Methods to assist in the diagnosis of tumors, evaluation of treatment effectiveness, monitoring of recurrence, assessment of recurrence risk, or assessment of the risk of developing tumors.

Abstract

The objective is to identify biomarkers that can monitor the disease state of tumors and to provide methods for determining the effectiveness of tumor treatment, monitoring recurrence, assessing the risk of recurrence, predicting prognosis, or assisting in diagnosis. [Solution] Focusing on the methylation status of ctDNA as a regulatory mechanism for epigenetic gene modification in tumor development, we found that certain genes are highly methylated in tumor tissue from tumor patients compared to non-tumor tissue, and that changes in the methylation level of these genes in ctDNA correlate with the disease state of the tumor. Therefore, we discovered that this can be used as a biomarker to monitor the disease state of tumors.

Description

Technical Field

[0001] The present invention relates to a method for assisting in the diagnosis of tumors, determination of treatment efficacy, monitoring of recurrence, evaluation of recurrence risk, or evaluation of onset risk, and a kit for the diagnosis of tumors, determination of treatment efficacy, monitoring of recurrence, evaluation of recurrence risk, or evaluation of onset risk.

Background Art

[0002] In Japan, malignant tumors account for 24.6% of all causes of death, the largest proportion. As examination methods for malignant tumors, gastrointestinal endoscopy and CT examination are frequently used at present. However, endoscopy is invasive to patients, and repeated CT examinations pose a problem of radiation exposure. As an auxiliary diagnosis, although tumor markers such as CEA and CA19-9 in blood are often used, except for some carcinomas such as prostate cancer, the sensitivity is often less than 30% (Non-Patent Document 4). Also, worldwide, surgery is the standard radical treatment for solid cancers including gastric cancer, but many patients experience recurrence after surgery, and despite the development of various chemotherapy methods, the prognosis is poor (Non-Patent Documents 1 and 2). For example, in the past, several approaches such as image examination and measurement of serum tumor markers have been used to evaluate the progression of gastric cancer (Non-Patent Document 3). However, the sensitivity of tumor markers such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) is not sufficient (Non-Patent Document 4). In addition, the detection sensitivity of peritoneal metastasis is low, and there is a risk of radiation exposure, so frequent use of CT is not suitable for evaluating the state of tumors (Non-Patent Document 5).

[0003] Circulating tumor DNA (ctDNA) is tumor-derived DNA released into the blood by necrosis or apoptosis of tumor cells. Patent Document 1 discloses a method for evaluating the effect of treatment for malignant neoplasms having a step of measuring the amount of ctDNA in blood derived from a subject. It has been reported that the amount of ctDNA in blood correlates with the disease progression state in various cancer types (Non-Patent Document 6).

[0004] To date, there have been reports of plasma biomarker studies using ctDNA methylation in several types of cancer (Non-Patent Documents 7-9). However, the analysis of methylation levels in the base sequence of a gene according to the present invention has not been disclosed. [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] International Publication No. 2017 / 094805 [Non-patent literature]

[0006] [Non-Patent Document 1] Hashimoto, T., Kurokawa, Y., Mori, M. &Doki, Y. Update on the Treatment of Gastric Cancer. JMA J 1, 40-49,2018 [Non-Patent Document 2] Yanagimoto, Y., Kurokawa, Y. & Doki, Y.Essential updates 2021 / 2022: Perioperative and surgical treatments for gastricand esophagogastric junction cancer. Ann Gastroenterol Surg 7, 698-708, 2023 [Non-Patent Document 3] Smyth, EC et al. Gastric cancer: ESMOClinical Practice Guidelines for diagnosis, treatment and follow-up. Ann.Oncol. 27, v38-v49, 2016 [Non-Patent Document 4] Shimada , H. , Noie , T. , Ohashi , M. , Oba , K. & Takahashi , Y. Clinical significance of serum tumor markers for gastriccancer: a systematic review of literature . Gastric Cancer 17.26-33

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[0007] The inventors have previously focused on tumor-derived DNA (ctDNA) released into peripheral blood due to necrosis or apoptosis of tumor tissue, and have reported on the clinical application of ctDNA detection in malignant tumors such as esophageal cancer and gastrointestinal stromal tumors (GIST). (Wada, N. et al., Detecting Secondary C-KIT Mutations in the Peripheral Blood of Patients with Imatinib-Resistant Gastrointestinal Stromal Tumor. Oncology 2016; 90: 112-117; Hagi, T. et al. Molecular Barcode Sequencing for Highly Sensitive Detection of Circulating Tumor DNA in Patients with Esophageal Squamous Cell Carcinoma. Oncology 2020; 98: (222-229) Targeted deep sequencing of free plasma DNA from gastric cancer patients was performed, and a correlation was shown between tumor status and TP53-ctDNA mutations (Hamakawa, T. et al. Monitoring gastric cancer progression with circulating tumor DNA. Br. J. Cancer 112, 352-356, 2015). However, TP53 mutations are found in 90% of esophageal squamous cell carcinoma patients, but only in 24% of gastric cancer patients. From this, it was considered that detecting gene mutations in ctDNA may not be practical in gastric cancer, where gene mutations are rare.

[0008] The object of the present invention is to identify biomarkers that can monitor the disease state of a tumor, and to provide a method for assisting in determining the therapeutic effect of tumors, monitoring recurrence, evaluating the risk of recurrence, predicting prognosis, or diagnosing tumors. [Means for solving the problem]

[0009] The inventors of this invention conducted extensive research to solve the above problems and focused on the methylation state of ctDNA as a regulatory mechanism for epigenetic gene modification in tumor development. They discovered that certain genes are highly methylated in tumor tissue from tumor patients compared to non-tumor tissue, and that changes in the methylation level of these genes in ctDNA correlate with the disease state of the tumor. As a result, they found that these genes can be used as biomarkers to monitor the disease state of tumors, and thus completed the present invention.

