatives.

[0171] 2. The method of claim 1, wherein the cell-free nucleic acid molecule in (a) is obtained from the blood of the subject.

[0172] 3. The method of claim 2, wherein the blood comprises plasma, and wherein the cell-free nucleic acid molecules in (a) are obtained from the plasma.

[0173] 4. The method of claim 1, wherein the identifying of (c) comprises sequencing the hypermethylated cell-free nucleic acid molecules.

[0174] The method of claim 4 , wherein the sequencing comprises detecting methylation patterns.

[0175] The method of claim 5 , wherein detecting the methylation pattern comprises identifying hypermethylated regions.

[0176] 7. The method according to any one of claims 1-6, further comprising immunoprecipitating the hypermethylated cell-free nucleic acid molecules prior to sequencing.

[0177] 8. The method of claim 7, wherein the sequencing comprises sequencing the immunoprecipitated hypermethylated cell-free nucleic acid molecules.

[0178] 9. The method of claim 7, wherein immunoprecipitation comprises using a methylated nucleic acid binder to bind the hypermethylated cell-free nucleic acid molecules.

[0179] 10. The method of claim 9, wherein the methylated nucleic acid binder comprises an antibody against methylated nucleic acid.

[0180] The method according to claim 10 , wherein the antibody against methylated nucleic acid comprises an anti-5-methylcytosine (5-mC) antibody.

[0181] 12. The method of any one of claims 1-11, wherein the sequencing comprises subjecting the cell-free nucleic acid molecule or derivative thereof to a cfMeDIP-seq protocol.

[0182] 13. The method of any one of claims 1-12, wherein the cell-free nucleic acid molecule or derivative thereof is subjected to in vitro conditions sufficient to increase the methylation level of the cell-free nucleic acid molecule.

[0183] 14. The method of any one of claims 1-13, wherein the conditions sufficient to increase the methylation level of the cell-free nucleic acid molecules comprise an enzyme.

[0184] 15. The method of claim 14, wherein the enzyme comprises a CpG methyltransferase.

[0185] 16. The method according to any one of claims 1-15, wherein the cell-free nucleic acid molecule in (a) is obtained from a body fluid, the body fluid comprising at least one of urine, amniotic fluid, cerebrospinal fluid, prostatic fluid, tears or saliva.

[0186] 17. The method according to any one of claims 1-16, further comprising: a. providing a sheared genomic nucleic acid molecule, wherein the sheared genomic nucleic acid molecule is separated from the cell-free nucleic acid molecule; b. subjecting the sheared genomic nucleic acid molecule or its derivative to conditions sufficient to increase the methylation level of the sheared genomic nucleic acid molecule or its derivative, thereby producing a highly methylated genomic nucleic acid molecule; and c. identifying the sequence of the highly methylated genomic nucleic acid molecule or its derivative.

[0187] 18. The method according to claim 17, further comprising obtaining the sheared genomic nucleic acid molecule from the subject before (a).

[0188] 19. The method of claim 17, wherein the sheared genomic nucleic acid sample is not from the subject.

[0189] 20. The method of claim 17, wherein the sheared genomic nucleic acid molecules comprise a sequence length similar to a sequence length of a cell-free nucleic acid.

[0190] 21. The method of claim 17, wherein the identifying in c) comprises sequencing the hypermethylated genomic nucleic acid molecule or a derivative thereof.

[0191] 22. The method of claim 21, further comprising using the sequencing to identify the methylation pattern of the hypermethylated genomic nucleic acid molecule or its derivative.

[0192] 23. The method of claim 22, further comprising using the sequencing to identify highly methylated regions of the hypermethylated genomic nucleic acid molecules, wherein the highly methylated regions are regions that are at least 50% methylated.

[0193] 24. The method of claim 23, further comprising processing the hypermethylated regions identified in the hypermethylated cell-free nucleic acid with the hypermethylated regions identified in the hypermethylated genomic nucleic acid and identifying overlapping hypermethylated region windows.

[0194] 25. The method of claim 24, wherein the overlapping highly methylated region windows comprise overlapping highly methylated sequences of about 100 base pairs (bp) to 500 bp in length.

[0195] 26. The method of claim 25, wherein the overlapping highly methylated region windows comprise overlapping highly methylated sequences of about 250 bp to 350 bp in length.

[0196] 27. The method of any one of claims 24-26, wherein the plurality of identified overlapping hypermethylated region windows collectively comprise about 10% to about 20% of the whole genome.

