KR102222523B1 - A composition for predicting drug responsibility and uses thereof - Google Patents

Abstract

The present invention relates to a composition for predicting the reactivity to an oxaliplatin drug, which is mainly used for treating colorectal cancer. According to the present invention, the suitability of the use of a therapeutic agent for colorectal cancer patients before dosing is predicted, so that it is possible to provide basic information to use appropriate alternative anticancer drugs in a future treatment plan by a clinician, thereby being able to reduce the cost burden of a patient and increase a survival rate.

Description

A composition for predicting drug responsibility and uses thereof

The present invention relates to a composition for predicting the reactivity of an anticancer drug and a method for predicting the reactivity of an anticancer drug using the same.

In the case of colorectal cancer, most of them are adenocarcinoma, which starts histologically from the mucosa, and in rare cases, cancers such as carcinoid and lymphoma occur. According to data from the Central Cancer Registry released in 2017, 214,701 cases of cancer occurred in Korea in 2015, of which colorectal cancer was reported to be 2nd with 12.5% of the total, with 26,790 cases of men and women.

Colorectal cancer is very difficult to detect early with only symptoms without any other subjective symptoms. If detected in stage 1, 20% of all diagnoses can be cured by endoscopic surgery at early diagnosis, and nearly 95% are cured. In particular, since abdominal pain, diarrhea, and bloody stools, which are the main symptoms of colorectal cancer, are all present in advanced cancers of stage 3 or higher, these symptoms are often found to have already progressed considerably. If it is detected in stage 2/3, it is 60% of all diagnoses, followed by chemotherapy to prevent recurrence after radical surgery. At this time, it is estimated that about 60% of patients who do not require chemotherapy after surgery are estimated to have recurrence despite the chemotherapy in about 20%, so only about 20% of the patients actually see the effect of chemotherapy. I can infer. In other words, 60% of patients undergoing surgery in stage 2/3 can observe the course without weak treatment or treatment, but 20% of recurrences require stronger treatment or additional treatment, and biomarkers that can accurately determine this are insufficient. to be.

Combination of chemotherapy or chemoradiotherapy before surgery for colorectal cancer patients improves the survival rate compared to the case of performing surgery alone. Currently, three types of cytotoxic treatments that are commonly used in the treatment of colorectal cancer include 5-fluorouracil (5-Fu), oxaliplatin, and irinotecan, but various anti-cancer therapies depending on the characteristics of each patient. When attempting to, if the drug has resistance to the drug (drug resistance), the successful anticancer treatment effect is halved, thereby deteriorating the prognosis of colon cancer.

As such, even if the patient is treated with the drug for which drug resistance is predicted, the effect of the treatment cannot be guaranteed, resulting in a situation in which the time and cost of the clinician and patients are unnecessarily consumed. Therefore, when starting chemotherapy, it is necessary to determine the individual characteristics of the patient according to the treatment. It is a necessary situation.

One object of the present invention is to provide a composition capable of predicting the reactivity to drugs.

Another object of the present invention is to provide a kit capable of predicting the reactivity to drugs.

Another object of the present invention is to provide a method capable of predicting the reactivity to drugs.

However, the technical problem to be achieved by the present invention is not limited to the problems mentioned above, and other problems that are not mentioned will be clearly understood by those of ordinary skill in the art from the following description.

Hereinafter, various embodiments described herein are described with reference to the drawings. In the following description, for a thorough understanding of the present invention, various specific details, such as specific forms, compositions and processes, etc. are set forth. However, certain embodiments may be practiced without one or more of these specific details, or with other known methods and forms. In other instances, well-known processes and manufacturing techniques have not been described in specific details in order not to unnecessarily obscure the present invention. Reference throughout this specification to “one embodiment” or “an embodiment” means that a particular feature, form, composition, or characteristic described in connection with the embodiment is included in one or more embodiments of the present invention. Thus, the context of “in one embodiment” or “an embodiment” expressed in various places throughout this specification does not necessarily represent the same embodiment of the invention. Additionally, particular features, forms, compositions, or properties may be combined in one or more embodiments in any suitable manner.

Unless otherwise defined in the specification, all scientific and technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

According to an embodiment of the present invention, it relates to a composition for predicting drug reactivity comprising a probe represented by at least one of SEQ ID NOs: 1 to 27.