[0010] In other words, the present invention consists of the following. 1. A method for assisting in the diagnosis of tumors, evaluation of treatment effectiveness, monitoring of recurrence, assessment of recurrence risk, or assessment of disease risk, comprising the step of analyzing the methylation levels in the base sequences of at least two genes selected from the group consisting of SPG20, TFPI2, SEPT9, FBN1, and SDC2 in a biological sample taken from a subject. 2. The method according to paragraph 1, wherein the tumor is selected from the group consisting of gastric cancer, colorectal cancer, esophageal cancer, gastrointestinal stromal tumor, pancreatic cancer, glioblastoma, liver cancer, lung cancer, bile duct cancer, kidney cancer, prostate cancer, bladder cancer, breast cancer, uterine cancer, cervical cancer, ovarian cancer, salivary gland cancer, pharyngeal cancer, hematological cancer, lymphoma, sarcoma, skin cancer, brain tumor, and thyroid cancer. 3. The method according to item 1 or 2 above, wherein the tumor is gastric cancer. 4. The following genes in biological samples collected from subjects: SPG20, TFPI2, SEPT9, FBN1, SDC2, ALX4, ARMCX1, BCAT1, BMP3, CACNA1G, CDKN2A, CDO1, COL4A2, DCC, DKK2, DLX5, EYA2, EYA4, FGF5, FOXF1, GRASP, INA, IRAK3, IRF4, MAL, MDFI, MGMT, NDRG4, NGFR, NPY, PDGFRA, RASSF1, RASSF2, SFRP2 The method according to any one of items 1 to 3 above, comprising the step of analyzing the methylation level in the base sequence of any gene selected from the group consisting of SHOX2, SLC6A15, SNCA, SOCS1, SSTR2, ST8SIA1, THBS1, TMEFF2, UNC5C, and VIM, wherein the analysis step includes analyzing the methylation level in the base sequences of at least two genes selected from the group consisting of SPG20, TFPI2, SEPT9, FBN1, and SDC2 genes. 5. The method according to any one of paragraphs 1 to 4 above, wherein the analysis step includes analyzing the methylation level in the base sequence of SPG20 and one or more genes selected from the group consisting of TFPI2, SEPT9, FBN1, and SDC2. 6. The method according to any one of paragraphs 1 to 5 above, wherein the analysis step includes analyzing the methylation levels in the base sequences of the SPG20, TFPI2, SEPT9, FBN1, and SDC2 genes. 7. The method according to any one of paragraphs 1 to 6 above, wherein the analysis step further includes analyzing the methylation level in the base sequence of one or more genes selected from the group consisting of the following genes in the biological sample: APC, CMTM3, CNRIP1, ESX1, FOXI2, HLTF, IGF2, IGFBP3, IKZF1, MLH1, NEUROG1, NPTX2, RUNX3, SLC16A7, SOX1, SOX21, TP73, WIF1, and ZSCAN18. 8. The method according to any one of paragraphs 1 to 7 above, wherein the base sequence of each gene analyzed in the analysis step includes a base sequence of length 500 to 3000 bp within ±3000 bp upstream and downstream of the transcription start site. 9. When the methylation level is higher than the cut-off value, it indicates that the subject has a tumor, has a high risk of recurrence, or has a high risk of onset, or a decrease in the methylation level indicates a therapeutic effect, or an increase in the methylation level indicates the presence of recurrence, the method according to any one of items 1 to 8 above. 10. The method according to any one of items 1 to 9 above, wherein the biological sample is a blood-derived sample. 11. A kit for diagnosing a tumor, determining a therapeutic effect, monitoring recurrence, evaluating a recurrence risk or evaluating an onset risk, comprising a probe or primer adapted to specifically hybridize or amplify the nucleotide sequence of any two or more genes selected from the group consisting of SPG20, TFPI2, SEPT9, FBN1 and SDC2. 12. Further, the following genes: any one or two or more genes selected from the group consisting of ALX4, ARMCX1, BCAT1, BMP3, CACNA1G, CDKN2A, CDO1, COL4A2, DCC, DKK2, DLX5, EYA2, EYA4, FGF5, FOXF1, GRASP, INA, IRAK3, IRF4, MAL, MDFI, MGMT, NDRG4, NGFR, NPY, PDGFRA, RASSF1, RASSF2, SFRP2, SHOX2, SLC6A15, SNCA, SOCS1, SSTR2, ST8SIA1, THBS1, TMEFF2, UNC5C and VIM; the kit according to item 11 above, comprising a probe or primer adapted to specifically hybridize or amplify the nucleotide sequence thereof.

Advantages of the Invention

[0011] The present invention can provide a method for assisting in the diagnosis of a tumor, determination of a therapeutic effect, monitoring of recurrence, evaluation of a recurrence risk or evaluation of an onset risk.

Brief Description of the Drawings

[0012] [Figure 1]The heat map of the methylation β-value ratio (T / N ratio) of tumor tissues and non-tumor tissues of 44 genes that were determined to have increased methylation specifically in gastric cancer of the TCGA cohort is shown. The T / N ratio is represented by -1.2 to 1.2 by common logarithm transformation. (Example 1) [Figure 2] The heat map of the methylation β-values of 44 genes in the plasma of 16 cases of gastric cancer at the time of postoperative recurrence is shown. (Example 2) [Figure 3] The heat map of the methylation level of genes in the plasma of 16 cases of gastric cancer at the time of postoperative recurrence. The heat map of the average methylation level of 5 cluster A genes (SPG20, FBN1, SDC2, TFPI2 and SEPT9) is shown. The methylation level is represented as a β-value and represents a continuous measurement value from 0 (not methylated at all) to 1 (completely methylated). (Example 2) [Figure 4] The Kaplan-Meier method total survival curves of the hypermethylation group and the hypomethylation group (a), the high CEA or CA19-9 value group and the low value group (b) obtained by hierarchical cluster analysis of 5 cluster A genes are shown. (Example 3) [Figure 5] The relationship between ctDNA methylation and disease progression in case 3 is shown. The vertical axis of the graph shows the average β-values of 5 cluster A genes (SPG20, FBN1, SDC2, TFPI2 and SEPT9). (Example 4) [Figure 6] The relationship between the ctDNA methylation level and disease progression in 40 non-cancer carriers and 125 gastric cancer patients before treatment is shown. (Example 5) [Figure 7] The change in the ctDNA methylation level before and after surgery in R0 surgical cases (excluding cases where ctDNA methylation is negative both before and after surgery) is shown. (Example 5) [Figure 8] The recurrence-free survival (RFS) in cases (16 cases) with positive ctDNA methylation level after surgery and cases (49 cases) with negative ctDNA methylation level after surgery is shown. (Example 5) [Figure 9] The fluctuation of the ctDNA methylation level before and after chemotherapy in 39 cases of patients with unresectable advanced or recurrent gastric cancer is shown. (Example 5) [Modes for carrying out the invention]

[0013] The present invention relates to a method for assisting in the diagnosis of tumors, evaluation of treatment effectiveness, monitoring of recurrence, assessment of recurrence risk, or assessment of the risk of developing tumors (hereinafter sometimes simply referred to as "the method of the present invention") and a kit for the diagnosis of tumors, evaluation of treatment effectiveness, monitoring of recurrence, assessment of recurrence risk, or assessment of the risk of developing tumors (hereinafter sometimes simply referred to as "the kit of the present invention").

[0014] The method of the present invention includes the step of analyzing the methylation levels in the base sequences of at least two genes selected from the group consisting of SPG20, TFPI2, SEPT9, FBN1, and SDC2 in a biological sample taken from a subject.

[0015] In one embodiment, the method of the present invention involves the following genes in a biological sample taken from a subject: SPG20, TFPI2, SEPT9, FBN1, SDC2, ALX4, ARMCX1, BCAT1, BMP3, CACNA1G, CDKN2A, CDO1, COL4A2, DCC, DKK2, DLX5, EYA2, EYA4, FGF5, FOXF1, GRASP, INA, IRAK3, IRF4, MAL, MDFI, MGMT, NDRG4, NGFR, NPY, PDGFRA, RA The procedure includes a step of analyzing the methylation level in the base sequence of any gene selected from the group consisting of SSF1, RASSF2, SFRP2, SHOX2, SLC6A15, SNCA, SOCS1, SSTR2, ST8SIA1, THBS1, TMEFF2, UNC5C, and VIM, and the analysis step includes analyzing the methylation level in the base sequence of at least two genes selected from the group consisting of SPG20, TFPI2, SEPT9, FBN1, and SDC2 genes.

[0016] In one embodiment, the analysis step includes analyzing the methylation level in the base sequence of SPG20 and one or more genes selected from the group consisting of TFPI2, SEPT9, FBN1, and SDC2.

[0017] In one embodiment, the analysis step includes analyzing the methylation level in the base sequences of TFPI2 and one or more genes selected from the group consisting of SPG20, SEPT9, FBN1, and SDC2.

[0018] In one embodiment, the analysis step includes analyzing the methylation level in the base sequences of SEPT9 and one or more genes selected from the group consisting of SPG20, TFPI2, FBN1, and SDC2.

[0019] In one embodiment, the analysis step includes analyzing the methylation level in the base sequence of FBN1 and one or more genes selected from the group consisting of SPG20, TFPI2, SEPT9, and SDC2.

[0020] In one embodiment, the analysis step includes analyzing the methylation level in the base sequence of SDC2 and one or more genes selected from the group consisting of SPG20, TFPI2, SEPT9, and FBN1.

[0021] In one embodiment, the analysis step includes analyzing the methylation levels in the base sequences of the SPG20, TFPI2, SEPT9, FBN1, and SDC2 genes.