[0197] 28. The method of any one of claims 24-26, wherein the window comprises approximately 500,000 to 2.5 million highly methylated regions.

[0198] 29. A method for identifying differentially methylated regions (DMRs), comprising analyzing and comparing the methylation patterns of multiple overlapping sequence read windows between a diseased sample and a control sample, and identifying high-methylated or low-methylated regions in the diseased sample as differentially methylated regions (DMRs) compared to the control sample.

[0199] 30. The method of claim 29, wherein analyzing the methylation patterns of the plurality of overlapping sequence read windows comprises immunoprecipitating cell-free nucleic acids in the diseased sample, and then analyzing the methylation patterns of the immunoprecipitated cell-free nucleic acids.

[0200] 31. The method of claim 29, wherein the method identifies DMRs that are not found using the whole genome as background.

[0201] 32. The method of claim 29, wherein the method produces a greater number of DMRs than using the whole genome as background.

[0202] 33. The method of claim 29, wherein the method produces at least twice the number of DMRs compared to using the whole genome as background.

[0203] 34. The method of claim 29, wherein the method can identify DMRs more quickly than using the whole genome as a background.

[0204] 35. A method of analyzing a sample, comprising analyzing the differentially methylated region (DMR) identified in any one of claims 29-34 in the diseased sample, wherein the method identifies the sample as a diseased sample or a normal sample.

[0205] 36. The method of claim 35, wherein the method provides better discrimination than using whole genome DMRs.

[0206] 37. The method of claim 35, wherein the method provides differentiation between cancer samples and normal samples.

[0207] 38. The method of claim 37, wherein the method provides differentiation between different types of cancer.

[0208] 39. The method of claim 1, further comprising providing therapeutic intervention to the subject upon identification of the sequence.

[0209] 40. The method of claim 39, wherein providing the therapeutic intervention comprises providing therapy to the subject.

[0210] 41. The method of claim 39, wherein providing the therapeutic intervention comprises providing a report identifying the sequence of the hypermethylated cell-free nucleic acid molecule or derivative thereof.

[0211] 42. The method of claim 39, wherein providing the therapeutic intervention comprises providing a report providing a therapeutic recommendation to the subject for treating a disease associated with the hypermethylated cell-free nucleic acid molecule or derivative thereof.

[0212] 43. The method of claim 39, wherein providing the therapeutic intervention comprises treating the subject for a disease associated with the hypermethylated cell-free nucleic acid molecule or derivative thereof.

[0213] 44. The method of claim 42 or 43, wherein the disease is cancer.

[0214] Example

[0215] Example 1: Providing Cell-Free DNA

[0216] This example demonstrates methods and systems for identifying all genomic regions that are most susceptible to pull-down by methylated DNA binders.

[0217] 5mC antibodies are used as methylated DNA binders to identify background genomic region sets that are enriched by binders when methylated. In this example, 5mC antibodies are used for cfMeDIP-seq assays to distinguish cancer and control samples using plasma cell-free DNA (cfDNA). Using this validated genomic region set instead of the whole genome as background significantly improves the ability to identify differentially methylated regions (DMRs) between different conditions (e.g., cancer patients and healthy donors or between different cancer types).

[0218] Commercially available normal donor plasma (Cedarlane, Cat#IPLASNAE50ML) was used as the starting material, and cfDNA was extracted using the Apostle MiniMax High Efficiency cfDNA Isolation Kit (Apostle, Cat#C40605) according to the manufacturer's protocol. Before the in vitro methylation step, the eluted cfDNA (called native cfDNA) was quantified by Qubit.

[0219] Commercial K562 genomic DNA (Promega, Cat#DD2011) was sheared on a Covaris LE-220 sonicator and then size-selected using 1.2X-1.7X bead ratios by AMPure XP beads (Beckman Coulter, Cat#A63882) to generate DNA that mimics the size of cfDNA. Prior to the in vitro methylation step, size-selected sheared DNA (referred to as cfDNA mimics) was quantified by Qubit.

[0220] like Fig.14 As shown, samples from both natural cfDNA and sheared genomic DNA (cfDNA mimics) were used. Natural cfDNA can more closely represent the fragmentation pattern observed in clinical samples, while cfDNA mimics can better represent the whole genome. Both natural cfDNA and cfDNA mimics were fully methylated in vitro and subjected to the cfMeDIP-seq protocol to identify all genomic regions that can be pulled down by the 5mC antibody.

