A method for determining cancer risk

Abstract

A method for determining the risk of gastrointestinal cancer in a human subject is provided, the method comprising: determining whether there is a mutation in at least one CTCF binding site (CBS) overlapping region or a portion thereof or a flanking sequence, or at least one non-CBS region or a portion thereof or a flanking sequence in a biological sample of the subject, wherein the presence of a mutation in at least one CBS overlapping region or a portion thereof or a flanking sequence, and / or at least one non-CBS region or a portion thereof or a flanking sequence indicates a risk of gastrointestinal cancer in the subject. A method for treating gastrointestinal cancer in a human subject and a related kit are also provided.

Description

Technical Field

[0001] The present disclosure relates broadly to methods of determining cancer risk, methods of treating cancer, and related polynucleotides and kits. Background Art

[0002] Noncoding DNA constitutes more than 98% of the human genome and has many functional elements necessary for regulating gene expression and maintaining chromosome structure. However, mainly due to cost reasons, cancer genomics research has been limited to the analysis of somatic DNA changes in protein coding regions to date, largely ignoring the noncoding regions of 98% of the human genome and >95% of the transcribed genome. Therefore, important noncoding regulatory regions (e.g., gene promoters, enhancers, untranslated regions, and thousands of noncoding RNAs) remain largely unknown "black boxes" in cancer genomes.

[0003] Mutations in noncoding regions can drive cancer by dysregulating oncogenes and tumor suppressor genes, as evidenced by recent studies demonstrating recurrent point mutations in the TERT promoter in multiple cancer types and TAL1 enhancer insertions in T-cell acute lymphoblastic leukemia. Although previous pan-cancer analyses of tumor genomes have suggested regulatory driver mutations, these studies are often not adequately powered to identify tissue-specific noncoding driver mutations, as hundreds of samples are often required to reliably identify driver mutations in a single cancer type. 6 Recently, studies have been conducted on breast cancer, liver cancer, 8 and pancreatic cancer 9 Genome-wide mutational landscape of tumors to identify cancer-specific noncoding drivers However, for most other cancer types, including gastrointestinal cancer, the prevalence and impact of noncoding tissue-specific driver mutations remain unknown.

[0004] Comprehensive genetic and molecular analyses have identified novel molecular subtypes and genetic drivers of gastrointestinal cancer / adenocarcinoma. Studies have also investigated the extent and impact of mutational signatures and epigenetic dysregulation in the gastrointestinal cancer genome. However, not all elements of gastric cancer disease development are known. As a leading cause of cancer death worldwide, additional understanding of disease development / progression in gastrointestinal cancer is needed.

[0005] Therefore, there is a need to provide alternative methods for determining cancer risk, methods for treating cancer, and related polynucleotides and kits. Summary of the invention

[0006] In one aspect, a method for determining a risk of gastrointestinal cancer in a human subject is provided, the method comprising: determining in a biological sample of the subject whether a mutation is present in at least one of the CTCF-binding site (CBS) overlapping regions shown in the following table:

[0007] chromosome Start site Termination site 6 50570094 50570120 8 71000992 71001012 1 209422184 209422222 2 49173770 49173816 4 182064578 182064613 X 104435106 104435140 14 70285576 70285601 6 73122084 73122123 8 70576141 70576184 13 36552821 36552860 3 164903700 164903728

[0008] or a portion thereof or a flanking sequence thereof,

[0009] or at least one of the non-CBS regions shown in the following table:

[0010]

[0011]

[0012] or a portion thereof or a flanking sequence thereof,

[0013] Wherein the presence of a mutation in at least one CBS overlapping region or a portion thereof or a flanking sequence thereof, and / or at least one non-CBS region or a portion thereof or a flanking sequence thereof, indicates a risk of gastrointestinal cancer in the subject.

[0014] In one embodiment, the method comprises determining in a biological sample of a subject whether a mutation is present in at least one of the CTCF-binding site (CBS) overlapping regions shown in the following table:

[0015] chromosome Start site Termination site 6 50570094 50570120 8 71000992 71001012 1 209422184 209422222 2 49173770 49173816 4 182064578 182064613 X 104435106 104435140 14 70285576 70285601 6 73122084 73122123 8 70576141 70576184 13 36552821 36552860 3 164903700 164903728

[0016] or a portion thereof or its flanking sequence.

[0017] In one embodiment, the method further comprises determining in the biological sample of the subject whether a mutation is present in at least one non-CBS region shown in the following table:

[0018] chromosome Start site Termination site 7 68391104 68391132 7 136495924 136495948 2 57627616 57627640 16 8381278 8381302 5 23824204 23824224 7 67614923 67614943 8 65161396 65161420 7 4937707 4937736 12 126996666 126996686 4 5415060 5415082 X 137405623 137405655 3 171164993 171165017 4 144748744 144748764 9 25481736 25481758 2 77150455 77150477 3 104801455 104801477 X 125548690 125548710 14 83046706 83046744

[0019] or a portion thereof or its flanking sequence.

[0020] In one embodiment, the method further comprises determining in a biological sample of the subject whether there is a mutation in at least one gene selected from the group consisting of KRAS, TP53, RHOA, ARID1A, PIK3CA, ERBB3 and PTEN, wherein the presence of a mutation in at least one CBS overlapping region or portion thereof or its flanking sequence, at least one non-CBS region or portion thereof or its flanking sequence, and / or at least one of said genes indicates a risk of gastrointestinal cancer in the subject.

[0021] In one embodiment, determining whether a mutation exists in a CBS overlapping region or a portion thereof or its flanking sequence, and / or a non-CBS region or a portion thereof or its flanking sequence, does not include determining whether a mutation exists in a region spanning more than 50 nucleotides.

[0022] In one embodiment, determining the presence or absence of a mutation in at least one CBS overlapping region or a portion thereof or its flanking sequence comprises contacting the biological sample with a primer having at least about 85% sequence identity to a sequence selected from the group consisting of SEQ ID NO.35, SEQ ID NO.36, SEQ ID NO.39, SEQ ID NO.40, SEQ ID NO.45, SEQID NO.46, SEQ ID NO.47, SEQ ID NO.48, SEQ ID NO.49, SEQ ID NO.50, SEQ ID NO.51, SEQID NO.52, SEQ ID NO.57, SEQ ID NO.58, SEQ ID NO.59, SEQ ID NO.60, SEQ ID NO.65, SEQID NO.66, SEQ ID NO.77, SEQ ID NO.78, SEQ ID NO.SEQ ID NO.83, SEQ ID NO.84 and combinations thereof.

[0023] In one embodiment, determining the presence or absence of a mutation in at least one non-CBS region or portion thereof or a flanking sequence thereof comprises contacting the biological sample with a primer having at least about 85% sequence identity to a sequence selected from the group consisting of SEQ ID NO.37, SEQ ID NO.38, SEQ ID NO.41, SEQ ID NO.42, SEQ ID NO.43, SEQ ID NO.44, SEQ ID NO.53, SEQ ID NO.54, SEQ ID NO.55, SEQ ID NO.56, SEQ ID NO.61, SEQ ID NO.62, SEQ ID NO.63, SEQ ID NO.64, SEQ ID NO.67, SEQ ID NO.68, SEQ ID NO.71, SEQ ID NO.72, SEQ ID NO.75, SEQ ID NO.76, SEQ ID NO.81, SEQ ID NO.82, SEQ ID NO.85, SEQ ID NO.86, SEQ ID NO.87, SEQ ID NO.88, SEQ ID NO.89, SEQ ID NO.90, SEQ ID NO.91, SEQ ID NO.92, SEQ ID NO.93, SEQ ID NO.94, SEQ ID NO.95 NO.91, SEQ ID NO.92 and their combinations.

[0024] In one embodiment, the method comprises determining whether a mutation is present in at least two of the CBS overlapping regions listed in the table or portions thereof or flanking sequences thereof.

[0025] In one embodiment, the method comprises determining whether a mutation is present in all CBS overlapping regions listed in the table, or portions thereof, or in their flanking sequences.

[0026] In one embodiment, the method comprises determining whether a mutation is present in all CBS overlapping regions or portions thereof or flanking sequences, and all non-CBS regions or portions thereof or flanking sequences listed in the table.

[0027] In one embodiment, the method has a detection sensitivity of not less than about 50%.

[0028] In one aspect, a method of treating gastrointestinal cancer in a human subject is provided, the method comprising: determining in a biological sample of the subject whether a mutation is present in at least one CTCF-binding site (CBS) overlapping region shown in the following table:

[0029]

[0030]

[0031] or a portion thereof or a flanking sequence thereof,

[0032] or in one of the non-CBS regions shown in the following table:

[0033]

[0034]

[0035] or a portion thereof or a flanking sequence thereof,

[0036] Wherein if the biological sample shows the presence of a mutation in at least one CBS overlapping region or portion thereof or its flanking sequence and / or at least one non-CBS region or portion thereof or its flanking sequence, a therapeutic agent for treating gastrointestinal cancer is administered to the subject.

[0037] In one embodiment, the method comprises determining in a biological sample of a subject whether a mutation is present in at least one of the CBS overlapping regions shown in the following table:

[0038]

[0039] or a portion thereof or its flanking sequence.

[0040] In one embodiment, the method further comprises determining in the biological sample of the subject whether a mutation is present in at least one non-CBS region shown in the following table:

[0041]

[0042]

[0043] or a portion thereof or its flanking sequence.

[0044] In one embodiment, the method further comprises determining in a biological sample of the subject whether a mutation is present in at least one gene selected from the group consisting of KRAS, TP53, RHOA, ARID1A, PIK3CA, ERBB3, and PTEN, wherein if the biological sample shows the presence of a mutation in at least one CBS overlapping region or portion thereof or its flanking sequence, at least one non-CBS region or portion thereof or its flanking sequence, and / or at least one of said genes, a therapeutic agent for treating gastrointestinal cancer is administered to the subject.

[0045] In one embodiment, the therapeutic agent is selected from chemotherapy, radiation therapy, immunotherapy, and combinations thereof.

[0046] In one embodiment, the biological sample is a fluid biological sample.

[0047] In one embodiment, the fluid biological sample is selected from the group consisting of: blood, plasma, serum, and combinations thereof.

[0048] In one embodiment, the gastrointestinal cancer is selected from gastric cancer, colorectal cancer, colon cancer, and rectal cancer.

[0049] In one aspect, a kit for detecting gastrointestinal cancer in a human subject is provided, the kit comprising reagents for detecting mutations in at least one of the CBS overlapping regions shown in the following table:

[0050]

[0051]

[0052] or a portion thereof or a flanking sequence thereof,

[0053] or at least one of the non-CBS regions shown in the following table:

[0054]

[0055]

[0056] or a portion thereof or its flanking sequence.

[0057] In one embodiment, the kit includes reagents for detecting mutations in at least one of the CBS overlapping regions shown in the following table:

[0058] chromosome Start site Termination site 6 50570094 50570120 8 71000992 71001012 1 209422184 209422222 2 49173770 49173816 4 182064578 182064613 X 104435106 104435140 14 70285576 70285601 6 73122084 73122123 8 70576141 70576184 13 36552821 36552860 3 164903700 164903728

[0059] or a portion thereof or a flanking sequence thereof,

[0060] In one embodiment, the kit further comprises reagents for detecting mutations in at least one of the non-CBS regions shown in the following table:

[0061]

[0062]

[0063] or a portion thereof or its flanking sequence.

[0064] In one embodiment, the kit further comprises a reagent for detecting a mutation in at least one gene selected from the group consisting of KRAS, TP53, RHOA, ARID1A, PIK3CA, ERBB3, and PTEN.

[0065] In one embodiment, the reagents for detecting mutations in at least one CBS overlapping region or a portion thereof or its flanking sequences include primers having at least about 85% sequence identity with a sequence selected from the following: SEQ ID NO.35, SEQ ID NO.36, SEQ ID NO.39, SEQ ID NO.40, SEQ ID NO.45, SEQ ID NO.46, SEQ ID NO.47, SEQ ID NO.48, SEQ ID NO.49, SEQ ID NO.50, SEQ ID NO.51, SEQ ID NO.52, SEQ ID NO.57, SEQ ID NO.58, SEQ ID NO.59, SEQ ID NO.60, SEQ ID NO.65, SEQ ID NO.66, SEQ ID NO.77, SEQ ID NO.78, SEQ ID NO.SEQ ID NO.83 and SEQ ID NO.84.

[0066] In one embodiment, the reagents for detecting mutations in at least one non-CBS region or portion thereof or its flanking sequence include primers having at least about 85% sequence identity to a sequence selected from the group consisting of SEQ ID NO.37, SEQ ID NO.38, SEQ ID NO.41, SEQ ID NO.42, SEQ ID NO.43, SEQ ID NO.44, SEQ ID NO.53, SEQ ID NO.54, SEQ ID NO.55, SEQ ID NO.56, SEQ ID NO.61, SEQ ID NO.62, SEQ ID NO.63, SEQ ID NO.64, SEQ ID NO.67, SEQ ID NO.68, SEQ ID NO.71, SEQ ID NO.72, SEQ ID NO.75, SEQ ID NO.76, SEQ ID NO.81, SEQ ID NO.82, SEQ ID NO.85, SEQ ID NO.86, SEQ ID NO.87, SEQ ID NO.88, SEQ ID NO.89, SEQ ID NO.90, SEQ ID NO.91 and SEQ ID NO. NO.92.

[0067] In one embodiment, the kit includes reagents for detecting mutations in at least two of the CBS overlapping regions listed in the table, or portions thereof, or flanking sequences thereof.

[0068] In one embodiment, the kit includes reagents for detecting mutations in all CBS overlapping regions listed in the table, or portions thereof, or in their flanking sequences.

[0069] In one embodiment, the kit includes reagents for detecting mutations in all CBS overlapping regions or portions thereof or flanking sequences and all non-CBS regions or portions thereof or flanking sequences listed in the table.

[0070] In one embodiment, the human subject is an Asian subject.

[0071] definition

[0072] The term "biological sample" refers to a sample obtained from a biological subject, including a sample of a biological tissue or fluid source obtained in vivo or in vitro. Therefore, a "biological sample" can be a solid biological sample or a liquid biological sample. Examples of "solid biological samples" include tumor biopsies, and examples of "fluid biological samples" or "liquid biological samples" include blood, serum, plasma, sputum, lavage fluid (e.g., peritoneal lavage fluid), cerebrospinal fluid, urine, semen, sweat, tears, saliva, etc. As used herein, the terms "blood", "plasma" and "serum" cover their fractions or processed parts. Similarly, if the sample is taken from a biopsy, swab, smear, etc., then "sample" covers the processed fractions or parts derived from a biopsy, swab, smear, etc.

[0073] When it comes to nucleic acid sequences, the term "mutation" will be broadly interpreted as referring to changes in nucleic acid sequences compared to a reference sequence (usually a wild-type sequence). A wild-type sequence generally refers to a sequence found in normal cells rather than in abnormal or cancer cells (e.g., gastrointestinal cancer cells). Examples of wild-type sequences include SEQ ID NO.1-34. The term "mutation" includes deletions, additions, insertions and / or substitutions. "Mutations" can be inherited, naturally occurring or introduced. When it comes to coding genes, the term "mutation" generally refers to, but is not limited to, sequence variants of proteins that encode proteins that are different (e.g., in function, conformation, structure, etc.) from proteins encoded by wild-type genes.

[0074] As used herein, the term "gene" refers primarily to coding sequences, but may also include some or all of the surrounding regulatory elements or introns. Thus, when referring to coding genes, "mutations" may also include deletions, additions, insertions and / or substitutions in the regulatory elements or introns of the coding genes. The term "gene" also includes artificial or recombinant genes generated from cDNA or genomic DNA, including recombinant genes based on splice variants.

[0075] All genomic coordinates used in this article to describe chromosome regions are specified according to the human reference genome construction GRCh37. When it comes to being used for chromosome regions in this article, "starting site" generally refers to the position of the first sequence defining the starting chromosome region, and "termination site" generally refers to the position of the last sequence defining the terminal of the chromosome region. For example, the CBS overlapping region with genomic coordinates "Chromosome 6, starting site: 50570094, termination site: 50570120" is based on the human reference genome construction GRCh37, spanning the region from position 50570094 (including endpoints) to position 50570120 (including endpoints) on chromosome 6. Therefore, the mutation in the above-mentioned CBS overlapping region covers the mutation occurring anywhere in the region from position 50570094 (including endpoints) to position 50570120 (including endpoints) on chromosome 6 according to the human reference genome construction GRCh37.

[0076] When referring to nucleic acid sequences in this article, the term "overlap region" will be broadly interpreted as including a nucleic acid region that contains at least a portion or all of a nucleic acid sequence. For example, the term "CTCF-binding site (CBS) overlap region" includes a nucleic acid region that contains at least a portion or all of a CBS. Therefore, a "CBS overlap region" can include sequences that are not part of a CBS, as long as at least one nucleotide in the region is a constituent nucleotide of the CBS (i.e., part of the CBS).

[0077] When referring to a nucleic acid sequence, the term "one or more flanking sequences" will be broadly interpreted as referring to any one or more sequences / one or more base pairs / one or more nucleotides that are immediately adjacent to the nucleic acid sequence at the 5' end and the 3' end of the nucleic acid sequence. The length of a "flanking sequence" is generally about 1-10 nucleotides / base pairs, for example, 2, 3, 4, 5, 6, 7, 8, 9 or 10 nucleotides / base pairs. Therefore, when referring to a CBS overlap region, the term "one or more flanking sequences" refers to a continuous sequence of no more than 10 nucleotides / base pairs that is immediately adjacent to the 5' end of the CBS overlap region and the 3' end of the CBS overlap region. For example, the "one or more flanking sequences" of the CBS overlapping region "Chromosome 6, start site: 50570094, end site: 50570120" covers one or more nucleotides / one or more base pairs at positions 50570084 (including the end point) to 50570093 (including the end point) and one or more nucleotides / one or more base pairs at positions 50570121 (including the end point) to 50570130 (including the end point).