In the present invention, the "probe" refers to a substance capable of specifically binding to a target substance to be detected in a sample, and is specifically attached to a target gene through the binding to identify the target gene and/ Or, it is a concept in a broad sense that comprehensively includes a probe to be detected. The type of probe is not limited as a material commonly used in the art, but preferably may be a peptide nucleic acid (PNA), a locked nucleic acid (LNA), a peptide, a polypeptide, a protein, RNA, or DNA. More specifically, the probe is a biomaterial that includes an organism-derived or similar thing or a thing produced in vitro, for example, enzymes, proteins, antibodies, microorganisms, animal and plant cells and organs, neurons, DNA, and It may be RNA, DNA includes cDNA, genomic DNA, oligonucleotide, RNA includes genomic RNA, mRNA, oligonucleotide, and examples of proteins include antibodies, antigens, enzymes, peptides, and the like.

The probe of the present invention is a marker capable of detecting DNA methylation, and more specifically, may be a probe that binds to a CpG methylation site, and may be bound to identify changes in DNA methylation patterns. I can.

In the present invention, the "CpG" refers to a CG site, and refers to a sequence in which cytosine (Cytosine; C) and guanine (G) are in line. CpG does not mean a mere sequence of sequences, but a state in which C and G are connected as one with a phosphate interposed therebetween. Thus, CpG is distinguished from C-G in which a cytosine base and a guanine base form a complementary hydrogen bond pair in double-stranded DNA. The proportion of CG base pairs in the entire genome varies from species to species, and is about 42% in humans. Usually, unmethylated cytosine is easily converted to urasil (U), so the frequency of occurrence of CG sequences is not very high. The methylated cytosine is unaffected. CpG island refers to a portion in which a sequence of cytosine and guanine linked by a phosphate group is at least 200 bp or more consecutive, and mammalian CpG islands have a size of 0.4 kb to 4 kb. If you look at the whole genome, CpG, which is about 1%, is concentrated, and it is a region with a CG base pair ratio of 50% or more within the range of approximately 300 to 2000 base pairs in the double-stranded DNA length.

In the present invention, the "DNA methylation" is one of the very important biochemical processes in the development of higher organisms. The combination of bases in which cytosine (C) is followed by guanine (G) in total DNA is 1/16, which is about 3 to 4% in probability. However, there are places where CG combinations are concentrated in specific regions of DNA, which are called CpG islands, and these structures are concentrated at promoter positions corresponding to gene switches. DNA methylation is known to occur mainly in the cytosine of CpG islands in the promoter region of a specific gene. It refers to the attachment of a methyl group (-CH3) to the 5th carbon of cytosine, one of the bases. However, not all cytosine has a methyl group attached, and methylation proceeds only when cytosine (C) is followed by guanine (G). It is a mechanism in which the function of the gene is lost even without mutations in the coding sequence, since the expression of a specific gene is blocked by interfering with the binding of the transcription factor. DNA methylation reliably changes the gene phenotype in cells. These changes can be inherited through cell differentiation, and DNA methylation is a critical part for general organismal evolution and cellular transformation. In various diseases including cancer, such abnormal methylation/demethylation in CpG islands has been reported, and attempts to be used for diagnosis of various diseases by investigating promoter methylation of disease-related genes have been actively made. CpG regions showing different methylation for each individual can be utilized as potential markers.

In the present invention, the probe represented by SEQ ID NO: 1 may be hybridized to the CpG region of the WIPF1 gene.

In the present invention, the probe represented by SEQ ID NO: 2 may be hybridized to the CpG site of the TOPAZ1 gene.

In the present invention, the probe represented by SEQ ID NO: 3 may be hybridized to the CpG site of the PRDM6 gene.

In the present invention, the probe represented by SEQ ID NO: 4 may be hybridized to the CpG site of the CSMD3 gene.

In the present invention, the probe represented by SEQ ID NO: 5 may be hybridized to the CpG region of the APOC2 gene.

In the present invention, the probe represented by SEQ ID NO: 6 may be hybridized to the CpG region of the APOC2 gene.

In the present invention, the probe represented by SEQ ID NO: 7 may be hybridized to the CpG region in the region between the FEM1C and TICAM2 genes.

In the present invention, the probe represented by SEQ ID NO: 8 may be hybridized to the CpG site of the SLC26A9 gene.

In the present invention, the probe represented by SEQ ID NO: 9 may be hybridized to the CpG region in the region between the CPO and KLF7 genes.