[0221] Methylation reactions using 150 ng of native cfDNA and 150 ng of cfDNA mimics as starting materials were set up using CpG methyltransferase (M.SssI) (ThermoFisher Scientific, Cat#EM0821) according to the manufacturer's protocol. Due to the low amount of cfDNA available, an alternative control sample was also set up along with the native cfDNA and cfDNA mimics to test for proper methylation. The alternative control sample was an in-house generated amplicon that was available in large quantities and had restriction sites corresponding to the methylation-sensitive restriction enzyme HpyCH4IV. For in vitro methylation, the volume of the starting material was supplemented to 16.6 μL with nuclease-free water before mixing with the following mastermix: 2 μL of 10X M.SssI buffer, 0.4 μL of 50X SAM, and 1 μL of M.SssI enzyme. The reaction was incubated at 37°C for 15 minutes and then inactivated at 65°C for 20 minutes. Methylated DNA was purified using Qiagen MinElute PCR Clean up kit (Qiagen, Cat#28004) according to the manufacturer's protocol and then quantified via Qubit.

[0222] According to the manufacturer's protocol, aliquots of the methylated surrogate control sample and the original surrogate control sample were subjected to methylation-sensitive restriction digestion using the restriction enzyme HpyCH4IV (NEB, Cat# R0619S). After purification of the digested products using the Qiagen MinElute PCR Clean up Kit, the original surrogate sample (multiple smaller products) was verified to be digested by TapeStation profiling, but the methylated surrogate control (single larger product) was not digested, indicating successful in vitro methylation.

[0223] Commercially available biobank plasma samples corresponding to cancer and normal control patients were extracted using the Apostle MiniMax High Efficiency cfDNA Isolation Kit (Apostle, Cat# C40605) according to the manufacturer's protocol, with volumes ranging from 1-3 mL. The isolated DNA was quantified by Qubit and analyzed using the Agilent TapeStation cfDNA Assay Kit to ensure that the % cfDNA determined by the TapeStation analysis software was ≥50%.

[0224] Immunoprecipitated (IP'ed) fractions and input controls were sequenced. Library preparation was performed using 10 ng of methylated native cfDNA and methylated cfDNA mimics, each in triplicate, supplemented with 0.1 ng of spike-in control DNA, using the Kapa Hyper Prep kit in combination with IDT xGen CS adapters (IDT, Cat#1080799), following the manufacturer's protocol with slight modifications. In brief, after end repair and A-tailing, incubation at 20°C for 30 minutes, 0.327 μM of xGen CS adapters were then ligated to the DNA. After purifying the adapter-ligated DNA using AMPure XP beads, 5% of the DNA was saved as the input control. The remaining DNA was combined with lambda filler DNA to increase the starting DNA amount to 100 ng before MeDIP. MeDIP was performed as previously published (Shen, SY, Burgener, JM, Bratman, SV, & De Carvalho, DD (2019). Preparation of cfMeDIP-seq libraries for methylome profiling of plasma cell-free DNA. Nature protocol, 14 (10), 2749-2780). The PCR cycle for the final IP'ed library amplification was set to 15 cycles, using IDT xGen UDI primers (IDT, Cat#10005922), followed by size selection using AMPure XP beads. The previously saved input control DNA was also amplified using the same PCR mastermix and protocol as used for MeDIP, reducing the number of PCR cycles to 10 cycles.

[0225] For cfDNA extracted from cancer patients and control patients, 10 ng of cfDNA aliquots from each patient were supplemented with 0.1 ng of spike-in control DNA and used for library preparation and MeDIP as described above with slight modifications. The IP'ed libraries were amplified for 14 cycles and then cleaned up with AMPure XP beads. The saved input controls for these samples were not sequenced but simply stored at -20 °C. The final libraries from cfMeDIP-seq were sequenced on a NovaSeq 6000 configured for paired-end 100 bp.

[0226] To analyze the data, first, for the six fully in vitro methylated samples, the IP'ed and input control libraries were run through our pipeline, which performs standard bioinformatics steps, including trimming the raw reads in the FASTQ files, aligning them to the human genome construct hg38 to generate BAM files, and generating (normalized) aligned read counts to measure methylation signals in genomic windows of size 300-bp.

