DNA methylation and gene expression as determinants of genome-wide cell-free DNA fragmentation

Abstract

For non-invasive disease detection and patient treatment, analysis of cell-free DNA (cfDNA) fragment ends in cancer patients provides a direct correlation between epigenetic changes and cfDNA fragmentation. In some embodiments, methods for the diagnosis and treatment of cancer include the step of detecting differences in methylation and expression that affect cell-free (cfDNA) size and coverage in a patient. In specific aspects, methods for determining circulating cfDNA fragmentation in a provided sample include the steps of assaying a genomic sequence to identify cfDNA termination sites; and analyzing the frequency of cfDNA breaks at multiple locations in the genomic sequence, thereby determining circulating cfDNA fragmentation. TIFF2026522392000007.tif55160

Description

[Technical Field]

[0001] This application claims the benefit of priority of U.S. Provisional Application No. 63 / 521,666, filed 17 June 2023, which is incorporated herein by reference in its entirety.

[0002] Description of research supported by the federal government. This invention was made with the support of the U.S. Government under grants CA006973, CA062924, CA121113, and CA233259 from the National Institutes of Health. The U.S. Government reserves certain rights in this invention.

[0003] field This disclosure relates to methods and compositions for evaluating and / or treating mammals (e.g., humans) with cancer. In particular, the methods relate to analyzing epigenetic changes and cfDNA fragmentation for non-invasive disease detection. [Background technology]

[0004] background Cell-free DNA (cfDNA) is a focus of research into blood-based biomarkers for the early detection and monitoring of cancer. Normally, cellular DNA is packaged and condensed within chromosomes by wrapping around the histone core. 1,2 During and after cell death, DNA is digested by DNAse, partly to prevent the release of unbound DNA that could act as self-antigens. 3~6 The DNA fragments tightly wrapped around the histone core are collectively called nucleosomes and appear to be protected from further digestion. 7 These fragments are typical of cfDNA and can be collected by simple blood sampling. The characteristics and origins of cfDNA fragmentation in blood are not yet fully understood. [Overview of the project]

[0005] overview A method for diagnosing and treating cancer includes a step of detecting differences in methylation and expression that affect cell-free (cfDNA) size and coverage in a patient.

[0006] In certain contexts, a method is provided for determining circulating cell-free DNA (cfDNA) fragmentation in a sample, the method comprising the steps of: assaying a genomic sequence to identify the cfDNA terminal locations; and analyzing the frequencies of cfDNA breaks at multiple locations in the genomic sequence, thereby determining circulating cell-free DNA (cfDNA) fragmentation.

[0007] In certain contexts, a method is provided for determining circulating cell-free DNA (cfDNA) fragmentation in a sample, the method comprising (a) assaying a cfDNA sequence to identify the location and terminal position of the cfDNA fragment in the genome; and (b) analyzing the frequency of cfDNA breaks at multiple locations in the genomic sequence, thereby determining circulating cell-free DNA (cfDNA) fragmentation.

[0008] In certain embodiments of the method described above, genome sequences are assayed by whole-genome sequencing or by obtaining whole-genome sequences from a database, and by pooling cfDNA sequences.

[0009] In certain embodiments, the analysis of cfDNA breakpoint frequencies includes calculating the ratio of the number of cfDNA fragments that begin or terminate at a particular location to the number of fragments that have a start or termination location within 50 bp surrounding that location.

[0010] In certain embodiments of the above method, the analysis of cfDNA breakpoint frequencies includes calculating the ratio of the number of cfDNA fragments that start or terminate at a particular location to the number of fragments that have a start or termination location within 50 bp surrounding that location.

[0011] In certain embodiments, cfDNA fragments containing similar terminal position sequences contain similar motifs. In certain embodiments, the motif contains thymine or adenine before the start of the cfDNA fragment sequence, and two cytosines (A / T|CC) or guanine following cytosine (A / T|CG) as the first two nucleotides of the cfDNA fragment sequence.

[0012] In certain embodiments, the motif contains thymine or adenine before the start of the cfDNA fragment sequence, and two cytosines (A / T|CC) or guanine following cytosine (A / T|CG) as the first two nucleotides of the cfDNA fragment sequence, or these sequences are reverse complements at the ends of the cfDNA fragments.

[0013] In certain embodiments, the frequency of the motif is increased in healthy subjects compared to subjects with cancer. For example, in such embodiments, the frequency of the motif is at least 0.5, 1.2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 150, or 200 percent higher in healthy subjects compared to the frequency of the motif in subjects with cancer. In certain embodiments, the frequency of the motif is decreased in healthy subjects compared to subjects with cancer. For example, in such embodiments, the frequency of the motif is at least 0.5, 1.2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 150, or 200 percent lower in healthy subjects compared to the frequency of the motif in subjects with cancer.

[0014] In certain embodiments, the frequency of the A / T|CC motif exceeds the frequency of the A / T|CG motif. In certain embodiments, the A / T|CG motif is located in proximity to the histone H1 linker or centered around 100 to 200 base pairs from the histone H1 linker. In certain embodiments, the internal region of the cfDNA fragment is enriched in adenine and thymine.

[0015] In certain embodiments, the method comprises mapping cfDNA fragments to the genome, and comparing the cfDNA fragment end sequences to methylated and unmethylated CpG sites of cfDNA from healthy subjects further comprising.

[0016] In certain embodiments, methylated CpGs are enriched at the ends of A / T|CG cfDNA fragment sequences.

[0017] In certain embodiments, the quantitative assessment of the enrichment of cfDNA fragment ends at CpGs is for each CpG, calculating the ratio of cfDNA fragments starting or ending at the CpG dinucleotide position to the number of cfDNA fragments having a start or end position within 50 bp of each CpG comprising.

[0018] In certain embodiments, in female subjects compared to male subjects, at the location of X chromosome CpG islands, cfDNA fragments obtained from the X chromosome in healthy subjects contain increased cfDNA fragments ending with CG and decreased cfDNA fragments ending with CCG. In certain embodiments, the cfDNA sequence coverage of the enriched cfDNA fragment end sequences includes increased methylation across the region of the methylated CpG island compared to the decreased or less frequent occurrence of the cfDNA fragment end sequences.

[0019] In certain embodiments, gene expression at the transcription start site (TSS) is inversely correlated with cfDNA coverage at the TSS.

[0020] The subjects, such as mammals, particularly humans, may have previously undergone cancer treatment to treat cancer. The method may further include a step of administering cancer treatment after the step of determining circulating cell-free DNA (cfDNA) fragmentation, for example, the cancer treatment administered may be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiotherapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T-cell therapy, targeted therapy, or a combination thereof. The method may also include a step of monitoring the mammal for the presence of cancer after cancer treatment and / or after determining circulating cell-free DNA (cfDNA) fragmentation.

[0021] In another context, a method is provided for diagnosing cancer and treating the subject, and this method is The process involves obtaining a sample from a subject, assaying changes in circulating cfDNA fragment size compared to normal and tumor-derived cell-free DNA (cfDNA) controls, assaying CpG methylation at the ends of cfDNA fragments, and evaluating fragment end representation at CG and CCG sites through low-coverage whole-genome cfDNA analysis; diagnosing a subject with cancer; and treating the subject with one or more chemotherapy agents, radiotherapy, surgery, or a combination thereof. Preferably includes the following. In certain embodiments, an increase in cfDNA fragments terminating at N|CCG compared to a normal control constitutes a diagnosis of cancer. In certain embodiments, a decrease in cfDNA fragments terminating at N|CCG is an indicator of a cancer-free subject. In certain embodiments, an increase in cfDNA fragments terminating at CG compared to a normal control constitutes a diagnosis of cancer. In certain embodiments, cfDNA fragments are obtained from genomic regions with increased methylation compared to a normal genome methylation control. In certain embodiments, it further includes incorporating the distribution of fragment end positions at CG and CCG sites in a gradient boosted tree machine learning model.

[0022] In another context, methods for diagnosing and treating subjects diagnosed with cancer include: assaying a genomic sequence to identify genome-wide CpG methylation; determining circulating cell-free DNA (cfDNA) fragmentation by analyzing the frequencies of cfDNA breaks at multiple locations in the genomic sequence, wherein recurrent cfDNA fragment end enrichment at CpG sites correlates with higher genome-wide methylation levels and smaller cfDNA fragments that constitute a diagnosis of cancer; diagnosing a subject with cancer if hypomethylation and / or increased gene expression or decreased cfDNA fragment size is detected; and treating the subject with one or more chemotherapy, radiation, surgery, or a combination thereof. In certain embodiments, the genomic sequence is assayed by whole-genome sequencing or by obtaining the whole-genome sequence from a database, and by pooling cfDNA sequences.

[0023] In certain embodiments, the analysis of cfDNA breakpoint frequencies includes calculating the ratio of the number of cfDNA fragments that begin or terminate at a particular location to the number of fragments that have a start or termination location within 50 bp surrounding that location.

[0024] In certain embodiments, cfDNA fragments containing similar terminal sequences contain similar motifs. In certain embodiments, the motifs contain a thymine or adenine prior to the beginning of the cfDNA fragment sequence, and two cytosines (A / T|CC) or cytosines followed by guanines (A / T|CG) as the first two nucleotides of the cfDNA fragment sequence.

[0025] In certain embodiments, the frequency of motifs is increased in healthy subjects compared to subjects with cancer. In certain embodiments, the frequency of A / T|CC motifs exceeds the frequency of A / T|CG motifs. In certain embodiments, A / T|CG motifs are located adjacent to the histone H1 linker or centered 100 to 200 base pairs from the histone H1 linker. In certain embodiments, the internal region of the cfDNA fragment is enriched with adenine and thymine. In certain embodiments, the method further includes the steps of mapping the cfDNA fragment to the genome and comparing the terminal sequence of the cfDNA fragment with methylated and unmethylated CpG sites of cfDNA from a healthy subject. In certain embodiments, methylated CpG is enriched at the ends of the A / T|CG cfDNA fragment sequence. In certain embodiments, quantitative evaluation of the enrichment of cfDNA fragment ends at CpG is performed. For each CpG, calculate the ratio of cfDNA fragments that start or terminate at the CpG dinucleotide position to the number of cfDNA fragments that have a start or termination position within 50 bp of the surrounding CpG. Includes.

[0026] In certain embodiments, compared to male subjects, female subjects showed an increase in cfDNA fragments obtained from the X chromosome in healthy subjects at the location of the X chromosome CpG island, with a decrease in cfDNA fragments that terminated at CG and a decrease in cfDNA fragments that terminated at CCG.

[0027] In certain embodiments, the cfDNA sequence coverage of an enriched cfDNA fragment terminal sequence includes increased methylation across the entire region of a methylated CpG island compared to a reduced or less frequent occurrence of the cfDNA fragment terminal sequence. In certain embodiments, gene expression at a transcription start site (TSS) is inversely correlated with cfDNA coverage at the TSS. The subject, e.g., mammals, particularly humans, may have previously undergone cancer treatment to treat cancer. Cancer treatment may include surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiotherapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T-cell therapy, targeted therapy, or a combination thereof. The method may also include a step of monitoring the mammal for the presence of cancer after cancer treatment.

[0028] In another context, a method for determining a mammalian cfDNA fragmentation profile includes the steps of: processing cfDNA fragments obtained from a mammalian sample to form a sequencing library; subjecting the sequencing library to whole genome sequencing (e.g., low-coverage whole genome sequencing) to obtain sequenced fragments; mapping the sequenced fragments to the genome to obtain a window of mapped sequences; and analyzing the window of mapped sequences to determine the cfDNA fragment length. The mapped sequences may include windows of tens to thousands. The windows of mapped sequences may be non-overlapping windows. The windows of mapped sequences may contain up to approximately 250,000, 500,000, 1,000,000, 2,000,000, 3,000,000, 4,000,000, 5,000,000, or 6,000,000 or more base pairs, respectively. A cfDNA fragmentation profile can be determined within each window. The cfDNA fragmentation profile may include the median fragment size. The cfDNA fragmentation profile may include the fragment size distribution. A cfDNA fragmentation profile may include the ratio of small cfDNA fragments to large cfDNA fragments within a window of mapped sequences. A cfDNA fragmentation profile may span the entire genome. A cfDNA fragmentation profile may span a subgenome segment (e.g., a segment within a portion of a single chromosome). In certain embodiments, a reference cfDNA fragmentation profile may be a cfDNA fragmentation profile of a healthy mammal. In certain embodiments, a reference cfDNA fragmentation profile may be created by determining the cfDNA fragmentation profile in a sample obtained from a healthy mammal. In certain embodiments, a reference DNA fragmentation pattern may be a reference nucleosome cfDNA fragmentation profile. In certain embodiments, a cfDNA fragmentation profile may include a median fragment size, where the median fragment size of the cfDNA fragmentation profile is shorter than the median fragment size of the reference cfDNA fragmentation profile.In certain embodiments, the cfDNA fragmentation profile may include a fragment size distribution, the fragment size distribution of the cfDNA fragmentation profile differing from the fragment size distribution of a reference cfDNA fragmentation profile by at least 4, 6, 8, 10, 12, 14, 16, 18, or 20 nucleotides.

[0029] In certain embodiments, the cfDNA fragmentation profile may include the ratio of small cfDNA fragments to large cfDNA fragments within a window of the mapped sequence, where small cfDNA fragments are at most 40, 60, 80, or 100 bp to 110, 120, 130, 140, or 150 bp in length, and large cfDNA fragments are at most 151 bp to 220 bp in length, and the correlation of fragment ratios in the cfDNA fragmentation profile is lower than the correlation of fragment ratios in the reference cfDNA fragmentation profile.

[0030] In certain embodiments, a cfDNA fragmentation profile may include sequence coverage of small cfDNA fragments in a genome-wide window. In certain embodiments, a cfDNA fragmentation profile may include sequence coverage of large cfDNA fragments in a genome-wide window. In certain embodiments, a cfDNA fragmentation profile may include sequence coverage of small and large cfDNA fragments in a genome-wide window. The comparison step may include comparing a cfDNA fragmentation profile across the entire genome to a reference cfDNA fragmentation profile. The comparison step may include comparing a cfDNA fragmentation profile across subgenome segments to a reference cfDNA fragmentation profile. Mammals may have previously undergone cancer treatment to treat cancer.

[0031] In another context, a method is provided for diagnosing and treating subjects diagnosed with cancer, the method comprising: (a) assaying a genomic sequence to identify genome-wide CpG methylation; (b) determining circulating cell-free DNA (cfDNA) fragmentation by analyzing the frequency of cfDNA breaks at multiple locations in the genomic sequence, wherein repeated cfDNA fragment end enrichment at CpG sites correlates with higher genome-wide methylation levels and smaller cfDNA fragments that constitute a diagnosis of cancer; (c) diagnosing the subject with cancer if hypomethylation and / or increased gene expression or decreased cfDNA fragment size is detected; and (d) treating the subject with one or more chemotherapy, radiation, surgery, or a combination thereof.

[0032] In certain embodiments, genome sequences are assayed by whole-genome sequencing or by obtaining whole-genome sequences from a database, and by pooling cfDNA sequences. In certain embodiments, the analysis of cfDNA breakpoint frequencies involves calculating the ratio of the number of cfDNA fragments that start or terminate at a particular location to the number of fragments that have a start or termination location within 50 bp surrounding that location. In certain embodiments, cfDNA fragments containing similar terminal location sequences contain similar motifs. In certain embodiments, a motif contains a thymine or adenine preceding the beginning of a cfDNA fragment sequence, and two cytosines (A / T|CC) or cytosines followed by guanines (A / T|CG) as the first two nucleotides of the cfDNA fragment sequence. In certain embodiments, the frequency of motifs is increased in healthy subjects compared to subjects with cancer. In certain embodiments, the frequency of A / T|CC motifs is higher than the frequency of A / T|CG motifs. In certain embodiments, the A / T|CG motif is located adjacent to the histone H1 linker or centered 100 to 200 base pairs from the histone H1 linker. In certain embodiments, the internal region of the cfDNA fragment is enriched with adenine and thymine. In certain embodiments, the method may further include the steps of mapping the cfDNA fragment to the genome and comparing the terminal sequence of the cfDNA fragment with methylated and unmethylated CpG sites of cfDNA from a healthy subject. In certain embodiments, methylated CpG is enriched at the ends of the A / T|CG cfDNA fragment sequence. In certain embodiments, quantitative evaluation of the enrichment of the cfDNA fragment ends in CpG is performed. For each CpG, calculate the ratio of cfDNA fragments that start or terminate at the CpG dinucleotide position to the number of cfDNA fragments that have a start or termination position within 50 bp of the surrounding CpG. This includes: In certain embodiments, in female subjects compared to male subjects, cfDNA fragments obtained from the X chromosome in healthy subjects at the location of X chromosome CpG islands include increased cfDNA fragments terminating at CG and decreased cfDNA fragments terminating at CCG. In certain embodiments, the cfDNA sequence coverage of enriched cfDNA fragment terminal sequences includes increased methylation across the entire region of methylated CpG islands compared to said cfDNA fragment terminal sequences with decreased or less frequent occurrences. In certain embodiments, gene expression at transcription start sites (TSS) is inversely correlated with cfDNA coverage at TSSs.