In the present invention, the probe represented by SEQ ID NO: 10 may be hybridized to the CpG region in the region between the PDCD6IP and LOC101928135 genes.

In the present invention, the probe represented by SEQ ID NO: 11 may be hybridized to the CpG region in the region between the ZIC4 and LINC02010 genes.

In the present invention, the probe represented by SEQ ID NO: 12 may be hybridized to the CpG region of the C4orf22 gene.

In the present invention, the probe represented by SEQ ID NO: 13 may be hybridized to the CpG site of the KPNA5 gene.

In the present invention, the probe represented by SEQ ID NO: 14 may be hybridized to the CpG region of the ULBP2 gene.

In the present invention, the probe represented by SEQ ID NO: 15 may be hybridized to the CpG site of the PTPRN2 gene.

In the present invention, the probe represented by SEQ ID NO: 16 may be hybridized to the CpG site of the NRG1 gene.

In the present invention, the probe represented by SEQ ID NO: 17 may be hybridized to the CpG region of the GPIHBP1 gene.

In the present invention, the probe represented by SEQ ID NO: 18 may be hybridized to the CpG site of the FRMD3 gene.

In the present invention, the probe represented by SEQ ID NO: 19 may be hybridized to the CpG region in the region between the PTPN5 and MRGPRX1 genes.

In the present invention, the probe represented by SEQ ID NO: 20 may be hybridized to the CpG site of the KRT2 gene.

In the present invention, the probe represented by SEQ ID NO: 21 may be hybridized to the CpG region in the region between the FOXA1 and TTC6 genes.

In the present invention, the probe represented by SEQ ID NO: 22 may be hybridized to the CpG region in the region between ONECUT1 and LINC02490 genes.

In the present invention, the probe represented by SEQ ID NO: 23 may be hybridized to the CpG region in the region between LOC102724596 and the PHB gene.

In the present invention, the probe represented by SEQ ID NO: 24 may be hybridized to the CpG site of the SPAG4 gene.

In the present invention, the probe represented by SEQ ID NO: 25 may be hybridized to the CpG region in the region between the LOC101928126 and SLC5A3 genes.

In the present invention, the probe represented by SEQ ID NO: 26 may be hybridized to the CpG region of the CACNG2 gene.

In the present invention, the probe represented by SEQ ID NO: 27 may be hybridized to the CpG site of the MTSS1 gene.

In one example of the present invention, the probe may include a probe represented by at least one of SEQ ID NOs: 1 to 27.

In another example of the present invention, the probe may include a probe represented by at least one of SEQ ID NOs: 1 to 6.

In another example of the present invention, the probe may include a probe represented by at least one of SEQ ID NOs: 1 to 6, and may include a probe represented by SEQ ID NO: 7.

In another example of the present invention, the probe may include a probe represented by at least one of probes represented by at least one of SEQ ID NOs: 1 to 6 and 8 to 27.

In another example of the present invention, the probe may include a probe represented by at least one of SEQ ID NOs: 1 to 6, and may include a probe represented by at least one of SEQ ID NOs: 8 to 27.

In another example of the present invention, the probe is a probe represented by SEQ ID NOs: 1 to 6, or a probe represented by SEQ ID NOs: 1 to 7, or a probe represented by SEQ ID NOs: 1 to 6, and SEQ ID NOs: 8 to 27. It may include a displayed probe.

If necessary, the probe of the present invention may be labeled with a fluorescent substance, a radioactive substance, a bioluminescent substance, a chemiluminescent substance, an enzyme, a receptor, or a ligand. More specifically, the labeling material may be a fluorescent material, and the fluorescent material is a green fluorescent protein (GFP), an enhanced green fluorescent protein (EGFP), or a modified red fluorescent protein. protein; mRFP). The labeling method is a technique well known in the art, and can be performed through a conventional method.

The probe of the present invention binds to the CpG site so that the degree of DNA methylation can be confirmed, and furthermore, it is possible to predict the therapeutic responsiveness of the drug in a target individual based on this.