[0227] Using a standard peak calling algorithm, all genome-wide regions enriched in the IP'ed fraction were identified and compared to the input control. These regions were defined as the "new background" file, also referred to as background regions. Highly methylated signal peaks / genomic regions for fully in vitro methylated samples were identified using the "narrow peaks" setting with a q value < 0.01 using the standard MACS2 peak calling software. Other thresholds, such as q < 0.1, can also be used. The three IP'd libraries derived from natural cfDNA were compared to the corresponding input controls, and the same comparison was performed for libraries from three cfDNA mimic samples. Peaks from these two sample sets were merged, and genomic windows of size 300 bp that overlapped with these narrow peaks were identified. These windows (n = 1,218,413) constitute the background region, which was experimentally verified to be effectively pulled down by the 5mC antibody. This background region is significantly smaller than the whole genome (n = 9,583,339 windows).

[0228] Then, to test the usefulness of the background region file, the methylation signals from these 300-bp windows were obtained from breast cancer (n=14), colorectal cancer (n=15), or lung cancer (n=30) patient samples and from control samples (n=59) in count and RPKM (reads per kilobase per million mapped reads) format using MEDIPS software. In addition, counts and RPKMs were obtained for all 300-bp windows from chromosomes 1 to 22 for these same 118 samples to obtain "whole genome" methylation signals.

[0229] Next, all cancer samples (n=59) were compared to all control samples (n=59) to obtain differentially methylated regions (DMRs) / 300-bp windows that either gained methylation in cancer, termed "high DMRs," or lost methylation in cancer, termed "low DMRs." These DMRs were identified using 300-bp windows in background regions and 300-bp windows across the genome alone using the same threshold (q<0.1) for both backgrounds using DESeq2 software.

[0230] Compared to using the whole genome as background, the background region file can identify a much larger number of DMRs between cases and controls, such as Figure 2 and Figure 3 The volcano plot in Figure 2 highlights the ability to identify DMRs between conditions compared to using the whole genome as background. Fig. 9 As shown in the Venn diagram in Figure 2, more DMRs were identified using background regions than using the whole genome as background, and most whole genome DMRs overlapped with DMRs identified using background regions. In addition, the background region file allowed for faster calculation of cancer vs. control DMRs ( Figure 8 ). Finally, the set of DMRs identified using the background region file was more effective in distinguishing cases from controls than the limited set of DMRs identified using the whole genome background. This is evident in the heatmap showing the Z-scores of RPKM obtained from the MEDIPS software. Figure 4 As shown, DMRs from background regions showed that various cancer sample clusters could be clearly distinguished from control samples, while using genome-wide DMRs ( Figure 5 ), some cancer cases clustered with healthy controls. Figure 4 and Figure 5 The same standard clustering algorithm (Ward with Euclidean distance) is used. The graph generated by Uniform Manifold Approximation and Projection (UMAP) ( Figure 6 and Figure 7 ) also showed that DMRs from background regions were more effective in distinguishing cancer from control samples compared with genome-wide DMRs.

[0231] Taken together, these data indicate that DMRs generated from background regions identified in fully methylated samples more clearly distinguish cancer from control samples than DMRs generated from the whole genome. In addition, these DMRs are generated faster and with lower computational intensity. This experiment demonstrates the ability to identify genomic regions that work well with methylation assays, with the ultimate goal of accurately detecting tumor signals in patient samples.

[0232] In summary, the use of modified DNA binders (e.g., 5mC antibodies in cfMeDIP-seq experiments) allows for more efficient and accurate interpretation of experiments. The present invention can improve any method that relies on affinity purification of modified DNA, such as cfMeDIP-seq, MDB-seq, 5hmC antibodies (hMeDIP-seq), 5hmC-SEAL, etc.

[0233] Example 2: Peak calling method

[0234] De novo peak calling was performed on a per sample basis using the open source peak calling tool MACS2. The MACS2 "callpeak" function can be called with either a "wide" or "narrow" option. In this example, the MACS2 "callpeak" function was called with the "wide" option enabled, a "wide cutoff" of 0.1, a q-value threshold of 0.01, and the choice to retain duplicates that had not been removed by the upstream process. The input sample binary alignment map (BAM) was read filtered for unique molecular identifiers (UMIs) and optical duplicates. Peak annotations were then generated by an algorithm that determined signals that were significantly different from the background average coverage. The result is a peak annotation file containing coordinates representing peak "regions of interest" (ROIs). The peak ROI represents the genomic location where the methylation signal in the sample was observed, such as Fig.11 and Fig.12 The "wide" option in MACS2 was able to generate new peak ROIs that were not captured using MACS2 callpeak with the "narrow" function.