[0033] definition Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as those commonly understood by those skilled in the art to which this invention belongs. Furthermore, terms, for example, those defined in commonly used dictionaries, should be interpreted as having a meaning consistent with their meaning in the context of the relevant art, and should not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0034] As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural unless otherwise explicitly stated by the context. Furthermore, the terms “including,” “includes,” “having,” “has,” and “with,” or their variations thereof, are intended to be inclusive, as with the term “comprising,” to the extent that they are used in the detailed description and / or claims.

[0035] The terms “about” or “approximately” mean that a particular value is within an acceptable margin of error as determined by those skilled in the art. This depends in part on how the value is measured or determined, i.e., on the limits of the measurement system. For example, “about” may mean within a range of 1 or a standard deviation greater than 1, according to convention in the art. Or, “about” may mean within 20%, 10%, 5%, or 1% of a given value or range. Or, particularly with respect to biological systems or biological processes, the term may mean within an order of five times a given value, or even within twice a given value. Wherever a particular value is described in this application and claims, unless otherwise specified, the term “about” should be understood as meaning that the particular value is within an acceptable margin of error.

[0036] The terms “aligned,” “alignment,” “mapped,” or “the process of aligning,” or “the process of mapping” refer to one or more sequences that have been found to match, in terms of the order of nucleic acid molecules, with known sequences derived from a reference genome. Such alignments may be performed manually or by computer algorithms. An example is the Efficient Local Alignment of Nucleotide Data (ELAND) computer program distributed as part of the Illumina Genomics Analysts pipeline. Sequence read matching in alignment may be 100% sequence match or less than 100% (incomplete match).

[0037] As used herein, the term "cancer" means a disease, condition, trait, genotype, or phenotype characterized by uncontrolled cell proliferation or replication, as known in the art, including liver cancer (including hepatocellular carcinoma (HCC)), lung cancer (including non-small cell lung cancer), gastric cancer, colorectal cancer, and, for example, leukemia, e.g., acute myeloid leukemia (AML), chronic myeloid leukemia (CML), acute lymphoblastic leukemia (ALL), and chronic lymphocytic leukemia; AIDS-related cancers, e.g., Kaposi's sarcoma; breast cancer; bone cancers, e.g., osteosarcoma, chondrosarcoma, Ewing's sarcoma, fibrosarcoma, giant cell tumor, adamantinoma, and chordoma; brain cancers, e.g., meningioma, glioblastoma, low-grade astrocytoma, oligo Oligodendrocytoma, pituitary tumors, Schwann cell tumors, and metastatic brain cancer; various lymphomas, e.g., mantle cell lymphoma, non-Hodgkin lymphoma, adenoma, squamous cell carcinoma, laryngeal cancer, head and neck cancers, gallbladder cancer and bile duct cancer, retinal cancers, e.g., retinoblastoma, esophageal cancer, gastric cancer, multiple myeloma, ovarian cancer, uterine cancer, thyroid cancer, testicular cancer, endometrial cancer, melanoma, bladder cancer, prostate cancer, pancreatic cancer, sarcoma, Wilms' tumor, cervical cancer, head and neck cancers, skin cancer, nasopharyngeal cancer, liposarcoma, epithelial carcinoma, renal cell carcinoma, gallbladder adenocarcinoma, parotid adenocarcinoma, endometrial sarcoma, multidrug-resistant cancer; and proliferative diseases and conditions, e.g., angiogenesis associated with tumor angiogenesis.

[0038] The terms “cell-free nucleic acid,” “cell-free DNA,” or “cfDNA” refer to nucleic acid fragments that circulate within an individual’s body (e.g., in the bloodstream) and originate from one or more healthy cells, and / or one or more cancer cells. Furthermore, cfDNA may originate from other sources, such as viruses or fetuses.

[0039] The term "cfDNA sequence coverage" refers to the average number of cfDNA molecules that overlap at a particular location.

[0040] The term "circulating tumor DNA" or "ctDNA" refers to nucleic acid fragments derived from tumor cells or other types of cancer cells that can be released into the bloodstream of an individual as a result of biological processes such as apoptosis or necrosis of dying cells, or that can be actively released by viable tumor cells.

[0041] As used herein, the terms “comprising,” “comprise,” and “comprised,” and their inflections, are intended to be inclusive or open-ended with respect to any defined or described element of an item, composition, apparatus, method, process, system, etc., thereby indicating that the defined or described item, composition, apparatus, method, process, system, etc. includes the specified element, or, where applicable, its equivalent, and other elements, and still falls within the scope / definition of the defined item, composition, apparatus, method, process, system, etc.

[0042] "Diagnostic" or "diagnosed" means determining the presence or nature of a pathological condition. Diagnostic methods differ in terms of sensitivity and specificity. The "sensitivity" of a diagnostic assay is the percentage of diseased individuals that test positive (the percentage of "true positives"). Individuals with the disease that are not detected by this assay are "false negatives." Subjects that are not diseased and test negative in this assay are called "true negatives." The "specificity" of a diagnostic assay is 1 minus the false positive rate, and the "false positive" rate is defined as the proportion of disease-free individuals that test positive. A condition may not be definitively diagnosed by a particular diagnostic method, but it is sufficient if the method provides a positive indicator that aids in diagnosis.

[0043] As used herein, "effective dose" means the amount that produces a therapeutic or preventive benefit.

[0044] As used herein, the terms “fragmentation profile,” “location-dependent differences in fragmentation patterns,” and “location-dependent differences in fragment size and coverage across the genome” are synonymous and can be used interchangeably. In some embodiments, the process of determining a cfDNA fragmentation profile in a mammal can be used to determine if a mammal has cancer. For example, cfDNA fragments obtained from a mammal (e.g., derived from a sample obtained from a mammal) can be subjected to low-coverage-hole genome sequencing, and the sequenced fragments can be mapped to the genome (e.g., in a non-overlapping window) and evaluated to determine a cfDNA fragmentation profile. As described herein, the cfDNA fragmentation profiles of mammals with cancer are heterogeneous (e.g., in terms of fragment length) compared to the cfDNA fragmentation profiles of healthy mammals (e.g., mammals without cancer). Accordingly, this disclosure also provides methods and materials for evaluating, monitoring, and / or treating mammals (e.g., humans) that have or are suspected of having cancer. In some embodiments, this document provides methods and materials for determining if a mammal has cancer. For example, by evaluating a sample obtained from a mammal (e.g., a blood sample), the presence of cancer in the mammal, and optionally the primary tissue of the cancer, can be determined, at least partially, based on the mammal's cfDNA fragmentation profile. In some embodiments, methods and materials are provided for monitoring whether a mammal has cancer. For example, by evaluating a sample obtained from a mammal (e.g., a blood sample), the presence of cancer in the mammal can be determined, at least partially, based on the mammal's cfDNA fragmentation profile. In some embodiments, methods and materials are provided for determining whether a mammal has cancer and for treating the mammal by administering one or more cancer treatments to the mammal. For example, by evaluating a sample obtained from a mammal (e.g., a blood sample), it can be determined whether a mammal has cancer, at least partially, based on the mammal's cfDNA fragmentation profile, and one or more cancer treatments can be administered to the mammal.

[0045] The term "genomic nucleic acid" or "genomic DNA" refers to nucleic acids containing chromosomal DNA derived from one or more healthy (e.g., non-tumor) cells or tumor cells. In various embodiments, genomic DNA can be extracted from cells derived from blood cell lineages such as leukocytes (WBCs).

[0046] "Optional" or "optional" means that the description includes both the cases in which the event or situation described later occurs and the cases in which it does not occur.

[0047] As used herein and in the appended claims, the term “or” is used in general to include “and / or” unless otherwise explicitly defined in the context.

[0048] Parenteral administration of immunogenic compositions includes, for example, subcutaneous (sc), intravenous (iv), intramuscular (im), or intrasternal injection or infusion.

[0049] The terms “patient,” “individual,” and “subject” are used synonymously herein and refer to the mammalian subject to be treated, with human patients preferred. In some embodiments, the methods of the present invention are used in the development of disease animal models in laboratory animals, including, but not limited to, rodents such as mice, rats, and hamsters, as well as primates, for veterinary use.

[0050] As used herein, the term “reference genome” may refer to a digital or previously identified nucleic acid sequence database assembled as a representative example of a species or subject. A reference genome may be assembled from nucleic acid sequences derived from multiple subjects, samples, or organisms and may not necessarily represent the nucleic acid composition of a single individual. Reference genomes may be used to map sequencing reads derived from a sample to chromosomal locations. For example, reference genomes used for human subjects and many other organisms can be found at the National Center for Biotechnology Information (ncbi.nlm.nih.gov).

[0051] The term "read segment" or "read" refers to any nucleotide sequence containing sequence reads obtained from an individual and / or a nucleotide sequence derived from the initial sequence read from a sample obtained from an individual.

[0052] The terms “sample,” “patient sample,” and “biological sample” encompass a variety of sample types obtained from patients, individuals, or subjects and can be used in diagnostic, prognostic, and / or monitoring assays. Patient samples may be obtained from healthy subjects, patients with disease, or patients with lung cancer. In certain embodiments, the “provided” sample may be obtained by the person (or machine) performing the assay, or by another person (or machine) and transferred to the person (or machine) performing the assay. Furthermore, samples obtained from patients may be divided, and only a portion may be used for diagnosis. Furthermore, a sample, or a portion thereof, may be stored under conditions that preserve the sample for later analysis. Specifically, this definition includes blood and other liquid samples of biological origin (including, but not limited to, peripheral blood, serum, plasma, umbilical cord blood, amniotic fluid, cerebrospinal fluid, urine, saliva, feces, and synovial fluid), solid tissue samples, e.g., biopsy specimens or tissue cultures or cells and their offspring derived therefrom. In certain embodiments, a sample includes cerebrospinal fluid. In certain embodiments, a sample includes a blood sample. In other embodiments, a sample includes a plasma sample. In yet another embodiment, serum samples are used. The definition of “sample” also includes samples that, after acquisition, have been manipulated or washed by any method, for example, centrifugation, filtration, precipitation, dialysis, chromatography, treatment with reagents, or concentrated for a particular cell population. These terms further encompass clinical samples and also include cells in culture, cell supernatants, tissue samples, organs, etc. Samples may also include fresh-frozen and / or formalin-fixed, paraffin-embedded tissue blocks, for example, blocks prepared from clinical or pathological biopsy material, blocks prepared for pathological analysis or immunohistochemical study.

[0053] The term "sequence read" refers to a nucleotide sequence read from a sample obtained from an individual. Sequence reads can be obtained by various methods known in the art.

[0054] As defined herein, a “therapeutically effective” amount (i.e., an effective dose) of a compound or drug means an amount sufficient to produce a desired therapeutic (e.g., clinical) outcome. The composition may be administered once or multiple times daily, including once every other day, or once or multiple times weekly. Those skilled in the art will understand that certain factors, including but not limited to the severity of the disease or disorder, previous treatments, the subject’s overall health and / or age, and other pre-existing conditions, may influence the dose and timing required to effectively treat the subject. Furthermore, treatment of a subject with a therapeutically effective amount of the compound of the present invention may consist of a single treatment or a series of treatments.

[0055] As used herein, terms such as “to treat,” “to treat,” and “treatment” refer to reducing or relieving a disorder and / or symptoms associated with the disorder. It is understood that treating a disorder or condition does not require the complete elimination of the disorder, condition, or symptoms associated therewith, though this is not excluded.

[0056] Genes: All genes, gene names, and gene products disclosed herein are intended to correspond to homologs derived from any species to which the compositions and methods disclosed herein are applicable. When a gene or gene product derived from a particular species is disclosed, this disclosure is intended to be illustrative only and should not be construed as limiting unless explicitly indicated by the relevant context. Thus, for example, the genes or gene products disclosed herein are intended to encompass homologous and / or orthologous genes and gene products derived from other species.

[0057] Scope: Throughout this application, various aspects of the invention can be presented in the form of a range. It should be understood that the description in the form of a range is for convenience and simplification only and should not be interpreted as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to specifically disclose all possible subranges and the individual numbers within those ranges. For example, a description of a range such as 1 to 6 should be considered to specifically disclose subranges such as 1 to 3, 1 to 4, 1 to 5, 2 to 4, 2 to 6, 3 to 6, and the individual numbers within those ranges, such as 1, 2, 2.7, 3, 4, 5, 5.3, and 6. This applies regardless of the width of the range.

[0058] Any composition or method provided herein may be combined with one or more of the other compositions and methods provided herein. [Brief explanation of the drawing]

[0059] The patent or application file shall include at least one drawing made in color. A copy of the patent or patent application publication accompanied by the color drawing will be provided by the Japan Patent Office upon request and payment of the required fees.