In the present invention, the "drug" may be used interchangeably with "cancer treatment", "anticancer treatment" or "anticancer agent", and the drug has a mechanism of killing cancer cells by acting on cancer cells as well as cancer stem cells. It may be a drug or a drug that has an anticancer effect by acting as a drug or other mechanism. For example, oxaliplatin, 5-fluorouracil, nitrogen mustard, imatinib, rituximab, erlotinib, neratinib, lapatinib, gefitinib, vandetanib, nirotinib, semasanib, bosutinib, wick Citinib, Cediranib, Restaurtinib, Trastuzumab, Gefitinib, Bortezomib, Sunitinib, Carboplatin, Bevacizumab, Cisplatin, Cetuximab, Viscumalbum, Asparaginase, Tretinoin, Hyde Roxycarbamide, Dasatinib, Estramustine, Gemtuzumab ozogamycin, Ibritumomab Tucetan, Heptaplatin, Methylaminolevulinic Acid, Amsacrine, Alemtuzumab, Procarbazine, Alprostadil, Holmium nitrate chitosan, gemcitabine, doxyfluridine, pemetrexed, tegafur, capecitabine, gimeracin, oteracil, azacitidine, methotrexate, uracil, cytarabine, fluorouracil, fludagabine, Enocitabine, flutamide, decitabine, mercaptopurine, thioguanine, cladribine, carmophor, raltitrexed, docetaxel, paclitaxel, irinotecan, belotecan, topotecan, vinorelbine, etoposide, vincristine , Vinblastine, Teniposide, Doxorubicin, Idarubicin, Epirubicin, Mitoxantrone, Mitomycin, Bleromycin, Daunorubicin, Dactinomycin, Pirarubicin, Aclarubicin, Pepromycin , Temsirolimus, temozolomide, busulfan, ifosfamide, cyclophosphamide, melpharan, altrethamine, dacarbazine, thiotepa, nimustine, chlorambucil, mitoractol, leucovorin, tretonin , Exemestane, aminoglutesimide, anagrelide, navelbin, padazole, tamoxifen, toremifene, testolactone, anastrozole, letrozole, borosol, bicalutamide, lomustine and carmu It may be a drug containing one or more selected from the group consisting of stins, and preferably may be oxaliplatin, but if it corresponds to a kind of drug that can be considered to be the same as oxaliplatin, it is not limited thereto.

In the present invention, "Oxaliplatin" is a kind of anticancer agent classified as a platinum family, and inhibits DNA repair and DNA synthesis through cross-linking of DNA. Examples of platinum-based anticancer drugs include Carboplatin, Cisplatin, Nedaplatin, Triplatin tetranitrate, Phenanthriplatin, Satraplatin, Pico Platin (Picoplatin) and the like exist If it corresponds to a drug that acts on the same mechanism as oxaliplatin, it is not limited to the drugs mentioned above.

In the present invention, the "drug responsiveness" refers to the degree of effectiveness of treatment with drugs for an individual, preferably a cancer patient. For example, the term “increased responsiveness” or “good responsiveness” when used in connection with the treatment of cancer patients can refer to an increase in the effectiveness of a drug as measured using any method known in the art. As another example, a cancer patient's response to a drug can be characterized as a complete or partial response. As another example, the increased responsiveness of cancer patients to drugs can be characterized as overall survival, disease-free survival, target response rate, time to tumor progression, time to progression-free survival, or treatment failure.

In the present invention, "prediction" refers to an action of a patient predicting the course and outcome of a disease with respect to a drug or drug set. More specifically, prognosis prediction is interpreted to mean any action that predicts the course of the disease after treatment by comprehensively considering the patient's physiological or environmental condition, and the course of the disease after treatment may vary depending on the patient's physiological or environmental condition. Can be.

In the present invention, the term "cancer" refers to a mammalian physiological state characterized by uncontrolled cell proliferation. Examples of cancer include colon cancer, breast cancer, glioma, thyroid cancer, lung cancer, liver cancer, pancreatic cancer, head and neck cancer, gastric cancer, urinary tract cancer, kidney cancer, prostate cancer, testicular cancer, cervical cancer, ovarian cancer, endometrial cancer, melanoma, fallopian tube cancer. , Uterine cancer, blood cancer, bone cancer, skin cancer, brain cancer, vaginal cancer, endocrine cancer, parathyroid cancer, ureteral cancer, urethral cancer, bronchial cancer, bladder cancer, bone marrow cancer, acute lymphocytic or lymphoblastic leukemia, acute or chronic lymphocytic leukemia, Acute non-lymphocytic leukemia, brain tumor, cervical cancer, chronic myelogenous leukemia, bowel cancer, T-zone lymphoma, esophageal cancer, gall bladder cancer, Ewing's sarcoma, tongue cancer, Hopkins lymphoma, Capocis sarcoma, mesothelioma, multiple myeloma, neuroblastoma , Non-Hopkin's lymphoma, osteosarcoma, neuroblastoma, mammary cancer, cervical cancer, penis cancer, retinoblastoma, skin cancer, and uterine cancer, and more preferably colon cancer, but is not limited thereto.