[0078] As used herein, the term "isolated" refers to a nucleic acid or polynucleotide removed from its natural environment. An "isolated" nucleic acid or polynucleotide is typically partially purified. The term includes, for example, recombinant nucleic acids that are incorporated into a vector, into an autonomously replicating plasmid or virus, or into the genomic DNA of a prokaryote or eukaryote, or that exist as a separate molecule independent of any other sequence.

[0079] As used herein, the expression "determining the risk of cancer" broadly includes determining / detecting the presence of cancer (including recurrent and / or metastatic cancer). Thus, the expression "determining the risk of gastrointestinal cancer" includes determining / detecting the presence of gastrointestinal cancer (including recurrent and / or metastatic gastrointestinal cancer).

[0080] As used herein, the term "therapeutically effective amount" of a compound will be the amount of an active agent that is capable of preventing or at least slowing down (mitigating) medical conditions such as autoimmune diseases, inflammation, and cancer. The dosage and administration of the compounds, compositions, and formulations of the present disclosure can be determined by a person of ordinary skill in the field of clinical pharmacology or pharmacokinetics. See, for example, Mordenti and Rescigno, (1992) Pharmaceutical Research.9:17-25; Morenti et al., (1991) Pharmaceutical Research.8:1351-1359; and Mordenti and Chappell, "The use of interspecies scaling in toxicokinetics" in Toxicokinetics and New Drug Development, Yacobi et al. (eds.) (Pergamon Press: NY, 1989), pp. 42-96. The therapeutic use of an effective amount of an active agent of the present disclosure will depend, for example, on the therapeutic goal, route of administration, and condition of the patient. Therefore, it may be necessary for the therapist to titrate the dose and change the route of administration as needed to obtain the best therapeutic effect.

[0081] The term "Asian" includes a human classification of people who are derived from an ethnic group in Asia. For example, a person with any original ethnic origin originating from the Far East, Southeast Asia, or the Indian subcontinent, including, for example, China (including Hong Kong), Singapore, Malaysia, and India.

[0082] Unless otherwise specified, the terms "coupled" or "connected" as used in this specification are intended to encompass either direct connection or connection through one or more intermediate means.

[0083] When referring to two elements, the term "associated" as used herein refers to a generalized relationship between the two elements. The relationship includes, but is not limited to, a physical, chemical, or biological relationship. For example, when element A is associated with element B, element A and element B may be directly or indirectly connected to each other, or element A may contain element B, or vice versa.

[0084] When referring to two elements, the term “adjacent” used herein means that one element is immediately adjacent to the other element, and may be, but is not limited to, elements contacting each other, or may further include elements separated by one or more other elements disposed therebetween.

[0085] The term "and / or", such as "X and / or Y", is understood to mean "X and Y" or "X or Y", and should be construed to provide explicit support for both meanings or either meaning.

[0086] In addition, in the description herein, whenever used, the word "substantially" should be understood to include, but not limited to, "entirely" or "completely", etc. In addition, whenever used, terms such as "including", "comprising", etc. are intended to be non-restrictive descriptive language, because they broadly include the elements / components listed after these terms, except for other components not explicitly listed. For example, when "including" is used, a reference to "a" feature is also intended to be a reference to "at least one" of the feature. Terms such as "consisting of", "consisting of...", etc. can be considered as subsets of terms such as "including", "comprising", etc. in appropriate contexts. Therefore, in the embodiments disclosed herein using terms such as "including", "comprising", etc., it will be understood that these embodiments provide teachings for the corresponding embodiments using terms such as "consisting of", "consisting of...", etc. In addition, whenever used, terms such as "about", "approximately", etc. generally refer to reasonable changes, for example, a change of + / - 5% of the disclosed value, or a change of 4% of the disclosed value, or a change of 3% of the disclosed value, a change of 2% of the disclosed value, or a change of 1% of the disclosed value.

[0087] In addition, in this specification, certain values ​​may be disclosed within a certain range. The values ​​showing the endpoints of a range are intended to show a preferred range. Where a range has been described, it is intended that the range covers and teaches all possible subranges and single values ​​within the range. That is, the endpoints of a range should not be interpreted as inflexible limitations. For example, the description of a range of 1% to 5% is intended to have specifically disclosed subranges of 1% to 2%, 1% to 3%, 1% to 4%, 2% to 3%, etc., as well as values ​​within the range individually, such as 1%, 2%, 3%, 4% and 5%. The above specific disclosures are intended to apply to any depth / width of the range.

[0088] In addition, when describing some embodiments, the present disclosure may have disclosed a method and / or process as a specific order of steps. However, unless otherwise required, it will be understood that the method or process should not be limited to the specific order of steps disclosed. Other step orders are also possible. The specific order of steps disclosed herein should not be interpreted as overly restrictive. Unless otherwise required, the method and / or process disclosed herein should not be limited to the steps performed in the written order. The order of the steps can be changed and still remain within the scope of the present disclosure.

[0089] Implementation plan description

[0090] Exemplary, non-limiting embodiments of the present disclosure are disclosed below. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention belongs.

[0091] In various embodiments, a method of obtaining / predicting a clinical phenomenon in a test subject is provided, the method comprising determining biological data associated with one or more non-coding polynucleotide sequences / regions in a biological sample of the test subject.

[0092] In various embodiments, the clinical phenomenon includes at least one of the following: the presence / likelihood / tendency / risk of a proliferative disease in the test subject, the presence / likelihood / tendency / risk of recurrence / relapse of a proliferative disease in the test subject, the presence / likelihood / tendency / risk of metastasis of a proliferative disease in the test subject, an indication of the life expectancy / survival rate / time to death of a test subject with a proliferative disease, or the efficacy of a treatment regimen for a test subject with a proliferative disease.

[0093] In various embodiments, proliferative diseases are cancers.Cancer can be metastatic and / or recurrent.In various embodiments, cancer can include but is not limited to liver cancer, liver hepatocellular carcinoma, pancreatic cancer, pancreatic adenocarcinoma, lung cancer, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma, breast cancer and gastrointestinal cancer.Cancer can be adenocarcinoma, sarcoma, carcinoid, gastrointestinal stromal tumor (GIST) and / or lymphoma.In some embodiments, cancer includes gastrointestinal cancer.In some embodiments, gastrointestinal cancer can include but is not limited to stomach / stomach (stomach / gastric) cancer, colorectal cancer, colon cancer and rectal cancer.Gastric cancer can be intestinal type, diffuse type and / or mixed type.In some embodiments, cancer does not include the gastric adenocarcinoma subtype of the tumor with high level microsatellite instability (MSI).

[0094] In various embodiments, the test subject is a human subject. In some embodiments, the human subject is an Asian subject. In some embodiments, the Asian subject is a Chinese subject.

[0095] In various embodiments, the biological data includes mutations. In various embodiments, the biological data includes 1 to 50 mutations. In various embodiments, the biological data includes at least one mutation, at least two mutations, at least three mutations, at least four mutations, at least five mutations, at least six mutations, at least seven mutations, at least eight mutations, at least nine mutations, at least ten mutations, at least 11 mutations, at least 12 mutations, at least 13 mutations, at least 14 mutations, at least 15 mutations, at least 16 mutations, at least 17 mutations, at least 18 mutations, at least 19 mutations, at least 20 mutations, at least 21 mutations, at least 22 mutations, at least 23 mutations, at least 24 mutations, at least 25 mutations, at least 26 mutations, at least 27 mutations, at least 28 mutations, at least 29 mutations, at least 30 mutations, at least 31 mutations, at least 32 mutations, at least 33 mutations, or at least 34 mutations.

[0096] In some embodiments, one or more non-coding polynucleotide sequences / regions include non-coding sequences / regions of genes or within genes. Non-coding sequences / regions of genes or within genes may include introns of the gene. Non-coding sequences / regions of genes or within genes may include sequences / regions that are not gene exons. In some embodiments, one or more non-coding polynucleotide sequences / regions include intergenic sequences / regions.

[0097] In some embodiments, one or more non-coding polynucleotide sequences / regions include sequences / regions that are enriched / located in conserved sequences.

[0098] In some embodiments, each of the one or more non-coding polynucleotide sequences / regions is not adjacent to a TERT gene. In some embodiments, at least 1 gene, at least 2 genes, at least 3 genes, at least 4 genes, or at least 5 genes are present in a region spanning a TERT gene and each of the one or more non-coding polynucleotide sequences / regions that are not adjacent to a TERT gene. In some embodiments, each of the one or more non-coding polynucleotide sequences / regions that are not adjacent to a TERT gene is located at least about 180 kilobase pairs (kbp), at least about 177 kbp, at least about 170 kbp, at least about 160 kbp, at least about 150 kbp, at least about 140 kbp, at least about 130 kbp, at least about 120 kbp, at least about 110 kbp, at least about 100 kbp, at least about 90 kbp, at least about 80 kbp, at least about 70 kbp, at least about 60 kbp, at least about 50 kbp, at least about 40 kbp, at least about 30 kbp, at least about 20 kbp, or at least about 10 kbp from a TERT gene.

[0099] In various embodiments, one or more non-coding polynucleotide sequences / regions are located on a human chromosome selected from the group consisting of human chromosome 1, human chromosome 2, human chromosome 3, human chromosome 4, human chromosome 5, human chromosome 6, human chromosome 7, human chromosome 8, human chromosome 9, human chromosome 12, human chromosome 13, human chromosome 14, human chromosome 16, human chromosome X, and any combination thereof. In various embodiments, one or more non-coding polynucleotide sequences / regions are not located on a human chromosome selected from the group consisting of human chromosome 10, human chromosome 11, human chromosome 15, human chromosome 17, human chromosome 18, human chromosome 19, human chromosome 20, human chromosome 21, human chromosome 22, human chromosome Y, and any combination thereof.

[0100] In some embodiments, the one or more non-coding polynucleotide sequences / regions include the polynucleotide sequences / regions shown in Table 1 below:

[0101] Table 1

[0102]

[0103]

[0104]

[0105] Therefore, in some embodiments, a method for determining the risk of cancer / predicting the likelihood of cancer / detecting cancer in a test subject is provided, the method comprising: determining in a biological sample of the subject whether a mutation exists in at least one region shown in Table 1 or a portion thereof or its flanking sequence.

[0106] In various embodiments, one or more non-coding polynucleotide sequences / regions comprise the sequence shown in SEQ ID NO.1, SEQ ID NO.2, SEQ ID NO.3, SEQ ID NO.4, SEQ ID NO.5, SEQ ID NO.6, SEQ ID NO.7, SEQ ID NO.8, SEQ ID NO.9, SEQ ID NO.10, SEQ ID NO.11, SEQ ID NO.12, SEQ ID NO.13, SEQ ID NO.14, SEQ ID NO.15, SEQ ID NO.16, SEQ ID NO.17, SEQ ID NO.18, SEQ ID NO.19, SEQ ID NO.20, SEQ ID NO.21, SEQ ID NO.22, SEQ ID NO.23, SEQ ID NO.24, SEQ ID NO.25, SEQID NO.26, SEQ ID NO.27, SEQ ID NO.28, SEQ ID NO.29, SEQ ID NO.30, SEQ ID NO.31, SEQ ID NO.32, SEQ ID NO.33 or SEQ ID NO.34.

[0107] In various embodiments, one or more non-coding polynucleotide sequences / regions include a transcription factor binding site (TFBS) or a portion thereof. In some embodiments, one or more non-coding polynucleotide sequences / regions include a CTCF (11-zinc finger protein or CCCTC-binding factor) binding site (CBS) or a CTCF binding motif and its flanking sequence / region / border, optionally, wherein the flanking sequence / region / border length is no more than about 10bp, no more than about 9bp, no more than about 8bp, no more than about 7bp, no more than about 6bp, no more than about 5bp, no more than about 4bp, no more than about 3bp, no more than about 2bp or no more than about 1bp. In some embodiments, the CTCF binding motif has the following position frequency matrix (JASPAR ID: MA0139.1 and UniProt ID: P49711):

[0108]

[0109]

[0110] CTCF is a DNA binding protein that is essential for maintaining genome structure by mediating short-range and long-range chromosome contacts. CTCF, together with the cohesin complex, organizes chromatin into large topologically associated domains (TADs), insulating local chromosome neighborhoods from adjacent regions. Therefore, disruption of CTCF binding can lead to dysregulation of gene expression. In cancer, it was found that CTCF binding was disrupted by various mechanisms, such as changes in DNA copy number across domain boundaries, microdeletions within CBS, and high methylation of CBS. These changes in CBS can drive cancer progression by allowing ectopic expression of oncogenes. It is worth noting that an increase in the genome-wide somatic mutation rate across CBS was found in several cancer types, indicating that the mutation and DNA repair processes in CBS may act differently relative to other genomic regions, resulting in an increase in the overall mutation load of such sites in cancer. More notably, the inventors also identified 11 CBS overlapping regions that were surprisingly significantly mutated even after controlling the mutation rate of the whole genome elevated in CBS.

[0111] Thus, in some embodiments, the mutation comprises a mutation in a CBS or CTCF binding motif or its flanking sequences / regions / borders.

[0112] In addition, in some embodiments, mutations within one or more non-coding polynucleotide sequences / regions include mutations that cannot be fully explained by the genome-wide elevated mutation rate of the one or more non-coding polynucleotide sequences / regions. In some embodiments where the mutation is located within a CBS, the mutation includes mutations that cannot be fully explained by the genome-wide elevated mutation rate of the CBS (e.g., the mutation is a mutation that remains significant after adjustment with a CBS-specific background mutation model).

[0113] Therefore, in some embodiments, a method for determining the risk of gastrointestinal cancer / predicting the likelihood of gastrointestinal cancer / detecting gastrointestinal cancer in a human subject is provided, the method comprising: determining in a biological sample of the subject whether there is a mutation in a CTCF-binding site (CBS) overlapping region or a portion thereof or a flanking sequence thereof, optionally, wherein the CBS overlapping region is as shown in Table 2 below:

[0114] Table 2

[0115]

[0116]

[0117] The presence of a mutation in at least one CBS overlapping region or a portion thereof or a flanking sequence thereof indicates a risk of gastrointestinal cancer (e.g., an increased or elevated risk of gastrointestinal cancer) or a likelihood that the subject has gastrointestinal cancer or is susceptible to gastrointestinal cancer or the subject has gastrointestinal cancer or is susceptible to gastrointestinal cancer.

[0118] In some embodiments, a method for determining the risk of gastrointestinal cancer in a human subject is provided, the method comprising: determining in a biological sample of the subject whether there is a mutation in at least one CBS overlapping region or a portion thereof or a flanking sequence thereof shown in Table 2; wherein the presence of a mutation in at least one CBS overlapping region or a portion thereof or a flanking sequence thereof indicates a risk of gastrointestinal cancer in the subject.

[0119] In some embodiments, the TFBS or CBS overlapping region includes the sequences shown in SEQ ID NO.1, SEQ ID NO.3, SEQ ID NO.6, SEQ ID NO.7, SEQ ID NO.9, SEQ ID NO.10, SEQ ID NO.14, SEQ ID NO.15, SEQ ID NO.18, SEQ ID NO.24 and SEQ ID NO.28.

[0120] In some embodiments, the one or more non-coding polynucleotide sequences / regions include a sequence / region that is not a TFBS (or CBS) or a portion thereof.

[0121] Therefore, in some embodiments, a method for determining the risk of gastrointestinal cancer / predicting the likelihood of gastrointestinal cancer / detecting gastrointestinal cancer in a human subject is provided, the method comprising: determining whether there is a mutation in a non-CBS region or a portion thereof or a flanking sequence thereof in a biological sample of the subject, optionally, wherein the non-CBS region is as shown in Table 3 below:

[0122] Table 3

[0123] chromosome Start site Termination site 7 68391104 68391132 7 136495924 136495948 2 57627616 57627640 16 8381278 8381302 5 23824204 23824224 7 67614923 67614943 8 65161396 65161420 7 4937707 4937736 12 126996666 126996686 4 5415060 5415082 X 137405623 137405655 3 171164993 171165017 4 144748744 144748764 9 25481736 25481758 2 77150455 77150477 3 104801455 104801477 X 125548690 125548710 14 83046706 83046744

[0124] The presence of a mutation in at least one non-CBS region or a portion thereof or a flanking sequence thereof indicates a risk of gastrointestinal cancer or a likelihood that the subject has or is susceptible to gastrointestinal cancer or the subject has or is susceptible to gastrointestinal cancer.

[0125] Therefore, in some embodiments, the method also includes determining in a biological sample of the subject whether there is a mutation in at least one non-CBS region or portion thereof or its flanking sequence shown in Table 3, wherein the presence of a mutation in at least one CBS overlapping region or portion thereof or its flanking sequence, and / or at least one non-CBS region or portion thereof or its flanking sequence indicates a risk of gastrointestinal cancer in the subject.

[0126] In some embodiments, the present invention provides a method for determining the risk of gastrointestinal cancer in a human subject, the method comprising: determining in a biological sample of the subject whether there is a mutation in at least one CTCF-binding site (CBS) overlapping region or a portion thereof or its flanking sequence shown in Table 2 or in at least one non-CBS region or a portion thereof or its flanking sequence shown in Table 3, wherein the presence of a mutation in at least one CBS overlapping region or a portion thereof or its flanking sequence, and / or at least one non-CBS region or a portion thereof or its flanking sequence indicates a risk of gastrointestinal cancer in the subject.