[0022] In one embodiment, the analysis step further includes analyzing the methylation level of the base sequence of one or more genes selected from the group consisting of the following genes in a biological sample: APC, CMTM3, CNRIP1, ESX1, FOXI2, HLTF, IGF2, IGFBP3, IKZF1, MLH1, NEUROG1, NPTX2, RUNX3, SLC16A7, SOX1, SOX21, TP73, WIF1, and ZSCAN18. The method of the present invention may further analyze the methylation level of the base sequence of known or future discovered genes other than the 63 genes listed above in the analysis step. The analysis of each gene may be performed simultaneously or separately.

[0023] DNA methylation occurs when a methyl group is added to a cytosine base, and when the frequency of methylation increases at CpG sites around the transcription start site of a gene, gene expression is suppressed. The mechanism of the present invention is not particularly limited, but in oncology, it is thought that methylation of tumor suppressor genes suppresses gene expression and is involved in the mechanisms of carcinogenesis and tumor progression. By detecting this methylation of tumor suppressor genes as ctDNA in peripheral blood, it can be used as a highly sensitive biomarker for early detection of malignant tumors, disease progression monitoring, and prognosis prediction. In previous plasma biomarker studies using ctDNA methylation, various methods such as pyrosequencing, methylation-specific PCR, and quantitative methylation digital PCR have been employed (Non-patent documents 7-9), but these can only evaluate methylation at specific CpG sites. In the examples described below, the inventors first employed bisulfite sequencing using next-generation sequencing (NGS), which can evaluate methylation at a wider range of CpG sites, and established a method for identifying highly sensitive ctDNA methylated genes. Furthermore, they established a method employing digital PCR, which can detect ctDNA present in only trace amounts in peripheral blood with high sensitivity and low cost. Localized gene methylation normally occurs at the CpG (5'-cytosine-phosphate-guanine-3') site, which is not normally methylated. In the examples described below, in order to identify ctDNA methylated genes detectable in colorectal cancer and gastric cancer, the three major cancers in Japan, the methylation levels of the base sequences containing CpG sites of the following 63 genes, which have been reported to be associated with methylation in colorectal cancer, were analyzed: SPG20(Spartin)、TFPI2(Tissue Factor Pathway Inhibitor 2)、SEPT9(Septin 9)、FBN1(Fibrillin1)、SDC2(Syndecan2)、ALX4(Homeobox ProteinAristaless-Like 4)、APC(AdenomatousPolyposis Coli Protein)、ARMCX1(ArmadilloRepeat Containing X-Linked1)、BCAT1(Branched Chain AminoAcid Transaminase 1)、BMP3(BoneMorphogenetic Protein 3)、CACNA1G(Calcium Voltage-Gated Channel Subunit Alpha1 G)、CDKN2A(Cyclin Dependent Kinase Inhibitor 2A)、CDO1(Cysteine Dioxygenase Type 1)、CMTM3(CKLF like MARVEL transmembranedomaincontaining 3)、CNRIP1(CannabinoidReceptor Interacting Protein 1)、COL4A2(Collagen Type IV Alpha 2 Chain)、DCC(DCC netrin 1 receptor)、DKK2(Dickkopf WNT Signaling Pathway Inhibitor 2)、DLX5(Distal-Less Homeobox 5)、ESX1(ESX Homeobox 1)、EYA2(EYA Transcriptional Coactivator And Phosphatase 2)、EYA4(EYA Transcriptional Coactivator AndPhosphatase 4)、FGF5(FibroblastGrowth Factor 5)、FOXF1(ForkheadBox F1)、FOXI2(Forkhead Box I2)、GRASP(Trafficking Regulator And ScaffoldProtein Tamalin)、HLTF(HelicaseLike TranscriptionFactor)、IGF2(Insulin Like Growth Factor 2)、IGFBP3(Insulin Like Growth Factor Binding Protein 3)、IKZF1(IKAROS Family Zinc Finger 1)、INA(Internexin Neuronal IntermediateFilament Protein Alpha)、IRAK3(Interleukin1 Receptor Associated Kinase 3)、IRF4(Interferon Regulatory Factor 4)、MAL(Mal, T Cell Differentiation Protein)、MDFI(MyoD Family Inhibitor)、MGMT(O-6-Methylguanine-DNA Methyltransferase)、MLH1(MutL Homolog 1)、NDRG4(NDRG Family Member 4)、NEUROG1(Neurogenin 1)、NGFR(NerveGrowthFactor Receptor)、NPTX2(NeuronalPentraxin 2)、NPY(Neuropeptide Y)、PDGFRA(Platelet Derived GrowthFactorReceptor Alpha)、RASSF1(RasAssociation Domain Family Member 1)、RASSF2(Ras Association Domain Family Member 2)、RUNX3(RUNX Family Transcription Factor 3)、SFRP2(Secreted Frizzled Related Protein 2)、SHOX2(Short Stature Homeobox 2)、SLC16A7(Solute Carrier Family 16 Member 7)、SLC6A15(Solute Carrier Family 6 Member 15)、SNCA(Synuclein Alpha)、SOCS1(Suppressor Of Cytokine Signaling 1)、SOX1(SRY-Box Transcription Factor1), SOX21 (SRY-Box Transcription Factor 21), SSTR2 (Somatostatin Receptor 2), ST8SIA1 (ST8 Alpha-N-Acetyl-Neuraminide Alpha-2,8-Sialyltransferase 1), THBS1 (Thrombospondin 1), TMEFF2 (Transmembrane Protein With EGFLikeAnd Two Follistatin Like Domains 2), TP73 (Tumor Protein P73), UNC5C (Unc-5 Netrin Receptor C), VIM (Vimentin), WIF1 (WNTInhibitory Factor 1) and ZSCAN18 (ZincFinger And SCAN Domain Containing 18).

[0024] The base sequence information for the 63 genes listed above can be obtained from publicly available databases, such as the UCSC Genome Browser (https: / / genome.ucsc.edu) provided by the University of California, Santa Cruz (UCSC), and the database provided by the National Center for Bioinformation (NCBI) (http: / / www.ncbi.nlm.nih.gov / ).

[0025] In the examples described later, analysis of the above 63 genes revealed that 44 genes were significantly hypermethylated in tumor tissue of recurrent gastric cancer patients compared to non-tumor tissue: SPG20, TFPI2, SEPT9, FBN1, SDC2, ALX4, ARMCX1, BCAT1, BMP3, CACNA1G, CDKN2A, CDO1, COL4A2, DCC, DKK2, DLX5, EYA2, EYA4, FGF5, FOXF1, GRASP, INA, IRAK3, We identified IRF4, MAL, MDFI, MGMT, NDRG4, NGFR, NPY, PDGFRA, RASSF1, RASSF2, SFRP2, SHOX2, SLC6A15, SNCA, SOCS1, SSTR2, ST8SIA1, THBS1, TMEFF2, UNC5C, and VIM, and confirmed that the hypermethylated group, classified by hierarchical cluster analysis of plasma DNA of these genes, had significantly worse overall survival compared to the hypomethylated group. Furthermore, we identified five genes with particularly high methylation levels among these 44 genes—SPG20, TFPI2, SEPT9, FBN1, and SDC2—and confirmed that their methylation status in plasma DNA correlated with the disease state of gastric cancer.

[0026] The nucleotide sequences of the analyzed regions of the SPG20 (NCBI EntrezGene ID:23111), TFPI2 (NCBI Entrez Gene ID:7980), SEPT9 (NCBI Entrez Gene ID:10801), FBN1 (NCBI Entrez Gene ID:2200), and SDC2 (NCBI Entrez Gene ID:6383) genes in the examples described later are shown in sequence numbers 1 to 5. The locations of the analyzed regions in hg19 (UCSC Human Genome 19) are, respectively, chr13:36919626-36921096, chr7:93519367-93520128, chr17:75368620-75370662, chr15:48936742-48938924, and chr8:97505565-97507808.