[0060] [Figure 1]Figures 1A–1C demonstrate the enrichment of motifs at the ends of cfDNA fragments and at the ends of repeated cfDNA fragments. 3 bp motifs are located around the fragment, including the outer 1 nucleotide and the first 2 nucleotides of the fragment. Vertical lines indicate the beginning of the fragment. Figure 1A: Frequency of 3 bp DNA motifs at the ends of DNA fragments after sonication shearing, at the ends of cfDNA fragments, and at the ends of cfDNA with “preferred” ends observed in at least 5% of repeated sequences. Relative frequencies are normalized by the observed occurrence of 3 bp motifs in the human genome. Figure 1B: Increased motif priority at repeated cfDNA fragment end locations, measured by the increase in the ratio of the number of cfDNA fragments starting or stopping at a particular location to the total amount of cfDNA fragments overlapping with a + / - 50 nucleotide window around that location. This included enrichment of T|CC and A|CC, as well as T|CG and A|CG, at these repeated cfDNA end locations. Figure 1C: X-ray crystal structure of PDB entry 7COW44. The TCG motif is red, and the nucleosome protein is shown on the gray surface, with a histone H1 linker at the top. Bases within 5 angstroms of the H1 linker are shown as spheres. [Figure 2-1]Figures 2A-2E demonstrate that DNA methylation is a determinant of cfDNA fragmentation. a. The frequency of CpGs observed at various positions within cfDNA fragments, counted from cfDNA cleavage, differs between methylated and unmethylated CpG sites. Unmethylated CpGs show a more uniform distribution throughout the cfDNA fragment, while methylated CpGs show concentration at the beginning of the cfDNA fragment. Figure 2B: The priority of a fragment to start at a CpG increases, and the level of methylation of that CpG increases. The priority of a cfDNA fragment to start at a CpG is measured by the ratio of cfDNA fragments that start at a CpG to the total amount of cfDNA fragment that overlaps with the + / - 50 base window around the first base of that CpG. Figure 2C: The opposite relationship is observed when cytosine precedes the CpG: if the CpG in this motif is not methylated, there is a priority for cfDNA fragments to start at a CCG. Figure 2D: CpGs on the X chromosome are known to be methylated differently in male and female individuals. X inactivation due to methylation of CpG islands leads to differences in the priority of cfDNA fragment end positions among these CpGs. With increasing methylation, more fragments start from CpGs, while fragments start from CCGs decrease. Figure 2E: No differences in methylation are observed between male and female individuals on autosomes. [Figure 2-2] Refer to the explanation in Figure 2-1. [Figure 3-1]Figures 3A–3G demonstrate the effects of CpG methylation and gene expression on cfDNA fragment coverage and size. Figure 3A: Sequence coverage in a CpG island region, aligned by mean methylation of CpG islands. Figure 3B: Sequence coverage in a transcription start site region, aligned by mean gene expression in myeloid cells. Figure 3C: Mean cfDNA fragment size in a CpG island region, aligned by mean methylation of CpG islands. Figure 3D: Mean cfDNA fragment size in a transcription start site region, aligned by mean gene expression in myeloid cells. Figure 3E: Correlation matrix (right) of significant gene set enrichment analysis of gene expression, DNA methylation in CpG islands, cfDNA fragment coverage across transcription start sites, and cfDNA coverage across CpG islands, all related to the same gene. Figure 3F: Representative gene set analysis (left) of KEGG neuron receptor-ligand interactions, a gene set with decreased expression and increased methylation in WBCs, showing increased cfDNA coverage at TSS and CpG sites. Figure 3G: cfDNA fragment size distribution (top) and cumulative diagram (bottom) for fragments with mutations (tumor-derived) compared to wild-type fragments (mainly leukocytes), fragments from regions with high-expression transcription start sites compared to fragments from regions with low-expression transcription start sites, and fragments from regions with methylated CpG islands compared to fragments from regions with unmethylated CpG islands. Figure 3G: Data from human isogeneic xenografts (IDH1 R132H mutant compared to IDH1 wild-type human glioblastoma cell line) showing increased coverage of human cfDNA in regions with increased methylation and decreased coverage in regions with increased gene expression across the top n regions for differential methylation or gene expression. [Figure 3-2] Refer to the explanation in Figure 3-1. [Figure 3-3] Refer to the explanation in Figure 3-1. [Figure 3-4] Refer to the explanation in Figure 3-1. [Figure 4]Figures 4A and 4B show a comparison of cfDNA fragment terminal motifs in differential methylation regions in individuals with and without pancreatic cancer. Figure 4A: Aggregate ratio of cfDNA fragments that started or terminated at specific motifs containing CpG, showing differential methylation between cfDNA from healthy individuals and pancreatic cancer tissue. The largest increase in signal was observed in pancreatic cancer patients (n=34), while cfDNA from patients with other cancers (colorectal cancer n=27, ovarian cancer n=28, lung cancer n=39, and breast cancer n=54) showed intermediate signals between those found in pancreatic cancer patients and individuals without cancer (n=244). Figure 4B: Predictions of this signal for detecting pancreatic cancer are shown as receiver operating characteristic curves compared to DELFI, while the DELFI approach was shown to be optimally predictive when combined with methylation-based signals in an ensemble model. [Figure 5] Figure 5 demonstrates the relative frequencies of nucleotides across 144 bp cfDNA fragments. The bar graph shows the relative frequencies of nucleotides across all 144 bp cfDNA fragments from 543 healthy individuals. The observed frequencies exhibit a 10.4 base periodicity in the alternating appearance of nucleotides A and T (more flexible) and C and G (less flexible). [Figure 6] Figure 6 demonstrates that the proportion of cfDNA fragments that begin or terminate at specific CpG-containing motifs is dependent on the CpG methylation status. Increased CpG methylation results in more fragments beginning at that site, while the opposite relationship is observed for fragments beginning at the CCG. This effect is independent of the genomic context in which the CpG appears, such as a CpG island, CpG shore, CpG shelf, or open sea. [Figure 7A]Figures 7A and 7B show the amount of cfDNA fragments that begin at motifs containing CpGs. The top two histograms (light blue and dark blue) show the normalized amount of fragments that begin at each position around the motif, obtained by aggregating low-coverage WGS of cfDNA from 543 cancer-free individuals. In the light blue histogram, the motif contains unmethylated CpGs (β value < 0.3). In the dark blue histogram, the motif contains methylated CpGs (β value > 0.7). The third histogram (brown) shows the difference between the top two histograms, indicating an increase in fragments that begin at a CpG when the CpG is methylated. If an additional cytosine is located before the CpG, and the subsequent CpG is methylated, a large decrease in the amount of cfDNA fragments that begin at that cytosine is also observed. Figure 7A shows the effect of methylation on cfDNA fragments that start around a 3 bp motif, and Figure 7B shows the effect of methylation on fragments that start around a 4 bp motif. [Figure 7B] Refer to the explanation in Figure 7A. [Figure 8] Figure 8 demonstrates that cfDNA fragment ends are affected by differences in innate methylation on the X chromosome between male and female individuals due to X inactivation. Similar to Figures 2D and 2E, which focus on CpG islands on the X chromosome and autosomes, fragments that begin at CpG Shore on the X chromosome differ between males and females due to increased methylation at these locations on the second X chromosome. The opposite pattern is observed at CpG shelf and open-sea CpG on the X chromosome. In these regions, female individuals show fewer fragments that begin at CpG and more fragments that begin at CCG, indicating less methylation of these CpGs on the female X chromosome. The opposite relationship is observed when cytosine precedes the CpG. In contrast, on autosomes, the amount of cfDNA fragments that begin at CpG or CpG preceded by cytosine was shown to be indistinguishable between males and females. [Figure 9]Figures 9A and 9B demonstrate the relationship between CpG island methylation, gene expression, and cfDNA coverage. Figure 9A: The dot plot shows that decreased CpG island methylation (lower β values) is associated with lower cfDNA coverage in CpG islands, while increased CpG island methylation (higher β values) is associated with higher cfDNA coverage in CpG islands. Figure 9B shows an inverse relationship between gene expression and cfDNA coverage around transcription start sites, where increased gene expression is associated with decreased cfDNA coverage, and vice versa. [Figure 10-1] Figures 10A–10E demonstrate the effects of methylation and gene expression on cfDNA coverage and fragment size in large regions surrounding CpG islands and transcription start sites. Figure 10A: Cumulative coverage and average cfDNA fragment size (centered around the center of the CpG islands) around 1000 most methylated and 1000 least methylated CpG islands (Figure 10C) show reduced coverage and smaller fragments in less methylated CpG islands. Figure 10B: Cumulative coverage and average cfDNA fragment size (centered around the transcription start sites) around 1000 most expressed genes and 1000 least expressed genes (Figure 10D) show reduced coverage and smaller fragment sizes around more expressed genes. The effect is most pronounced in the regions adjacent to the transcription start site. Figure 10E: Average methylation around the center of the CpG islands (centered around the center of the CpG islands) of 1000 most methylated and 1000 least methylated CpG islands. [Figure 10-2] Refer to the explanation in Figure 10-1. [Figure 11A]Figures 11A and 11B demonstrate the effects of gene expression and CpG island methylation on cfDNA coverage and the proportion of cfDNA fragments that start or terminate at CpG. Figure 11A. Cumulative cfDNA coverage around the transcription start site of the top 1000 most expressed genes and the top 1000 least expressed genes, both among all genes and in subgroups of genes with unmethylated CpG islands (β value < 0.3) or methylated CpG islands (β value > 0.7). Figure 11B. When genes are aligned by the amount of methylation of their associated CpG islands (left), the proportion of cfDNA fragments that start at CpG in these genes shows a similar increase. Aligning genes by expression (right) shows a much more limited effect on the proportion of cfDNA fragments that start at CpG in these genes. It was concluded that methylation has an independent effect on the amount of fragments that start at CpG compared to expression. [Figure 11B] Refer to the explanation in Figure 11A. [Figure 12A] Figures 12A and 12B show the number of expressed genes and methylation sites in leukocytes and cancer tissue. Figure 12A: Healthy leukocytes show genes that are expressed at a statistically significantly lower rate than in cancer tissue. Figure 12B: Leukocytes show CpG islands that are statistically significantly more methylated than in cancer tissue. BRCA: Breast cancer, COAD: Colon adenocarcinoma, LIHC: Hepatocellular carcinoma of the liver, LUAD: Lung adenocarcinoma, LUSC: Lung squamous cell carcinoma, OV: Ovarian cancer, PAAD: Pancreatic adenocarcinoma. [Figure 12B] Refer to the explanation in Figure 12A. [Figure 13]Figure 13 (including Figures 13A and 13B): Multivariate analysis showing the effects of CpG island methylation, RNA expression, and nucleosome positioning on cfDNA coverage and size. Figure 13A: Forest plot showing the regression coefficients of the model for coverage by nucleosome positioning score (WPS), RNA expression (RNA), and an index of methylation status (Meth=1 if β≧0.5, otherwise 0) as independent predictors. Both RNA and WPS were scaled to have unit standard deviations. Quadratic terms (WPS2) and cubic terms (WPS3) were included to model the nonlinear relationship between coverage and WPS. Furthermore, the inventors dependent the relationship between coverage and WPS on RNA expression level and methylation status through the addition of interaction terms. Analysis of variance (ANOVA) comparing the model with only terms WPS and RNA (WPS + WPS2 + WPS3 + RNA + WPS x RNA + WPS2 x RNA + WPS3 x RNA) to the full model shown in panel (a) was statistically significant (F5,18377=22.0, p<0.0001). This indicates that methylation is useful in explaining variations in coverage beyond the effects of nucleosome positioning and RNA expression alone. Similarly, ANOVA comparing the model with only terms WPS and Meth (WPS + WPS2 + WPS3 + Meth + WPS x Meth + WPS2 x Meth + WPS3 x Meth) to the full model demonstrated that RNA expression is statistically significant and useful in explaining variations in coverage beyond the effects of WPS and methylation alone (F5,18377=1271.2, p<0.0001). Corresponding ANOVA with WPS as linear or quadratic models was also significant throughout the analysis (p<0.0001). Figure 13B: Forest plot showing results for models with cfDNA fragment size (bp) as the dependent variable, and WPS, RNA, and Meth as independent variables. Furthermore, quadratic and cubic terms for WPS allowed for nonlinearity between fragment size and WPS.ANOVA analyses comparing the full model (panel b) with the model without methylation and the model without RNA expression were statistically significant (Meth ANOVA: F5,18539, p=1.3e-10; RNA ANOVA: F5,18539, p<0.0001). [Modes for carrying out the invention]

[0061] Detailed explanation The disclosures herein provide relationships between methylation, expression, and cfDNA fragmentation in healthy subjects and subjects with cancer. Furthermore, these disclosures also provide an analysis of features related to nucleosome positioning and cleavage-related motifs of cfDNA fragments in both healthy individuals and cancer patients. These disclosures also provide how epigenetic marks induce specific fragmentation patterns of cfDNA and how these relate to both methylation and gene expression. The following examples section demonstrates that differences in cfDNA fragmentation between healthy individuals and cancer patients can be identified using differentially methylated CpGs in a specific sequence context.

[0062] CpG methylation Epigenetics refers to genetic changes in gene expression that are not caused by changes in DNA. The most well-defined epigenetic change is the DNA methylation of cytosine by the DNA methyltransferase enzyme. Cytosine bound to guanine is called a CpG dinucleotide, and these are usually found in CpG-rich regions called CpG islands. A CpG island is defined as a sequence range of at least 200 bp, with a GC percentage exceeding 50% and a CpG observation-to-prediction (Obs / Exp) ratio exceeding 60%. The predicted number of CpG dimers within a window is calculated by multiplying the number of "C"s in the window by the number of "G"s in the window and dividing by the length of the window. A CpG island is defined as a region exceeding 500 bp with a guanine-cytosine content exceeding 55%. Up to 60% of CpG islands are located within the 5' regulatory (promoter) region of a gene. However, CpG islands that are not located within promoter regions can also be found within the coding and non-coding regions of genes, which may be targets for de novomethylation in cancer and aging. DNA methylation affects a variety of cellular processes, including apoptosis, the cell cycle, DNA damage repair, growth factor response, signaling, and tumor structure, all of which may contribute to the onset and progression of cancer.

[0063] Methylated cytosine can be found in CpG islands, shor regions, shelves, open-sea regions, and regions surrounding transcription sites of coding genes (-200 to -1500 bp, 5' untranslated region (UTR), and exon 1), as well as in gene bodies, the 3'UTR, and other / open-sea regions derived from genome-wide association studies. Shor regions are considered to be 0–2 kb regions from CpG islands, shelves are 2–4 kb regions from CpG islands, and other / open-sea regions are isolated CpG sites within the genome that do not have a specific name.

[0064] Therefore, in certain embodiments, genome sequences are assayed to determine circulating cell-free DNA (cfDNA) fragmentation by identifying the degree of genome-wide CpG methylation and the frequency of cfDNA breakpoints at multiple locations within the genome sequence, where repeated cfDNA fragment end enrichment at CpG sites correlates with higher genome-wide methylation levels and smaller cfDNA fragments, which are used to diagnose cancer.

[0065] In certain embodiments, the method further includes the steps of mapping a cfDNA fragment to a genome and comparing the terminal sequence of the cfDNA fragment with methylated and unmethylated CpG sites of cfDNA from a healthy subject. Methylated CpGs are enriched at the ends of A / T|CG cfDNA fragment sequences, and quantitative evaluation of the enrichment of cfDNA fragment ends in CpGs is performed. For each CpG, calculate the ratio of cfDNA fragments that start or terminate at the CpG dinucleotide position to the number of cfDNA fragments that have a start or termination position within 50 bp of the surrounding CpG. This includes: In certain embodiments, in female subjects compared to male subjects, cfDNA fragments obtained from the X chromosome in healthy subjects at the location of X chromosome CpG islands include increased cfDNA fragments terminating at CG and decreased cfDNA fragments terminating at CCG. In certain embodiments, the cfDNA sequence coverage of enriched cfDNA fragment terminal sequences includes increased methylation across the entire region of methylated CpG islands compared to said cfDNA fragment terminal sequences with decreased or less frequent occurrences. In certain embodiments, gene expression at transcription start sites (TSS) is inversely correlated with cfDNA coverage at TSSs.

[0066] cfDNA fragmentation profile A cfDNA fragmentation profile may include one or more cfDNA fragmentation patterns. The cfDNA fragmentation patterns may include any suitable cfDNA fragmentation patterns. Examples of cfDNA fragmentation patterns include, but are not limited to, the median fragment size, fragment size distribution, the ratio of small to large cfDNA fragments, and cfDNA fragment coverage. In some embodiments, the cfDNA fragmentation pattern includes two or more (e.g., two, three, or four) of the median fragment size, fragment size distribution, the ratio of small to large cfDNA fragments, and cfDNA fragment coverage. In some embodiments, the cfDNA fragmentation profile may be a genome-wide cfDNA profile (e.g., a genome-wide cfDNA profile in a genome-wide window). In some embodiments, the cfDNA fragmentation profile may be a targeted region profile. The targeted region may be any suitable part of the genome (e.g., a chromosomal region). Examples of chromosomal regions from which cfDNA fragmentation profiles can be determined as described herein include, but are not limited to, portions of chromosomes (e.g., portions of 2q, 4p, 5p, 6q, 7p, 8q, 9q, 10q, 11q, 12q, and / or 14q) and chromosomal arms (e.g., chromosomal arms of 8q, 13q, 11q, and / or 3p). In some embodiments, the cfDNA fragmentation profile may include two or more target region profiles.

[0067] In some embodiments, cfDNA fragmentation profiles can be used to identify changes in cfDNA fragment length (e.g., alterations). These alterations may be genome-wide or to alter one or more target regions / locuses. The target regions may be any region containing one or more cancer-specific alterations. In some embodiments, cfDNA fragmentation profiles can be used to identify (for example, identify simultaneously) about 10 to about 500 changes (e.g., about 25 to about 500, about 50 to about 500, about 100 to about 500, about 200 to about 500, about 300 to about 500, about 10 to about 400, about 10 to about 300, about 10 to about 200, about 10 to about 100, about 10 to about 50, about 20 to about 400, about 30 to about 300, about 40 to about 200, about 50 to about 100, about 20 to about 100, about 25 to about 75, about 50 to about 250, or about 100 to about 200 changes).

[0068] cfDNA fragmentation profiles can be obtained using any suitable method. In some embodiments, cfDNA derived from mammals (e.g., mammals with or suspected of having cancer) can be processed into a sequencing library, which can then be subjected to whole-genome sequencing (e.g., low-coverage whole-genome sequencing), mapped to the genome, and analyzed to determine cfDNA fragment lengths. The mapped sequences can be analyzed within a non-overlapping window covering the genome. The window can be of any suitable size. For example, a window may be several thousand to several million bases long. As a non-limiting example, a window may be approximately 5 megabases (Mb) long. Any suitable number of windows can be mapped. For example, tens to several thousand windows can be mapped in the genome. For example, hundreds to several thousand windows can be mapped in the genome. Within each window, cfDNA fragmentation profiles can be determined.

[0069] In some embodiments, the methods and materials described herein may also include machine learning. For example, machine learning can be used to identify mutation frequencies, changes in fragmentation profiles (e.g., using cfDNA fragment coverage, cfDNA fragment size, chromosome coverage, and mtDNA).