[0127] In some embodiments, determining whether there is a mutation in each of one or more non-coding polynucleotide sequences / regions includes determining whether there is at least one mutation, at least two mutations, at least three mutations, at least four mutations, at least five mutations, at least six mutations, at least seven mutations, at least eight mutations, at least nine mutations, or at least ten mutations in each of the non-coding polynucleotide sequences / regions. For example, determining whether there is a mutation in a non-coding polynucleotide sequence / region selected from chr 4:144748744-144748764, chr 7:136495924-136495948, chr 8:70576141-70576184, chr 2:77150455-77150477, and chr 7:68391104-68391132 includes determining whether there are at least three mutations, at least four mutations, at least five mutations, at least six mutations in the non-coding polynucleotide sequence / region. In various embodiments, each of the one or more non-coding polynucleotide sequences / regions is no more than about 50 bp, no more than about 49 bp, no more than about 48 bp, no more than about 47 bp, no more than about 46 bp, no more than about 45 bp, no more than about 44 bp, no more than about 43 bp, no more than about 42 bp, no more than about 41 bp, no more than about 40 bp, no more than about 39 bp, no more than about 38 bp, no more than about 37 bp, no more than about 36 bp, no more than about 35 bp, no more than about 34 bp, no more than about 33 bp, no more than about 32 bp, no more than about 31 bp, no more than about 30 bp, no more than about 29 bp, no more than about 28 bp, no more than about 27 bp, no more than about 26 bp, no more than about 25 bp, no more than about 24 bp, no more than about 23 bp, no more than about 22 bp, no more than about 21 bp, no more than about 20 bp, no more than about 19 bp, no more than about 18 bp, no more than about 17 bp, no more than about 16 bp or no more than about 15 bp. Advantageously, mutations in the one or more non-coding polynucleotide sequences / regions are dense and concentrated, and thus the one or more non-coding polynucleotide sequences / regions are very suitable as biomarkers in liquid biopsy assays.

[0128] Thus, in various embodiments, determining whether a mutation exists in a CBS overlapping region or a portion thereof or its flanking sequence, and / or a non-CBS region or a portion thereof or its flanking sequence, does not include determining whether a mutation exists in a region spanning more than 50 nucleotides. In various embodiments, determining whether a mutation exists in a CBS overlapping region or a portion thereof or its flanking sequence, and / or a non-CBS region or a portion thereof or its flanking sequence, includes determining whether a mutation exists in a region spanning no more than 50 nucleotides.

[0129] In various embodiments, the mutation may include a point mutation. The mutation may also include one selected from the following: an insertion mutation, a deletion mutation, a substitution mutation, an insertion / deletion mutation (indel) or any combination thereof. The mutation may also include a missense mutation, a splice site mutation, a frameshift mutation, a nonsense mutation, etc. In some embodiments, the mutation includes a mutation selected from the following: AT>CG substitution, AT>GC substitution, T>G substitution, T>C substitution, T>A substitution and any combination thereof.

[0130] In various embodiments, the mutation includes a mutation at a position selected from the group consisting of: position 1, position 2, position 3, position 8, position 9, position 10, position 11, position 12, position 17, position 18, position 19, and any combination thereof of a CBS or CTCF binding motif, but is not limited thereto. In various embodiments, the mutation does not include a mutation at a position selected from the group consisting of: position 4, position 5, position 6, position 7, position 13, position 14, position 15, position 16, and any combination thereof of a CBS or CTCF binding motif, but is not limited thereto.

[0131] In some embodiments, wherein the mutations are located in one or more non-CBS regions, a plurality of said non-CBS regions, the mutations, overall, are not enriched in mutations selected from the group consisting of: AT>CG substitutions, AT>GC substitutions, and any combination thereof.

[0132] In some embodiments, the mutation includes a mutation in a sequence / region / border flanking the CBS. The sequence / region / border flanking the CBS may include a sequence / region / border flanking the 5' end of the CBS, or alternatively / additionally include a sequence / region / border flanking the 3' end of the CBS. The sequence / region / border flanking the CBS may be about 1-10 nucleotides / base pairs, for example, 2, 3, 4, 5, 6, 7, 8, 9 or 10 nucleotides / base pairs.

[0133] Advantageously, the sensitivity of the method can be improved by combining mutations in non-coding polynucleotide sequences / regions with protein coding regions that are frequently (and concentratedly) mutated in gastrointestinal cancer. The sensitivity of the method can also be improved by combining two or more mutations in non-coding polynucleotide sequences / regions.

[0134] Therefore, in various embodiments, the method further comprises determining biological data associated with the gene sequence / region or its fragment in a biological sample of a test subject. In some embodiments, wherein the biological data comprises the presence of mutations within the gene sequence / region or its fragment.

[0135] In various embodiments, the method further comprises determining from a biological sample at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight genes selected from KRAS, TP53, RHOA, ARID1A, PIK3CA, ERBB3, PTEN and HLA-B or mutations associated therewith. In some embodiments, the method further comprises determining from a biological sample whether there is also a mutation in at least one region encoding a protein selected from KRAS, TP53, RHOA, ARID1A, PIK3CA, ERBB3, PTEN and HLA-B. In some embodiments, the method further comprises determining in a biological sample whether there is a mutation in at least one gene selected from the following: KRAS, TP53, RHOA, ARID1A, PIK3CA, ERBB3, PTEN, wherein at least one CBS overlapping region or a portion thereof or a flanking sequence thereof, at least one non-CBS region or a portion thereof or a flanking sequence thereof, and / or at least one of the genes present a mutation, indicating a risk of gastrointestinal cancer in the subject. In some embodiments, the method further includes determining in a biological sample of the subject whether a mutation is present in at least one gene selected from the group consisting of: KRAS, TP53, and RHOA, wherein the presence of a mutation in at least one CBS overlapping region or a portion thereof or its flanking sequence, at least one non-CBS region or a portion thereof or its flanking sequence, and / or in at least one of the genes indicates a risk of gastrointestinal cancer in the subject.

[0136] In various embodiments, the method comprises determining in a biological sample of the subject a mutation in or associated with at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, or at least 34 of the non-coding polynucleotide sequences / regions in Table 1.

[0137] In various embodiments, the method comprises determining from a biological sample obtained from the subject the presence or absence of a mutation in at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or all 11 of the non-coding regions that overlap / are TF binding sites or CBSs shown in Table 2, and / or determining from a biological sample the presence or absence of a mutation in at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, or all 18 of the non-coding regions that do not overlap with the TF binding sites or CBSs shown in Table 3.

[0138] In some embodiments, the method comprises determining whether a mutation exists in at least two of the CBS overlapping regions or portions thereof or flanking sequences shown in Table 2. In some embodiments, the method comprises determining whether a mutation exists in all of the CBS overlapping regions or portions thereof or flanking sequences shown in Table 2. In some embodiments, the method comprises determining whether a mutation exists in all of the CBS overlapping regions or portions thereof or flanking sequences shown in Table 2 and Table 3, and all of the non-CBS regions or portions thereof or flanking sequences.

[0139] In various embodiments, the detection sensitivity / accuracy of the method is not less than about 15%, not less than about 20%, not less than about 25%, not less than about 30%, not less than about 40%, not less than about 45%, not less than about 50%, not less than about 55%, not less than about 60%, not less than about 65%, not less than about 70%, not less than about 75%, not less than about 85%, not less than about 90%, or not less than about 95%. In some embodiments, the detection sensitivity of the method is not less than about 50%.

[0140] In various embodiments, the method is at least one of a diagnostic method, a prognostic method, a disease monitoring method, a method for detecting recurrence, or a method for detecting metastasis. In some embodiments, the method comprises an early detection method or a monitoring method for disease recurrence / recurrence and / or metastasis.

[0141] In various embodiments, the method is an in vitro method. In various embodiments, the method is an ex vivo method.

[0142] In various embodiments, the method further comprises obtaining a biological sample from the test subject prior to the determination step. In some embodiments, the step of obtaining the biological sample from the test subject is a non-surgical step, a non-invasive step, or a minimally invasive step. In some embodiments, the step of obtaining the biological sample from the test subject comprises drawing a blood sample from the test subject or obtaining a tumor biopsy from the test subject. In some embodiments, the method further comprises removing particulate blood components from the blood sample to leave plasma for the determination step. In some embodiments, the particulate blood components are selected from red blood cells, white blood cells, platelets, and combinations thereof.

[0143] In various embodiments, the biological sample is a solid biological sample. In some embodiments, the solid biological sample includes a solid sample derived from a tumor tissue (e.g., a solid block tumor). The solid sample derived from a tumor tissue may include a tumor biopsy. In various embodiments, the biological sample is a fluid biological sample. In some embodiments, the fluid biological sample may include, but is not limited to, blood, plasma, serum, and combinations thereof.

[0144] In various embodiments, the biological sample includes cell-free DNA or circulating DNA of tumor cells. In various embodiments, the length of cell-free DNA or circulating DNA of tumor cells is no more than about 250 bp, no more than about 240 bp, no more than about 230 bp, no more than about 220 bp, no more than about 210 bp, no more than about 200 bp, no more than about 190 bp, no more than about 180 bp, no more than about 170 bp, no more than about 160 bp, no more than about 150 bp, no more than about 140 bp, no more than about 130 bp, no more than about 120 bp, no more than about 110 bp, no more than about 100 bp, no more than about 90 bp, no more than about 80 bp, no more than about 70 bp, no more than about 60 bp or no more than about 50 bp, but is not limited thereto.

[0145] In various embodiments, the method requires no more than about 20 ml, no more than about 19.5 ml, no more than about 19 ml, no more than about 18.5 ml, no more than about 18 ml, no more than about 17.5 ml, no more than about 17 ml, no more than about 16.5 ml, no more than about 16 ml, no more than about 15.5 ml, no more than about 15 ml, no more than about 14.5 ml, no more than about 14 ml, no more than about 13.5 ml, no more than about 13 ml, no more than about 12.5 ml, no more than about 12 ml, no more than about 11.5 ml, no more than about 11 ml, no more than about 10.5 ml, no more than about 10 ml, no more than about 9.5 ml, no more than about 9 ml, no more than about 8. 5 ml, no more than about 8 ml, no more than about 7.5 ml, no more than about 7 ml, no more than about 6.5 ml, no more than about 6 ml, no more than about 5.5 ml, no more than about 5 ml, no more than about 4.5 ml, no more than about 4 ml, no more than about 3.5 ml, no more than about 3 ml, no more than about 2.5 ml, no more than about 2 ml, no more than about 1.5 ml, no more than about 1 ml, no more than about 0.9 ml, no more than about 0.8 ml, no more than about 0.7 ml, no more than about 0.6 ml, no more than about 500 microliters, no more than about 450 microliters of a biological sample, no more than about 400 microliters of a biological sample, no more than about 350 microliters of a biological sample, or no more than about 300 microliters of a biological sample.

[0146] In various embodiments, a method for treating a proliferative disease in a subject in need thereof is provided, the method comprising: providing a biological sample from the subject; determining the presence of a mutation associated with one or more non-coding polynucleotide sequences / regions in the biological sample of the test subject, wherein if the sample shows the presence of a mutation, the subject is treated with an agent for treating the proliferative disease, optionally, wherein the agent may include, but is not limited to, chemotherapy, radiotherapy, combination therapy, alternative / complementary therapy, and immunotherapy. In some embodiments, a method for treating gastrointestinal cancer in a human subject is provided, the method comprising determining in the biological sample of the subject whether there is a mutation in at least one CTCF-binding site (CBS) overlapping region or a portion thereof or its flanking sequence shown in Table 2, or in one of the non-CBS regions or a portion thereof or its flanking sequences shown in Table 3, wherein if the biological sample shows the presence of a mutation in at least one CBS overlapping region or a portion thereof or its flanking sequence and / or at least one non-CBS region or a portion thereof or its flanking sequence, a therapeutic agent for treating gastrointestinal cancer is administered to the subject.

[0147] Therefore, in some embodiments, a method for treating gastrointestinal cancer in a human subject is provided, the method comprising: determining in a biological sample of the subject whether there is a mutation in at least one CBS overlapping region or a portion thereof or a flanking sequence thereof shown in Table 2, wherein if the biological sample shows the presence of a mutation in at least one CBS overlapping region or a portion thereof or a flanking sequence thereof, a therapeutic agent for treating gastrointestinal cancer is administered to the subject. In some embodiments, the method further comprises determining in a biological sample of the subject whether there is a mutation in at least one non-CBS region or a portion thereof or a flanking sequence thereof shown in Table 3, wherein if the biological sample shows the presence of a mutation in at least one CBS overlapping region or a portion thereof or a flanking sequence thereof, and / or at least one non-CBS region or a portion thereof or a flanking sequence thereof, a therapeutic agent for treating gastrointestinal cancer is administered to the subject. In some embodiments, the method further comprises determining in the biological sample of the subject whether there is a mutation in at least one gene selected from the following: KRAS, TP53, RHOA, ARID1A, PIK3CA, ERBB3 and PTEN, wherein if the biological sample shows a mutation in at least one CBS overlapping region or a portion thereof or its flanking sequence, at least one non-CBS region or a portion thereof or its flanking sequence, and / or at least one of the genes, then administering to the subject a therapeutic agent for treating gastrointestinal cancer. In some embodiments, the method further comprises determining in the biological sample of the subject whether there is a mutation in at least one gene selected from the following: KRAS, TP53 and RHOA, wherein if the biological sample shows a mutation in at least one CBS overlapping region or a portion thereof or its flanking sequence, at least one non-CBS region or a portion thereof or its flanking sequence, and / or at least one of the genes, then administering to the subject a therapeutic agent for treating gastrointestinal cancer.

[0148] In some embodiments, the therapeutic agent may include, but is not limited to, chemotherapy, radiation therapy, immunotherapy, and combinations thereof. In some embodiments, a therapeutically effective amount of a therapeutic agent is administered to a subject.

[0149] In various embodiments, the method includes a liquid biopsy assay. Thus, in various embodiments, the method may include various existing experimental steps / methods for targeted sequencing (e.g., amplicon sequencing, capture probes, etc.) of one or more non-coding polynucleotide sequences / regions in a liquid biopsy.

[0150] The detection of the presence of mutations can be performed according to any of the many methods available to those skilled in the art. In various embodiments, the determination step is performed by performing a determination capable of detecting the presence of mutations in one or more non-coding polynucleotide sequences / regions. In some embodiments, the determination may include, but is not limited to, DNA sequencing methods, next generation sequencing (NGS) methods, whole genome sequencing (WGS) methods, whole exon sequencing (WES) methods, panel sequencing methods, paired end sequencing methods, DNA microarray methods, multiple ligation-dependent probe amplification (MLPA) methods, single-stranded conformation polymorphism (SSCP) methods, denaturing gradient gel electrophoresis (DGGE) methods, heteroduplex analysis methods, restriction fragment length polymorphism (RFLP) methods, polymerase chain reaction (PCR) methods, molecular inversion probes, digital droplet PCR, fluorescent probe PCR, quantitative PCR, allele-specific PCR, etc.

[0151] In various embodiments, determining whether there is a mutation in one or more non-coding polynucleotide sequences / regions includes contacting the biological sample with a reagent for detecting a mutation in one or more non-coding polynucleotide sequences / regions. In some embodiments, reagents may include, but are not limited to, primers, probes, capture agents, dyes, labels, nucleotides, salts, buffers, various additives, PCR enhancers, and combinations thereof. Therefore, in some embodiments, the assay step includes contacting the biological sample with a primer / probe for hybridization with one or more non-coding polynucleotide sequences / regions under stringent conditions.

[0152] In various embodiments, the probe / primer comprises SEQ ID NO.35, SEQ ID NO.36, SEQ ID NO.37, SEQ ID NO.38, SEQ ID NO.39, SEQ ID NO.40, SEQ ID NO.41, SEQ ID NO.42, SEQ ID NO.43, SEQ ID NO.44, SEQ ID NO.45, SEQ ID NO.46, SEQ ID NO.47, SEQ ID NO.48, SEQ ID NO.49, SEQ ID NO.50, SEQ ID NO.51, SEQ ID NO.52, SEQ ID NO.53, SEQ ID NO.54, SEQ ID NO.55, SEQ ID NO.56, SEQ ID NO.57, SEQ ID NO.58, SEQ ID NO.59, SEQ ID NO.60, SEQ IDNO.61, SEQ ID NO.62, SEQ ID NO.63, SEQ ID NO.64, SEQ ID NO.65, SEQ ID NO.66, SEQ IDNO.67, SEQ ID NO.68, SEQ ID NO.69, SEQ ID NO.70, SEQ ID NO.71, SEQ ID NO.72, SEQ ID NO.73, SEQ ID NO.74, SEQ ID NO.75, SEQ ID NO.76, SEQ ID NO.77, SEQ ID NO.78, SEQ ID NO.79, SEQ ID NO.80, SEQ ID NO.81, SEQ ID NO.82, SEQ ID NO.83, SEQ ID NO.84, SEQ ID NO.85, SEQ ID NO.86, SEQ ID NO.87, SEQ ID NO.88, SEQ ID NO.89, SEQ ID NO.90, SEQ ID NO.91 or SEQ ID NO.92 has a sequence identity of at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 85% or at least about 99%. It should be understood that other probes / primers available to one of ordinary skill in the art based on the sequence of one or more non-coding polynucleotide sequences / regions and based on well-known criteria for designing sequence-specific probes / primers may also be used.

[0153] In various embodiments, determining whether a mutation is present in at least one CBS overlapping region or a portion thereof or a flanking sequence thereof comprises contacting the biological sample with a reagent, optionally a primer for detecting a mutation in at least one CBS overlapping region shown in Table 2. In various embodiments, determining whether a mutation is present in at least one CBS overlapping region or a portion thereof or its flanking sequence comprises contacting the biological sample with a primer having at least about 85% sequence identity to a sequence selected from the group consisting of SEQ ID NO.35, SEQ ID NO.36, SEQ ID NO.39, SEQ ID NO.40, SEQ ID NO.45, SEQ ID NO.46, SEQ ID NO.47, SEQ ID NO.48, SEQ ID NO.49, SEQ ID NO.50, SEQ ID NO.51, SEQ ID NO.52, SEQ ID NO.57, SEQ ID NO.58, SEQ ID NO.59, SEQ ID NO.60, SEQ ID NO.65, SEQ ID NO.66, SEQ ID NO.77, SEQ ID NO.78, SEQ ID NO.SEQ ID NO.83, SEQ ID NO.84 and combinations thereof. In various embodiments, determining whether a mutation is present in at least one CBS overlapping region comprises contacting the biological sample with a primer pair having at least about 85% sequence identity with a sequence pair such as, but not limited to, SEQ ID NOs. 35 and 36, SEQ ID NOs. 39 and 40, SEQ ID NOs. 45 and 46, SEQ ID NOs. 47 and 48, SEQ ID NOs. 49 and 50, SEQ ID NOs. 51 and 52, SEQ ID NOs. 57 and 58, SEQ ID NOs. 59 and 60, SEQ ID NOs. 65 and 66, SEQ ID NOs. 77 and 78, and SEQ ID NOs. 83 and 84.