[0027] In this specification, "methylation level" means the frequency of base methylation in the base sequence of a gene, and can be expressed, for example, as the amount of methylated DNA present in the target nucleic acid, or as a value that quantifies that amount. The type of nucleic acid is not particularly limited and may be, for example, plasma cfDNA, FFPE DNA, etc., but plasma cfDNA is preferred from the viewpoint of low invasiveness and ctDNA content. In one example, the methylation level can be expressed as the β value, which is the ratio of the number of reads with evidence of methylation to the number of reads read by next-generation sequencing (NGS) in a bisulfite sequence. In another example, the methylation level can be expressed as the methylation ratio calculated by normalizing the copy number of the methylated target sequence, measured by digital PCR after treatment with a restriction enzyme that specifically recognizes unmethylated target sites, with respect to the copy number of the reference gene (e.g., hTERT, etc.). When analyzing the methylation levels in the base sequences of two or more genes, the average value or sum of the methylation levels (e.g., β value, methylation ratio) of each gene may be used as a representative methylation level for the target. Hereafter, when referring to "methylation level," the concept of representative methylation levels is also included. When using the sum of the methylation levels of each gene, the methylation level of each gene may be individually compared with the cutoff value to determine whether it is positive or negative, and the methylation level of genes determined to be negative may be treated as "0".

[0028] In methylation level analysis, the methylation level of a predetermined length (e.g., 100-6000 bp, 200-4000 bp, 500-3000 bp, 600-2500 bp, 700-2000 bp, 200-1000 bp, etc.) of a base sequence in a predetermined region of each gene (e.g., part or all of the promoter region, CpG islands, within ±3000 bp, ±2000 bp, ±1500 bp, or ±1000 bp upstream and downstream of the transcription start site) is analyzed. The base sequence contains at least one CpG, preferably has a GC content of 45% or more, more preferably 50% or more, more preferably 55% or more, and more preferably 60% or more. Examples of such base sequences include sequences of sequence numbers 1-5.

[0029] Methylation levels are, for example, as reported by Yu, C. et al. (Yu, C. et al. Repeatability of methylation measures using a QIAseqtargeted methylpanel and comparison with the Illumina HumanMethylation450 assay. BMC Res. Notes 14, 394, 2021) and / or as provided by the Bisulfite Sequencing manuals of QIAGEN ("Detecting DNA methylation", QIAGEN, https: / / resources.qiagenbioinformatics.com / manuals / clcgenomicsworkbench / current / index.php?manual=Detecting_DNA_methylation.html, "Map Bisulfite Reads to Reference", QIAGEN, https: / / resources.qiagenbioinformatics.com / manuals / clcgenomicsworkbench / current / index.php?manual=Map_Bisulfite_Reads_Reference.html, "Call Methylation"). These levels are obtained by analyzing DNA methylation analysis methods such as bisulfite sequencing, based on "Call Methylation Levels" (QIAGEN, https: / / resources.qiagenbioinformatics.com / manuals / clcgenomicsworkbench / current / index.php?manual=Call_Methylation_Levels.html) and "Create RRBS-fragment Track" (QIAGEN, https: / / resources.qiagenbioinformatics.com / manuals / clcgenomicsworkbench / current / index.php?manual=Create_RRBS_fragment_Track.html).In bisulfite sequencing, bisulfite (bisulfite) treatment converts unmethylated cytosine to uracil, while methylated cytosine remains unchanged. This allows for the analysis of the presence or absence of methylation and the level of methylation in subsequent sequencing.

[0030] In this specification, "subject" or "subject" refers to a human or non-human mammal (e.g., monkey, sheep, pig, horse, cow, mouse, rat, cat, dog, etc.), preferably a human.

[0031] In this specification, "biological sample" is not particularly limited as long as it is a biological sample containing tumor-derived DNA. Examples of such biological samples include bodily fluids, tissues (including tumor tissue), and cultures obtained by culturing cells or tissues collected from living organisms (tumor patients, people suspected of having tumors). Examples of bodily fluids include blood-derived samples such as whole blood, plasma, and serum, as well as lymph fluid, ascites, urine, tears, saliva, bone marrow fluid, and papillary secretions. Examples of tissues include tissue excisions from tissues (tumors) obtained by surgery or biopsy, and tissue biopsy materials obtained from tissues obtained by preoperative endoscopic examination. Among these, blood-derived samples are preferred from the viewpoint of low invasiveness and ctDNA content, and plasma is more preferred. The timing of biological sample collection is not particularly limited and can include, for example, at the time of initial tumor diagnosis, after surgical resection (curative resection), before, during, and after adjuvant chemotherapy, at the time of recurrence diagnosis, and during follow-up. Biological sample collection and methylation level analysis may be performed at the same time or at a predetermined time apart.

[0032] In this specification, “therapeutic effect” means tumor reduction, disappearance, inhibition of growth, and / or absence of recurrence and metastasis. In this specification, “tumor” is used as a concept that includes malignant and benign tumors, and is not particularly limited to any excessive group of autonomous new cells that occur in animals, including humans. The present invention is applicable to a wide range of tumors, and the type of tumor is not particularly limited, but examples include gastric cancer, colorectal cancer, esophageal cancer, gastrointestinal stromal tumors, pancreatic cancer, glioblastoma, liver cancer, lung cancer, bile duct cancer, kidney cancer, prostate cancer, bladder cancer, breast cancer, uterine cancer, cervical cancer, ovarian cancer, salivary gland cancer, pharyngeal cancer, hematological cancer, lymphoma, sarcoma, skin cancer, brain tumor, thyroid cancer, etc. Preferably, gastric cancer, colorectal cancer, esophageal cancer, and gastrointestinal stromal tumors, and particularly preferably gastric cancer.

[0033] In this specification, "risk of recurrence" means the possibility of recurrence and / or metastasis after a predetermined period from the time a biological sample is taken from the subject (for example, approximately 1 month, 3 months, 6 months, 12 months, 24 months, 36 months, 48 ​​months, and 60 months). The assessment of the risk of recurrence may also be, for example, a prediction of the recurrence rate after a certain period following treatment. In this specification, "high risk of recurrence" means that the possibility of recurrence and / or metastasis within a predetermined period from the time a biological sample is taken from the subject is higher compared to tumor patients showing a cutoff value.

[0034] In this specification, "risk of developing the disease" refers to the possibility of developing the disease after a predetermined period from the time a biological sample is collected from the subject (for example, approximately 1 month, 3 months, 6 months, 12 months, 24 months, 36 months, 48 ​​months, and 60 months). In this specification, "high risk of developing the disease" means that the probability of developing the disease within a predetermined period from the time a biological sample is collected from the subject is higher compared to those who meet the cutoff value.

[0035] In one embodiment, the method of the present invention analyzes the methylation level of genes in biological samples taken from a subject at any one or more point in time (for example, at the time of initial tumor diagnosis, before, during, and after neoadjuvant chemotherapy, before and after surgical resection (curative resection), before, during, and after adjuvant chemotherapy, during follow-up, at the time of recurrence, before, during, and after chemotherapy). If necessary, the analysis may be performed at regular intervals, for example, daily, every 1 to 7 days, every 2 to 4 weeks, every 1 to 12 months, or every 1 to 5 years. In one embodiment, if the methylation level obtained from the second or subsequent analysis is compared with the methylation level obtained from the previous analysis, it can be determined or aided in determining that the treatment performed between the previous analysis and the current analysis has had a therapeutic effect, and / or if the methylation level has increased or remains unchanged, it can be determined or aided in determining that the treatment performed between the previous analysis and the current analysis has had no therapeutic effect. In one embodiment, if the methylation level obtained from the second or subsequent analysis is compared with the methylation level obtained from the previous analysis and the methylation level has increased, it can be determined or assist in such determination, and / or if the methylation level has decreased or remains unchanged, it can be determined or assist in such determination that there is no recurrence. In one embodiment, if the methylation level obtained from the second or subsequent analysis is compared with the methylation level obtained from the previous analysis and the methylation level has increased, it can be determined or assist in such determination that there is a tumor, a high risk of recurrence, or a high risk of developing the disease, and / or if the methylation level has decreased or remains unchanged, it can be determined or assist in such determination that there is no tumor, a low risk of recurrence, or a low risk of developing the disease. Here, the criteria for determining whether the "methylation level has increased" or "methylation level has decreased" are not particularly limited, but for example, it may be an increase or decrease of 10% or more compared to the previous methylation level, preferably an increase or decrease of 20%, more preferably an increase or decrease of 30% or more, more preferably an increase or decrease of 50% or more, and more preferably an increase or decrease of 100% or more. If the change is less than the said criteria, it may be determined that "the methylation level has not changed."