[0235] De novo peak calls were obtained from the three fully methylated samples. The peak ROIs for each sample were then merged into a consensus set of feature annotations using the open source tool mumerge, a probabilistic method for merging ROIs across multiple samples. Mumerge treats ROIs as probability distributions, taking into account any different conditions between samples (e.g., cell type, treatment, technical replicates) to produce a joint probability distribution describing the highest likelihood location for that ROI. Fig.11 and Fig.12 As shown, this approach yields higher resolution than simply merging all overlapping ROIs to create a consensus annotation set (which tends to exaggerate the size of the true ROI). This approach improves the resolution of features rather than shifting them to the tail of the true peak ROI. In addition, probabilistic features are useful for determining whether the ROI of variation is related to other genomic features of interest (e.g. Fig.11 The relative proximity of the p53 transcription factor motif in the annotation track shown in is important.

[0236] Once the consensus file is generated, counts are generated for these peak ROIs for use in feature selection and diagnostic algorithms, such as Fig.13 Using this approach, "quiescent" regions, or regions with few reads in control samples, can be derived, which can be used to determine changes in response to medical conditions such as cancer. Given the very low number of reads captured from ctDNA, the improvement in methylation ROI resolution will better distinguish between healthy and disease states.

[0237] Although preferred embodiments of the present invention have been described herein, those skilled in the art will appreciate that changes may be made thereto without departing from the spirit of the present invention or the scope of the appended claims. All documents disclosed herein, including documents in the appended reference list, are incorporated by reference.

Claims

1. A method for generating a methylated genomic region sequencing copy library, the method comprising: a) providing a cell-free nucleic acid molecule of a subject and providing a sheared genomic nucleic acid molecule, wherein the sheared genomic nucleic acid molecule is separated from the cell-free nucleic acid molecule; b) subjecting the cell-free nucleic acid molecule or derivative thereof to conditions sufficient to increase the methylation level of the cell-free nucleic acid molecule or derivative thereof, and subjecting the sheared genomic nucleic acid molecule or derivative thereof to conditions sufficient to increase the methylation level of the sheared genomic nucleic acid molecule or derivative thereof, thereby producing a hypermethylated cell-free nucleic acid molecule and a hypermethylated genomic nucleic acid molecule, respectively; c) incubating the hypermethylated cell-free nucleic acid molecules and the hypermethylated genomic nucleic acid molecules to enrich for the first methylated genomic region of the hypermethylated cell-free nucleic acid molecules and the second methylated genomic region of the hypermethylated genomic nucleic acid molecules; as well as d) combining the first methylated genomic region of the hypermethylated cell-free nucleic acid molecule and the second methylated genomic region of the hypermethylated genomic nucleic acid molecule to generate a library of sequenced copies of the methylated genomic regions.

2. The method of claim 1, wherein the cell-free nucleic acid molecule is a cell-free deoxyribonucleic acid (DNA) molecule.

3. The method of claim 2, wherein the cell-free DNA molecule is derived from a biological sample of the subject. The method according to claim 3 , wherein the biological sample comprises a blood sample or a plasma sample.

5. The method according to any one of claims 1-4, before (d), further comprising subjecting the first methylated genomic region of the hypermethylated cell-free nucleic acid molecule and the second methylated genomic region of the hypermethylated genomic nucleic acid molecule to conditions sufficient to amplify the first methylated genomic region of the hypermethylated cell-free nucleic acid molecule and the second methylated genomic region of the hypermethylated genomic nucleic acid molecule.

6. The method of claim 5, further comprising sequencing the amplified first methylated genomic region of the hypermethylated cell-free nucleic acid molecule to generate a first plurality of sequencing reads, and sequencing the amplified second methylated genomic region of the hypermethylated genomic nucleic acid molecule to generate a second plurality of sequencing reads.

7. The method of claim 6, wherein the sequencing does not comprise bisulfite sequencing.

8. The method of any one of claims 1-7, further comprising obtaining a first input control of the cell-free nucleic acid molecules, wherein the input control comprises the hypermethylated cell-free nucleic acid molecules derived from b).

9. The method of claim 8, wherein the first input control is not subjected to the enrichment step as in c).

10. The method of claim 9, further comprising amplifying and sequencing the first input control under sufficient conditions to generate a plurality of first input control sequencing reads.

11. The method according to any one of claims 1-7, further comprising obtaining a second input control of the sheared genomic nucleic acid molecule, wherein the second input control comprises the hypermethylated sheared genomic nucleic acid molecule derived from b).