[0154] In various embodiments, determining whether a mutation is present in at least one non-CBS region or portion thereof or its flanking sequence comprises contacting the biological sample with a reagent, which is optionally a primer for detecting a mutation in at least one non-CBS region or portion thereof or its flanking sequence shown in Table 3. In various embodiments, determining the presence or absence of a mutation in at least one non-CBS region or portion thereof or a flanking sequence thereof comprises contacting the biological sample with a primer having at least about 85% sequence identity to a sequence selected from the group consisting of SEQ ID NO.37, SEQ ID NO.38, SEQ ID NO.41, SEQ ID NO.42, SEQ ID NO.43, SEQ ID NO.44, SEQ ID NO.53, SEQ ID NO.54, SEQ ID NO.55, SEQ ID NO.56, SEQ ID NO.61, SEQ ID NO.62, SEQ ID NO.63, SEQ ID NO.64, SEQ ID NO.67, SEQ ID NO.68, SEQ ID NO.71, SEQ ID NO.72, SEQ ID NO.75, SEQ ID NO.76, SEQ ID NO.81, SEQ ID NO.82, SEQ ID NO.85, SEQ ID NO.86, SEQ ID NO.87, SEQ ID NO.88, SEQ ID NO.89, SEQ ID NO.90, SEQ ID NO.91, SEQ ID NO.92, SEQ ID NO.93, SEQ ID NO.94, SEQ ID NO.95 In various embodiments, determining the presence or absence of a mutation in at least one non-CBS region comprises contacting the biological sample with a primer pair having at least about 85% sequence identity to a sequence pair such as, but not limited to, SEQ ID NOs. 37 and 38, SEQ ID NOs. 41 and 42, SEQ ID NOs. 43 and 44, SEQ ID NOs. 53 and 54, SEQ ID NOs. 55 and 56, SEQ ID NOs. 61 and 62, SEQ ID NOs. 63 and 64, SEQ ID NOs. 67 and 68, SEQ ID NOs. 69 and 70, SEQ ID NOs. 71 and 72, SEQ ID NOs. 73 and 74, SEQ ID NOs. 75 and 76, SEQ ID NOs. 79 and 80, SEQ ID NOs. 81 and 82, SEQ ID NOs. 85 and 86, SEQ ID NOs. 87 and 88, SEQ ID NOs. 89 and 90, SEQ ID NOs. 91 and 92, and the like.

[0155] In various embodiments, a kit comprising reagents is provided for hybridizing to one or more non-coding polynucleotide sequences / regions under stringent conditions, or to a polynucleotide having at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 85%, or at least about 99% sequence identity to the one or more non-coding polynucleotide sequences / regions. In some embodiments, the reagents may include, but are not limited to, primers, probes, capture agents, dyes, labels, nucleotides, salts, buffers, various additives, PCR enhancers, and combinations thereof.

[0156] In various embodiments, a kit for detecting gastrointestinal cancer in a human subject is provided, the kit comprising reagents for detecting mutations in one or more non-coding polynucleotide sequences / regions shown in Table 1 or portions thereof or flanking sequences thereof. In some embodiments, the reagents comprise SEQ ID NO.35, SEQ ID NO.36, SEQ ID NO.37, SEQ ID NO.38, SEQ ID NO.39, SEQ ID NO.40, SEQ ID NO.41, SEQ ID NO.42, SEQ ID NO.43, SEQ ID NO.44, SEQ ID NO.45, SEQ ID NO.46, SEQ ID NO.47, SEQ ID NO.48, SEQ ID NO.49, SEQ ID NO.50, SEQ ID NO.51, SEQ ID NO.52, SEQ ID NO.53, SEQ ID NO.54, SEQ ID NO.55, SEQ ID NO.56, SEQ ID NO.57, SEQ ID NO.58, SEQ ID NO.59, SEQ ID NO.60, SEQ ID NO.61, SEQ ID NO.62, SEQ ID NO.63, SEQ ID NO.64, SEQ ID NO.65, SEQ ID NO.66, SEQ ID NO.67, SEQ ID NO.68, SEQ The sequences shown in SEQ ID NO.69, SEQ ID NO.70, SEQ ID NO.71, SEQ ID NO.72, SEQ ID NO.73, SEQ ID NO.74, SEQ ID NO.75, SEQ ID NO.76, SEQ ID NO.77, SEQ ID NO.78, SEQ ID NO.79, SEQ ID NO.80, SEQ ID NO.81, SEQ ID NO.82, SEQ ID NO.83, SEQ ID NO.84, SEQ ID NO.85, SEQ ID NO.86, SEQ ID NO.87, SEQ ID NO.88, SEQ ID NO.89, SEQ ID NO.90, SEQ ID NO.91, and SEQ ID NO.92 have a sequence identity of at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 85% or at least about 99%.

[0157] In various embodiments, a kit for detecting gastrointestinal cancer in a human subject is provided, the kit comprising: a reagent for detecting a mutation in at least one CBS overlapping region or a portion thereof or a flanking sequence thereof shown in Table 2, or at least one non-CBS region or a portion thereof or a flanking sequence thereof shown in Table 3. In some embodiments, a kit for detecting gastrointestinal cancer in a human subject is provided, the kit comprising a reagent for detecting a mutation in at least one CBS overlapping region or a portion thereof or a flanking sequence thereof shown in Table 2. In some embodiments, reagents for detecting mutations in at least one CBS overlapping region or a portion thereof or its flanking sequences include primers having at least about 85% sequence identity with, for example but not limited to, the following sequences: SEQ ID NO.35, SEQ ID NO.36, SEQ ID NO.39, SEQ ID NO.40, SEQ ID NO.45, SEQ ID NO.46, SEQ ID NO.47, SEQ ID NO.48, SEQ ID NO.49, SEQ ID NO.50, SEQ ID NO.51, SEQ ID NO.52, SEQ ID NO.57, SEQ ID NO.58, SEQ ID NO.59, SEQ ID NO.60, SEQ ID NO.65, SEQ ID NO.66, SEQ ID NO.77, SEQ ID NO.78, SEQ ID NO.SEQ ID NO.83 and SEQ ID NO.84, etc. In some embodiments, the kit includes a primer pair having at least about 85% sequence identity to a sequence selected from the group consisting of SEQ ID NOs. 35 and 36, SEQ ID NOs. 39 and 40, SEQ ID NOs. 45 and 46, SEQ ID NOs. 47 and 48, SEQ ID NOs. 49 and 50, SEQ ID NOs. 51 and 52, SEQ ID NOs. 57 and 58, SEQ ID NOs. 59 and 60, SEQ ID NOs. 65 and 66, SEQ ID NOs. 77 and 78, and SEQ ID NOs. 83 and 84.In some embodiments, the kit comprises a primer pair having the following sequences: SEQ ID NO.35 and 36 or a sequence pair having at least about 85% sequence identity thereto; SEQ ID NO.39 and 40 or a sequence pair having at least about 85% sequence identity thereto; SEQ ID NO.45 and 46 or a sequence pair having at least about 85% sequence identity thereto; SEQ ID NO.47 and 48 or a sequence pair having at least about 85% sequence identity thereto; SEQ ID NO.49 and 50 or a sequence pair having at least about 85% sequence identity thereto; and SEQ ID NO.51 and 52 or a sequence pair having at least about 85% sequence identity thereto. In some embodiments, the kit comprises reagents for detecting mutations in at least two of the CBS overlapping regions shown in Table 2, or portions thereof, or flanking sequences thereof. In some embodiments, the kit comprises reagents for detecting mutations in all of the CBS overlapping regions shown in Table 2, or portions thereof, or flanking sequences thereof. In some embodiments, the kit includes reagents for detecting mutations in all CBS overlapping regions or portions thereof or flanking sequences thereof and all non-CBS regions or portions thereof or flanking sequences thereof shown in Tables 2 and 3.

[0158] In some embodiments, the kit further comprises a reagent for detecting a mutation in at least one non-CBS region or a portion thereof or a flanking sequence thereof shown in Table 3. In some embodiments, a kit for detecting gastrointestinal cancer in a human subject is provided, the kit comprising a reagent for detecting a mutation in at least one CBS overlapping region or a portion thereof or a flanking sequence thereof shown in Table 3. In some embodiments, the reagents for detecting mutations in at least one non-CBS region or portion thereof or in its flanking sequence include primers having at least about 85% sequence identity to a sequence selected from the group consisting of SEQ ID NO.37, SEQ ID NO.38, SEQ ID NO.41, SEQ ID NO.42, SEQ ID NO.43, SEQ ID NO.44, SEQ ID NO.53, SEQ ID NO.54, SEQ ID NO.55, SEQ ID NO.56, SEQ ID NO.61, SEQ ID NO.62, SEQ ID NO.63, SEQ ID NO.64, SEQ ID NO.67, SEQ ID NO.68, SEQ ID NO.71, SEQ ID NO.72, SEQ ID NO.75, SEQ ID NO.76, SEQ ID NO.81, SEQ ID NO.82, SEQ ID NO.85, SEQ ID NO.86, SEQ ID NO.87, SEQ ID NO.88, SEQ ID NO.89, SEQ ID NO.90, SEQ ID NO.91 and SEQ ID NO. NO. 92. In some embodiments, the kit includes primer pairs having at least about 85% sequence identity to sequences such as, but not limited to, SEQ ID NOs. 37 and 38, SEQ ID NOs. 41 and 42, SEQ ID NOs. 43 and 44, SEQ ID NOs. 53 and 54, SEQ ID NOs. 55 and 56, SEQ ID NOs. 61 and 62, SEQ ID NOs. 63 and 64, SEQ ID NOs. 67 and 68, SEQ ID NOs. 69 and 70, SEQ ID NOs. 71 and 72, SEQ ID NOs. 73 and 74, SEQ ID NOs. 75 and 76, SEQ ID NOs. 79 and 80, SEQ ID NOs. 81 and 82, SEQ ID NOs. 85 and 86, SEQ ID NOs. 87 and 88, SEQ ID NOs. 89 and 90, and SEQ ID NOs. 91 and 92, etc.

[0159] In various embodiments, the probe / primer length is no more than about 30 bp, no more than about 29 bp, no more than about 28 bp, no more than about 27 bp, no more than about 26 bp, no more than about 25 bp, no more than about 24 bp, no more than about 23 bp, no more than about 22 bp, no more than about 21 bp, no more than about 20 bp, no more than about 19 bp, no more than about 18 bp, no more than about 17 bp, no more than about 16 bp, no more than about 15 bp, no more than about 14 bp, no more than about 13 bp, no more than about 12 bp, no more than about 11 bp, no more than about 10 bp, no more than about 9 bp, no more than about 8 bp, no more than about 7 bp, no more than about 6 bp or no more than about 5 bp.

[0160] In various embodiments, the kit further comprises a probe / primer for hybridizing with a gene sequence. In some embodiments, the gene is selected from KRAS, TP53, RHOA, ARID1A, PIK3CA, ERBB3, PTEN, and HLA-B.1. In some embodiments, the kit further comprises a reagent for detecting a mutation in at least one gene selected from the following: KRAS, TP53, RHOA, ARID1A, PIK3CA, ERBB3, and PTEN. In some embodiments, the kit further comprises a reagent for detecting a mutation in at least one gene selected from the following: KRAS, TP53, RHOA.

[0161] In various embodiments, the kit comprises at least about 10, at least about 20, at least about 30, at least about 40, at least about 50, at least about 60, or at least about 68 probes / primers.

[0162] In various embodiments, isolated nucleic acids or polynucleotides are also provided that comprise a mutation in each of one or more of the non-coding polynucleotide sequences / regions shown in Table 1 or in a polynucleotide having at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 85%, or at least about 99% sequence identity with each of one or more of the non-coding polynucleotide sequences / regions shown in Table 1.

[0163] In various embodiments, there is provided a method, product or use as described herein. Sequence Listing <110> A*STAR <120> A method for determining cancer risk <130> 1900226 <160> 92 <170> PatentIn version 3.5 <210> 1 <211> 27 <212> DNA <213> Homo sapiens <400> 1 ctctagtgga attttttcag tactgca 27 <210> 2 <211> 29 <212> DNA <213> Homo sapiens <400> 2 ggattttttt aaaaaaaatt tttttttat 29 <210> 3 <211> twenty one <212> DNA <213> Homo sapiens <400> 3 ctggcaaaac ttcggtgcca a 21 <210> 4 <211> 25 <212> DNA <213> Homo sapiens <400> 4 tactggctta agttttggcc cagca 25 <210> 5 <211> 25 <212> DNA <213> Homo sapiens <400> 5 atttttccaa aaatttttgc aatta 25 <210> 6 <211> 39 <212> DNA <213> Homo sapiens <400> 6 aggtcctttg ttgccaccta gtggccatta attgcacta 39 <210> 7 <211> 47 <212> DNA <213> Homo sapiens <400> 7 tagcctgtag ttacttgatt tcaccactag atggtgatct tgtatta 47 <210> 8 <211> 21 <212> DNA <213> Homo sapiens <400> 8 aggagagtac gtgcatgtgt g 21 <210> 9 <211> 36 <212> DNA <213> Homo sapiens <400> 9 cgcaacctac aaaactgcca ccaggtggcg ctcacg 36 <210> 10 <211> 35 <212> DNA <213> Homo sapiens <400> 10 cagctcgccc tctagtggta attttatgaa ttgcc 35 <210> 11 <211> 25 <212> DNA <213> Homo sapiens <400> 11 aataaataaa taaacaaaca aacaa 25 <210> 12 <211> 21 <212> DNA <213> Homo sapiens <400> 12 agcaagccta agtgtggtct g 21 <210> 13 <211> 21 <212> DNA <213> Homo sapiens <400> 13 tgagagaaac ttagggtttc a 21 <210> 14 <211> 26 <212> DNA <213> Homo sapiens <400> 14 tgcacttata aattctacca ccagag 26 <210> 15 <211> 40 <212> DNA <213> Homo sapiens <400> 15 gtggcgcatc tcagccacaa gatggcagca gtgcttcgtg 40 <210> 16 <211> 25 <212> DNA <213> Homo sapiens <400> 16 cataatctga aaatttttgc aaaga 25 <210> 17 <211> 30 <212> DNA <213> Homo sapiens <400> 17 tgctggctta agttcagggc cacgctgccc 30 <210> 18 <211> 44 <212> DNA <213> Homo sapiens <400> 18 gtgttttcca aaatgaccac taggtggtag tctagttcaa ctaa 44 <210> 19 <211> 21 <212> DNA <213> Homo sapiens <400> 19 gtaagaaaac ttaggtgtaa a 21 <210> 20 <211> 21 <212> DNA <213> Homo sapiens <400> 20 aagaagtaca agactttgag g 21 <210> 21 <211> 23 <212> DNA <213> Homo sapiens <400> 21 ttttagctga agttcagtgg aat 23 <210> 22 <211> 21 <212> DNA <213> Homo sapiens <400> 22 gatttttttt taaaaagtta t 21 <210> 23 <211> 33 <212> DNA <213> Homo sapiens <400> 23 tatctcacta ataaaggaag aattcatcac ttt 33 <210> 24 <211> 40 <212> DNA <213> Homo sapiens <400> 24 taatatcaca attcgccact tggtgtcact agacctccag 40 <210> 25 <211> 21 <212> DNA <213> Homo sapiens <400> 25 cttttttttt aaaaaaaaag c 21 <210> 26 <211> 25 <212> DNA <213> Homo sapiens <400> 26 aataaataaa taaacaaaca aacat 25 <210> 27 <211> 21 <212> DNA <213> Homo sapiens <400> 27 aattttgttt gtttatttat t 21 <210> 28 <211> 29 <212> DNA <213> Homo sapiens <400> 28 aatggcctac ttagataagt gcctgcctc 29 <210> 29 <211> twenty one <212> DNA <213> Homo sapiens <400> 29 ctggtcagag agcaggagga a 21 <210> 30 <211> twenty three <212> DNA <213> Homo sapiens <400> 30 ttcacctaaa acttatcccc tta 23 <210> 31 <211> twenty three <212> DNA <213> Homo sapiens <400> 31 atttgtagaa agtttgtgag ctg 23 <210> 32 <211> twenty three <212> DNA <213> Homo sapiens <400> 32 aatggagtca agttttctga aat 23 <210> 33 <211> twenty one <212> DNA <213> Homo sapiens <400> 33 ttgacagaac ttgttacatt t 21 <210> 34 <211> 39 <212> DNA <213> Homo sapiens <400> 34 ttggacaaac ttgacttaaa taataggcaa gggacacct 39 <210> 35 <211> twenty two <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 35 cctgtccagg tattagagaa gg 22 <210> 36 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 36 aacccctcca cctccttttt 20 <210> 37 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 37 ctgacttgca ccactcatgc 20 <210> 38 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 38 gatgggagga ctgtttgagg 20 <210> 39 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 39 ttctcctgca atgtttctcg 20 <210> 40 <211> twenty two <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 40 tcaccttatt tgactgcttt gc 22 <210> 41 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 41 ccatgcagtg gttacagtgg 20 <210> 42 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 42 agaggtggtg gaggtgattg 20 <210> 43 <211> twenty two <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 43 agagcaggat aaggcatatt ca 22 <210> 44 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 44 tgggacattt tccctttcaa 20 <210> 45 <211> twenty two <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 45 cactgcctac agccaataat ca 22 <210> 46 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 46 accctgcctt ttctttcatt 20 <210> 47 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 47 ggggtgcaga gaagaatctg 20 <210> 48 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 48 atgccaccct ttgttagtgg 20 <210> 49 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 49 ctttgcgtgg tgaaaagaaa 20 <210> 50 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 50 ttgctgctgc tccaaagtta 20 <210> 51 <211> twenty one <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 51 ccagcagttg gtcaatgata a 21 <210> 52 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 52 gctaaaaggg caattgttgg 20 <210> 53 <211> twenty four <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 53 tgatgtctgt gatgttttca agtg 24 <210> 54 <211> twenty one <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 54 tgtcaatttt gagccctcat c 21 <210> 55 <211> twenty one <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 55 ttggaactcc ctacccctaa a 21 <210> 56 <211> twenty one <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 56 catggataac cagcacagac a 21 <210> 57 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 57 ggtagggcac ttgctgtgtt 20 <210> 58 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 58 ctgcacctaa gggacagcag 20 <210> 59 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 59 ctgccctctg ttggtgagag 20 <210> 60 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 60 tagcgcatca gcattgactc 20 <210> 61 <211> twenty one <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 61 aaatggactc tctgccactg a 21 <210> 62 <211> twenty two <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 62 caggacaaag agacaagtgg ag 22 <210> 63 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 63 tgccttctaa accctcctga 20 <210> 64 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 64 gtctttgtga tggcccatgt 20 <210> 65 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 65 aagagcatca ccccatcaag 20 <210> 66 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 66 ctggagccaa gcaaagatgt 20 <210> 67 <211> twenty two <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 67 ctgctgcctt tatgttgaaa tg 22 <210> 68 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 68 gcaaaaccag gaagaagcag 20 <210> 69 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 69 cagggctttg aggatggtag 20 <210> 70 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 70 ctgggaatta gctcgcaaag 20 <210> 71 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 71 caaggaacaa agagcccaac 20 <210> 72 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 72 atggagaagg cagagcttca 20 <210> 73 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 73 gcttccaggc taaagcacaa 20 <210> 74 <211> twenty one <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 74 tccaggctat gtgaatgttg a 21 <210> 75 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 75 tggacatggt aggtccttga 20 <210> 76 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 76 tgtggtccca tatggacttg 20 <210> 77 <211> twenty two <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 77 gaacactctc aaacaaccaa ca 22 <210> 78 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 78 aatggggcct tacagaaaaa 20 <210> 79 <211> twenty two <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 79 ccagacatta ttggacttcc tg 22 <210> 80 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 80 tcattcccaa atcactgtca 20 <210> 81 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 81 cactccagcc tgggtaacag 20 <210> 82 <211> twenty two <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 82 gctatttggg gattaacagt ga 22 <210> 83 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 83 gggtctgtgg atctctggaa 20 <210> 84 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 84 ccggtatcaa attggtggag 20 <210> 85 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 85 tcatcagcac cagaaaccag 20 <210> 86 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 86 tggattctaa tgggggaaaa 20 <210> 87 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 87 catgaaacct cttgcaacca 20 <210> 88 <211> twenty two <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 88 ggctctgttt aacaaccagc tc 22 <210> 89 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 89 acacagccaa actgcatcaa 20 <210> 90 <211> twenty two <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 90 aactatgggt tgctcttgct tt 22 <210> 91 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 91 gccacattca aagccattct 20 <210> 92 <211> 20 <212> DNA <213> Artificial sequence <220> <223> Oligonucleotide sequence <400> 92 ctcaaggtgt cccttgccta 20 BRIEF DESCRIPTION OF THE DRAWINGS