[0036] In one embodiment, the method of the present invention can determine, or assist in, that if the methylation level obtained by analyzing a biological sample taken from a subject is higher than the cutoff value, the subject has a tumor or a high probability of having one, a high risk of recurrence, or a high risk of developing the disease, and / or, if the methylation level is lower than the cutoff value, the subject does not have a tumor or a high probability of having one, a low risk of recurrence, or a low risk of developing the disease, and / or, if the methylation level obtained by analyzing a biological sample taken from a subject is higher than the cutoff value, the subject has a tumor or a high probability of having one, or there are residual molecular lesions after surgery or there are high probability of having one, and / or, if the methylation level is lower than the cutoff value, the subject does not have a tumor or a high probability of having one, or there are no and / or, if there are no residual molecular lesions after surgery or there are high probability of having one, and / or, In one embodiment, when analyzing the methylation levels of two or more genes, the methylation level of each gene can be compared with a cutoff value set individually to determine whether it is positive or negative. If the number of positive genes among the analyzed genes is above an arbitrarily set threshold, the ctDNA methylation level of the biological sample can be determined to be positive. A subject from whom a biological sample with a positive ctDNA methylation level has been collected can be determined to have a tumor or a high probability thereof, and / or have a poor prognosis or a high probability thereof. The threshold for the number of positive genes can be set to any number from 1 or more to less than or equal to the number of genes analyzed (for example, 1, 2, 3, 4, 5, 10, 20, 30, 40, etc.).

[0037] The cutoff value in this invention is not particularly limited and can be set empirically by accumulating analytical data. One example of how to set the cutoff value is to set it to a value higher than the median methylation level of biological samples taken from healthy individuals who have been previously confirmed not to have tumors. For example, the cutoff value is set to the median value plus the product of the IQR (Interquartile Range; Q3-Q1; simply put, the value obtained by subtracting the lower 25th percentile from the upper 75th percentile of the data) and an arbitrary coefficient (not particularly limited, for example, any value between 0.1 and 10.0, e.g., 1.0, 1.5, 2.0, 3.0, etc.). Another example of how to set the cutoff value is to set the cutoff value from a range that is higher than the methylation level of biological samples taken from healthy individuals and lower than the methylation level of biological samples taken from tumor patients, based on the results of analyzing the methylation levels of biological samples taken from tumor patients and healthy individuals who have been previously confirmed not to have tumors. Another example of setting the cutoff value is to analyze the methylation levels of biological samples taken from multiple tumor patients who underwent treatment, and set the cutoff value within a range that is higher than the methylation level of biological samples taken from those who showed a therapeutic effect and lower than the methylation level of biological samples taken from those who did not show a therapeutic effect. Another example of setting the cutoff value is to analyze the methylation levels of biological samples taken from patients with recurrent tumors and those who have remained recurrence-free for a specified period, and set the cutoff value within a range that is higher than the methylation level of biological samples taken from recurrence-free survivors and lower than the methylation level of biological samples taken from patients with recurrent tumors. Another example of setting the cutoff value is to analyze the methylation levels of biological samples taken from multiple healthy individuals, and set the cutoff value within a range that is higher than the methylation level of biological samples taken from those who did not develop tumors for a specified period and lower than the methylation level of biological samples taken from those who developed tumors within a specified period.Another example of how to set the cutoff value is to set it within a range that is higher than the methylation level of biological samples taken from patients whose overall survival time from the time of biological sample collection is longer than a predetermined period, and lower than the methylation level of biological samples taken from patients whose overall survival time from the time of biological sample collection is shorter than the predetermined period, based on the results of analyzing the methylation levels of biological samples taken from multiple tumor patients. In setting the cutoff value based on the results of Example 2 described later, the β value obtained by analyzing the methylation level of plasma can be any value between 0.0710 and 0.1599, or any value between 0.0418 and 0.2159.

[0038] The methylation level may be analyzed by known methods or methods to be developed in the future, for example, by bisulfite sequencing using next-generation sequencing (NGS). Alternatively, it can be analyzed by PCR using primers and / or probes designed to amplify a base sequence of a predetermined length (e.g., 100-6000 bp, 200-4000 bp, 500-3000 bp, 600-2500 bp, 700-2000 bp, 200-1000 bp, etc.) in a predetermined region of each gene (e.g., part or all of the promoter region, CpG islands, within ±3000 bp, ±2000 bp, ±1500 bp, or ±1000 bp upstream and downstream of the transcription start site), such as digital PCR. The base sequence contains at least one CpG, preferably has a GC content of 45% or more, more preferably 50% or more, more preferably 55% or more, and more preferably 60% or more. One example of methylation level analysis using PCR is to extract DNA from a biological sample taken from a subject as needed, treat it with restriction enzymes containing CpG sequences as recognition sequences (e.g., BstU I and Hha I), and then perform digital PCR. This restriction enzyme treatment cleaves unmethylated target CpG sites and prevents PCR amplification, while methylated target CpG sites are not cleaved and are amplified by PCR. Another example of methylation level analysis using PCR is to extract DNA from a biological sample taken from a subject as needed, treat it with bisulfite, and then perform digital PCR. Bisulfite treatment converts unmethylated sequences to bisulfite, making them incompatible with primers and / or probes, so only methylated sequences are amplified. The methylation level can be expressed as the amount of amplification of the target sequence by the above PCR.

[0039] The kit of the present invention comprises probes or primers adapted to specifically hybridize or amplify the base sequence of one or more genes selected from the group consisting of the following genes: SPG20, TFPI2, SEPT9, FBN1, and SDC2.

[0040] In one embodiment, the kit of the present invention comprises probes or primers adapted to specifically hybridize or amplify the nucleotide sequences of the SPG20, TFPI2, SEPT9, FBN1, and SDC2 genes.

[0041] In one embodiment, the kit of the present invention further comprises probes or primers adapted to specifically hybridize or amplify the nucleotide sequences of one or more genes selected from the group consisting of the following genes: ALX4, ARMCX1, BCAT1, BMP3, CACNA1G, CDKN2A, CDO1, COL4A2, DCC, DKK2, DLX5, EYA2, EYA4, FGF5, FOXF1, GRASP, INA, IRAK3, IRF4, MAL, MDFI, MGMT, NDRG4, NGFR, NPY, PDGFRA, RASSF1, RASSF2, SFRP2, SHOX2, SLC6A15, SNCA, SOCS1, SSTR2, ST8SIA1, THBS1, TMEFF2, UNC5C, and VIM.

[0042] In one embodiment, the kit of the present invention further includes the following genes: ALX4, ARMCX1, BCAT1, BMP3, CACNA1G, CDKN2A, CDO1, COL4A2, DCC, DKK2, DLX5, EYA2, EYA4, FGF5, FOXF1, GRASP, INA, IRAK3, IRF4, MAL, MDFI, MGMT, NDRG4, NGFR, NPY, PDGFRA, RASSF1, RASSF2, SFRP2, SHOX2, SLC6A15, SNCA, SOCS1, SST The invention comprises probes or primers adapted to specifically hybridize or amplify the base sequence of one or more genes selected from the group consisting of R2, ST8SIA1, THBS1, TMEFF2, UNC5C, VIM, APC, CMTM3, CNRIP1, ESX1, FOXI2, HLTF, IGF2, IGFBP3, IKZF1, MLH1, NEUROG1, NPTX2, RUNX3, SLC16A7, SOX1, SOX21, TP73, WIF1, and ZSCAN18.