12. The method of claim 11, wherein the second input control is not subjected to the enrichment step as in c).

13. The method of claim 12, further comprising amplifying and sequencing the second input control under sufficient conditions to generate a plurality of second input control sequencing reads.

14. The method of any one of claims 1-13, wherein sequencing reads of the first methylated genomic region of the hypermethylated cell-free nucleic acid molecule are obtained by comparing the first plurality of sequencing reads to the plurality of first input control sequencing reads.

15. The method of any one of claims 1-13, wherein sequencing reads of the second methylated genomic region of the hypermethylated cell-free nucleic acid molecule are obtained by comparing the second plurality of sequencing reads to the plurality of second input control sequencing reads.

16. The method of any one of claims 1-15, further comprising generating sequencing reads enriched for methylated DNA regions from a cell-free DNA sample of a subject having or suspected of having a disease or condition, and generating sequencing reads enriched for methylated DNA regions from a cell-free DNA sample of a healthy subject.

17. The method of claim 16, further comprising generating a first plurality of differentially methylated regions (DMRs) mapped to the library of sequenced copies of methylated genomic regions.

18. The method of claim 16, further comprising generating a second plurality of differentially methylated regions (DMRs) mapped to the whole genome sequenced copy library.

19. The method of claim 17 or claim 18, wherein the first plurality of DMRs or the second plurality of DMRs comprises hypermethylation or hypomethylation.

20. The method of claim 17 or claim 18, wherein the first plurality of DMRs is larger than the second plurality of DMRs.

21. The method of any one of claims 17-19, wherein when the first plurality of DMRs are used to classify diseased samples and healthy samples, the diseased samples are better separated from the healthy samples than when the second plurality of DMRs are used.

22. The method of claim 21, wherein the separation of the diseased samples and the healthy samples is shown by a gene heat map.

23. The method of claim 21, wherein the separation of the diseased samples and the healthy samples is shown by a Uniform Manifold Approximation and Projection (UMAP) plot.

24. The method of any one of claims 17-23, wherein the run time for classifying the diseased samples and the healthy samples is reduced compared to using the second plurality of DMRs.

25. The method of any one of claims 16-24, wherein the disease or condition is cancer or a tumor.

26. The method of claim 25, wherein the cancer is breast cancer, bladder cancer, colorectal cancer, endometrial cancer, prostate cancer, kidney cancer, pancreatic cancer, or lung cancer.

27. The method of claim 25, wherein the cancer is advanced cancer.

28. The method of claim 25, wherein the cancer is an early stage cancer.

29. The method of any one of claims 1-28, wherein b) comprises using a binder that binds to one or more methylated nucleotides of a methylated region of the cell-free nucleic acid molecule or the sheared genomic nucleic acid molecule.

30. The method of claim 29, wherein the binder comprises a protein comprising a methyl-CpG-binding domain.

31. The method of claim 30, wherein the protein is MBD2 protein.

32. The method of claim 29, wherein the conjugate comprises an antibody.

33. The method of claim 32, wherein the antibody is a 5-mC antibody.

34. The method of claim 32, wherein the antibody is a 5-hydroxymethylcytosine antibody.

35. The method of claim 29, wherein the binder exhibits a reduced level of non-specific binding to non-methylated nucleotides of the cell-free nucleic acid molecule or the sheared genomic nucleic acid molecule.

36. The method according to any one of claims 1-35, before c), further comprising mixing the hypermethylated cell-free nucleic acid molecules or the hypermethylated genomic nucleic acid molecules with filler deoxyribonucleic acid (DNA) molecules.

37. The method of claim 36, wherein the filler DNA is used in step b) to enrich for multiple methylated regions in the cell-free nucleic acid molecules or derivatives thereof or the sheared genomic nucleic acid molecules or derivatives thereof.

38. The method of any one of claims 36-37, wherein the filler DNA has a length of about 50 base pairs (bp) to about 800 base pairs.

39. The method of claim 38, wherein the length is about 100 bp to about 600 bp.

40. The method of claim 38, wherein the length is about 200 bp to about 600 bp.

41. The method of any one of claims 6-40, wherein the sequencing comprises peak calling to identify an enriched portion of the first plurality of sequencing reads or the second plurality of sequencing reads.

42. The method of claim 41, wherein narrow peak calling is used to identify hypermethylated regions.

43. The method of claim 41, wherein broad peak calling is used to identify hypermethylated regions.

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