[0164] Figure 1 is a summary of the data. (A) Gastric tumor samples were grouped by cohort and sorted by SNV count within each cohort. The insets show the coverage, SNV count, indel count, mutation spectrum, molecular subtype, and Lauren classification for each sample. (B) Correlation between epigenetic features and somatic mutation rates in different tumor subtypes. Error bars represent the sem of the correlation coefficient. (C) Principal component analysis of the contribution of epigenetic features to the variation in mutation rates of individual tumors. The stacked bar chart shows the contribution of individual epigenetic features to the first two principal components.

[0165] Figure 2 shows genome-wide analysis of non-coding indel recurrence. (A) Workflow of the method for detecting recurrently mutated non-coding regions. (B) Genome-wide negative log P-values ​​for indel recurrence for 21 bp regions with at least 1 indel. The horizontal line represents the Bonferroni-adjusted P-value of 0.01. (C) Negative log P-values ​​for indel recurrence in the non-coding regions of each gene merged. The top 3 significantly mutated genes are highlighted. The horizontal line represents the Bonferroni-adjusted P-value of 0.01. (DF) Expression of LIPF (D), PGC (E), and MUC6 (f) genes in normal gastric samples, tumors with wild-type target genes, and tumors with target genes with non-coding indels.

[0166] Figure 3 shows a genome-wide analysis of non-coding SNV hotspots. (A) Negative log P-values ​​for SNV recurrence for all 21bp regions within the genome, showing only regions with at least 3 mutations. Significant mutation hotspots overlapping with CBS are highlighted. The horizontal line represents the Bonferroni-adjusted P-value of 0.01. (B) Log odds ratios of hotspot mutations and non-hotspot mutation enrichment in transcription factor binding regions and conserved regions. Error bars represent the sem of the log odds ratio. (C) Gastric cancer samples classified by molecular subtype, with each row representing a significant mutation hotspot. Mutated samples are highlighted in black in the matrix. The mutation load for each sample is shown in the bottom panel. The right panel annotates the position of each hotspot relative to the annotated functional region.

[0167] Figure 4 is an analysis of CBS mutations in different gastric cancer subtypes. (AD) Mutation counts per tumor around CBS in four gastric cancer subtypes. (E) Elevated mutation rate at CBS compared to flanking regions. (F) Somatic replacement patterns within CTCF motifs for hotspot mutations and all mutations, respectively. (G) Negative log P-values ​​of recurrent mutations for all CBSs assessed with a CBS-specific background model. Figure 3A CBS hotspots identified in are highlighted and labeled. Horizontal lines indicate Bonferroni-adjusted P-values ​​of 0.01.

[0168] Fig. 5 shows the correlation between CBS hotspot mutation and cis gene expression.(A, D, G) Correlation between CBS hotspot mutation status and adjacent gene expression level (two-sided Wilcoxon rank sum test). Upregulated genes are shown above the X-axis, and downregulated genes are shown below the X-axis. Non-expressed genes are shown with empty circles on the X-axis (normalized counts in all samples <10).(B, E, H) Reference sequences and mutant alleles at 3 CBS hotspots. Mutations in tumors with expression data are underlined (black underline: TCGA tumors, gray underline: SG tumors).(C, F, I) Gene expression of CENPQ (C), KCNQ5 (F) and SPG20 (I) in normal gastric tissue and tumors with or without mutations at corresponding CBS hotspots. P-values ​​are adjusted using the Benjamini-Hochberg method.

[0169] Figure 6 shows a pan-cancer analysis of recurrent mutations at 11 CBS mutation hotspots. (A) A subset of samples with mutations in at least one CBS hotspot in different cancer types. (B) Mutation rates of CBS in different cancer types. (C) Recurrent mutations in individual CBS hotspots in different cancer types.

[0170] Fig. 7 is an analysis of the correlation between CBS mutations and chromosomal instability. (A) The distance from CBS hotspots and other non-CBS mutation hotspots to the nearest CNV breakpoint. (B) The distance from the ring boundary CBS and non-ring boundary CBS to the nearest CNV breakpoint. (C) The correlation between mutation rate and SCNA breakpoint density. (D) The correlation between normalized mutation rate and SCNA breakpoint density, correcting the background mutation rate in each group. Error bars represent sem (E) Violin plots show the VAF distribution of somatic mutations in diploid regions of individual tumors. The VAFs of mutations at CBS hotspots are marked with red vertical lines. (F) Comparison between the VAFs of CBS hotspot mutations and the VAFs of non-silent coding mutations on GC driver genes. The darker dots represent the median VAFs in each group. The dotted lines match mutations from the same sample. P-values ​​are calculated by paired Wilcoxon rank sum tests.

[0171] Figure 8 is a summary of mutation data from 212 gastric cancer genomes. (A) A total of 212 gastric cancer whole genome sequences from 4 sources were collected and processed uniformly to obtain high-confidence somatic mutation identification. (B) Mutation counts and coverage of individual tumors from 4 cohorts. (C) Individual samples are plotted by their mutation counts on the y-axis for the CG>AT mutation score on the x-axis. Seven samples were removed due to data corruption. 13 tumors with low mutation counts were removed because they may be low-quality samples. Finally, 5 samples showing oxidative DNA damage characteristics (high CG>AT mutation scores) were removed. (D) After unified alignment and mutation identification, the mutation spectra of tumors from the 4 cohorts were similar.

[0172] Figure 9 shows the features used in each background mutation model. The sequence and epigenetic features most correlated with the somatic mutation rate were selected by LASSO regression. Features selected in (A) SNV background model, (B) indel background model and (C) CBS-specific background model.

[0173] Fig.10 The log odds ratios of enrichment of hotspot mutations and non-hotspot mutations in constitutive transcription factor binding regions are shown. Error bars represent the sem of the log odds ratios.

[0174] Fig.11 is a mutation hotspot analysis using a 41 bp window. Negative log P-values ​​for SNV recurrence for all 41 bp regions genome-wide are shown, only regions with at least 3 mutations. 17 / 34 hotspots remained significant, and only 2 additional hotspots were identified. The horizontal lines represent the Bonferroni-adjusted P-value of 0.01 and 1% FDR, respectively.

[0175] Fig.12 The correlation between the CBS mutation rate and COSMIC signature for each sample is shown.

[0176] Figure 13 shows the negative log P-values ​​of recurrent mutations plotted against the number of mutant samples in each noncoding region. (A) Whole genome SNV hotspot model. Significant mutation hotspots overlapping with CBS are highlighted in grey. (B) CBS-specific model. CBS hotspots identified in (A) are highlighted in grey. (C) Whole genome indel hotspot model. Regions with 2 significant mutations are highlighted in grey. (D) Gene-based indel recurrence model. Genes with 3 significant mutations are highlighted in grey.

[0177] Figure 14 shows the distribution of mutations within each CBS hotspot. (AR) Somatic replacement patterns within each CBS hotspot. CBS hotspots identified from genome-wide analysis of non-coding SNV hotspots are highlighted in grey. The Y-axis shows mutation counts and the X-axis shows position relative to the CTCF motif.

[0178] Fig.15 The chromatin neighborhood of the CBS hotspot at chr6:50570094-50570120 is shown. Candidate genes with expression changes associated with the hotspot mutation status are highlighted in gray. Arches represent constitutive CTCF loops defined by Hnisz et al., Science, 2016. Heatmap shows normalized Hi-C interaction frequencies in IMR90 cells (Dixon et al., Nature, 2012). TADs are named by Dixon et al., Nature, 2012.

[0179] Fig.16 The chromatin neighborhood of the CBS hotspot at chr6:73122084-73122123 is shown. Candidate genes with expression changes associated with the hotspot mutation status are highlighted in gray. Arches represent constitutive CTCF loops defined by Hnisz et al., Science, 2016. Heatmap shows normalized Hi-C interaction frequencies in IMR90 cells (Dixon et al., Nature, 2012). TADs are named by Dixon et al., Nature, 2012.

[0180] Fig.17 The chromatin neighborhood of the CBS hotspot at chr13:36552821-36552860 is shown. Candidate genes with expression changes associated with the hotspot mutation status are highlighted in gray. Arches represent constitutive CTCF loops defined by Hnisz et al., Science, 2016. Heatmap shows normalized Hi-C interaction frequencies in IMR90 cells (Dixon et al., Nature, 2012). TADs are named by Dixon et al., Nature, 2012.

[0181] Figure 18 shows the correlation between CBS hotspot mutations and candidate gene expression, using expression data from 14 tumors in the Singapore cohort. (AC) Gene expression of CENPQ (A), KCNQ5 (B), and SPG20 (C) in matched normal gastric tissue, tumors with wild type at the corresponding CBS hotspot, and tumors with mutations at the corresponding CBS hotspot. P-values ​​of Wilcoxon rank sum tests are shown.

[0182] Figure 19 shows the correlation between CBS hotspot mutations and residual expression of candidate genes after correction for tumor purity and copy number. (AC) Gene expression of CENPQ (A), KCNQ5 (B), and SPG20 (C) in tumors with wild-type and mutations at the corresponding CBS hotspots. P-values ​​of Wilcoxon rank sum tests are shown.

[0183] Figure 20 shows the evolutionary conservation of the consensus CTCF motif and flanking sequences. (A) Average PhyloP score of ±5 flanking bases and CTCF binding motifs for all mutated CBSs. (B-C) Two CBS hotspots (B involves the hotspot upstream of CENPQ) where mutations at the 5' flank of the CTCF binding motif coincide with conserved bases.

[0184] Fig.21 The distances from the ring boundary CBS and non-ring boundary CBS to the nearest CNV breakpoint are shown for GS tumors.

[0185] Fig. 22 Mutation rates of tissue-specific CBS in different cancer types are shown.

[0186] Fig.23 is a waterfall plot showing that mutations were detected in at least one noncoding hotspot in 44%-56% of 9 liquid biopsy samples from colorectal cancer patients. Example

[0187] Example embodiments of the present disclosure will be better understood and apparent to one of ordinary skill in the art from the following examples, tables and, if applicable, in conjunction with the accompanying drawings. The example embodiments should not be construed as limiting the scope of the present disclosure.

[0188] Gastric adenocarcinoma mutation map

[0189] The whole genome sequences of 212 gastric adenocarcinoma tumors and matched normal samples collated from four different sources were analyzed (data not shown). All samples were processed uniformly using the precise somatic mutation calling pipeline ( Fig. 8A -B). Briefly, a random forest classifier predicting high-confidence somatic mutation calls (SNVs and indels) was trained by combining four independent mutation callers. This approach achieved >85% accuracy on an independent test dataset of set somatic mutations. Twenty low-quality samples with fewer than 400 mutation calls were excluded from the discovery cohort ( Figure 8C In addition, five samples with strong enrichment of C>A substitutions (a sign of oxidative damage during DNA preparation) were removed ( Figure 8C). Somatic mutations in CDS regions, immunoglobulin loci, and regions with poor mappability were also removed from further analysis. After unified processing, samples from the four cohorts showed comparable somatic mutation count distributions and similar mutation spectra ( Figure 1A and Fig. 8A ). The ICGC cohort had slightly fewer mutations per tumor, which may be due to the lower sequencing depth of this cohort.

[0190] Previous studies have identified four molecular subtypes of gastric adenocarcinoma: EBV-positive (EBV) tumors, tumors with high levels of microsatellite instability (MSI), tumors exhibiting copy number instability (CIN), and tumors that are genomically stable (GS). We investigated the correlation between somatic mutation rates in the four cancer subtypes and epigenetic profiles of gastric tissue obtained from the Roadmap Epigenomics project. In general, somatic mutation rates were negatively correlated with histone marks at open chromatin regions (DNasel hypersensitivity) and active promoters (H3K4me3) and enhancers (H3K27ac). Figure 1B The loss of somatic mutations in open chromatin regions may be due to enhanced accessibility to DNA repair machinery. Notably, somatic mutations in the EBV subtype were less correlated with histone signatures and replication timing than in the CIN and GS subtypes, suggesting that additional mutational biases may exist in EBV-infected tumors.

[0191] Tumors belonging to the MSI subtype showed significantly different correlations between epigenetic features and mutation patterns. Little association between mutation rate and open chromatin marks or replication timing was observed in MSI tumors. This may be because mismatch repair (MMR)-deficient MSI tumors have been shown to lose MMR-coupled enhanced repair efficiency at early replicating open chromatin regions. In addition, the MSI mutational profile was found to show a strong positive correlation with heterochromatin (H3K9me3) and repressive domains (H3K27me3). Figure 1B This is in contrast to previous studies that reported that mutations generated after MMR inactivation are no longer enriched in heterochromatin regions, suggesting that changes in mutation rates across the genome are primarily a result of MMR. Instead, the current data suggest that, in addition to MMR, other repair or mutational processes may further contribute to changes in the GC mutational landscape. Principal component analysis (PCA) of the correlation matrix between mutational profiles and epigenetic covariates of individual tumors also revealed that MSI tumors are distinct clusters ( Figure 1C Therefore, a small number of MSI tumors (N = 19) were removed from the discovery cohort to ensure that all tumors had similar mutational biases.

[0192] A statistical framework for mutation hotspot identification

[0193] In order to identify positive selection in cancer genomes, it is necessary to establish an accurate background mutation rate model to calibrate covariates (features) that affect regional mutation rate changes, such as local sequence background and chromatin profiles. A series of genetic and epigenetic features that may be associated with GC somatic mutation rates were considered. The features include 33 universal and 36 gastric-specific chromatin features, 133 transcription factor binding profiles, and DNA replication time profiles. In order to simulate the effect of local sequence background on mutation rate, previous studies have considered the single-nucleotide or trinucleotide sequence background of each mutation. However, since mutation rates may also be affected by wider sequence backgrounds, an amplified sequence background model that considers the effects of trinucleotide (1bp flanking) and pentanucleotide (2bp flanking) backgrounds on the mutation probability of each base was used. LASSO logistic regression was used to identify the most predictive epigenetic and sequence background features (Figure 9). These features were used to assess sample-specific background mutation probabilities and to identify single focal regions (21bp) in samples that exhibited mutation recurrences beyond those expected by chance ( Figure 2A ; Methods). Overlapping significantly mutated regions were merged to obtain a list of unique hotspots.