[0043] The kit of the present invention may be a kit for analyzing the methylation level of the base sequence of the above gene in a biological sample.

[0044] In one embodiment, the probes or primers included in the kit of the present invention are adapted to specifically hybridize or amplify a base sequence of a predetermined length (e.g., 100-6000 bp, 200-4000 bp, 500-3000 bp, 600-2500 bp, 700-2000 bp, 200-1000 bp, etc.) in a predetermined region of each gene (e.g., part or all of the promoter region, a CpG island, within ±3000 bp, ±2000 bp, ±1500 bp, or ±1000 bp upstream and downstream of the transcription start site). The base sequence contains at least one CpG, preferably has a GC content of 45% or more, more preferably 50% or more, more preferably 55% or more, and more preferably 60% or more.

[0045] The kit of the present invention may include additional components as needed. For example, it may further include means for collecting biological samples from a subject, means for preparing samples, control or reference samples, and / or instructions for use. [Examples]

[0046] To aid in understanding the present invention, the invention will be specifically described below with reference to examples, but it goes without saying that the present invention is not limited to these examples. The following examples were carried out in accordance with the Declaration of Helsinki and approved by the Ethics Review Committee at Osaka University Hospital. Written informed consent was obtained from all patients before sampling.

[0047] Patients and samples Plasma methylation analysis was performed on 16 patients who received chemotherapy for recurrent gastric cancer after surgery at Osaka University Hospital between July 2019 and August 2021. Details of the 16 patients are shown in Table 1. Plasma samples were collected from all 16 patients (Cases 1-16). In Case 3, blood samples were collected and stored at five points in time: before neoadjuvant chemotherapy, before and after surgery, during adjuvant chemotherapy, and at the time of recurrence. Clinical staging was performed based on the 15th edition of the Japanese Gastric Cancer Guidelines.

[0048] [Table 1] In the table, * indicates the condition at the time of blood collection and the chemotherapy regimen, M is male, F is female, por is poorly differentiated adenocarcinoma, mod is moderately differentiated adenocarcinoma, wel is well-differentiated adenocarcinoma, Cx is chemotherapy, PTX is paclitaxel, RAM is ramucirumab, IRI is irinotecan, and DTX is docetaxel.

[0049] DNA preparation Tumor / Normal (T / N) paired tissues capable of isolating a sufficient amount of DNA were obtained from formalin-fixed paraffin-embedded (FFPE) slides of gastric specimens resected during primary surgery in 10 cases (Cases 1-10). DNA from the tissue samples was isolated using the Gene read® DNAFFPE Kit (QIAGEN, Hilden, Germany). Blood samples were collected in 10 mL Vacutainer® ethylenediaminetetraacetate disodium dihydrate (EDTA-2Na) blood collection tubes. Plasma was centrifuged at 1600 g for 10 minutes at 4°C, and then recentrifuged at 16000 g for 10 minutes at 4°C to separate it from blood cells. Centrifugation was performed within 2 hours of blood collection, and plasma samples were stored at -80°C until DNA extraction. DNA was isolated from plasma samples using the Apostle MiniMax® High Efficiency cfDNA Isolation Kit (BeckmanCoulter, CA, USA).

[0050] Methylation panel and TCGA cohort We used QIAseq Targeted DNA panels (DHS-002Z, QIAGEN, Hilden, Germany) targeting thousands of CpG sites in 63 cancer-specific genes shown in Table 2. To examine the methylation status of these 63 genes in gastric cancer, we used the Stomach Adenocarcinoma (STAD) dataset from TCGA (The CancerGenomeAtlas, http: / / tcga-data.nci.nih.gov / tcga / tcgaHome2.jsp) and the UCSC Xena browser (https: / / xena.ucsc.edu). Gene methylation levels were scored using β values ​​ranging from 0 (not methylated at all) to 1 (fully methylated). Methylation β value data for each case was downloaded from the Illumina Human Methylation 27 and 450 platforms (https: / / xenabrowser.net) and analyzed for the 63 genes. The mean methylation β value was calculated for each of the 63 genes. We analyzed the differences using the T / N ratio as a selection criterion to identify genes that are hypermethylated in tumor tissue compared to non-tumor tissue using β values. To narrow down the hypermethylated genes in gastric cancer, genes in the TCGA cohort were selected based on i) the ratio of β values ​​in tumor tissue to non-tumor tissue (β value T / N ratio) > 1, and ii) the P value determined by the Wilcoxon rank-sum test comparing tumor and non-tumor tissues < 0.05.

[0051] [Table 2-1]

[0052] [Table 2-2]

[0053] Bisulfite conversion and next-generation sequencing (NGS) 5 ng of plasma DNA and 200 ng of FFPE DNA were bisulfite-converted using the EPITECTFast Bisulfite Sequencing Conversion Kit (QIAGEN). The bisulfite-converted DNA was used as an input template to create a target library according to the QIAseq Targeted MethylPanel protocol. The resulting dataset was then sequenced using Illumina Miseq™ 2x150bp at V2 Chemistry (Illumina, CA, USA). Paired-end reads were mapped to the human genome reference (hg19). Data were analyzed at the GeneGlobe Data Analysis Center.

[0054] methylated β value For each CpG site, the DNA methylation level was defined as a β value and calculated by dividing the methylation coverage (number of reads with evidence of methylation at that location) by the context coverage (number of reads that fit the selected methylation context), based on the report by Yu, C. et al. (Yu, C. et al. Repeatability of methylation measures using aQIAseqtargeted methyl panel and comparison with the IlluminaHumanMethylation450 assay. BMC Res. Notes 14, 394, 2021). The analysis region was within ±1,500 base pairs upstream and downstream of the transcription start site (TSS). If the context coverage was less than 10 times, the methylation value was defined as "missing". Finally, the β value for each gene was calculated as the average β value of the CpG sites it was contained in. Highly methylated genes in gastric cancer were evaluated by comparing the β values ​​of tumor tissue and non-tumor tissue. The selection of the top five hypermethylated genes was based on hierarchical clustering in a heatmap of methylation β-value T / N ratio.

[0055] statistical analysis Hierarchical clustering was performed using Ward's method of least variance. Overall survival (OS) was defined as the time from the date of blood collection to the date of death from any cause. Survival rates were estimated using the Kaplan-Meier method and compared using the log-rank test. All statistical analyses were performed using JMP PRO software (JMP version 17.0.0; SAS Institute, Cary, NC).

[0056] (Example 1) Evaluation of gene methylation status in the TCGA cohort The methylation status of 63 genes in gastric cancer patients was analyzed using the TCGA-STAD dataset. Methylation β-value data for 63 genes were downloaded from the Illumina Human Methylation27 and 450 platforms and analyzed. As a result, 44 of these genes were found to be significantly hypermethylated in tumor tissue compared to non-tumor tissue (Table 3), and these 44 genes were subsequently analyzed. In Table 3, "*" indicates genes that did not meet either of the following criteria: i) the ratio of β-values ​​in tumor tissue to non-tumor tissue (β-value T / N ratio) > 1, or ii) the P-value < 0.05 determined by the Wilcoxon rank-sum test comparing tumor and non-tumor tissue.