[0070] In some embodiments, the process of determining a cfDNA fragmentation profile in a mammal can be used to determine if the mammal has cancer. For example, cfDNA fragments obtained from a mammal (e.g., derived from a sample obtained from a mammal) can be subjected to low-coverage-hole genome sequencing, and the sequenced fragments can be mapped to the genome and evaluated to determine the cfDNA fragmentation profile. As described herein, the cfDNA fragmentation profile of a mammal with cancer is heterogeneous (e.g., in terms of fragment length) compared to the cfDNA fragmentation profile of a healthy mammal (e.g., a mammal without cancer). Therefore, methods and materials for evaluating, monitoring, and / or treating a mammal (e.g., a human) that has or is suspected of having cancer are also provided. In some embodiments, methods and materials for determining if a mammal has cancer are provided. For example, a sample obtained from a mammal (e.g., a blood sample) can be evaluated to determine the presence of cancer in the mammal, and optionally the tissue of origin of the cancer, at least partially based on the mammal's cfDNA fragmentation profile. In some embodiments, methods and materials for monitoring if a mammal has cancer are provided. For example, a sample obtained from a mammal (e.g., a blood sample) can be evaluated to determine whether the mammal has cancer, at least partially based on its cfDNA fragmentation profile. In some embodiments, methods and materials are provided for determining whether a mammal has cancer and for treating the mammal by administering one or more cancer treatments. For example, a sample obtained from a mammal (e.g., a blood sample) can be evaluated to determine whether the mammal has cancer, at least partially based on its cfDNA fragmentation profile, and one or more cancer treatments can be administered to the mammal.

[0071] In some embodiments, tumor-derived DNA can be detected using cfDNA fragmentation profiles. For example, tumor-derived DNA can be detected using cfDNA fragmentation profiles by comparing a cfDNA fragmentation profile of a mammal with or suspected of having cancer with a reference cfDNA fragmentation profile (e.g., a cfDNA fragmentation profile of a healthy mammal, and / or a nucleosome DNA fragmentation profile of a healthy cell derived from a mammal with or suspected of having cancer). In some embodiments, the reference cfDNA fragmentation profile is a pre-created profile derived from a healthy mammal. For example, a reference cfDNA fragmentation profile in a healthy mammal can be determined using the method provided herein, and that reference cfDNA fragmentation profile can be stored (e.g., on a computer or other electronic storage medium) for future comparison with a tested cfDNA fragmentation profile in a mammal with or suspected of having cancer. In some embodiments, the reference cfDNA fragmentation profile of a healthy mammal (e.g., a stored cfDNA fragmentation profile) is determined across the entire genome. In some embodiments, the reference cfDNA fragmentation profile of a healthy mammal (e.g., a stored cfDNA fragmentation profile) is determined across subgenome segments.

[0072] In some embodiments, cfDNA fragmentation profiles can be used to determine if a mammal (e.g., human) has cancer (e.g., liver cancer, colorectal cancer, lung cancer, breast cancer, stomach cancer, pancreatic cancer, cholangiocarcinoma, and / or ovarian cancer).

[0073] cfDNA fragmentation profiles can include cfDNA fragment size patterns. cfDNA fragments can be of any suitable size. For example, cfDNA fragments can range in length from approximately 50 base pairs (bp) to approximately 400 bp.

[0074] cfDNA fragmentation profiles may include cfDNA fragment size distributions. As described herein, mammals with cancer may have cfDNA fragment size distributions that are more variable than those in healthy mammals. In some embodiments, the size distribution may be inside the target region. Healthy mammals (e.g., mammals without cancer) may have a target region cfDNA fragment size distribution of about 1 or less than about 1. In some embodiments, mammals with cancer may have a target region cfDNA fragment size distribution that is longer than that in healthy mammals (e.g., 10, 15, 20, 25, 30, 35, 40, 45, 50 bp, or longer, or any number of base pairs between these values). In some embodiments, mammals with cancer may have a target region cfDNA fragment size distribution that is shorter than that in healthy mammals (e.g., 10, 15, 20, 25, 30, 35, 40, 45, 50 bp, or shorter, or any number of base pairs between these values). In some embodiments, the size distribution can be genome-wide. Healthy mammals (e.g., mammals without cancer) may have very similar genome-wide distributions of short and long cfDNA fragments. In some embodiments, mammals with cancer may have one or more changes (e.g., increases and decreases) in cfDNA fragment size genome-wide. One or more changes can be any suitable chromosomal region of the genome. For example, one change may be in one part of a single chromosome. Examples of chromosomal parts that may contain one or more changes in cfDNA fragment size include, non-limitingly, parts of 2q, 4p, 5p, 6q, 7p, 8q, 9q, 10q, 11q, 12q, and 14q. For example, the change may span a single chromosomal arm (e.g., an entire chromosomal arm).

[0075] cfDNA fragmentation profiles can include the ratio of small cfDNA fragments to large cfDNA fragments, and the correlation of fragment ratios to a reference fragment ratio. With respect to the ratio of small cfDNA fragments to large cfDNA fragments as used herein, small cfDNA fragments can be approximately 100 bp to 150 bp long. With respect to the ratio of small cfDNA fragments to large cfDNA fragments as used herein, large cfDNA fragments can be approximately 151 bp to 220 bp long. Mammals with cancer may have lower (e.g., 1 / 2, 1 / 3, 1 / 4, 1 / 5, 1 / 6, 1 / 7, 1 / 8, 1 / 9, 1 / 10, or less) fragment ratio correlations (e.g., correlation of cfDNA fragment ratios to a reference DNA fragment ratio, such as the DNA fragment ratio from one or more healthy mammals) than healthy mammals. Healthy mammals (e.g., mammals without cancer) may have a fragment ratio correlation (e.g., correlation of cfDNA fragment ratios to reference DNA fragment ratios, such as the ratio of DNA fragments from one or more healthy mammals) of about 1 (e.g., about 0.96). In some embodiments, mammals with cancer may have a fragment ratio correlation (e.g., correlation of cfDNA fragment ratios to reference DNA fragment ratios, such as the ratio of DNA fragments from one or more healthy mammals) that is, on average, lower than the fragment ratio correlation in healthy mammals (e.g., correlation of cfDNA fragment ratios to reference DNA fragment ratios, such as the ratio of DNA fragments from one or more healthy mammals).

[0076] A cfDNA fragmentation profile may include coverage of all fragments. Coverage of all fragments may include a coverage window (e.g., a non-overlap window). In some embodiments, coverage of all fragments may include a window for small fragments (e.g., fragments approximately 100 bp to 150 bp in length). In some embodiments, coverage of all fragments may include a window for large fragments (e.g., fragments approximately 151 bp to 220 bp in length).

[0077] In certain embodiments, cfDNA fragmentation profiles can be used to identify the molecular origin of cfDNA in a patient and to identify genomic and chromatin features associated with changes in fragmentation.

[0078] In some embodiments, cfDNA fragmentation profiles can be used to identify the primary site of cancer (e.g., liver cancer, colorectal cancer, lung cancer, breast cancer, gastric cancer, pancreatic cancer, bile duct cancer, or ovarian cancer). For example, cfDNA fragmentation profiles can be used to identify localized cancers. If the cfDNA fragmentation profile includes a target region profile, one or more modifications described herein can be used to identify the primary site of cancer. In some embodiments, one or more modifications in chromosomal regions can be used to identify the primary site of cancer.

[0079] cfDNA fragmentation profiles can be obtained using any suitable method. In some embodiments, cfDNA derived from mammals (e.g., mammals with or suspected of having cancer) can be processed into a sequencing library, which can then be subjected to whole-genome sequencing (e.g., low-coverage whole-genome sequencing) to map to the genome, analyze, and determine the cfDNA fragment lengths. The mapped sequences can be analyzed within a non-overlapping window covering the genome. The window can be of any suitable size. For example, the window may be several thousand to several million base pairs long. As a non-limiting example, the window may be approximately 5 megabases (Mb) long. Any suitable number of windows can be mapped. For example, tens to several thousand windows can be mapped in the genome. For example, hundreds to several thousand windows can be mapped in the genome. Within each window, a cfDNA fragmentation profile can be determined.

[0080] In some embodiments, the methods and materials described herein may also include machine learning. For example, machine learning can be used to identify changes in the fragmentation profile (e.g., using cfDNA fragment coverage, cfDNA fragment size, chromosomal coverage, and mtDNA). Various machine learning algorithms can be used to analyze the fragmentation profile. For example, a stochastic gradient boosting model (GBM; see, e.g., Friedman et al., 2001 Ann Stat 29:1189-1232; and Friedman et al., 2002 Comput Stat Data An 38:367-378) can be used to distinguish between healthy individuals and cancer patients using the fragmentation profile. For all 504 bins, the GC-corrected total coverage and short fragment coverage can be centered and scaled to have a mean of 0 and a standard deviation of 1 for each sample. Further features include Z-scores and mitochondrial representations (log10-transformed proportions of reads mapped to mitochondria) for each of the 39 autosomal arms. To estimate the prediction error of this approach, 10-fold cross-validation can be used, as described elsewhere (see, e.g., Efron et al., 1997 J Am Stat Assoc 92, 548-560). In each cross-validation run, feature selection was performed only on the training data, and bins with high correlation (correlation > 0.9) or near-zero variance were excluded. Stochastic gradient boosted machine learning can be implemented using the R package gbm package. To average the prediction errors from patient randomization across multiple folds, the 10-fold cross-validation method can be repeated.

[0081] In some embodiments, the machine learning model is a neural network (NN). In certain embodiments, the neural network is a convolutional neural network, a recurrent neural network, or a deep learning neural network. In some embodiments, the machine learning model is a random forest, a logistic regression, or an unsupervised clustering model. In other embodiments of the neural network model, the model may be a deep neural network (DNN) or a higher-order neural network (HONN) with multiple locally and fully connected hidden layers. In the case of a DNN, a restricted Boltzmann machine (RBM) can be used to pre-train the neural nodes in the input and connection layers. In the case of an HONN, a mean-covariance RBM can be used to pre-train the neural nodes in the input and connection layers.

[0082] In certain embodiments, a computer system for obtaining access to database files and running one or more software programs may include a server, a data storage device, a network, and a user interface device. The server may be a hypervisor-based system running one or more guest partitions that host an operating system having a module containing server configuration information. In further embodiments, the system may include a storage controller, or a storage server configured to manage data communication between the data storage device and the server or other components communicating with the network. In alternative embodiments, the storage controller may be connected to the network.

[0083] In certain embodiments, user interface devices are referred to in a broad sense and are intended to encompass appropriate processor-based devices such as desktop computers, laptop computers, personal digital assistants (PDAs) or tablet computers, smartphones, or other mobile communication devices that can access a network. In further embodiments, user interface devices can access the Internet or other wide area networks or local area networks to access web applications or web services hosted by servers and provide a user interface for users to input or receive information. Networks can facilitate data communication between servers and user interface devices. Networks may include, but are not limited to, direct connections between PCs, local area networks (LANs), wide area networks (WANs), modem-to-modem connections, the Internet, combinations of the above, or any other communication networks currently known or to be developed in the field of networking that enable two or more computers to communicate with each other.

[0084] In certain embodiments, a computer system comprises a central processing unit ("CPU") connected to a system bus. The CPU may be a general-purpose CPU or microprocessor, a graphics processing unit ("GPU"), and / or a microcontroller. The CPU can execute various logical instructions according to this embodiment. The computer system may also comprise random access memory (RAM), which includes synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous dynamic RAM (SDRAM), and the like. The computer system may utilize RAM to store various data structures used by software applications. The computer system may also comprise read-only memory (ROM), which may include PROM, EPROM, EEPROM, optical memory, and the like. ROM may store configuration information for booting the computer system. RAM and ROM hold user data and system data, and both RAM and ROM may be accessed randomly.

[0085] A computer system may also include I / O adapters, communication adapters, user interface adapters, and display adapters. In certain embodiments, I / O adapters and / or user interface adapters enable a user to interact with the computer system. In further embodiments, a display adapter may display a graphical user interface (GUI) associated with software or a web-based application on a display device such as a monitor or touchscreen.

[0086] An I / O adapter can connect one or more storage devices from among hard drives, solid-state storage devices, flash drives, compact disc (CD) drives, floppy disk drives, and tape drives to a computer system. Data storage may be a separate server connected to the computer system via a network connection to the I / O adapter. A communication adapter may be adapted to connect the computer system to a network that may be one or more of a LAN, WAN, and / or the Internet. A user interface adapter connects user input devices such as a keyboard, pointing device, and / or touchscreen to the computer system. A display adapter may be driven by the CPU to control the display on a display device.

[0087] A computer system is provided as an example of one type of computing device that can be adapted to perform the functions of a server and / or user interface device. Various embodiments and / or steps of the cancer detection model disclosed herein can be implemented using any suitable processor-based device, not limited to, personal data assistants (PDAs), tablet computers, smartphones, computer game consoles, and multiprocessor servers. Furthermore, various embodiments of the cancer detection method of this disclosure can be implemented on application-specific integrated circuits (ASICs), very large-scale integrated circuits (VLSIs), or other circuits. In fact, a person skilled in the art can utilize any number of suitable structures capable of performing logical operations according to the embodiments described. For example, the computer system may be virtualized for access by multiple users and / or applications.

[0088] When the various methods, steps, and parameter calculations disclosed herein are implemented within firmware and / or software, the various functions described above can be stored as one or more instructions or codes in a computer-readable medium. Examples include non-temporary computer-readable medium coded in data structures and computer-readable medium coded in computer programs. Computer-readable medium includes physical computer storage media. The storage medium can be any available medium accessible by a computer. For example, and not limited to, such computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage devices, magnetic disk storage devices, or any other (e.g., cloud-based) medium that can be used to store desired program code in the form of instructions or data structures and is accessible by a computer. Disks and discs include compact discs (CDs), laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs. Generally, a disk reproduces data magnetically, while a disc reproduces data optically. The combination of the above is also included within the scope of computer-readable media.

[0089] In addition to storage on computer-readable media, instructions and / or data may be provided as signals on a transmission medium included in a communication device. For example, a communication device may include transceivers having signals indicating instructions and data. The instructions and data are configured to cause one or more processors to perform the functions embodied herein.

[0090] Treatment method The methods described herein include steps for identifying a mammal as having cancer. These methods include steps for extracting cell-free DNA (cfDNA) from a biological sample of interest, constructing a genomic library from the extracted cfDNA, sequencing individual cfDNA molecules to obtain fragmentation profiles, analyzing the characteristics of nucleosome positioning and cleavage-related motifs of cfDNA fragments in both healthy individuals and cancer patients, and analyzing epigenetic marks that give rise to specific patterns of cfDNA fragmentation and how these are related to both methylation and gene expression. Using this information, it is possible to identify differences in cfDNA fragmentation between healthy individuals and cancer patients using differentially methylated CpGs in a specific sequence context.

[0091] In another context, the methods for diagnosing cancer and treating the affected area are: The process involves obtaining a sample from a subject, assaying changes in circulating cfDNA fragment size compared to normal and tumor-derived cell-free DNA (cfDNA) controls, assaying CpG methylation at the ends of cfDNA fragments, and evaluating fragment end expression at CG and CCG sites through low-coverage-hole genomic cfDNA analysis; diagnosing a subject with cancer; and treating the subject. This includes: In certain embodiments, an increase in cfDNA fragments terminating at N|CCG compared to a normal control is a diagnosis of cancer. In certain embodiments, a decrease in cfDNA fragments terminating at N|CCG is an indicator of a cancer-free subject. In certain embodiments, an increase in cfDNA fragments terminating at CG compared to a normal control is a diagnosis of cancer. In certain embodiments, cfDNA fragments are obtained from genomic regions with increased methylation compared to a normal methylation control of the genome. In certain embodiments, this further includes incorporating the distribution of fragment end positions at CG and CCG sites in a gradient boosted tree machine learning model.

[0092] In another aspect, a method for diagnosing and treating a subject diagnosed with cancer includes the steps of: assaying a genomic sequence to identify genome-wide CpG methylation; determining circulating cell-free DNA (cfDNA) fragmentation by analyzing the frequency of cfDNA breakpoints at multiple locations in the genomic sequence, wherein repeated cfDNA fragment end enrichment at CpG sites correlates with higher genome-wide methylation levels and smaller cfDNA fragments that constitute a diagnosis of cancer; diagnosing the subject with cancer if hypomethylation and / or increased gene expression or decreased cfDNA fragment size is detected; and treating the subject. In certain embodiments, the genomic sequence is assayed by whole-genome sequencing or by obtaining a whole-genome sequence from a database, and by pooling cfDNA sequences. In certain embodiments, the analysis of cfDNA breakpoint frequency includes calculating the ratio of the number of cfDNA fragments that start or terminate at a particular location to the number of fragments that have a start or termination location within 50 bp surrounding that location. In certain embodiments, cfDNA fragments containing similar terminal sequences contain similar motifs. In certain embodiments, the motifs contain thymine or adenine prior to the beginning of the cfDNA fragment sequence, and two cytosines (A / T|CC) or cytosines followed by guanine (A / T|CG) as the first two nucleotides of the cfDNA fragment sequence. In certain embodiments, the frequency of the motifs is increased in healthy subjects compared to subjects with cancer. In certain embodiments, the frequency of A / T|CC motifs exceeds the frequency of A / T|CG motifs. In certain embodiments, A / T|CG motifs are located adjacent to the histone H1 linker or centered 100 to 200 base pairs from the histone H1 linker. In certain embodiments, the internal region of the cfDNA fragments is enriched with adenine and thymine. In certain embodiments, the method further includes the steps of mapping a cfDNA fragment to a genome and comparing the terminal sequence of the cfDNA fragment with methylated and unmethylated CpG sites of cfDNA derived from a healthy subject.In certain embodiments, methylated CpG is enriched at the ends of A / T|CG cfDNA fragment sequences.