[0194] Recurrent indels in gastric lineage-specific genes

[0195] This statistical framework was used to identify somatic mutation hotspots (indels and point mutations) in the noncoding genome ( Figure 2B and Figure 3A ). The highest indel hotspot was located ~100 kbp upstream of the AFDN gene, which is frequently translocated in leukemias and downregulated in multiple cancer types. Due to the lack of paired tumor expression data for mutant samples, the effect of hotspot mutations on AFDN gene expression could not be detected. The second most significant indel hotspot was located in the intron of the PGC gene, which encodes the precursor of the gastric protease pepsinogen (see Table 4 below). PGC is expressed at 11940 TPM in the stomach, 39 TPM in the lung, and <= 2 TPM in all other tissues in GTEx. Interestingly, a recent study reported that LIPF, a lineage-specific gastric lipase, has extensive indel enrichment in gastric cancer. Assuming that other lineage-specific genes may show similar indel enrichment patterns, gene-based recurrence analysis was performed to identify all genes with extensive indel enrichment in their non-coding regions (combining the promoter, untranslated region, and intronic regions of each gene; Methods). Interestingly, the top 3 genes in this analysis are all lineage-specific genes that are highly expressed in gastric tissue: LIPF, PGC, and MUC6 ( Figure 2C; Table 5 below). MUC6 encodes a mucin glycoprotein that is a major component of the intestinal mucosa and is expressed at 133 TPM in gastric tissue, 38 TPM in pancreas, and <= 2 TPM in all other tissues in GTEx. However, consistent with previous reports, noncoding indels in these 3 recurrently mutated lineage-specific genes were not associated with expression changes ( Figure 2D -F).

[0196] Table 4 Indel hotspots. Non-coding indel hotspots of significant mutations were identified by whole-genome scanning of 21 bp windows.

[0197] chromosome Starting termination P-value length # Mutation samples Adjusted P-value chr6 168136120 168136140 6.45E-16 21 4 1.63E-06 chr6 41709379 41709409 1.93E-14 31 4 4.90E-05

[0198] Table 5 Non-coding indel-enriched genes

[0199] Gene name chromosome Gene start Gene termination Gene length # Mutation samples P-value Adjusted P-value LIPF chr10 90424198 90438571 14807 16 1.89E-17 6.39E-13 PGC chr6 41704449 41721847 16717 7 6.17E-08 2.08E-03 MUC6 chr11 1012821 1036706 17851 8 4.92E-07 1.66E-02

[0200] Mutation hotspots enriched in CBS in gastric cancer

[0201] Then, we performed genome-wide analysis of SNVs in noncoding regions and identified 34 significant mutation hotspots (Bonferroni-adjusted P -value < 0.01; Figure 3A ; Table 6 below). These hotspots are enriched in conserved sequences and TF binding regions, indicating that many hotspot mutations can destroy functional elements ( Figure 3B Strikingly, of the 34 mutational hotspots, 11 were located in CBS ( Figure 3A and 3C The majority of mutations in the CBS hotspot occurred in CIN tumors (71%, P = 0.012 by two-sided Fisher's exact test), which are the most common gastric cancer subtype, accounting for ∼50% of all GC cases ( Figure 3C The remaining 23 non-CBS hotspots often overlapped with genic regions but never colocalized with TF binding regions. In addition, a lack of somatic mutations at gastric-specific TFBSs was observed in non-hotspot mutations ( Figure 3B In summary, gastric tissue TFBS comprised approximately 1% of the genome, but only 0.58% of non-hotspot mutations were located in these regions. A similar lack of mutations was observed in constitutive TFBS ( Fig.10This is surprising because two recent studies have found that somatic mutation rates are elevated at transcription factor binding sites (TFBSs), and this higher overall mutational load at TFBSs could be explained by reduced accessibility to nucleotide excision repair (NER) enzymes at these sites. This phenomenon has been primarily observed in melanoma and lung adenocarcinoma, where NER plays an important role in repairing carcinogen-induced DNA damage. In contrast, the present findings demonstrate that NER and TF occupancy are not responsible for regional mutational biases in GC.

[0202] To test whether the 21 bp window size was sufficient to capture most mutation hotspots, the hotspot analysis was repeated using a larger 41 bp window. Overall, the ranking of hotspots remained stable ( Fig.11 ). 17 / 34 hotspots remained significant, and only 2 additional hotspots were identified (P<0.01, Bonferroni correction).

[0203] Table 6 SNV hotspots. Non-coding SNV hotspots of significant mutations were identified by whole genome scanning of 21 bp windows.

[0204]

[0205] Differential CBS mutation burden among gastric cancer subtypes

[0206] Despite the general absence of somatic mutations at TFBSs in GI tumors, several studies have reported an increased mutation rate at CBSs in GI tumors. Indeed, when examining all CBSs across the genome, a 3-fold increase in mutation rate was found at CBSs (11 mutations / Mb) compared with their 1 kb flanking regions (3.6 mutations / Mb). In addition, the frequency of mutations at CBSs varied widely between tumors of different molecular subtypes. In CIN and GS tumors, the somatic mutation rate at CBSs was 7.1-fold and 4.7-fold higher compared with flanking regions, respectively ( Figure 4A -E). In MSI tumors, there is no enrichment of somatic mutations at the CBS, likely due to impaired DNA mismatch repair. Surprisingly, EBV tumors that are not MMR-deficient have only a modest 1.7-fold increase in mutational load at the CBS. Therefore, the enrichment of somatic mutations at the CBS is unlikely to be a result of differential DNA repair alone.

[0207] Consistent with another finding in colorectal cancer, the inventors found that somatic mutations at CTCF motifs, including CBS hotspot mutations, were mainly AT>CG and AT>GC substitutions ( Figure 4F), suggesting that hotspot mutations arise through the same mutational process as other CBS mutations. The mutational pattern at CBS hotspots is generally similar to that of all CBSs. However, although the conserved base at position 9 of the 19-bp CTCF binding motif is generally the most common mutation position in CBSs, CBS hotspot mutations have the highest enrichment in the 4-bp sequence flanking the 5' end of the CTCF motif. In addition, the C>T change, which is relatively common in all CBS mutations, is much less common in CBS hotspot mutations (Fisher's exact test P-value = 4.4×10 -07 ). These differences may indicate functional differences between CBS hotspot mutations and non-hotspot mutations.

[0208] Hotspots remain significant in CBS-specific models

[0209] To specifically test whether CBS hotspots could be explained by the genome-wide elevated mutation rate at CBS, a CBS-specific background mutation model was constructed. Because CBS mutation rates vary between tumor subtypes, the model further included tumor subtype as a covariate. Similarly, because CBSs located at chromatin loop boundaries have a higher somatic mutation load than non-boundary CBSs, the CBS-specific background model distinguished between CBSs inside and outside chromatin loop boundaries. CTCF loop domains have not been analyzed in gastric tissue but tend to be invariant across cell types. Therefore, a set of constitutive CTCF domains shared by three cell lines (CM12878, Jurkat, and K562) was used to define CTCF loop boundaries. In addition, because the mutation spectrum at CBS is different from the global genome mutation spectrum, LASSO logistic regression was performed to identify sequence background features associated with somatic mutation rates at CBS. To identify other mutational processes that may be involved in the occurrence of CBS mutations, the correlation between the proportion of CBS mutations in each tumor and the percentage contribution of each COSMIC mutational signature to each tumor was calculated. Although CBS mutations are known to be positively associated with Signature 17, we found that CBS mutations were also strongly negatively associated with COSMIC mutational signature 1, an age-related signature (Pearson correlation = -0.41; Fig.12 ). Therefore, the percentage contribution of mutational signatures 1 and 17 in each individual was added as a covariate. Finally, the model also corrected for replication timing and local mutation rate. Using this model, the 9 / 11 CBS hotspot remained significant at a Bonferroni-corrected significance threshold of 0.01, and the other 2 hotspots were borderline at adjusted P-values ​​of 0.025 and 0.086 ( Figure 4G). In addition, another 7 CBS became significant in restricted hypothesis testing (Table 7 below; Figures 13-14). Therefore, mutations at these specific sites cannot be explained by the genome-wide elevated mutation rate at CBS, indicating that mutations at these focal sites may be positively selected in gastric tumors.

[0210] Table 7 CBS with recurrent mutations under CBS-specific background model

[0211]

[0212] CBS hotspot mutations associated with altered gene expression

[0213] We next examined the possibility that CBS hotspots were associated with altered expression of nearby genes. We restricted our analysis to 4 CBS hotspots with at least 3 mutant samples with gene expression data in the TCGA cohort (N = 35 samples). We validated our results using expression data from the SG cohort (N = 14 samples). Since chromatin structure is generally invariant across cell types and there are no published Hi-C data from gastric tissue, we used the published Hi-C data from IMR90 cells by Dixon et al. to examine the 3D chromatin structure around each hotspot ( Figure 15-17 ). The flanking TAD boundaries closest to each hotspot were identified, and the correlation between the mutational status of each hotspot and the expression of genes within the two adjacent TADs was tested. Three of the four hotspots were found to have genes with nominally altered expression (Figure 5), and two of them remained significant after correction for multiple testing in each region.

[0214] The first hotspot identified was located in CBS on chromosome 6 and had mutations in 12 samples ( Figure 5A -C). In the mutant samples, the expression of two neighboring genes CENPQ and MUT, ∼1 Mb upstream of the hotspot, was significantly increased (P = 0.007 and 0.0021, respectively, adjusted P = 0.026 and 0.042, respectively, two-sided Wilcoxon rank sum test; Figure 5A -C). Similar CENPQ expression trends were observed using expression data from the SG cohort ( Fig.18A ). CENPQ is a subunit of the centromere complex and is involved in mitotic progression and chromosome segregation. Interestingly, the tumor with the highest CENPQ expression had a mutation at the highly conserved position 9 of the CTCF motif, while the other two tumors had a mutation at position 2 of the CTCF motif. This suggests that different mutations in the same hotspot may have different damaging potentials. However, formal assessment of this role requires a larger set of tumor samples with CBS mutations and available RNA-Seq data.

[0215] The next hotspot detected was located on chromosome 6, with 9 mutations. Tumors mutated at this hotspot had significantly lower expression of the KCNQ5 gene (Wilcoxon P = 0.0059, adjusted P = 0.047), which is located ∼200 kb downstream of the hotspot ( Figure 5D -F). A similar trend in KCNQ5 expression was observed using expression data from the SG cohort ( Fig.18B ). A recent study by Umer et al. found the same mutational hotspot by analyzing motif-breaking mutations. Using electrophoretic mobility shift assays, Umer et al. demonstrated that chromosome 6:73,122,103A>G mutations disrupt CTCF binding. In addition, it has been reported that CTCF is involved in the spatial organization of the KCNQ5 locus and that knockdown of CTCF downregulates KCNQ5 expression.

[0216] At the third hotspot located on chromosome 13, SPG20 expression was reduced by an average of 3-fold in mutant tumors (Wilcoxon P = 0.045, adjusted P = 0.65; Figure 5G -I). However, only 3 tumors with expression data had mutations in this hotspot, and the expression change was not significant after correction for multiple testing. A larger sample size is needed to assess whether this is a spurious or true correlation. A similar SPG20 expression trend was observed using expression data from the SG cohort ( Fig.18C ). SPG20 is involved in the trafficking of epidermal growth factor receptor and was previously found to be significantly mutated in exons of esophageal cancer.

[0217] In all 3 cases, the expression changes of these genes proved to be significant after correcting for variation in DNA copy number and tumor purity between samples (Figure 19). Since CBS is essential in maintaining chromosome structure, these CBS hotspot mutations are likely to cause altered expression of nearby cancer driver genes by disrupting local chromosome organization. Indeed, using a set of constitutive CTCF-CTCF loops, chromatin contacts between the KCNQ5 and SPG20 loci and their corresponding CBS hotspots were observed (Figure 19). Figure 16-17 Interestingly, these three genes were also differentially expressed in GC tumors compared with normal gastric tissues. CENPQ was upregulated in tumors compared with normal gastric samples (Wilcoxon P = 0.0028; Figure 5C), while both KCNQ5 and SPG20 were downregulated in tumors (Wilcoxon P = 3.2 × 10 -7 and 0.00082; Fig. 5F and 5I). Therefore, the expression of these 3 genes may be altered in GC through other mechanisms. Indeed, KCNQ5 and SPG20 were found to be downregulated in colorectal cancer compared with normal mucosa due to promoter hypermethylation. These results further support the contribution of these genes to GC tumorigenesis.

[0218] Many hotspot mutations are located on the 5' flank of the consensus CTCF motif ( Figure 4F Previous studies have found increased conservation of sequences flanking weaker CTCF and REST binding sites, suggesting that sequence context is important for TF binding at these sites. The evolutionary conservation of the CTCF binding motif and its flanking sequences was examined. Overall, the 5' flank of the CTCF motif is not conserved ( Fig. 20A ). However, in the hotspot upstream of CENPQ, clusters of mutations in the 5' flank co-occur with conserved bases ( Fig. 20B In addition, another CBS hotspot with nine 5' flanking mutations coinciding with a highly conserved base was found ( Fig. 20C ). Such hotspot mutations affect the conserved 5' flank of the CTCF motif and may disrupt the context-specific binding of CTCF.

[0219] We also examined the possibility that mutations in the flanking regions of the CTCF motif could create or disrupt binding motifs for other TFs. 55 To predict the binding scores of wild-type and mutant sequences of 472 transcription factors. However, mutations with predicted TF binding changes were found only at 3 CBS sites (Table 8 below). Finally, some mutations at the CBS flanks may also be passenger mutations due to the overall elevated mutation rate at the CBS. Although this model identified individual CBS regions with overall mutation enrichment, it did not allow us to distinguish between passenger mutations and driver mutations within these regions.

[0220] Table 8 DeepBind analysis of hotspot mutations flanking CTCF-binding motifs

[0221]

[0222] CBS hotspots frequently mutated in gastrointestinal cancers

[0223] Overall, 25% of all gastric tumors were mutated in at least one of the 11 CBS hotspots, representing the second most mutated functional region in gastric cancer after TP53 (50% of gastric tumors). To investigate whether these hotspots could also play a role in other cancer types, the recurrence of these 11 hotspots in 826 non-hypermutated tumors of 18 other cancer types was examined (Figure 6). Strikingly, 19% of colorectal cancer tumors were found to be mutated in one or more CBS hotspots ( Fig. 6A and Fig. 22 ). Since colorectal cancer has pathological and molecular similarities with gastric cancer, CBS hotspot mutations may drive cancer progression in colorectal cancer through mechanisms similar to those in gastric cancer. CBS hotspots are mutated at lower frequencies in breast cancer, liver cancer, lung cancer, pancreatic cancer, and lymphoma. Interestingly, although melanoma and bladder cancer also have high genome-wide mutation rates at CBS, there are no CBS hotspot mutations in these two cancer types. Similarly, mutations at all CBS hotspots that have been previously reported in COSMIC or other genome-wide studies of gastrointestinal tumors were found (Table 9 below). This suggests that CBS hotspot mutations are generated and act in a cancer-specific manner.

[0224] Table 9 CBS hotspot mutations identified in previous whole-genome studies of gastrointestinal tumors and the COSMIC database

[0225]

[0226] legend

[0227] #Mutations in COSMIC: Number of somatic mutations confirmed at CBS hotspots in COSMICv83

[0228] #Mutations in Katainen et al.: Mutation clusters identified by Katainen et al. (Table S4)

[0229] #Mutations in Umer et al.: CBS with at least 2 motif-breaking mutations in Table S5 of Umer et al.

[0230] Katainen et al. studied colorectal cancer; Umer et al. studied liver cancer, gastric cancer, esophageal cancer, and pancreatic cancer

[0231] Cancer Type: Cancer types with mutations at CBS hotspots identified in previous studies

[0232] BRCA Breast Cancer

[0233] CRC Colorectal cancer

[0234] ESAD Esophageal cancer

[0235] GC Gastric cancer

[0236] HCC Hepatocellular carcinoma

[0237] KC Kidney Cancer

[0238] LYMP Lymphoma

[0239] OV Ovarian cancer

[0240] PACA Pancreatic Cancer

[0241] PRAD Prostate cancer

[0242] CBS mutations are associated with chromosomal instability

[0243] CBS mutations were most highly enriched in CIN tumors, which are characterized by increased chromosomal aneuploidy. If mutations at CBS in CIN tumors were associated with somatic copy number alteration (SCNA) breakpoints, follow-up testing was indicated. Strikingly, the distance between the CBS hotspot and its nearest SCNA breakpoint was significantly shorter in mutated tumors than in unmutated tumors (P = 0.0018, two-sided Wilcoxon rank sum test; Fig. 7A In contrast, non-CBS mutation hotspots did not show this correlation (P = 0.53). In the same sample, the median distance between CBS hotspot mutations and their nearest SCNA breakpoints was ∼1 Mbp, significantly shorter than the ∼2 Mbp distance for non-CBS hotspots ( Fig. 7A To investigate whether this correlation between CBS mutations and SCNA breakpoints was specific to CBS hotspots, we extended the analysis to all CBSs. Interestingly, we found that CBS mutations were associated with the occurrence of nearby SCNA breakpoints in the same samples, especially for mutations affecting CBSs at loop boundaries (Wilcoxon P = 5.7 × 10 -16 ; Figure 7B In contrast, when 1Mb windows of the genome were grouped according to SCNA breakpoint density, the normalized CBS mutation rate was found to be positively correlated with SCNA breakpoint density ( Figure 7C Altogether, these results highlight a general link between regional chromosomal instability and mutations at CBS hotspots and boundary CBSs.

[0244] Since the CBS mutation rate is also elevated in GS tumors ( Figure 4B ), we next investigated whether there was a similar correlation between CBS mutations and SCNA in GS tumors. Although mutated CBS also tended to be closer to SCNA breakpoints than unmutated CBS in GS tumors, this difference was not statistically significant ( Fig.21 ), and the relative difference was greater in CIN (2.17-fold difference in distance to the nearest breakpoint) compared with GS (1.58-fold difference) tumors. This may indicate that coupling of CBS mutations with nearby chromosomal instability is specific to CIN tumors or a process that is exacerbated in CIN tumors.