[0057] [Table 3-1]

[0058] [Table 3-2]

[0059] [Table 3-3]

[0060] Next, methylation status profiling of 44 genes selected from the TCGA cohort was performed using targeted bisulfite sequencing of DNA samples obtained from T / N paired tissues of 10 cases. Figure 1 shows heatmaps of the ratio of methylated β values ​​(T / N ratio) in tumor tissue and non-tumor tissue for the 44 genes in the 10 cases. Hierarchical cluster analysis of all 44 genes revealed four major clusters (A, B, C, and D) as shown in Figure 1. The genes in cluster A (SPG20, FBN1, SDC2, TFPI2, SEPT9) showed the highest mean T / N ratio (Cluster A, 3.424; Cluster B, 1.693; Cluster C, 1.827; Cluster D, 1.120).

[0061] (Example 2) Analysis of plasma methylation status Methylation analysis of 44 genes was performed using targeted bisulfite sequencing of DNA samples collected from the plasma of 16 patients with recurrent gastric cancer after surgery. Figure 2 shows a heatmap of the methylation β values ​​of these 44 genes. Hierarchical cluster analysis of these genes divided the 16 patients into a hypermethylated group and a hypomethylated group, with mean β values ​​of 0.1599 and 0.0710 for the 44 genes, respectively. Hierarchical cluster analysis of the five genes (SPG20, FBN1, SDC2, TFPI2, and SEPT9) in cluster A classified the patients into the same two groups, with mean β values ​​of 0.2159 and 0.0418 for the five genes, respectively (Figure 3). Even when the 16 patients were divided into two groups using an overall mean of 0.0854 as a cutoff value, the same patients remained in the hypermethylated group (Table 4).

[0062] [Table 4]

[0063] (Example 3) Prediction of overall survival based on plasma methylation status Using the Kaplan-Meier method, we evaluated the groups obtained from hierarchical cluster analysis of five cluster A genes. The hypermethylated group had significantly worse overall survival from the date of blood collection compared to the hypomethylated group (log-rank P=0.009) (Figure 4a). On the other hand, when serum CEA or CA19-9 values ​​measured simultaneously with ctDNA blood collection were divided into two groups using twice the upper limit of normal as a cutoff value, no statistically significant difference in overall survival was observed between the high CEA or CA19-9 group and the normal CEA or CA19-9 group (log-rank P=0.208) (Figure 4b).

[0064] (Example 4) Evaluation of the relationship between plasma methylation status and clinical tumor status Figure 5 shows the relationship between methylation levels of five genes and each point in the clinical course in Case 3. This patient had stage III gastric cancer with lymph node metastasis and underwent total gastrectomy with R0 resection following neoadjuvant chemotherapy. Adjuvant chemotherapy was administered postoperatively, but peritoneal and bilateral ovarian recurrence was observed on CT scan one year postoperatively. In this patient, blood samples were taken at five points in time, as described above. In plasma before neoadjuvant chemotherapy, the mean β values ​​of the five significant genes were highest at 0.054, but this value decreased to 0.026 after neoadjuvant chemotherapy and remained stable during postoperative chemotherapy. However, at the time of recurrence, the methylation level rose again to 0.039. In this patient, neither CEA nor CA19-9, tumor markers conventionally used in routine clinical practice, were elevated above the upper limit of normal at any point in time. In summary, compared to non-tumor tissue, 44 genes were significantly hypermethylated in tumor tissue, with SPG20, TFPI2, SEPT9, FBN1, and SDC2 genes being particularly hypermethylated. Patients with high plasma ctDNA methylation levels of these five genes had a significantly worse prognosis, and the average plasma ctDNA methylation levels of these five genes were closely correlated with the clinical state of the tumor. This suggests that these genes can be used as a more sensitive biomarker to monitor tumor status than conventional tumor markers such as CEA and CA19-9, which do not clearly indicate prognosis, and that they can also be used for screening for the early detection of cancer.

[0065] (Example 5) Evaluation of plasma methylation status by digital PCR using methylation-sensitive restriction enzymes To measure the ctDNA methylation levels of five genes, or some of them, namely SPG20, FBN1, SDC2, TFPI2, and SEPT9, we used methylation-sensitive restriction enzyme treatment and digital PCR as a less expensive and simpler method than the high-cost NGS. Specifically, primers and probes were prepared to measure the ctDNA methylation levels of the five genes identified in this study, targeting gastric cancer patients. The ctDNA methylation levels (also known as "methylated ctDNA (Met-ctDNA) levels") were then quantified using digital PCR (dPCR) after methylation-sensitive restriction enzyme (MSRE) treatment.

[0066] The primers and probes for MSRE-dPCR were designed as follows. Multiplex digital PCR was performed using the QuantStudio® Absolute Q® system (Thermo Fisher Scientific) to calculate the absolute copy number of methylated DNA and the endogenous control, hTERT. Three duplex reactions were performed on each DNA sample to measure five genes and hTERT: (1) hTERT and methylated SEPT9; (2) methylated SPG20 and methylated TFPI2; and (3) methylated FBN1 and methylated SDC2. The sequences of the primers and probes are shown in Table 5 below. The primers and probes for hTERT and SEPT9 were designed based on previously reported primer and probe sequences (Oncotarget. 2018Mar30;9(24):16974-16987). SEPT9 was designed to include three methylation-sensitive enzyme recognition sites. SPG20, TFPI2, and FBN1 were designed to contain two methylation-sensitive enzyme recognition sites each, while SDC2 was designed to contain five. If all of these sites are methylated, enzymatic cleavage does not occur, and amplification by dPCR becomes possible. On the other hand, hTERT was designed to lack recognition sites for the methylation-sensitive enzymes HhaI and Bsh1236I, and therefore is always amplified as long as human DNA is present. For each target gene, a 40 μL 20× probe-primer mix was prepared with the following composition: forward primer (100 μM) 7.2 μL, reverse primer (100 μM) 7.2 μL, probe (10 μM) 20 μL, and RNase-free water 5.6 μL.

[0067] [Table 5]

[0068] Methylation-sensitive restriction enzyme incubation was performed using the following procedure: To 10 μL of DNA isolated from a plasma sample using the Apostle MiniMax® High Efficiency cfDNA Isolation Kit (BeckmanCoulter, CA, USA), 1 μL of GeneAmp 10x PCR Buffer II (Thermo Fisher Scientific), 1 μL of 25 mmol / L MgCl2 (Thermo Fisher Scientific), 10 units of HhaI (Thermo Fisher Scientific), 10 units of Bsh1236I (Thermo Fisher Scientific), and 20 units of Exonuclease I (Thermo Fisher Scientific) were added, resulting in a total volume of 16 μL. The mixture was incubated at 37°C for 16 hours. Subsequently, the enzymes were inactivated by heating at 98°C for 10 minutes.

[0069] The reaction mixture for digital PCR (dPCR) consisted of the following components: 0.5 μL of 20×FAM probe / primer mix, 0.5 μL of 20×VIC (or HEX) probe / primer mix, 2 μL of 5×Absolute Q DNAdPCR Mix, and 3 μL of RNase-free water. After making a total of 6 μL, 4 μL of enzyme-treated DNA was added to make a final reaction volume of 10 μL. Of this, 9 μL was dispensed into each well of a MAP16 plate, and 15 μL of Absolute Q Isolation Buffer was added on top before loading into the Absolute Q instrument. The thermal cycling conditions were as follows: preheating at 96°C for 10 minutes, followed by denaturation at 96°C for 5 seconds and annealing / extension at 60°C for 20 seconds × 40 cycles.

[0070] Data analysis was performed using QuantStudio® Absolute Q® Digital PCR Software. The amount of methylated DNA in each assay was normalized relative to hTERT, and the "methylation ratio" was calculated using the following formula: Methylation ratio=(Methylatedtarget DNA copies / hTERT copies)×100 The methylation ratios for each of the five genes were calculated for each sample. For each of the five genes, the median + IQR of the methylation ratios for 40 non-cancer patients shown in Table 6 was set as the cutoff value. Specifically, the cutoff values ​​were SEPT9: 0.341, SPG20: 1.035, TFPI2: 2.130, FBN1: 1.192, and SDC2: 0.727. Positive or negative results were determined for each gene using the above cutoff values. If one or more, two or more, or three or more of the five genes were positive, the ctDNA methylation level of each plasma sample was determined to be positive, and the sensitivity and specificity were calculated for each case. Furthermore, for genes determined to be negative, the methylation ratio was treated as 0, and the sum of the methylation ratios of the five genes was used as the representative methyl ctDNA methylation level of the sample.