[0093] In certain embodiments, quantitative evaluation of cfDNA fragment end enrichment in CpG is, For each CpG, calculate the ratio of cfDNA fragments that start or terminate at the CpG dinucleotide position to the number of cfDNA fragments that have a start or termination position within 50 bp of the surrounding CpG. This includes, in certain embodiments, in female subjects compared to male subjects, cfDNA fragments obtained from the X chromosome in healthy subjects at the location of the X chromosome CpG island include increased cfDNA fragments terminating at CG and decreased cfDNA fragments terminating at CCG. In certain embodiments, the cfDNA sequence coverage of the enriched cfDNA fragment terminal sequences includes increased methylation across the entire region of the methylated CpG island compared to the cfDNA fragment terminal sequences of reduced or less frequent occurrences. Mammals may have previously undergone cancer treatment to treat cancer. The method may also include a step of monitoring the mammal for the presence of cancer after cancer treatment.

[0094] In certain embodiments, the subject is diagnosed with cancer, for example, early-stage cancer. In certain embodiments, the type and stage of cancer are identified, and the subject is treated with one or more cancer therapies.

[0095] In some embodiments, methods and materials are provided herein for evaluating, monitoring, and / or treating mammals (e.g., humans) that have or are suspected of having cancer. In some embodiments, methods and materials are provided for determining that a mammal has cancer.

[0096] In some embodiments, methods and materials are provided for determining that a mammal has cancer and for treating the mammal by administering one or more treatments. In some embodiments, the mammal may be monitored (or selected for enhanced monitoring) and / or undergo further diagnostic tests during or after a course of cancer treatment (e.g., any of the cancer treatments described herein). In some embodiments, monitoring may include evaluating the mammal by, for example, evaluating a sample (e.g., a blood sample) obtained from the mammal having or suspected of having cancer, in order to determine the characteristics of nucleosome positioning and cleavage-related motifs of cfDNA fragments in both healthy individuals and cancer patients, and to analyze epigenetic marks that give rise to specific patterns of cfDNA fragmentation and how these relate to both methylation and gene expression. Using this information, differentially methylated CpGs in a specific sequence context may be used to identify differences in cfDNA fragmentation between healthy individuals and cancer patients, identify the response to treatment, and / or identify the mammal having cancer (e.g., residual cancer).

[0097] Any suitable mammal may be evaluated, monitored, and / or treated as described herein. A mammal may be a mammal that has liver cancer. A mammal may be a mammal suspected of having liver cancer. Examples of mammals that may be evaluated, monitored, and / or treated as described herein include, but are not limited to, humans, primates such as monkeys, dogs, cats, horses, cattle, pigs, sheep, mice, and rats.

[0098] Any suitable sample of mammalian origin can be evaluated as described herein (e.g., for DNA fragmentation patterns). In some embodiments, the sample may contain DNA (e.g., genomic DNA). In some embodiments, the sample may contain cfDNA (e.g., circulating tumor DNA (ctDNA)). In some embodiments, the sample may be a bodily fluid sample (e.g., a liquid biopsy). Examples of samples that may contain DNA and / or polypeptides include, but are not limited to, blood (e.g., whole blood, serum, or plasma), amniotic membrane, tissue, urine, cerebrospinal fluid, saliva, sputum, bronchoalveolar lavage fluid, bile, lymph, cystic fluid, feces, ascites, Papanicolaou smear, breast milk, and exhaled condensate.

[0099] Mammalian-derived samples evaluated as described herein may contain any appropriate amount of cfDNA. In some embodiments, the sample may contain a limited amount of DNA. For example, cfDNA fragmentation profiles can be obtained from samples containing less DNA than typically required for other cfDNA analysis methods, such as those described in Phallen et al., 2017 Sci Transl Med 9; Cohen et al., 2018 Science 359:926; Newman et al., 2014 Nat Med 20:548; and Newman et al., 2016 Nat Biotechnol 34:547.

[0100] In some embodiments, the sample can be processed (for example, to isolate and / or purify DNA and / or polypeptides from the sample). For example, the isolation and / or purification of DNA may include cell lysis (using, for example, detergents and / or surfactants), protein removal (using, for example, proteases), and / or RNA removal (using, for example, RNases). Another example of the isolation and / or purification of polypeptides may include cell lysis (using, for example, detergents and / or surfactants), DNA removal (using, for example, DNases), and / or RNA removal (using, for example, RNases).

[0101] Cancer may be cancer at any stage. In some embodiments, cancer may be early-stage cancer. In some embodiments, cancer may be asymptomatic cancer. In some embodiments, cancer may be residual disease and / or recurrence (e.g., after surgical resection and / or cancer therapy). Cancer may be any type of cancer. Examples of cancer types that may be evaluated, monitored, and / or treated as described herein include, but are not limited to, colorectal cancer, lung cancer, breast cancer, gastric cancer, pancreatic cancer, cholangiocarcinoma, and ovarian cancer.

[0102] When treating a mammal having or suspected of having liver cancer as described herein, the mammal may be subjected to one or more cancer treatments. The cancer treatment may be any appropriate cancer treatment. One or more cancer treatments described herein may be administered to the mammal at any appropriate frequency (e.g., once or multiple times over a period ranging from several days to several weeks). Examples of cancer treatments include, but are not limited to, adjuvant chemotherapy, neoadjuvant chemotherapy, radiotherapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T-cell therapy (e.g., T cells having chimeric antigen receptors and / or wild-type or modified T-cell receptors), targeted therapies such as the administration of kinase inhibitors (e.g., kinase inhibitors targeting specific genetic damage such as translocation or mutation) (e.g., kinase inhibitors, antibodies, bispecific antibodies), signaling inhibitors, bispecific antibodies or antibody fragments (e.g., BiTE), monoclonal antibodies, immune checkpoint inhibitors, surgery (e.g., surgical resection), or combinations thereof. In some embodiments, cancer treatment can reduce the severity of cancer, lessen the symptoms of cancer, and / or reduce the number of cancer cells present in the mammal.

[0103] In some embodiments, cancer treatment may include immune checkpoint inhibitors. Non-exclusive examples of immune checkpoint inhibitors include nivolumab (Opdivo), pembrolizumab (Keytruda), atezolizumab (Tecentriq), avelumab (Bavencio), durvalumab (Imfinzi), and ipilimumab (Yervoy).

[0104] Cancer therapy generally includes various combination therapies with chemo-based and radiation-based treatments. Combination chemotherapy includes, for example, cisplatin (CDDP), carboplatin, procarbazine, mechloretamine, cyclophosphamide, camptothecin, ifosfamide, melphalan, chlorambucil, busulfan, nitrosourea, dactinomycin, daunorubicin, doxorubicin, bleomycin, plicomycin, mitomycin, etoposide (VP16), tamoxifen, raloxifene, estrogen receptor conjugates, taxol, gemcitabien, navelbine, famesil-protein transferase inhibitors, transplatinum, 5-fluorouracil, vincristine, vinblastine and methotrexate, temazolomide (aqueous form of DTIC), or any of the aforementioned analogues or derivative variants. The combination of chemotherapy and biological therapy is known as biochemotherapy. Chemotherapy may also be administered in a series of low doses, known as metronomic chemotherapy.

[0105] Further combination chemotherapy may include, for example, alkylating agents such as thiotepa and cyclosphosphamide; alkyl sulfonates such as busulfan, improsulfan, and piposulfan; aziridines such as benzodopa, carbocon, meturedopa, and uredopa; altretamine, triethylenemelamine, triethylenephosphoramide, triethylenethiophosphoramide, and trimethylolome Ethyleneimine and methylamelamine, including lamin (trimethylolomelamime); acetogenins (especially bulatacin and bulatacinone); camptothecin (including its synthetic analog topotecan); bryostatin; callistatin; CC-1065 (including its adzelesin, carzelesin, and biceresin synthetic analogs); cryptophycin (especially cryptophycin 1 and cryptophycin 1); Icin 8); Dorastatin; Duocalmycin (including synthetic analogs KW-2189 and CB1-TM1); Erytherobin; Pancratistatin; Sarcodictyin; Spongistatin; Nitrogen mustard, e.g., chlorambucil, chlornafadin, chlorophosphamide, estramustine, ifosfamide, mechloretamine, mechloretamine oxide hydrochloride, melphalan, Novetamine Novembichin, phenesterine, prednimustine, trophosphamide, uracil mustard; nitrosourea, e.g., carmustine, chlorozotocin, fotemustine, lomustine, nimustine, and ranimustine; antibiotics, e.g., engine antibiotics (e.g., calitiamycin, especially calitiamycin γII and calitiamycin ωII); dynemicin, including dynemicin A; bisphosphonates, e.g., clodronate; esperamicin;Furthermore, neocardinostatin chromophore and related pigment protein enediin antibiotics (antiobiotics) chromophore, acrasinomycin, actinomycin, authrarmycin, azaserin, bleomycin, kactinomycin, carabicin, carminomycin, cardinophilin, chromomycinis, dactinomycin, daunorubicin, detorubicin, 6-diazo-5-oxo-L-norleucine, doxorubicin (morpholino-doxol) (including bicine, cyanomorpholino-doxorubicin, 2-pyrrolino-doxorubicin, and deoxydoxorubicin), epirubicin, esorubicin, idarubicin, marcelomycin, mitomycin, e.g., mitomycin C, mycophenolic acid, nogaramycin, olibomycin, peplomycin, potfiromycin, puromycin, quelamycin, rodorubicin, streptonigrin, streptozocin, tubercidine, yubenimex, zinosta Tin and zolbicin; antimetabolites, e.g., methotrexate and 5-fluorouracil (5-FU); folate analogs, e.g., denopterin, pteropterin, trimethrexate; purine analogs, e.g., fludarabine, 6-mercaptopurine, thiamiprine, thioguanine; pyrimidine analogs, e.g., ancitabine, azacitidine, 6-azauridine, carmofur, cytarabine, dideoxyuridine, doxifluridine, enocitabine, phloxuridine; androgens, e.g., carsterone, propionic acid Dromostanolone, epithiostanol, mepitiostane, testolactone; anti-adrenal agents, e.g., mitotane, trilostane; folic acid supplements, e.g., folinic acid; acegraton; aldofamide glycoside; aminolevulinic acid; enyluracil; amsacrin; bestrabusil; bisanthren; edatraxate; defofamine; demecoltin; diaziquan; elformithine; eriptinium acetate; eposylone; etogluside; gallium nitrate; hydroxyurea;Lentinan; lonidynin; mytansinoids, e.g., mytansin and ansamitocin; mitogwazone; mitoxantrone; mopidanmol; nitraerine; pentostatin; fenamet; pirarubicin; losoxantrone; podophyllinic acid; 2-ethylhydrazide; procarbazine; PSK polysaccharide complex; razoxane; rhizoxin; schizo Firan (sizofiran); spirogermanium; tenuazonic acid; triadiquan; 2,2',2''-trichlorotriethylamine; trichothecene (especially T-2 toxin, verracurin A, roridin A, and anguidine); urethane; vindesine; dacarbazine; mannomustine; mitobronitol; mitractol; pipobromane; gacytosine; arabinoside ("Ara-C") ;Cyclophosphamide;Taxoids, e.g., paclitaxel and docetaxel gemcitabine;6-thioguanine;Mercaptopurine;Platinum-coordinated complexes, e.g., cisplatin, oxaliplatin, and carboplatin;Vinblastine;Platinum;Etoposide (VP-16);Ifosfamide;Mitoxantrone;Vincristine;Vinorelbine;Novantrone;Teniposide;Edatrexate;Daunomycin;Aminopterin;Xeloda;Ibandronate;Irinotecan (e.g.) This includes CPT-11; the topoisomerase inhibitor RFS2000; difluoromethylornithine (DMFO); retinoids, such as retinoic acid; capecitabine; carboplatin, procarbazine, plicomycin, gemcitabien, navelbine, farnesyl-protein transferase inhibitors, transplatinum, and any pharmaceutically acceptable salts, acids, or derivatives thereof.

[0106] Immunotherapy generally relies on the use of immune effector cells and molecules that target and destroy cancer cells. Immune effectors may be antibodies specific to certain markers on the surface of tumor cells, for example. Antibodies may serve as effectors of therapy on their own, or they may mobilize other cells that actually cause cell death. Antibodies may also be conjugated with drugs or toxins (such as chemotherapeutic agents, radionuclides, lysine A chain, cholera toxin, pertussis toxin, etc.) and serve simply as targeted agents. Alternatively, effectors may be lymphocytes carrying surface molecules that directly or indirectly interact with tumor cell targets. Various effector cells include cytotoxic T cells and NK cells, as well as genetically modified variants of these cell types that express chimeric antigen receptors.

[0107] Immunotherapy may include the suppression of T regulatory cells (Tregs), myeloid-derived suppressor cells (MDSCs), and cancer-associated fibroblasts (CAFs). In some embodiments, immunotherapy is a tumor vaccine (e.g., whole tumor cell vaccines, peptides, and recombinant tumor-associated antigen vaccines) or adoptive cell therapy (ACT) (e.g., T cells, natural killer cells, TILs, and LAK cells). T cells may be engineered with chimeric antigen receptors (CARs) or T cell receptors (TCRs) for specific tumor antigens. As used herein, a chimeric antigen receptor (or CAR) can refer to any engineered receptor that, when expressed on a T cell, confers the specificity of the CAR to the T cell. Once produced using standard molecular techniques, T cells expressing chimeric antigen receptors can be introduced into a patient by techniques such as adoptive cell transfer. In some aspects, the T cells are activated CD4 and / or CD8 T cells in an organism, characterized by γ-IFN-producing CD4 and / or CD8 T cells, and / or enhanced cytolytic activity compared to before combination administration. The CD4 and / or CD8 T cells may show increased release of cytokines selected from the group consisting of IFN-γ, TNF-α, and interleukins. The CD4 and / or CD8 T cells may also be effector memory T cells. In certain embodiments, the CD4 and / or CD8 effector memory T cells are CD44 high CD62L low It is characterized by having the expression of [something].

[0108] Immunotherapy may also be a cancer vaccine comprising one or more cancer antigens, in particular proteins or immunogenic fragments thereof, DNA or RNA encoding the said cancer antigens, in particular proteins or immunogenic fragments thereof, cancer cell lysates, and / or protein preparations from tumor cells. As used herein, cancer antigens are antigenic substances present in cancer cells. In principle, any protein produced in cancer cells that has an abnormal structure due to mutation can function as a cancer antigen. In principle, cancer antigens may be products of mutated oncogenes and tumor suppressor genes, products of other mutated genes, overexpressed or abnormally expressed cellular proteins, cancer antigens produced by oncoviruses, carcinoembryonic antigens, altered cell surface glycolipids and glycoproteins, or cell type-specific differentiation antigens. Examples of cancer antigens include abnormal products of the ras and p53 genes. Other examples include tissue differentiation antigens, mutated protein antigens, oncovirus antigens, oncotesticular antigens, and vascular-specific or stromal-specific antigens. Tissue differentiation antigens are tissue differentiation antigens specific to a particular type of tissue.

[0109] Immunotherapy may be an antibody, for example, a part of a polyclonal antibody preparation or a monoclonal antibody. The antibody may be a humanized antibody, a chimeric antibody, an antibody fragment, a bispecific antibody, or a single-chain antibody. Antibodies disclosed herein include, but are not limited to, Fab, Fab' and F(ab')2, Fd, single-chain Fvs(scFv), single-chain antibodies, disulfide-linked Fv(sdfv), and antibody fragments containing either a VL or VH domain. In some aspects, the antibody or fragment specifically binds to epidermal growth factor receptor (EGFR1, Erb-B1), HER2 / neu(Erb-B2), CD20, vascular endothelial growth factor (VEGF), insulin-like growth factor receptor (IGF-1R), TRAIL-receptor, epithelial cell adhesion molecules, carcinoembryonic antigen, prostate-specific membrane antigen, mucin-1, CD30, CD33, or CD40.