[0245] Mutational hotspots at CTCF binding sites are coupled to chromosomal instability in gastrointestinal cancer

[0246] The inventors have performed a comprehensive and unbiased analysis of non-coding SNVs and indels in 212 GC genomes, the largest cohort studied to date. In addition to the previously identified indel enrichment at LIPF, the analysis identified two other gastric lineage-specific genes with extensive enrichment of non-coding indels (PGC and MUC6). The results showed that the accumulation of indels in gastric cancer occurred in multiple lineage-specific genes. However, the indels of these 3 genes were not associated with changes in gene expression. Therefore, the functional consequences of these indels remain unclear. Strikingly, genome-wide analysis of somatic SNVs revealed 34 significant mutation hotspots (Bonferroni-adjusted P-value <0.01), which were disproportionately enriched in CBS. It has been reported that the mutation load at CBS in colorectal cancer is increased, and another study confirmed the widespread high mutation at CBS in 11 cancer types. These two studies generally regard CBS mutations as passenger mutations, but they did not explore the hypothesis that subsets of these mutated CBS may experience positive selection in individual cancer types. In fact, recent studies on motif-breaking mutations identified recurrent CBS mutations that disrupt CTCF binding, confirming the motif-breaking potential of CBS mutations. Here, the inventors used a large gastric cancer genomic cohort in combination with rigorous statistics, showing that the mutation rate at 11 specific CBSs was unexpectedly high and could not be explained solely by the mutation load of the whole genome at the CBS, indicating that there was positive selection at these sites. Among the 4 CBS hotspots examined, 3 were associated with nominally significant expression changes in neighboring genes (CENPQ, KCNQ5, and SPG20), and this correlation was verified in an independent tumor cohort. In addition, mutations at these CBS hotspots may also have remote or spatiotemporal regulatory effects on gene expression that are not captured by the bulk tumor transcriptome profile. In summary, these analyses point out that these CBS hotspots are potential drivers in GC and support the following hypothesis: driver mutations may appear as a byproduct of the increased mutation load at the CBS, followed by positive selection at specific CBSs. This is comparable to the model of genomic rearrangement hotspots in breast cancer, in which rearrangements initially arise from defective homologous recombination repair and rearrangements affecting cancer risk loci are subsequently positively selected, forming rearrangement hotspots.

[0247] Gastric tumors of the genomically unstable subtype (CIN) were found to exhibit the highest mutation rate at the CBS compared with tumors of other GC subtypes. Furthermore, CBS mutations were associated with the occurrence of nearby chromosomal breakpoints, suggesting a general link between CBS mutations and genomic instability. Previous studies have proposed a model in which genomic higher-order interactions directly prepare the way for chromosomal breakage. An important unanswered question is whether these processes are coupled, and if so, what is the temporal order of CBS mutations and chromosomal breakage. Interestingly, the somatic variant allele frequencies (VAFs) of CBS hotspot mutations support that these are often clonal and may be early events in tumor evolution ( Fig. 7E ). In addition, the VAF of CBS hotspot mutations was found to be comparable to non-silent coding mutations of known gastric cancer driver genes from the same samples (paired Wilcoxon P-value = 0.49; Figure 7F ).

[0248] Previous studies have found that kilobase-sized regions of high mutation (termed "Kataegis") often co-occur with genomic rearrangements in cancer. Importantly, the current data suggest that the mutational mechanism underlying the correlation between CBS mutations and DNA breakpoints is different from that of kataegis. Although kataegis are characterized by C>T and C>G substitutions, CBS mutations are primarily T>G and T>C substitutions. In addition, kataegis are defined by clusters of mutations with inter-mutation distances <1 kb. CBS hotspots are limited focal regions of <30 bps that include the CTCF motif and its 5' flanking sequence.

[0249] In the cohort used, only a subset of tumor samples had paired gene expression data (49 / 187 samples). This limits the ability to detect functional consequences of CBS hotspot mutations. Additional focused experiments involving transcriptome, copy number, and chromatin structure data should further elucidate the regulatory and functional roles of CBS mutations. The inventors did not find a common theme among the 23 important non-CBS hotspots. Among the non-CBS hotspots, 7 were introns, 1 was downstream of the gene, and the rest were intergenic. None of the genes associated with the hotspots were known cancer drivers. No mutation hotspots were observed near TERT, confirming that reactivation of TERT is very rare in gastric cancer. 2 For non-CBS hotspots that overlap with gene regions, focused functional validation experiments can be performed on a case-by-case basis.

[0250] The statistics of cancer driver identification are still limited by the knowledge of somatic mutation and repair processes. Although background models correct for many covariates of somatic mutation rates (e.g., epigenetic and sequence background features), current models do not account for this unknown mutational bias and may still produce false positives and false negatives.

[0251] In total, 25% of gastric cancer tumors and 19% of colorectal cancer tumors had mutations in at least 1 of the 11 CBS hotspots. Together, this analysis points to these CBS hotspots as common drivers of potential gastrointestinal cancers. Furthermore, the data support a general link between CBS mutations and chromosomal instability. This suggests that noncoding regulatory mutations can potentially drive tumor evolution by cooperating with cancer genome and epigenomic plasticity.

[0252] Use of biomarkers in liquid biopsy assays

[0253] Dying tumor cells continuously release DNA fragments (~160 nt in length) into the blood circulation. Detection and monitoring of this tumor-derived cell-free DNA (cfDNA) has the potential to revolutionize cancer detection and monitoring. This is particularly important for solid tumors where DNA samples can only be obtained by invasive methods. In addition, for the early detection of cancer, recurrence or metastasis, liquid biopsies may be the only available method to identify the presence of cancer cells. A key limitation of cfDNA assays is that cancer DNA fragments are only present in the blood at very low frequencies (usually <1%). Therefore, it is critical to design assays that target specific frequently mutated regions.

[0254] The present disclosure relates to a comprehensive and unified analysis of 212 gastric cancer genomes—a first in gastric cancer. A comprehensive statistical method was developed that combines epigenetic and sequence covariates to identify non-coding mutation hotspots in gastric cancer. 34 novel non-coding mutation hotspots were identified as potential drivers of gastric cancer (see Table 12 below). Importantly, because mutations occur in concentrated areas ("hotspots"), they are very suitable as biomarkers in liquid biopsy analysis. Overall, it is estimated that mutations in these 34 non-coding hotspots are detectable in ~54% of gastric cancer patients. Combining these non-coding regions with protein coding regions (e.g., KRAS, TP53, and RHOA) that are frequently and concentratedly mutated can increase this number to about 80% (see Table 10 below).

[0255] Table 10

[0256]

[0257] Column:

[0258] Cov: Patient coverage, estimated number of patients with mutations

[0259] AA length: the number of amino acids that frequently mutate in a gene

[0260] Size: Equivalent number of base pairs

[0261] Probe count: the number of non-overlapping 20 bp probes required to cover the region (ignoring exon-intron gaps)

[0262] Cov / Probe: Estimated patient coverage / probe

[0263] Cumulative Pr.: Cumulative probability of a patient having a mutation in these target regions (assuming independent mutations)

[0264] Cumulative # probes: The cumulative number of probes required

[0265] In addition, it is estimated that 29 of the 34 potential novel non-coding mutation hotspots are detectable in 45% of gastric cancer patients (see Table 13 below). Combining these 29 non-coding regions with frequently and concentratedly mutated protein coding regions (e.g., KRAS, TP53, and RHOA) can increase this number to about 76% (see Table 11 below).

[0266] Table 11

[0267]

[0268] The unique advantage of these noncoding regions in cfDNA assays is that the mutations are very dense and concentrated (many are comparable to KRAS hotspot mutations). This allows the design of compact targeted hybridization methods that can detect very low-frequency cancer DNA fragments in blood at low cost.

[0269] Additional information on 34 non-coding mutation hotspots

[0270] Table 12

[0271]

[0272]

[0273]

[0274] Column:

[0275] chr: Chromosome

[0276] Start: Starting coordinates (Hg19)

[0277] End: End coordinates

[0278] Width: hotspot width

[0279] N: Number of samples with mutations (among 168 non-MSI gastric cancer samples)

[0280] q-value: Bonferroni-corrected p-value of the hotspot

[0281] Region: Genomic location of hotspot

[0282] Allele frequency: the average allele frequency of hotspot mutations in tumor samples

[0283] Additional information on 29 non-coding mutation hotspots

[0284] Table 13

[0285]

[0286]

[0287] Column:

[0288] chr: Chromosome

[0289] Start: Starting coordinates (Hg19)

[0290] End: End coordinates

[0291] Width: hotspot width

[0292] N: Number of samples with mutations (among 168 non-MSI gastric cancer samples)

[0293] q-value: Bonferroni-corrected p-value of the hotspot

[0294] Region: Genomic location of hotspot

[0295] Allele frequency: the average allele frequency of hotspot mutations in tumor samples

[0296] Primer sequences

[0297] Table 14

[0298]

[0299]

[0300]

[0301] Detection of mutations in noncoding hotspots in colorectal cancer patients using a liquid biopsy assay

[0302] After informed consent, blood samples were collected from patients with metastatic colorectal cancer. Plasma was obtained from the patient's blood within 2 hours of venipuncture, and the blood was centrifuged at 1900g and 4°C for 10 minutes, and the plasma fraction was centrifuged for a second time at 16000g and 4°C for 10 minutes. The plasma was stored at -80°C until extraction. DNA was extracted from plasma using the QiaAmp circulating nucleic acid kit (Qiagen) according to the manufacturer's instructions. The inventors performed whole genome sequencing on cell-free DNA (cfDNA) samples and matched normal samples at 60-90X original coverage. Sequencing libraries were constructed from cfDNA using the Kapa Hyper Prep kit and 151bp paired-end sequencing was performed on the Illumina Hiseq4000. Sequencing reads were aligned to the human reference genome (hg19) using BWA. Stacked reads at 29 non-coding hotspots were generated using samtools. We found that 5 / 9 cfDNA samples had more than 3 mutant reads in at least 1 noncoding hotspot (55.6%), and 4 / 9 cfDNA samples had more than 4 mutant reads in at least 1 noncoding hotspot (44.4%) (see Fig.23 This is consistent with previous estimates that mutations in 29 noncoding hotspots can be detected in ∼45% of gastrointestinal cancer patients.

[0303] method

[0304] Gastric cancer whole genome sequence data

[0305] Whole genome sequencing was performed on 40 gastric GC tumors and matched normal samples from patients in Singapore (the study protocol was approved by the Institutional Review Board of the National University of Singapore). Informed consent was obtained from all participating patients. Genomic DNA (QIAGEN) was extracted from tumors and matched normal gastric tissues. Libraries with 300-400 bp insert lengths were constructed and paired-end sequencing of 101 bp or 151 bp was performed on an Illumina HiSeq instrument. As previously reported by TCGA 19 As described, tumors were divided into 4 molecular subtypes.

[0306] 40 GC tumors from TCGA (https: / / gdc.cancer.gov), 40 GC tumors from ICGC ( https: / / ega- archive.org / datasets / EGAD00001003132 ) and WGS data of 100 tumors from Wang et al. (HK) 20 Molecular subtypes of tumors from the TCGA cohort were defined by TCGA. For the HK cohort, only EBV and MSI subtype status were available. Molecular subtypes of tumors from the ICGC cohort were not available, but were determined using MSIseq61 One MSI sample was identified from the ICGC cohort.

[0307] Sequence alignment and somatic mutation identification

[0308] The raw sequencing data were uniformly processed using the bcbio-nextgen pipeline (v0.9.3). 62 Sequencing reads were aligned to the human reference genome (hg19). Picard-marked duplicate reads were removed. 63 Realignment of indel regions. Somatic mutations are identified by the following four independent mutation callers: VarScan 64 、MuTect 65 、VarDict 66 and FreeBayes 67 , using the default parameters of the bcbio-nextgen pipeline. As the nature of the analysis requires high specificity in somatic mutation calls, a random forest predictor, SMuRF, was developed and trained on artificially set true somatic mutations to identify high-confidence somatic mutation calls from the outputs of the four mutation callers. For each gastric cancer WGS sample, a set of high-confidence consensus calls was obtained by running the random forest prediction algorithm.

[0309] Additional filters for removing sequencing artifacts

[0310] Sequencing and mapping errors may result in false positive somatic calls. More false positives tend to be called in non-coding regions of the genome because these regions are rich in repetitive sequences and regions of low sequence complexity. Since downstream mutation recurrence analysis is very sensitive to recurrence artifacts in somatic mutation calls, additional post-processing filters were applied to eliminate potential false positive calls. The following candidate somatic mutation calls were removed:

[0311] (i) In the 1000 Genomes Project 68 Identification of candidate somatic mutations found at an allele frequency >1% in the genome (potential germline mutations)

[0312] (ii) Identification of candidate somatic mutations found in more than 10% of matched normal samples (potential systematic sequencing errors)

[0313] (iii) Candidate somatic mutation identification found in more than 1% of matched normal samples and within 20 bp of a common indel in the 1000 Genomes Project (potential errors caused by mapping errors near indels).

[0314] In addition, indel calls that overlapped with mononucleotide repeats of 8 bp or longer were removed. The final set of somatic SNVs and indels was obtained (data not shown).

[0315] Gene expression data

[0316] RNA sequencing was performed on 19 matched tumor-normal pairs. Total RNA was extracted using the Qiagen RNeasy Mini kit. RNA-seq libraries were constructed using the Illumina Stranded Total RNA Sample Prep Kit v2 (Illumina, San Diego, CA), Ribo-Zero Gold option (Epicentre, Madison, WI), and 1 μg of total RNA according to the manufacturer's instructions. The completed libraries were validated using an Agilent Bioanalyzer (Agilent Technologies, Palo Alto, CA) and applied to Illumina flow cells via the Illumina Cluster Station. RNA-Seq reads (2×101 bp) were aligned to the human genome (hg19) using TopHat2-2.0.12 (default parameters and --library-type fr-firststrand). The RNA-seq reads were aligned to the human genome (hg19) using Cufflinks. 68 Estimation of transcript abundance at the gene level. Normalized counts of RNA sequencing data from 35 tumors from the TCGA cohort were obtained from the Genomic Data Commons Portal.

[0317] Epigenetic and sequence covariates of somatic mutation rates

[0318] Somatic mutation rates are associated with epigenomic features (e.g., histone modifications and chromatin accessibility), especially those derived from the cell type from which the cancer originated. We assembled 36 gastric-specific and 24 general chromatin features that may influence gastric cancer mutation rates. From Roadmap Epigenomics 29These 66 histone modification profiles and chromatin accessibility profiles were obtained from internal data. P-value signal tracks of 853 DNasels and histone modification profiles of 111 primary tissues and cell types were obtained from the Roadmap Epigenomics project. Among them, 27 epigenetic profiles were from gastric-related tissues. For the 24 histone marks that were not measured in gastric-related tissues, a meta-histone modification profile was created by taking the median profile of each mark in the measured tissues and cell types. In addition, histone modification profiles of H3K4Me1, H3K4me3 and H3K27Ac of 19 GC tumor / normal samples and 13 gastric cancer cell lines (FU97, KATO3, MKN7, NCC24, NCC59, OCUM1, RERF-GC-1B, SNU16, SNU1750, YCC3, YCC7, YCC21, YCC22) were included. 24,70 The median signal for each histone mark across all tumor samples, all normal samples, and all cell lines was used, respectively.

[0319] There is no replication time profile for gastric tissue. Therefore, the inventors used the 71 Mean values ​​of replication timing profiles for the 13 cell lines generated (Bj, Nhek, K562, Mcf7, Gm06990, Gm12812, Imr90, Hepg2, Helas3, Gm12801, Huvec, Gm12878, Gm12813).

[0320] From Ensembl Regulatory Build 72 Binding profiles of 132 transcription factors and a meta-profile of all transcription factor binding sites were obtained. Since no comprehensive TF binding assays have been performed in gastric tissue, a universal TF binding profile was used. In total, 194 candidate epigenetic covariates that could potentially provide information on GC somatic mutation rates were considered (data not shown).

[0321] To identify sequence context features that affect the accumulation of somatic mutations in GC, 1-mer, 3-mer, and 5-mer nucleotide motifs centered on the mutation site, as well as 1-bp and 2-bp left / right flanking motifs of the site were considered. All nucleotide context features were divided into reverse complementary pairs. Since indels tend to appear in multi-monomer sequences, especially poly-A and poly-T sequences, the presence of poly-A, poly-T, poly-G, and poly-C sequences at the indel site was used as a feature in the indel background mutation model.

[0322] Finally, local mutation rate was included as a covariate to account for other unknown factors that affect mutation rate. After masking CDS regions, immunoglobulin loci, and poorly mappable regions (mappability score < 1 in ENCODE 75mers Alignabilitytrack), local mutation rate was calculated for 100 kb non-overlapping regions across the genome.

[0323] PCA on epigenetic signatures

[0324] The genome was divided into 1 Mb non-overlapping windows. CDS regions, immunoglobulin loci, and poorly mappable regions were masked from the genomic windows. Windows smaller than 250 kb after masking were removed. The average signal for each epigenetic feature was calculated ( Figure 1B The Pearson correlations between epigenetic signatures and mutation rates of tumors were calculated. To identify the contribution of epigenetic signatures to the variation in mutation rates of individual tumors, PCA analysis was performed on the correlation matrix between mutation rates and epigenetic signatures of individual tumors using the prcomp function in R. The contribution of each feature to a principal component was calculated as the feature loading (rotated) divided by the sum of all feature loadings for that principal component.

[0325] Feature Selection Using LASSO Regression

[0326] The least absolute shrinkage and selection operator (LASSO) is a regular regression method commonly used for automatic feature selection. LASSO penalizes the sum of the absolute magnitudes of the regression coefficients, forcing some regression coefficients to shrink to zero, thereby selecting simpler and more interpretable models. The LASSO objective function can be written as:

[0327]

[0328] Where l is the negative log likelihood function and λ is the regularization parameter.

[0329] LASSO logistic regression was used to identify the most informative features for modeling somatic mutation rates in gastric cancer. Since it is computationally expensive to run logistic regression on all positions in the non-coding genome with a large number of predictors, the inventors used all mutation sites and an equal amount of randomly sampled non-mutation sites as input for feature selection in the LASSO logistic regression model. The inventors regressed the binary mutation status of each site relative to the average signal of each feature on an 11bp region centered on the site. The regularization parameter λ was selected by 10-fold cross validation so that the error of the selected model was within 1 standard deviation of the minimum error. LASSO regression and cross validation were performed using the "glmnet" package in R.