[0071] This method was applied to 40 non-cancer patients and 125 pre-treatment gastric cancer patients (76 with resectable gastric cancer (cStage 1-3) and 49 with unresectable / recurrent gastric cancer (cStage 4)), as shown in Table 6. The ctDNA methylation levels were calculated by combining the measurement results of the five genes. As a result, ctDNA methylation levels increased significantly in the order of non-cancer patients, resectable gastric cancer, and unresectable gastric cancer (Figure 6, left). Furthermore, in resectable cases, a significant increase in ctDNA methylation levels was observed with increasing stage progression (Figure 6, right). Regarding sensitivity and specificity, the sensitivity was 80% and the specificity was 57% when one or more of the five genes were determined to be positive. The sensitivity was 64% and the specificity was 87% when two or more genes were determined to be positive. The sensitivity was 54% and the specificity was 97% when three or more genes were determined to be positive.

[0072] [Table 6]

[0073] Next, we examined the changes in methylated ctDNA before surgery and at the first postoperative outpatient visit (1 month post-surgery) in 65 R0 surgical cases shown in Table 7. R0 is a classification according to the R classification and indicates no residual tumor. Of the 61 resectable gastric cancer patients for whom blood samples could be collected before and after surgery, 33 cases showed a significant decrease in ctDNA methylation levels after surgery, excluding cases where ctDNA methylation levels were negative both before and after surgery (Figure 7). Furthermore, of the 65 resectable gastric cancer patients for whom blood samples could be collected after surgery, we analyzed the recurrence-free survival (RFS) from the time of blood sampling after surgery for cases with positive ctDNA methylation levels (16 cases) and cases with negative ctDNA methylation levels (49 cases). The results showed that the group with positive ctDNA methylation levels after surgery had a significantly worse RFS (Figure 8). These results confirm that ctDNA methylation levels are useful for detecting molecular residual disease (MRD), and that positive detection of ctDNA methylation levels after surgery is useful for predicting recurrence.

[0074] [Table 7]

[0075] Furthermore, in 39 patients with unresectable advanced or recurrent gastric cancer shown in Table 8, changes in ctDNA methylation levels before and after chemotherapy were examined. The results showed a significant increase in PD (Progressive Disease) cases and a significant decrease in Non-PD (Non-Progressive Disease) cases, showing good agreement with the efficacy assessment by CT, thus confirming its usefulness as a tumor disease progression evaluation marker (Figure 9).

[0076] [Table 8]

[0077] Based on the above, it was demonstrated that the five genes mentioned above reflect the amount of tumor tissue in the body with high sensitivity and precision, using a large number of cases (over 100). The method of the present invention can also be performed by digital PCR, making it highly versatile for clinical application. Furthermore, it can be applied to a wide range of cancers, including gastric cancer and colorectal cancer, which are common in Japanese people.

[0078] All publications, patents, and patent applications cited herein are incorporated in their entirety by such citation. [Industrial applicability]

[0079] This invention provides methods for evaluating the effectiveness of tumor treatment, monitoring recurrence, assessing the risk of recurrence, predicting prognosis, or assisting in diagnosis.

Claims

1. A method for assisting in the diagnosis of tumors, evaluation of treatment effectiveness, monitoring of recurrence, assessment of recurrence risk, or assessment of disease risk, comprising the step of analyzing the methylation levels in the base sequences of at least two genes selected from the group consisting of SPG20, TFPI2, SEPT9, FBN1, and SDC2 in a biological sample taken from a subject.

2. The method according to claim 1, wherein the tumor is selected from the group consisting of gastric cancer, colorectal cancer, esophageal cancer, gastrointestinal stromal tumor, pancreatic cancer, glioblastoma, liver cancer, lung cancer, bile duct cancer, kidney cancer, prostate cancer, bladder cancer, breast cancer, uterine cancer, cervical cancer, ovarian cancer, salivary gland cancer, pharyngeal cancer, hematological cancer, lymphoma, sarcoma, skin cancer, brain tumor, and thyroid cancer.

3. The method according to claim 1, wherein the tumor is gastric cancer.

4. The following genes were found in biological samples taken from subjects: SPG20, TFPI2, SEPT9, FBN1, SDC2, ALX4, ARMCX1, BCAT1, BMP3, CACNA1G, CDKN2A, CDO1, COL4A2, DCC, DKK2, DLX5, EYA2, EYA4, FGF5, FOXF1, GRASP, INA, IRAK3, IRF4, MAL, MDFI, MGMT, NDRG4, NGFR, NPY, PDGFRA, RASSF1, RASSF2, SFRP2, The method according to any one of claims 1 to 3, comprising the step of analyzing the methylation level in the base sequence of any gene selected from the group consisting of SHOX2, SLC6A15, SNCA, SOCS1, SSTR2, ST8SIA1, THBS1, TMEFF2, UNC5C, and VIM, wherein the analysis step comprises analyzing the methylation level in the base sequences of at least two genes selected from the group consisting of SPG20, TFPI2, SEPT9, FBN1, and SDC2 genes.

5. The method according to any one of claims 1 to 3, wherein the analysis step includes analyzing the methylation level in the base sequence of SPG20 and one or more genes selected from the group consisting of TFPI2, SEPT9, FBN1, and SDC2.

6. The method according to any one of claims 1 to 3, wherein the analysis step includes analyzing the methylation levels in the base sequences of the SPG20, TFPI2, SEPT9, FBN1, and SDC2 genes.

7. The method according to claim 4, wherein the analysis step further includes analyzing the methylation level in the base sequence of one or more genes selected from the group consisting of the following genes in the biological sample: APC, CMTM3, CNRIP1, ESX1, FOXI2, HLTF, IGF2, IGFBP3, IKZF1, MLH1, NEUROG1, NPTX2, RUNX3, SLC16A7, SOX1, SOX21, TP73, WIF1, and ZSCAN18.

8. The method according to any one of claims 1 to 3, wherein the base sequence of each gene analyzed in the analysis step includes a base sequence of length 500 to 3000 bp within ±3000 bp upstream and downstream of the transcription start site.

9. When the methylation level is higher than the cutoff value, it indicates the presence of a tumor, a high risk of recurrence, or a high risk of developing the disease. The decrease in the methylation level indicates that there is a therapeutic effect, or The method according to any one of claims 1 to 3, wherein the increase in the methylation level indicates the presence of recurrence.

10. The method according to any one of claims 1 to 3, wherein the biological sample is a blood-derived sample.

11. A kit for diagnosing tumors, determining treatment effectiveness, monitoring recurrence, assessing recurrence risk, or evaluating the risk of developing tumors, comprising probes or primers adapted to specifically hybridize or amplify the base sequences of two or more genes selected from the group consisting of SPG20, TFPI2, SEPT9, FBN1, and SDC2.

12. The kit according to claim 11, further comprising probes or primers adapted to specifically hybridize or amplify the base sequence of one or more genes selected from the group consisting of the following genes: ALX4, ARMCX1, BCAT1, BMP3, CACNA1G, CDKN2A, CDO1, COL4A2, DCC, DKK2, DLX5, EYA2, EYA4, FGF5, FOXF1, GRASP, INA, IRAK3, IRF4, MAL, MDFI, MGMT, NDRG4, NGFR, NPY, PDGFRA, RASSF1, RASSF2, SFRP2, SHOX2, SLC6A15, SNCA, SOCS1, SSTR2, ST8SIA1, THBS1, TMEFF2, UNC5C, and VIM.

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