[0110] Examples of monoclonal antibodies include trastuzumab (anti-HER2 / neu antibody); pertuzumab (anti-HER2 mAb); cetuximab (chimeric monoclonal antibody against epidermal growth factor receptor EGFR); panitumumab (anti-EGFR antibody); nimotuzumab (anti-EGFR antibody); zaltumumab (anti-EGFR mAb); nesitumumab (anti-EGFR mAb); MDX-210 (humanized anti-HER-2 bispecific antibody); MDX-210 (humanized anti-HER-2 bispecific antibody); MDX-447 (humanized anti-EGF receptor bispecific antibody); rituximab (chimeric mouse / human anti-CD20 mAb); obinutuzumab (anti-CD20 mAb); ofatumumab (anti-CD20 mAb); tositumomab-I131 (anti-CD20 mAb); ibritumomab tiuxetan (anti-CD20 mAb); Bevacizumab (anti-VEGF mAb); Ramucirumab (anti-VEGFR2 mAb); Ranibizumab (anti-VEGF mAb); Aflibercept (extracellular domains of VEGFR1 and VEGFR2 fused with IgGl Fc); AMG386 (angiopoietin-1 and -2 binding peptides fused with IgGl Fc); Darotuzumab (anti-IGF-1R mAb); Gemtuzumab Ozogamicin (anti-CD33 mAb); Alemtuzumab (anti-Campath-1 / CD52 mAb); Brentuximab Vedotin (anti-CD30 mAb); Catumakisomab (dual-specificity mAb targeting epithelial cell adhesion molecules and CD3); Naptumomab (anti-5T4 This includes, but is not limited to, mAbs; girentuximab (anti-carbonic anhydrase ix); or faretzumab (anti-folate receptor).Other examples include Panorex(trademark)(17-1A)(mouse monoclonal antibody); Panorex(MAb17-1A)(chimeric mouse monoclonal antibody); BEC2(in combination with anti-idiotype mAb mimicking the GD epitope)(with BCG); Oncolym(Lym-1 monoclonal antibody); SMART Ml95Ab, humanized 13'1 LYM-1(Oncolym), Ovarex(B43.13, anti-idiotype mouse mAb); 3622W94 mAb that binds to the EGP40(17-1A) pancarcinoma antigen on adenocarcinoma; Zenapax(SMART anti-Tac(IL-2 receptor); SMART M195 This includes antibodies such as Ab, humanized Ab, humanized; NovoMAb-G2 (pancalsinoma-specific Ab); TNT (chimeric mAb against histone antigens); TNT (chimeric mAb against histone antigens); Gliomab-H (monoclonal humanized Ab); GNI-250 Mab; EMD-72000 (chimeric EGF antagonist); LymphoCide (humanized IL.L.2 antibody); and antibodies targeting GD-2 such as MDX-260 bispecificity, ANA Ab, SMART IDIO Ab, SMART ABL 364 Ab, or ImmuRAIT-CEA.Further examples of antibodies include zanulimumab (anti-CD4 mAb), keriximab (anti-CD4 mAb); ipilimumab (MDX-101; anti-CTLA-4 mAb); tremilimmumab (anti-CTLA-4 mAb); daclizumab (anti-CD25 / IL-2R mAb); basiliximab (anti-CD25 / IL-2R mAb); MDX-1106 (anti-PD1 mAb); antibody against GITR; GC1008 (anti-TGF-β antibody); metelimumab / CAT-192 (anti-TGF-β antibody); lerdelimumab / CAT-152 (anti-TGF-β antibody); ID11 (anti-TGF-β antibody); denosumab (anti-RANKL mAb); BMS-663513 (humanized anti-4-IBB mAb); SGN-40 (humanized anti-CD40 mAb); CP870,893 (human anti-CD40 mAb); Infliximab (chimeric anti-TNF mAb); Adalimumab (human anti-TNF mAb); Certolizumab (humanized Fab anti-TNF); Golimumab (anti-TNF); Etanercept (extracellular domain of TNFR fused with IgG1 Fc); Beratacept (extracellular domain of CTLA-4 fused with Fc); Abatacept (extracellular domain of CTLA-4 fused with Fc); Belimumab (anti-B lymphocyte stimulator); Muromonab-CD3 (anti-CD3 mAb); Oterixizumab (anti-CD3 mAb); Teplizumab (anti-CD3 mAb); Tocilizumab (anti-IL6R This includes mAb);REGN88 (anti-IL6R mAb);ustekinumab (anti-IL-12 / 23 mAb);briakinumab (anti-IL-12 / 23 mAb);natalizumab (anti-α4 integrin);vedolizumab (anti-α4β7 integrin mAb);T1h (anti-CD6 mAb);epratuzumab (anti-CD22 mAb);efalizumab (anti-CD11a mAb);and atacicept (extracellular domain of transmembrane activator / calcium regulatory ligand interactor fused with Fc).

[0111] When monitoring mammals with or suspected of having cancer (for example, based at least in part on differentially methylated CpGs in a specific sequence context to identify differences in cfDNA fragmentation between healthy individuals and cancer patients) as described herein, the monitoring may be before, during, and / or after a course of cancer treatment. The monitoring methods provided herein can be used to determine the efficacy of one or more cancer treatments and / or to select mammals for enhanced monitoring. In some embodiments, monitoring may include identifying cfDNA fragmentation profiles as described herein. For example, a cfDNA fragmentation profile may be obtained before administering one or more cancer treatments to a mammal with or suspected of having cancer, the mammal may be administered one or more cancer treatments, and one or more samples may be analyzed during a course of cancer treatment. In some embodiments, the cfDNA fragmentation profile may change during a course of cancer treatment (for example, any of the cancer treatments described herein). For example, a cfDNA fragmentation profile indicating that a mammal has cancer may change to a cfDNA fragmentation profile indicating that the mammal does not have cancer. Such changes in the cfDNA fragmentation profile indicate that the cancer treatment is effective. Conversely, the cfDNA fragmentation profile may remain unchanged (e.g., identical or nearly identical) throughout a course of cancer treatment (e.g., any of the cancer treatments described herein). Such an unchanged cfDNA fragmentation profile indicates that the cancer treatment is ineffective.

[0112] In some embodiments, monitoring may include prior art capable of monitoring one or more cancer treatments (e.g., the efficacy of one or more cancer treatments). In some embodiments, mammals selected for enhanced monitoring may be subjected to diagnostic tests (e.g., any of the diagnostic tests disclosed herein) at an increased frequency compared to mammals not selected for enhanced monitoring. For example, mammals selected for enhanced monitoring may be subjected to diagnostic tests at a frequency of twice daily, once daily, twice weekly, once weekly, twice monthly, once monthly, four times a year, twice a year, once a year, or any of these frequencies. In some embodiments, mammals selected for enhanced monitoring may be subjected to one or more additional diagnostic tests compared to mammals not selected for enhanced monitoring. For example, mammals selected for enhanced monitoring may be subjected to two diagnostic tests, while mammals not selected for enhanced monitoring may be subjected to only one diagnostic test (or no diagnostic tests). In some embodiments, mammals selected for enhanced monitoring may also be selected for additional diagnostic tests. Once the presence of a tumor or cancer (e.g., cancer cells) is identified (e.g., by any of the various methods disclosed herein), it may be beneficial for the mammal to undergo both enhanced monitoring (e.g., to assess the progression of the tumor or cancer in the mammal and / or to assess the development of one or more cancer biomarkers, e.g., mutations) and further diagnostic testing (e.g., to determine the size and / or precise location (e.g., primary tissue) of the tumor or cancer). In some embodiments, one or more cancer treatments may be administered to mammals selected for enhanced monitoring after cancer biomarkers have been detected and / or after differentially methylated CpGs in a particular sequence context have neither improved nor worsened, in order to identify differences in cfDNA fragmentation between healthy individuals and cancer patients of the mammal. Any of the cancer treatments disclosed herein or known in the art may be administered.For example, mammals selected for enhanced monitoring can be further monitored, and if the presence of cancer cells persists throughout the enhanced monitoring period, cancer treatment can be administered. Additionally or alternatively, mammals selected for enhanced monitoring can be subjected to cancer treatment and further monitored as the treatment progresses. In some embodiments, after cancer treatment has been administered to mammals selected for enhanced monitoring, the enhanced monitoring may reveal one or more cancer biomarkers (e.g., mutations). In some embodiments, such one or more cancer biomarkers may provide a reason to administer another cancer treatment (e.g., resistance mutations may develop in cancer cells during the cancer treatment, and cancer cells containing such resistance mutations are resistant to the initial cancer treatment).

[0113] As described herein, when it is determined that a mammal has cancer (for example, based at least partially on the mammal's cfDNA fragmentation profile), such determination may occur before and / or during a course of cancer treatment. The methods for determining that a mammal has cancer, as provided herein, can be used as an initial diagnosis to identify the mammal (for example, before any course of treatment) and / or select it for further diagnostic testing. In some embodiments, once it is determined that a mammal has cancer, it may be subjected to further testing and / or selected for further diagnostic testing. In some embodiments, the methods provided herein can be used to select a mammal for further diagnostic testing at a point in time prior to when the prior art can diagnose that the mammal has early-stage cancer. For example, the methods for selecting a mammal for further diagnostic testing, as provided herein, can be used when the mammal has not been diagnosed by the prior art as having cancer and / or when it is not known that the mammal contains cancer. In some embodiments, mammals selected for further diagnostic testing may be subjected to diagnostic tests (e.g., any of the diagnostic tests disclosed herein) at an increased frequency compared to mammals not selected for further diagnostic testing. For example, mammals selected for further diagnostic testing may be subjected to diagnostic tests at a frequency of twice daily, once daily, twice weekly, once weekly, twice monthly, once monthly, four times a year, twice a year, once a year, or any of these frequencies. In some embodiments, mammals selected for further diagnostic testing may be subjected to one or more further diagnostic tests compared to mammals not selected for further diagnostic testing. For example, mammals selected for further diagnostic testing may be subjected to two diagnostic tests, while mammals not selected for further diagnostic testing may be subjected to only one diagnostic test (or no diagnostic tests at all).In some embodiments, diagnostic tests can determine the presence of cancer of the same type (e.g., having the same primary tissue) as the initially detected cancer (e.g., based at least partially on the mammalian cfDNA fragmentation profile). Additionally or alternatively, diagnostic tests can determine the presence of cancer of a different type than the initially detected cancer. In some embodiments, the diagnostic test is a scan. In some embodiments, the scan is computed tomography (CT), CT angiography (CTA), esophagography (barium swallowing), barium enema, magnetic resonance imaging (MRI), PET scan, ultrasound (e.g., endobronchial ultrasound, endoscopic ultrasound), radiography, or DEXA scan.

[0114] In some embodiments, diagnostic tests include physical examinations, e.g., anoscopy, bronchoscopy (e.g., autofluorescence bronchoscopy, white light bronchoscopy, guided bronchoscopy), colonoscopy, digital tomosynthesis of the breast, endoscopic retrograde cholangiopancreatography (ERCP), esophagogastroduodenoscopy, mammography, Papanicolaou smear examination, pelvic examination, and positron emission tomography / computed tomography (PET-CT) scans. In some embodiments, mammals selected for further diagnostic tests may also be selected for enhanced monitoring. Once the presence of a tumor or cancer (e.g., cancer cells) is identified (e.g., by any of the various methods disclosed herein), it may be beneficial for the mammal to undergo both enhanced monitoring (e.g., to assess the progression of the tumor or cancer in the mammal and / or to assess the development of one or more cancer biomarkers, e.g., mutations) and further diagnostic tests (e.g., to determine the size and / or exact location of the tumor or cancer). In some embodiments, a cancer treatment is administered to a mammal selected for further diagnostic testing after a cancer biomarker has been detected and / or after the mammal's cfDNA fragmentation profile has neither improved nor worsened. Any cancer treatment disclosed herein or known in the art may be administered. For example, a mammal selected for further diagnostic testing may undergo further diagnostic testing, and if the presence of a tumor or cancer is confirmed, a cancer treatment may be administered. Additionally or alternatively, a cancer treatment may be administered to a mammal selected for further diagnostic testing, and further monitoring may be performed as the cancer treatment progresses. In some embodiments, after a cancer treatment has been administered to a mammal selected for further diagnostic testing, additional testing may reveal one or more cancer biomarkers. In some embodiments, one or more such cancer biomarkers (e.g., mutations) may provide a reason to administer another cancer treatment (e.g., resistance mutations may develop in cancer cells during the cancer treatment, and cancer cells containing such resistance mutations are resistant to the initial cancer treatment). [Examples]

[0115] Example 1: DNA methylation and gene expression as determinants of genome-wide cell-free DNA fragmentation The development of high-throughput sequencing methods has made it possible to study the characteristics of cfDNA fragmentation, including those related to underlying nucleosomes. In healthy individuals, the positioning of not only nucleosomes but also larger chromatin compartments is remarkably similar to that in bone marrow cells and lymphocytes 8~10 . Similarly, the methylation profile of cfDNA in individuals without cancer is very similar to the DNA methylation of white blood cells 11 . Epigenetic changes are related to genome packaging 12 and chromatin structure 13 , as well as gene expression 14 . However, to date, research on the relationship between methylation, expression, and cfDNA fragmentation has been limited 15~17 . Furthermore, none of these studies have examined the fundamental impact of these changes on repeated cfDNA cleavage point motifs and fragment sizes

[0116] In this study, we analyzed the characteristics related to nucleosome positioning and cleavage point motifs of cfDNA fragments in both healthy individuals and cancer patients. It was shown how epigenetic marks cause specific fragmentation patterns of cfDNA and how they are related to both methylation and gene expression. Using this information, it was shown that differences in cfDNA fragmentation between healthy individuals and cancer patients can be identified using differentially methylated CpGs in specific sequence contexts

[0117] Methods Study population To analyze motif frequencies, repeated ends, and the relationship between gene expression and methylation, we used low-coverage whole-genome sequencing (WGS) of cfDNA (1 - 2×) obtained from 787 individuals without cancer and 182 cancer individuals 32cfDNA methylation in cancer-free individuals was previously analyzed using Illumina's Infinium methylation EPIC array. 11 The analysis was performed using and made available from the NCBI Gene Expression Omnibus (GEO) database (dataset identifier GSE122126). Cells contributing to cfDNA in non-cancer individuals have been previously used and validated. 9,11 This indicates that most of the cfDNA originated from bone marrow-derived cells. Average gene expression was previously published. 33 As such, it was derived from multiple myeloid cells.

[0118] Processing of cfDNA samples Whole genome libraries from cancer patients and cancer-free individuals were sequenced on the Illumina HiSeq2500 platform using 100 bp paired-end runs (200 cycles) with 1–2× coverage per genome. Prior to alignment, fastp software was used. 34 Adapter sequences were filtered from the reads using [tool name]. The sequence reads were aligned to the hg19 human reference genome using Bowtie235, and Sambamba [tool name] was used. 36 Duplicate reads were removed using MAPQ. Only reads with a MAPQ score of at least 30 or higher were retained. After alignment, each aligned pair was placed into the genomic segment corresponding to the sequenced DNA fragment using bedtools. 37 It was converted using [this method].

[0119] Frequency of motifs around the ends of cfDNA fragments The predicted frequency of 3 bp motifs in the human genome was calculated by counting the occurrences of each 3 bp motif in the human genome (hg19). To calculate the empirical frequency of 3 bp motifs at the ends of sheared (sonicated) fragments, the inventors used 10 lymphoblastoid cell lines. 38We used publicly available sequence data from [source]. The genomic DNA of these lymphoblastoid cell lines was fragmented by sonication using a Covaris M220 Focused Ultrasonicator. In this analysis, the data was reanalyzed in the same manner as when the cfDNA samples were analyzed. For 10 lymphoblastoid cell lines and low-coverage WGS from 543 cancer-free individuals, the number of 3 bp motifs was counted in the reverse complement of 3 bp motifs at the beginning and around the end of each fragment. A 3 bp motif consists of the outer base of the fragment followed by the first two bases of that fragment. Using these absolute numbers, the relative frequency of each of the 64 3 bp motifs was calculated.