[0330] glmnet(y~βX,family=logistic)

[0331] The inventors bootstrapped 100 samples with 50% of the data at each bootstrap and used the bootstrapped samples for LASSO regression. Assuming that the most informative features will be robustly selected, features selected in more than 95% of the bootstrapped samples were used for the final regression model.

[0332] Tumor-specific background mutation models

[0333] Patient-specific background mutation probabilities were estimated by fitting a logistic regression model on all genomic loci after masking CDS regions, immunoglobulin loci, and regions of poor mappability. Replication timing was discretized into 8 equally sized bins, local mutation rates were discretized into 10 equally sized bins, and chromatin features and TF binding profiles were binarized. -4 The cutoff values ​​binarized the P-value signal tracks of histone modification profiles from Roadmap Epigenomics. The ENCODE TF binding profiles were binarized according to the presence of peaks in any cell line assayed. Logistic regression was performed for each combination of covariates using frequency tables of mutation and non-mutation site counts. Logistic regression models were fitted separately to estimate the background mutation probability of SNVs and indels. This is to account for the different mutational processes that generate SNVs and indels, as well as the different uncertainties associated with SNV and indel identification.

[0334] glm(y~rep+epi+sequence+pid,family=logit)

[0335] Here, rep is the Repli-seq profile, epi represents the epigenetic feature, sequence represents the sequence background feature, and pid is the patient ID. The features used in each model are shown in FIG9 .

[0336] Poisson binomial model for mutation recurrence

[0337] For a specific target region, the probability p of mutation in tumor i i , is a function of the length of the region and the expected mutation rate of individual nucleotides in the region under the null hypothesis. Assume that q i,j is the mutation probability of nucleotide j in tumor i, and l is the length of the target region:

[0338]

[0339] Mutation recurrence is then modeled using a Poisson binomial distribution that accounts for inter-tumor variation in mutation rates. For a particular target region, the probability of a mutation occurring in k or more individuals is given by:

[0340]

[0341] Here, n is the total number of tumors sequenced, k is the number of tumors with mutations in the target region, and F m is the set of all subsets of k integers selected from {1,2,…,n}, A is F m A subset of c is the complement of set A, p i is the probability of mutation in tumor i, and p j is the probability of a mutation in tumor j. The Poisson binomial probability was calculated using an efficient and accurate normal approximation in the “poibin” R package.

[0342] Identification of mutational hotspots

[0343] Hot spot analysis aims to identify small focal regions with high mutation rates. By taking 10bp flanks on each side of each mutation, the inventors first considered the 21bp regions of all mutations. The mutation recurrence scores of all 21bp regions with 3 or more mutation samples (2 or more for indel) were then calculated. The P value of the mutation recurrence of each hot spot was calculated using the Poisson binomial model described in the previous section. The total number of hypotheses detected is equal to the number of bases in the non-coding genome covered. The Bonferroni correction was used to adjust the multiple tests of 2,533,374,732 hypotheses to keep the overall α at 0.01.

[0344] Identification of noncoding regions of genes with recurrent indels

[0345] The non-coding regions of genes with indel recurrence were scanned. Gene regions were defined by Ensembl v75 annotations. The inventors considered the merged non-coding regions of each gene by masking all coding regions of each gene and extending the gene boundaries by 1 kb to consider its promoter region. The mutation recurrence scores of all protein-coding genes and their single merged non-coding regions were calculated using the Poisson binomial model described in the previous section. The Bonferroni correction was used to keep the overall α at 0.01.

[0346] Enrichment of mutation hotspots in functional regions

[0347] The log odds ratios of hotspot mutation enrichment in TF binding regions and conserved DNA elements were calculated. Gastric-specific TFBS were defined as CHIP-seq peaks of TFs in any ENCODE cell line that overlapped with gastric tissue DNase1 hypersensitive sites (data from RoadmapEpigenomics). Constitutive TFBS were defined as P tfbs TFBS > 0.75, where P tfbs is the probability that a TFBS is bound by a TF in any given ENCODE cell line. tfbs All were obtained from ENSEMBL Regulatory Build. GERPs from hg19 and alignments with 36 mammals were downloaded from the UCSC Genome Browser. 73 Conserved elements generated.

[0348] The expected fraction of hotspot (or non-hotspot) mutations in a functional region type (p2) is the fraction of the genome that constitutes a functional region. The observed fraction of hotspot (or non-hotspot) mutations in a functional region is calculated by adding all mutations in the functional region type and dividing by the total number of mutations in the whole genome (p1). The log odds ratio of the enrichment of hotspot (or non-hotspot) mutations in a functional region type is given by:

[0349]

[0350] The LOD standard error calculation method is:

[0351]

[0352] The statistical significance of enrichment was assessed by Z-test.

[0353] Identification of gastric-specific CBS

[0354] From JASPAR 74 Obtain the position weight matrix of CTCF binding motifs using the MEME tool suite 76 FIMO 75 The function identified the genomic locations of CTCF binding motifs with a P-value threshold of 0.01. Gastric-specific CBSs were defined as CBS motifs that overlapped with both CTCF ChIP-seq peaks in at least one ENCODE cell line and DNase1 hypersensitive sites in gastric tissue from Roadmap epigenomics. 13 The set of constitutive CTCF-CTCF loops shared among three cell lines (GM12878, Jurkat and K562) was obtained in the supplementary information. CBSs overlapping with the boundaries of these constitutive CTCF loops were defined as boundary CBSs.

[0355] CBS-specific background model

[0356] For the CBS-specific background model, the model and the search space were restricted to the CBS region and its 5 bp flanking DNA.

[0357] glm(y CBS ~ rep+subtype+boundary+sequence+pid+mutsig1+mutsig17,family=logit)

[0358] Here, subtype is the tumor subtype, boundary indicates whether the CBS is located at a CTCF loop boundary, and mutsig1 and mutsig17 indicate the percentage contribution of signature 1 and signature 17 to the tumor. 76 To quantify the prevalence of each of the 30 COSMIC consensus mutational signatures in each tumor.

[0359] P values ​​for mutational recurrence were calculated for each CBS using the Poisson binomial model described in the previous section. A Bonferroni correction was applied to maintain an overall α of 0.01.

[0360] Motif analysis of hotspot mutations flanking the CTCF motif

[0361] The ±40bp sequence context around each mutation was extracted, and DeepBind was used to predict the binding scores of 472 TFs for each mutation to the reference sequence (ref score) and the mutant sequence (alt score). Since the binding scores output by DeepBind vary on an arbitrary scale and between different TF models, the background distribution of binding scores for each TF was estimated by applying DeepBind to 10,000 randomly sampled non-hotspot mutations. For a particular TF, if its reference sequence score is higher than 99.9% of random mutations and the score difference between its alternative sequence and the reference sequence (alt score - ref score) is less than 99.9% of random mutations for that TF, the mutation is predicted to be motif-destructive. If its alternative sequence score is higher than 99.9% of random mutations and the score difference between its alternative sequence and the reference sequence (alt score - ref score) is greater than 99.9% of random mutations for that TF, the mutation is predicted to create a motif for the specific TF.

[0362] Pan-cancer analysis of mutation recurrence at CBS hotspots

[0363] From Weinhold et al. 5 Somatic mutations for 858 tumors from 22 cancer types were downloaded from the Supplementary Information of . Highly mutated tumors with more than 200,000 mutations were excluded from the analysis. Cancer types with fewer than 10 samples were excluded from the analysis. Figure 6B For the calculation of the CBS mutation rate, CBS was defined as the CTCF motif that overlaps with the CTCF ChIP-seq peaks in at least one ENCODE cell line. The inventors further defined tissue-specific CBSs for 14 / 19 cancer types, where the DNasel profiles in the matching tissue types are available in Roadmap Epigenomics. Tissue-specific CBS was defined as the common CBS that falls under the DNasel peaks in the corresponding tissue. Fig. 22 The mutation rates at tissue-specific CBSs were shown.

[0364] SCNA breakpoint analysis

[0365] Copy number segmentation was generated using CNVkit 77 (bcbio-nextgen v0.9.3) with default settings. SCNA breakpoints were defined as the ends of non-diploid segments. Assuming a tumor purity of 50% (the estimated average purity of these tumors), non-diploid segments were defined as segments with log2(tumor coverage / normal coverage) < log2(1.5 / 2) or log2(tumor coverage / normal coverage) > log2(2.5 / 2).

[0366] Variant allele frequency analysis

[0367] A list of known gastric cancer driver genes was collected from the Cancer Gene Census 79 and the driver genes were identified by TCGA 19 and Wang et al. 20 TP53 was excluded from the analysis because TP53 often undergoes deletions and loss of heterozygosity. Nonsynonymous and truncating mutations on known gastric cancer driver genes were identified and their VAFs were compared with the VAFs of CBS hotspot mutations from the same samples using the matched Wilcoxon rank-sum test. Only mutations in diploid regions of each sample were included in the analysis.

[0368] Code availability

[0369] Some figures and statistical data in the paper were generated using R programming (data not shown). The source code of the somatic mutation recognizer SMuRF can be obtained in https: / / github.com / skandlab / SMuRF The source code for estimating the background mutation rate from genomic covariates and identifying non-coding mutation hotspots is available in https: / / github.com / skandlab / MutSpot

[0370] Data availability

[0371] ​SG tumor data: Sequence data have been deposited in the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, with accession number EGAS 00001002872.

[0372] TCGA tumor data: https: / / portal.gdc.cancer.gov / projects / TCGA-TAD

[0373] ICGC tumor data: https: / / ega-archive.org / datasets / EGAD00001003132

[0374] HK tumor data: https: / / ega-archive.org / datasets / EGAD00001000782

[0375] Roadmap Epigenomics Data: http: / / www.roadmapepigenomics.org / data /

[0376] Encoded data: ftp: / / ftp.ensembl.org / pub / release-85 / regulation / homo_ sapiens /

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[0457] application

[0458] It is believed that disclosures prior to the present disclosure have not rigorously tested the hypothesis that positive selection can still act on specific CBSs to drive cancer in individual tumor types even in the presence of an overall elevated mutational load at the CBS. Indeed, to accurately identify such genomic sites under positive selection, statistical tests must account for regional biases in mutational load. In the present disclosure, the inventors have used a packaged mutation identification method to uniformly and accurately identify somatic single nucleotide variants (SNVs) and insertions / deletions (indels) in 212 GC genomes. A comprehensive statistical method integrating epigenetic and sequence covariates was developed to accurately model background mutational processes and identify non-coding regions with mutational loads significantly above background, indicating positive selection and a role in gastric tumorigenesis.

[0459] An unbiased genome-wide scan of concentrated mutation hotspots (~20 bp, as TF binding motifs are typically <20 bp) observed significant enrichment of noncoding indels (insertions / deletions) in three gastric lineage-specific genes (LIPF, PGC, and MUC6). In addition, 11 overlapping CTCF binding sites (CBS) were identified in 34 focal regions (hotspots) of significant mutation and recurrence. These 11 CBS hotspots remained significant even after controlling for the genome-wide elevated mutation rate of CBS. In 3 of the 4 CBS hotspots detected, mutations were nominally associated with altered expression of neighboring genes (CENPQ, KCNQ5, SPG20). CBS hotspot mutations are enriched in tumors showing chromosomal instability, co-occur with adjacent chromosomal aberrations, and are common in gastric tumors (25%) and colorectal tumors (19%), but rare in other cancer types (CTCF hotspot mutations are frequent in gastrointestinal tumors (20-25%)). Thus, the present disclosure has identified mutational disruption of a specific CBS as a potential tissue-specific mechanism of tumorigenesis that is conserved in gastrointestinal cancers.

[0460] The hotspots in the present disclosure were further characterized by analyzing CBS-specific mutation biases, gene expression of neighboring genes, chromosomal instability, and the incidence of these mutations in other cancer types. In particular, the present disclosure has found that mutations at boundary CTCF binding sites are associated with neighboring chromosomal instability. In summary, the present disclosure has identified CBS hotspots as candidate drivers of gastrointestinal cancer and has elucidated the general connection between CBS mutations and chromosomal instability in gastrointestinal cancer.

[0461] It is worth noting that previous cases / embodiments or studies only covered mutations in protein coding regions in gastrointestinal cancer. It is believed that the non-coding regions of the mutations identified in the present disclosure have not been reported in gastrointestinal cancers including gastric cancer before. In addition, it is believed that no previous studies have used non-coding mutations in cancer cfFDNA assays. In fact, the identified non-coding regions / hotspots can only be reliably found in the case of> 150 samples / tumors (signal / noise ratio). Prior to the present disclosure, there was no such data set or study.

[0462] Advantageously, the CBS hotspots identified in the present disclosure are very dense and concentrated, and are very suitable for liquid biopsy methods for detecting and monitoring gastrointestinal cancer (e.g., gastric cancer) (especially by detecting, capturing and sequencing non-coding DNA fragments). This includes early detection and monitoring of disease recurrence and metastasis. More advantageously, the present disclosure also provides hybridization probes (or PCR primers) for capturing and sequencing 34 potential non-coding mutation hotspot regions (each ~20nt long).

[0463] The present disclosure has the potential to contribute new disease biomarkers, improved patient diagnostics, and entirely new treatment paradigms in the identification of novel oncogenic mutations.

[0464] Those skilled in the art will appreciate that other changes and / or modifications may be made to the embodiments disclosed herein without departing from the spirit or scope of the disclosure as broadly described. For example, in this specification, features of different exemplary embodiments may be mixed, combined, exchanged, merged, adopted, modified, included, etc. between different exemplary embodiments. Therefore, the present embodiments should be considered to be illustrative and not restrictive in all respects.

Claims

1. Use of a reagent in the preparation of a kit for detecting gastrointestinal cancer in a human subject, The reagent is used to determine the presence or absence of a mutation in a focal region having a high mutation rate in a fluid biological sample of a subject, wherein the fluid biological sample is selected from the group consisting of blood, plasma, serum, and combinations thereof, and The focus areas are: The CTCF-binding site (CBS) overlapping regions are shown in the table below: and non-CBS regions as shown in the following table: Wherein the presence of mutations in the focal region is detected, and the reagents include primers whose sequences are as follows: SEQ ID NO.35, SEQ ID NO.36, SEQ ID NO.39, SEQ ID NO.40, SEQ ID NO.45, SEQ ID NO.46, SEQ ID NO.47, SEQ ID NO.48, SEQ ID NO.49, SEQ ID NO.50, SEQ ID NO.51, SEQ ID NO.52, SEQ ID NO.57, SEQ ID NO.58, SEQ ID NO.59, SEQ ID NO.60, SEQ ID NO.65, SEQ ID NO.66, SEQ ID NO.77, SEQ ID NO.78, SEQ ID NO.83, SEQ ID NO.84, SEQ ID NO.37, SEQ ID NO.38, SEQ ID NO.41, SEQ ID NO.42, SEQ ID NO.43, SEQ ID NO.44, SEQ ID NO.53, SEQ ID NO.54, SEQ ID NO.55, SEQ ID NO.56, SEQ ID NO. NO.61, SEQ ID NO.62, SEQ ID NO.63, SEQ ID NO.64, SEQ ID NO.67, SEQ ID NO.68, SEQ ID NO.71, SEQ ID NO.72, SEQ ID NO.75, SEQ ID NO.76, SEQ ID NO.81, SEQ ID NO.82, SEQ ID NO.85, SEQ ID NO.86, SEQ ID NO.87, SEQ ID NO.88, SEQ ID NO.89, SEQ ID NO.90, SEQ ID NO.91, SEQ ID NO.92; and The presence of gastrointestinal cancer in the subject is detected, determined and / or diagnosed based on the presence of the mutation in the focal region.

2. The use according to claim 1, wherein the reagent is also used to determine in a subject whether there is a mutation in at least one gene selected from the following: KRAS, TP53, RHOA, ARID1A, PIK3CA, ERBB3 and PTEN, wherein the presence of a mutation in the focal region and at least one of the genes detects gastrointestinal cancer in the subject.

3. The use according to any one of claims 1-2, wherein determining whether a mutation is present in the focal region does not include determining whether a mutation is present in a region spanning more than 50 nucleotides.

4. A kit for detecting gastrointestinal cancer from a fluid biological sample of a human subject, the kit comprising: A reagent for detecting mutations in a focal area with a high mutation rate, wherein the reagent includes primers whose sequences are as follows: SEQ ID NO.35, SEQ ID NO.36, SEQ ID NO.39, SEQ ID NO.40, SEQ ID NO.45, SEQ ID NO.46, SEQ ID NO.47, SEQ ID NO.48, SEQ ID NO.49, SEQ ID NO.50, SEQ ID NO.51, SEQ ID NO.52, SEQ ID NO.57, SEQ ID NO.58, SEQ ID NO.59, SEQ ID NO.60, SEQ ID NO.65, SEQ ID NO.66, SEQ ID NO.77, SEQ ID NO.78, SEQ ID NO.83 and SEQ ID NO.84, SEQ ID NO.37, SEQ ID NO.38, SEQ ID NO.41, SEQ ID NO.42, SEQ ID NO.43, SEQ ID NO.44, SEQ ID NO.53, SEQ ID NO.54, SEQ ID NO.55, SEQ ID NO. NO.56, SEQ ID NO.61, SEQ ID NO.62, SEQ ID NO.63, SEQ ID NO.64, SEQ ID NO.67, SEQ ID NO.68, SEQ ID NO.71, SEQ ID NO.72, SEQ ID NO.75, SEQ ID NO.76, SEQ ID NO.81, SEQ ID NO.82, SEQ ID NO.85, SEQ ID NO.86, SEQ ID NO.87, SEQ ID NO.88, SEQ ID NO.89, SEQ ID NO.90, SEQ ID NO.91 and SEQ ID NO.92, and wherein the focus areas are: The CBS overlap areas are shown in the table below: and non-CBS regions as shown in the following table: The fluid biological sample is selected from the group consisting of blood, plasma, serum and combinations thereof.

5. The kit according to claim 4, further comprising a reagent for detecting a mutation in at least one gene selected from the group consisting of KRAS, TP53, RHOA, ARID1A, PIK3CA, ERBB3 and PTEN.

6. The kit of claim 4, wherein the human subject is an Asian subject.

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