[0120] To quantify the priority of cfDNA fragments terminating at specific locations in the genome, we calculated the ratio of cfDNA fragments terminating at this location (repeating fragment ends) to the number of cfDNA fragments (adjacent fragments) with 1 bp or more overlap within ±50 bp of this location. This ratio of repeating fragment ends to adjacent fragments was calculated by aggregating cfDNA fragments across all 543 individuals without cancer. This calculation was repeated for all locations in the hg19 reference genome consisting of high-quality alignments (mapq>30) with 200 or more adjacent fragments, thereby capturing repeats in non-repeating regions of the genome. Genomic locations with a high repeat rate were defined as those where the ratio of repeating fragment ends to adjacent fragments was at least 5%.

[0121] X-ray crystal structure of nucleosomes To identify the structure of DNA-bound nucleosomes, the Protein Data Bank (PDB; rcsb.org) was searched using the term "nucleosome," and the results were filtered for structures obtained from X-ray diffraction or cryo-EM, yielding 427 entries. DNA sequences (648) were downloaded from these structures and filtered for sequences of at least 167 bases in length. This identified 80 unique sequences from 51 PDB entries. These entries were visually validated, and those determined to be less than 167 bases or whose interaction with the H1 linker was disrupted by another DNA-binding protein were removed. As a result, 17 structures remained (Table 3). Motifs were considered properly positioned if they were within 5 angstroms of the H1 linker, or if a base 167 units away on the same strand was within 5 angstroms of the H1 linker.

[0122] Associating cfDNA fragment patterns with CpG methylation To determine whether the fragmentation pattern of cfDNA from cancer-free individuals is affected by methylation, four biologically distinct cohorts (young men, elderly men, young women, and elderly women) were studied. 11 Raw data derived from Illumina's Infinium methylationEPIC array from eight different cfDNA experiments were analyzed. A standard pipeline was used to process the Infinium array. 39Numerical scores (β values) ranging from 0 (unmethylated) to 1 (methylated) were obtained for each CpG and averaged across the entire sample to summarize the overall methylation level. CpG sites were labeled as unmethylated if the mean β value was <0.3 and as methylated if the mean β value was >0.7. For 543 cfDNA plasma samples from cancer-free individuals (Table 1), the location of Infinium CpG sites within cfDNA fragments was recorded using a single-nucleotide index. CpGs were grouped according to the mean β value of the Infinium array. The number of fragments starting or ending at a CpG site within a CpG group was counted, and this frequency was scaled by the number of fragments with any overlap within 50 bp of the start or termination site. Fragments were further classified by their 3 bp terminal motif and by whether the cfDNA fragment was located on a CpG island, shore, shelf, or open sea.

[0123] cfDNA sequence coverage and fragment size at CpG islands and transcription start sites (TSSs). To aggregate cfDNA fragment lengths within a single CpG island, the average length of cfDNA fragments starting at the CpG island was counted. This was conventionally referred to as position 0. This aggregation process was repeated at a position x bp from the CpG island, increasing by 1 bp from x=-3000 bp to x=+3000 bp, and then from x=-500000 bp to x=+500000 bp. This procedure was repeated for each CpG island. The average fragment length in TSS was aggregated in a similar manner, with position 0 representing TSS. Due to the use of low-coverage WGS, cfDNA fragments were pooled across all 543 non-cancer samples. Methylation data described previously. 11 and gene expression data from bone marrow cell lines 33 Using this method, we aligned regions to visualize patterns associated with CpG island methylation and gene expression (Figures 3A, 3B, 3C, 3D).

[0124] Gene set enrichment analysis Gene set enrichment analysis 40 This is the Molecular Signatures Database 43 Hallmark obtained from 41 and KEGG 42 The analysis was performed using gene sets. Average normalized counts across all healthy PBMC samples were used to rank genes in RNA sequencing. Average β values ​​for all CpG duplicate transcripts were used for ranking genes in methylation analysis. These metrics are identical to those previously used to rank genes to visualize cfDNA coverage surrounding CpG islands and transcription start sites. For coverage-based analysis, normalized fragment counts were used for overlapping transcription start sites or overlapping CpG islands within transcripts. 10,000 sorts were performed for each ranked set, which yielded the smallest unadjusted p-value of 1e-4. All gene sets that were moderately significant across all comparisons in any direction (unadjusted p<.1) were selected to be included in a heatmap showing enrichment scores by gene set (Figure 3G).

[0125] Multivariate model A generalized linear model was used to evaluate the relationship between aggregated mean cfDNA fragment size and total coverage at the RNA expression, WPS, and transcript levels with methylation. For methylation, the inventors calculated the mean β value at each CpG island across 97 blood samples processed on an Infinium array (see the study population section). CpG islands were mapped to transcripts by their proximity to the TSS using the R package annotatr (version 1.28.0). Transcripts were considered methylated if their mean β value was 0.5 or higher, and unmethylated otherwise. Mean RNA expression (mean TPM) across six myeloid cell lines was converted as log10(mean TPM+1) and then centered and scaled by the overall mean and standard deviation across all transcripts, respectively. WPS was aggregated per transcript in the interval of +1 to +10 bases from the TSS, and then centered and scaled. The total cfDNA coverage across all 543 non-cancerous samples was calculated for each base in the interval from -10 bp to -1 bp from the TSS and averaged, while the mean fragment size was calculated in the interval from -1480 bp to -1471 bp from the TSS. Intervals for summarizing cfDNA coverage, fragment size, and WPS were evaluated for all 10 bp genomic intervals within 2500 bp from the TSS. For regression analysis, the intervals that yielded the measurement with the highest absolute correlation to RNA expression were selected. Using these quantitative summaries described above, the expected normalized coverage DV of transcript i is given as follows: TIFF2026522392000002.tif4158

[0126] The expected fragment lengths were modeled in a similar manner. Coefficients from these models were estimated using a generalized linear model with the identity link function in R (version 4.3.2). Using analysis of variance (ANOVA), the inventors evaluated whether RNA expression helps explain the variation in coverage after adjusting for methylation and WPS by testing both the main effect of RNA expression and its interaction with methylation. The inventors performed a similar ANOVA to evaluate whether methylation can explain the variation in coverage after adjusting for the effects of RNA and WPS on coverage. Forest plots were created for each model, and estimated model coefficients were visualized in 95% confidence intervals using sjPlot (version 2.8.15).

[0127] Monte Carlo simulation of human cfDNA coverage in xenograft models For each of the six xenografts (three IDH1 R132H mutant xenografts and three IDH1 wild-type xenografts), coverage was calculated for the top 500, 1000, 2000, 3000, 4000, and 5000 most differentially methylated CpG islands or most differentially expressed genes. These coverages were normalized relative to the total size of these regions. Comparing IDH1 mutants and wild-types, the inventors determined whether the direction of the differences in the normalized coverage was consistent with the empirical expectation that higher coverage would be observed in methylated regions and lower coverage in expressed regions for each of the four possible comparisons (highly methylated regions in IDH mutants, high methylation in IDH wild-types, high expression in IDH mutants, and high expression in IDH wild-types). Under the null hypothesis that there is no difference in cfDNA coverage between IDH1 mutant and wild-type tumors in mice, to assess the likelihood of observing empirical agreement, the inventors rearranged mutant and wild-type labels and evaluated the agreement as described above. This operation was repeated 10,000 times. The inventors repeated this process for each of the 19 possible rearrangements of the sample labels and derived the agreement distribution under the null hypothesis. The p-value was calculated as the proportion of rearrangements that matched at or above the empirical agreement obtained from the unrearranged class labels. These analyses were repeated for each of the six region or gene list sizes shown above, resulting in a total of 24 comparisons.

[0128] Differentially methylated CpG-based tumor-specific cfDNA methylation patterns Using publicly available data, differentially methylated CpG data from individuals with and without cancer were evaluated. For pancreatic cancer, large cohort studies of differentially methylated regions have been published. 30Using these differentially methylated CpGs, subgroups were created based on the direction of differential methylation (non-cancer methylation vs. pancreatic cancer non-methylation; non-cancer non-methylation vs. pancreatic cancer methylation) and based on 3 bp and 4 bp motifs. A total of 16 different features were extracted for each sample based on the ratio of the aggregate of cfDNA fragments starting or ending at these motifs divided by the aggregate of fragments overlapping within the 101 bp window around the motif (Figure 4A). These features were used to create machine learning models, and their performance was compared to the original DELFI method 8. The ensemble model combining this methylation-based approach and DELFI showed a synergistic improvement in performance compared to using the two models individually (Figure 4B).

[0129] statistical methods Unless otherwise specified, all t-tests were performed using Welch's two-sample t-test. Statistical analysis was performed using R version 4.2.0.

[0130] result The frequency and composition of cfDNA start and termination sequences were investigated. This is because they have been previously described as being non-random and potentially related to cleavage by endogenous DNAse1. 8,19 To precisely pinpoint cfDNA end locations, cfDNA sequence data were pooled from low-coverage whole genome sequencing of a cohort of healthy individuals (n=543), and the frequency of cfDNA breakpoints was investigated at all possible locations in the genome. Only fragment reads with high sequence and mapping quality were considered, and a ratio was calculated by comparing the number of cfDNA fragments starting or ending at a particular location with the number of fragments having start or termination locations within 50 bp surrounding that location. This approach was necessary to identify repeating fragment end sequences and to account for differences in cfDNA fragment size and genome-wide coverage. 8 .

[0131] Evaluation of cfDNA fragments with more frequently observed terminal locations revealed enrichment of specific motifs. These typically include thymine or adenine prior to the beginning of the cfDNA fragment, and two cytosines (A / T|CC) or cytosine followed by guanine (A / T|CG) as the first two nucleotides of the cfDNA fragment (Figure 1A). It is hypothesized that the repeating terminal sequences within cfDNA fragments likely represent these locations protected by nucleosome occupancy, and the frequency of these base motifs was found to be further increased in healthy individuals at “preferred” repeating terminals (Figures 1A, 1B). The occurrence of A / T|CC and A / T|CG-preferred cfDNA fragment ends was observed at much higher rates within the genome than theoretically predicted (26.5 times for A / T|CC and 5.5 times for A / T|CG) (Figure 1A) (p<2.2e-16, 1-sample t-test), however, the frequency of DNA ends from fragments generated by sonication of genomic DNA was close to the theoretical abundance.

[0132] To understand the fundamental principles behind the enrichment of A / T|CG terminal sequences, we examined available X-ray crystal and cryo-EM structures of nucleosome-bound DNA. In 82% of the intrinsic structures, the A / T|CG motif was found either near the H1 linker or concentrated at a distance of 167 bp, the size of a typical cfDNA molecule (Figure 1C). In contrast to the fragment terminal sequences, the interior regions of the cfDNA fragments were rich in adenine and thymine, and the frequency of these nucleotides throughout the entire length of the fragment was observed to be 10–11 bp periodic (Figure 5). These observations were consistent with current predictions that DNA wraps around the histone core and that rigid DNA regions (rich in C and G) must be alternating with more flexible regions (rich in A and T) to wrap around the nucleosome nearly twice. 20 .

[0133] Given the overwhelming majority of fragment ends containing CG, it was questioned whether epigenetic marks at these sites could influence cfDNA fragmentation. cfDNA fragments were mapped to the genome, and their ends were evaluated against methylated and unmethylated CpG sites of healthy cfDNA obtained from a previously identified methylation array evaluating 850 K CpG sites (Moss J. et al. Nat Commun 9, 5068 (2018)). Methylated CpGs were observed to be concentrated at the ends of A / T|CG cfDNA fragments, while unmethylated CpGs were relatively uniformly distributed over the length of these molecules (Figures 2A, 7A, 7B). To quantitatively evaluate the concentration of fragment ends at CpGs, for each CpG, the proportion of cfDNA fragments starting or ending at this dinucleotide position was calculated relative to the number of fragments with start or termination sites within 50 bp of each CpG. It was observed that the proportion of preferred ends increased by approximately 2.4 times with increasing methylation levels (p<2.2e-16, t-test) (Figure 2B). Furthermore, CG cfDNA fragment ends were enriched approximately 2.2 times across the genome, for example, at methylated CpG locations in CpG islands, shores, shelves, and open seas (Figure 6), indicating that enrichment of methylated CpG fragments is a universal characteristic of cfDNA in these regions.

[0134] Methylated CG terminal sequences were observed to be preferentially enriched, even when they overlapped with frequently observed CC fragment terminal sequences. When an N|CC sequence was followed by guanine to form N|CCG, the frequency of typical N|CC terminal motifs decreased. This is because they competed with overlapping C|CG motifs (which are enriched when the CpG sites containing these sequences are methylated) (Figures 2B, 2C). The overall effect of this competition is such that it results in a dramatic decrease in N|CCG fragment terminal sequences at methylated CpG sites, which is even greater than the increase in the corresponding fragment ends in N|CG. For example, this is seen in the 3.7-fold decrease in T|CCG terminal sequences compared to a 2.2-fold increase in C|CG terminal motifs (p<2.2e-16, t-test) (p<2.2e-16, t-test).

[0135] It is well established that in females, one copy of the two X chromosomes is inactivated by methylation of CpG islands, whereas in males, these regions on a single X chromosome are not methylated. 21,22 To provide further biological evidence regarding the association between methylated CG and cfDNA fragmentation, cfDNA fragments originating from the X chromosome were compared between healthy individuals. Consistent with observations of methylation-induced fragment end enrichment, cfDNA fragments terminating at CG were enriched and fragment ends with CCG were preferentially reduced in female X chromosome CpG island locations compared to males (mean 0.39% for males vs. 0.48% for females at CG ends, p<2.2e-16, t-test; mean 0.68% for males vs. 0.54% for females at CCG ends, p<2.2e-16, t-test), although these differences were not observed in the autosomes of males and females (p=0.58 and p=0.80, t-test, respectively) (Figure 2D). This trend continued in the CpG shore, but CG fragment end enrichment was higher in males compared to females in the CpG shelf and open sea, which is consistent with previously reported increased methylation on the male X chromosome in these regions (Figure 8). 12,23 .

[0136] In addition to enrichment of cfDNA fragment terminal positions at epigenetic mark sites, cfDNA sequence coverage (the average number of overlapping cfDNA molecules at a particular location) was associated with methylation levels (r=0.6, p<2.2e-16, Pearson correlation test) (Figure 9A), and was observed to be up to 1.7 times higher across the entire region of methylated CpG islands compared to unmethylated ones (p<2.2e-16, t-test) (Figure 3A). Considering the relationship between CpG island methylation and expression, the relationship between gene expression and cfDNA fragmentation patterns at transcription start sites (TSSs) was evaluated. There was an inverse correlation between cfDNA coverage at TSSs and the expression levels of neighboring genes (r=-0.48, p<2.2e-16, Pearson correlation test) (Figure 9B). The overall level of cfDNA fragments overlapping the TSS of non-expressed genes was up to 3.7 times higher than in the regions of expressed genes, likely due to the lack of destabilizing effects of transcription factors on nucleosomes (p<2.2e-16, t-test) (Figure 3B). Consistent with the higher cfDNA coverage, changes in cfDNA fragment size were observed in these regions, including fragments 4–5 bp smaller in the region 800–1000 bp upstream of the TSS of highly expressed genes compared to non-expressed genes (164.5 bp vs. 168.6 bp, p<2.2e-16, t-test), or fragments 4–5 bp smaller in the region surrounding unmethylated CpG islands compared to highly methylated CpG (165.1 bp vs. 167.3 bp, p<2.2e-16, t-test) (Figures 3C, 3D). Investigations of broader regions surrounding TSS or CpG islands continued to show differences between expressed and unexpressed genes or unmethylated and methylated genes in regions extending up to 500 kb around these sites (Figures 10A-10D).

[0137] Analysis of cfDNA fragments adjacent to genes in KEGG and Hallmark gene sets revealed cfDNA coverage across the entire set of important genes identified in blood cells, including expression and CpG methylation. 11This was found to be consistent (p<0.1, gene set enrichment analysis) (Figure 4A, Table 7). This included higher cfDNA coverage in CpG island and TSS regions when methylation was increased and expression was decreased, and lower cfDNA coverage when methylation was decreased and expression was increased. For example, gene pathways not normally expressed in blood cells, including neuronal receptor-ligand interactions or olfactory receptor transmission, were normally methylated and more highly expressed in cfDNA fragments in regions containing CpG islands or TSS (Figure 3F). In contrast, genes utilized in hematopoiesis, including E2f transcription factor targets and blood cell metabolism genes, were shown to be more highly expressed, more frequently demethylated, and at lower cfDNA levels in the CpG or TSS regions of these genes (Figure 4B). Overall, cfDNA coverage was found to be associated with both CpG methylation and the expression of nearby genes (Figure 11A), but the enrichment of repeating cfDNA fragment ends in the CpG region was more closely associated with methylation levels than with gene expression (Figure 11B). These results highlight that DNA methylation is a fundamental feature influencing cfDNA fragmentation, which is related to but independent of gene expression.

[0138] To provide a direct and independent analysis of the effects of methylation or gene expression on cfDNA coverage, we evaluated human cfDNA fragmentation coverage in the plasma of mice transplanted with human tumors, with or without knock-in of the IDH1 chromatin modifier, which is known to be activated through our previous research (Parsons, DW et al. Science 321, 1807-1812 (2008)) and leads to widespread genome-wide methylation and expression changes, accompanied by a mutation at R132. Duncan, CG et al. Genome Res. 22, 2339-2355 (2012); Wei, S. et al. Oncogene 37, 5160-5174 (2018); Brennan, CW et al. Cell 155, 462-477 (2013). Mice were injected with either wild-type U87 glioblastoma cell lines (n=3) containing the R132H mutation, or modified U87 glioblastoma cell lines (n=3) containing the R132H mutation, and evaluated 20-30 days after tumor transplantation. After selecting human-derived cfDNA fragments from mouse plasma, high coverage of human cfDNA was observed in regions with increased methylation, while low coverage of cfDNA was observed in regions with increased expression (Figure 3G) (p<0.053, Monte Carlo simulation). This is consistent with our previous analysis results. This well-controlled analysis provides a direct causal relationship between genome-wide changes in epigenetic features and cfDNA fragmentation.

[0139] It has been widely reported that the overall size of cfDNA in cancer patients is smaller than that in healthy individuals. 24,25 , genome-wide changes in DNA methylation and gene expression during tumorigenesis 26 It was hypothesized that this might affect cfDNA fragmentation in cancer patients. To clearly compare tumor-derived cfDNA with wild-type cfDNA, changes in cfDNA fragment size of tumor-derived and WBC-derived cfDNA collected from 98 cancer patients were investigated using ultra-high-sensitivity NGS targeted sequencing.8,27 The tumor-derived cfDNA from these patients showed an average shift of 3.9 bp, which was similar to the cfDNA size differences observed in high-expression and low-expression TSS regions and CpG sites in methylated versus unmethylated regions of healthy cfDNA (Figure 3E). Since tumors typically show a higher number of expressed genes and a lower methylation state compared to leukocytes (Figures 12A, 12B), these results, as in previous studies26, 28, 29, support the idea that the smaller overall cfDNA fragment size observed in cancer patients may be partly due to changes in expression and methylation in cancer cells.

[0140] To identify the impact of differences in CpG methylation between healthy individuals and cancer patients on cfDNA fragment ends, previously identified regions were evaluated by comparing reduced-representation bisulfite sequencing (RRBS) data from laser-captured microdissectioned pancreatic ductal adenocarcinoma and normal pancreatic tissue, and the locations of these regions in cfDNA were determined by methyl DNA immunoprecipitation (MeDIP). 30 This was confirmed using [method / tool ​​name]. Next, fragment-end expression of CG and CCG sites was evaluated through low-coverage whole-genomic cfDNA analysis in patients with pancreatic cancer (n=34), colorectal cancer (n=27), ovarian cancer (n=28), lung cancer (n=39), or breast cancer (n=54) and individuals without cancer (n=244). 8In regions with increased CpG methylation in non-cancerous tissue, a preferential decrease in the amount of cfDNA fragments terminating at N|CCG was observed in individuals without cancer, compared to the large amounts of these fragments in pancreatic cancer patients and patients with other cancers (Figure 5A). In contrast, in regions with increased methylation in pancreatic cancer, an increase in cfDNA fragments terminating at CG was observed in cancer patients compared to the levels in individuals without cancer (Figure 5A). In all cases, the strongest signal was observed in pancreatic cancer patients, suggesting that the use of tumor-specific methylation sites led to improved performance in this tumor type. By incorporating the distribution of fragment end positions at these CG and CCG sites into a gradient boosted tree machine learning model, we were able to successfully distinguish between individuals with and without pancreatic cancer (cross-validation AUC = 0.87). Combining this approach with genome-wide fragmentation analysis (DELFI)8 incorporating fragment coverage and size improved the sensitivity of the combined method (AUC=0.94, 95% CI=0.90–0.96). These observations suggest that genome-wide DNA methylation can be used to detect cancer individuals using cfDNA fragment end expression at CG and CCG sites.

[0141] Multivariate regression models evaluating DNA methylation (Figures 3A, 3C), gene expression (Figures 3B, 3D), nucleosome positioning (Supplementary Figure 13), and their interactions revealed that each of these elements independently contributes to cfDNA coverage and fragment size (Figure 3F). In more complex models, including the interaction between DNA methylation and nucleosome positioning, and additional terms concerning the tripartite interaction between DNA methylation, gene expression, and nucleosome positioning, the relationship between methylation and coverage was qualitatively similar (Figure 13). These results highlight that DNA methylation is a fundamental feature influencing cfDNA fragmentation.

[0142] Consideration This study, in particular, demonstrated that genome-wide CpG methylation has a significant impact on cfDNA fragmentation. Repeating cfDNA sequences with CG fragment ends were enriched at methylation sites, resulting in an increase at N|CG sites and a decrease at competing N|CCG sites in a methylation level-dependent manner. Structural analysis of nucleosome-bound DNA showed that CG sequences are typically located near the histone H1 linker. These observations are consistent with previous molecular dynamics simulations. 31 In addition, this study demonstrates that methylation of the CG sequence can lead to a more stable interaction between methylated DNA and the H1 linker, thereby protecting cfDNA fragments bound to nucleosomes from degradation.

[0143] Methylated CpG not only affected the fragment end position but also resulted in increased circulating cfDNA volume in these regions. cfDNA fragmentation was similarly affected at both the individual gene level and within gene pathways in TSSs of downexpressed genes. cfDNA fragment size was altered by both methylation and expression changes and was observed not only in the vicinity of CG and TSS sites but also at distances of hundreds of thousands of base pairs. These determinants of cfDNA size, together with the overall increase in gene expression and decrease in methylation observed in human cancers in this study, provide evidence for the mechanism of the comprehensive reduction in cfDNA fragment length observed in cancer patients.

[0144] Incorporating cfDNA fragment end features at CpG sites into cross-validated machine learning models provides an approach that can be used to detect cancer independently of other cfDNA characteristics. This approach is considered to complement DELFI cfDNA fragmentation analysis, resulting in an improved method. Understanding the differences in methylation and expression that affect cfDNA size and coverage in cancer patients could lead to improvements in methods for evaluating genome-wide cfDNA fragmentation in the future. Integrating methylation and expression changes with other genome-wide epigenetic markers provides complementary insights into the origin and mechanisms of cfDNA fragmentation.

[0145] References TIFF2026522392000003.tif114145TIFF2026522392000004.tif205149TIFF2026522392000005.tif209149TIFF2026522392000006.tif136145

[0146] Other embodiments From the foregoing explanation, it is clear that the disclosures described herein may be modified and altered to suit various uses and conditions. Such modifications are also within the scope of the appended claims.

[0147] All references to sequences, patents, and publications herein are incorporated by reference to the same extent that each independent patent and publication is indicated in detail and individually to the extent that it is incorporated by reference. The various references herein do not constitute an admission by the applicant that any particular reference is “prior art” to its disclosure.

Claims

1. A method for determining the fragmentation of circulating cell-free DNA (cfDNA) in a sample, A step of assaying the cfDNA sequence to determine the location and terminal position of the cfDNA fragment in the genome; A process for analyzing the frequency of cfDNA breakpoints at multiple locations in the genome sequence. The method comprising, thereby determining circulating cell-free DNA (cfDNA) fragmentation.

2. The method according to claim 1, wherein the genome sequence is assayed by whole genome sequencing or by obtaining a whole genome sequence from a database, and by pooling cfDNA sequences.

3. The method according to claim 1, wherein the analysis of the frequency of the cfDNA breakpoints includes calculating the ratio of the number of cfDNA fragments that start or terminate at a specific location compared to the number of fragments that have a start or termination location within 50 bp surrounding the specific location.

4. The method according to claim 3, wherein a cfDNA fragment containing similar terminal position sequences contains similar motifs.

5. The method according to claim 4, wherein the motif comprises a thymine or adenine prior to the beginning of the cfDNA fragment sequence, and two cytosines (A / T|CC) or cytosines followed by guanines (A / T|CG) as the first two nucleotides of the cfDNA fragment sequence, or these sequences are the reverse complement at the end of the cfDNA fragment.

6. The method according to claim 5, wherein the frequency of the motif is increased or decreased in healthy subjects compared to subjects with cancer.

7. The method according to claim 6, wherein the frequency of A / T|CC motifs exceeds the frequency of A / T|CG motifs.

8. The method according to claim 7, wherein the A / T|CG motif is located in close proximity to the histone H1 linker or 100 to 200 base pairs away from the histone H1 linker.

9. The method according to any one of claims 1 to 8, wherein the internal region of the cfDNA fragment is enriched with adenine and thymine.

10. A step of mapping a cfDNA fragment to the genome, and A step of comparing the terminal sequence of the cfDNA fragment with the methylated and unmethylated CpG sites of cfDNA derived from a healthy subject. The method according to claim 1, further comprising:

11. The method according to claim 10, wherein methylated CpG is enriched at the end of the A / T|CG cfDNA fragment sequence.

12. Quantitative evaluation of cfDNA fragment end enrichment in CpG For each CpG, calculate the proportion of cfDNA fragments that start at a CpG dinucleotide and the proportion of cfDNA fragments that terminate at a CpG dinucleotide relative to the number of cfDNA fragments that have a start or termination position within 50 bp of the surrounding CpG. The method according to claim 11, including the method described in claim 11.

13. The method according to any one of claims 1 to 12, wherein the methylated CpG is reduced at the terminus of a cfDNA fragment (containing CpG at locations 2 and 3) that starts with a CCG trinucleotide.

14. The method according to any one of claims 1 to 13, wherein quantitative evaluation of cfDNA fragment terminology in CCGs includes calculating, for each CCG, the ratio of cfDNA fragments that start in the CCG trinucleotide to the number of cfDNA fragments in the region surrounding the first cytosine of the CCG trinucleotide (preferably 50 bp upstream and downstream from the specific position). A similar quantitative assessment of the reduction in cfDNA fragment ends in the reverse complement (CGG) of the CCG trinucleotide can be performed by calculating the ratio of cfDNA fragments terminating in the CCG trinucleotide to the number of cfDNA fragments in the region surrounding the last nucleotide of the trinucleotide.

15. The method according to any one of claims 1 to 14, wherein, compared to male subjects, the cfDNA fragments obtained from the X chromosome in healthy subjects include increased cfDNA fragments terminating at CG and decreased cfDNA fragments terminating at CCG at the locations of the X chromosome CpG island.

16. The method according to any one of claims 1 to 15, wherein the increased cfDNA fragment sequence coverage includes increased methylation across the entire region of the methylated CpG island.

17. The method according to any one of claims 1 to 16, wherein gene expression at transcription start sites (TSSs) is inversely correlated with cfDNA coverage at TSSs.

18. The method according to any one of claims 1 to 17, wherein the reduced CpG island methylation is associated with an increased amount of smaller cfDNA fragments in the region surrounding the CpG island.

19. The method according to any one of claims 1 to 18, wherein increased gene expression at a transcription start site is related to an increased amount of smaller cfDNA fragments in the region surrounding the transcription start site.

20. The method according to any one of claims 1 to 19, further comprising the step of treating the subject with one or more chemotherapeutic agents, radiotherapy, surgery, and combinations thereof.

21. A method for diagnosing cancer and treating the affected area, The process of obtaining a sample from the target, A step in assaying changes in circulating cfDNA fragment size compared to normal and tumor-derived cell-free DNA (cfDNA) controls. A process for assaying CpG methylation at the ends of cfDNA fragments by evaluating fragment end representation at CG and CCG sites through low-coverage whole-genome cfDNA analysis; A process for diagnosing a subject with cancer; and, A process of treating a subject with one or more chemotherapeutic agents, radiotherapy, surgery, or a combination thereof. The method, including the method described above.

22. The method according to claim 21, wherein an increase in cfDNA fragments terminating at N|CCG at sites of reduced methylation in cancer, compared to cfDNA from a healthy individual, is used to diagnose cancer.

23. The method according to claim 21 or 22, wherein a reduction in cfDNA fragments terminating at N|CCG at sites of increased methylation in cancer, compared to cfDNA from a healthy individual, serves as a diagnostic indicator for cancer.

24. The method according to any one of claims 21 to 23, wherein an increase in cfDNA fragments terminating at CG at sites of increased methylation in cancer, compared to cfDNA from a healthy individual, serves as a diagnostic indicator for cancer.

25. The method according to any one of claims 21 to 24, wherein a reduction in cfDNA fragments terminating at CG at sites associated with reduced methylation in cancer, compared to cfDNA from a healthy individual, serves as a diagnostic indicator for cancer.

26. The method according to any one of claims 21 to 25, further comprising incorporating the distribution of fragment terminal positions in CG and CCG regions in a gradient boosted tree machine learning model.

27. The method according to any one of claims 21 to 26, further comprising incorporating the distribution of fragment end positions and fragment size distributions in CG and CCG regions in a gradient boosted tree machine learning model.

28. A method for diagnosing and treating subjects diagnosed with cancer, A step of assaying genome sequences to identify genome-wide CpG methylation; A step of determining circulating cell-free DNA (cfDNA) fragmentation by analyzing the frequency of cfDNA breakpoints at multiple locations in the genome sequence, wherein recurrent cfDNA fragment end enrichment at CpG sites correlates with higher genome-wide methylation levels and smaller cfDNA fragments that contribute to the diagnosis of cancer; A step of diagnosing a subject as having cancer if hypomethylation and / or increased gene expression or a decrease in cfDNA fragment size is detected; and, A process of treating the subject with one or more chemotherapy, radiation, surgery, or a combination thereof. The method, including the method described above.

29. The method according to claim 28, wherein the genome sequence is assayed by whole genome sequencing or by obtaining a whole genome sequence from a database, and by pooling cfDNA sequences.

30. The method according to claim 29, wherein the analysis of cfDNA breakpoint frequencies includes calculating the ratio of the number of cfDNA fragments that start or terminate at a specific location to the number of fragments that have a start or termination location within 50 bp surrounding the specific location.

31. The method according to any one of claims 28 to 30, wherein a cfDNA fragment containing similar terminal position sequences contains similar motifs.

32. The method according to claim 31, wherein the motif comprises a thymine or adenine prior to the beginning of the cfDNA fragment sequence, and two cytosines (A / T|CC) or cytosines followed by guanines (A / T|CG) as the first two nucleotides of the cfDNA fragment sequence.

33. The method according to any one of claims 28 to 32, wherein the frequency of the aforementioned motif is increased in healthy subjects compared to subjects with cancer.

34. The method according to claim 33, wherein the frequency of A / T|CC motifs exceeds the frequency of A / T|CG motifs.

35. The method according to claim 33 or 34, wherein the A / T|CG motif is located in close proximity to the histone H1 linker or is centered 100 to 200 base pairs from the histone H1 linker.

36. The method according to any one of claims 28 to 35, wherein the internal region of the cfDNA fragment is enriched with adenine and thymine.

37. The process of mapping cfDNA fragments to the genome, and, A step of comparing the terminal sequence of the cfDNA fragment with the methylated and unmethylated CpG sites of cfDNA derived from a healthy subject. The method according to any one of claims 28 to 36, further comprising:

38. The method according to claim 37, wherein methylated CpG is enriched at the end of the A / T|CG cfDNA fragment sequence.

39. Quantitative evaluation of cfDNA fragment end enrichment in CpG For each CpG, calculate the ratio of cfDNA fragments that start or terminate at the CpG dinucleotide position to the number of cfDNA fragments that have a start or termination position within 50 bp of the surrounding CpG. The method according to any one of claims 28 to 38, including the method described in any one of claims 28 to 38.

40. The method according to any one of claims 28 to 39, wherein, compared to male subjects, in female subjects, the amount of cfDNA fragments obtained from the X chromosome in healthy subjects at the location of the X chromosome CpG island is increased, and the amount of cfDNA fragments that terminate at CG is decreased, and the amount of cfDNA fragments that terminate at CCG is decreased.

41. The method according to any one of claims 28 to 40, wherein the cfDNA sequence coverage of the enriched cfDNA fragment terminal sequence includes increased methylation across the entire region of the methylated CpG island compared to the cfDNA fragment terminal sequence with reduced or less frequent occurrences.

42. The method according to any one of claims 28 to 41, wherein gene expression at transcription start sites (TSSs) is inversely correlated with cfDNA coverage at TSSs.

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