In the present invention, the "colorectal cancer" is a condition classified as a malignant pathological state by, for example, colonoscopy and biopsy tissue. The clinical technology for diagnosing colorectal cancer is well known in the medical field. The large intestine belongs to the digestive system and is an organ located between the small intestine and the anus. The total length is about 1.5 meters on average and is classified into the appendix, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum from the right. Colon cancer is a malignant tumor that occurs in the colon and rectum, and cancer cells develop from the epithelial cells of the large intestine. Depending on the location of the cancer, cancer in the colon is called colon cancer, and cancer in the rectum is called rectal cancer. It is called cancer or colorectal cancer. In most examples of the present invention, the target subject is a human patient suspected of or actually diagnosed with colon cancer.

In the present invention, "stage of colorectal cancer" refers to phase 0 (in situ colon cancer), phase I (TlNO or T2NO), phase IIA (T3NO), phase IIB (T4NO), phase IIC (T4bN0), phase IIIA (T1N1, T2N1 or T1N2a), IIIB group (T3N1, T4aN1, T2N2a, T3N2a, T1N2b or T2N2b), IIIC group (T4aN2a, T3N2b, T4aN2b, T4bN1 or T4bN2), TM1b, and T4bN2 group (without TM1b and TNB) It is divided into (regardless). There is little or no involvement of lymph nodes in patients with early stage colorectal cancer. In the case of stage 2 colon cancer patients, the tumor invasion did not penetrate the serous layer, and there is no peripheral lymph node and distant metastasis, and the 5-year survival rate is reported to be approximately 70%, and in the case of stage 3 colon cancer patients, tumors It is not related to the degree of invasion, and there is metastasis to the surrounding lymph nodes, and there is no distant metastasis. In the case of stage 4 colorectal cancer patients, it is not related to the degree of invasion of the tumor barrier, and there is distant metastasis, but in most cases, metastasis is found in the surrounding lymph nodes, and the 5-year survival rate is reported to be approximately 5% or less. The survival rate of colorectal cancer patients depends on the pathological stage at the time of diagnosis. However, especially for colon cancer diagnosed as stage 2/3, the prognosis is different for each patient, and the responsiveness to the therapeutic drugs is also different for each patient. For effective treatment of colorectal cancer, the prognosis is based on the drug treatment responsiveness of colorectal cancer. It is more important than anything else to predict.

Methods of distinguishing patients with colorectal cancer and how to stage the disease are well known in the art, and manual examination, biopsy, patient and/or family history examination, and rectal examination, fecal occult blood examination, colonography. (Double contrast Barium-enema), colonoscopy, rectal and rectal anal ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), CT colonography, and positron It may also include an imaging technique such as emission tomography (PET). In addition, colorectal cancer can include, for example, surgery, radiation therapy, hormone therapy, anticancer therapy, or some combination thereof, and can be managed by several alternative strategies known in the art.

According to another embodiment of the present invention, it relates to a kit for predicting drug reactivity comprising the composition for predicting drug reactivity of the present invention.

In the present invention, "kit" refers to a tool capable of evaluating the expression level of a biomarker by labeling a probe or antibody that specifically binds to a biomarker component with a detectable label. In addition to directly labeling a detectable substance in relation to a probe or an antibody by reaction with a substrate, an indirect label conjugated with a color developing label by reactivity with other directly labeled reagents is also included. It may include a color developing substrate solution, a washing solution, and other solutions to react with the label, and may be prepared by including a reagent component to be used. In the present invention, the kit may be a kit including essential elements necessary for performing RT-PCR, and in addition to each primer pair specific for the marker gene, a test tube, reaction buffer, deoxynucleotide (dNTPs), Taq-polymerization Enzymes, reverse transcriptase, DNase, RNase inhibitors, sterile water, and the like may be included. In addition, the kit may be a kit for detecting a gene for predicting drug reactivity that includes essential elements required to perform a DNA chip. The DNA chip kit includes a substrate to which cDNA corresponding to a gene or a fragment thereof is attached as a probe, and the substrate may include a cDNA corresponding to a quantitative control gene or a fragment thereof. The kit of the present invention may be a digital PCR kit or a DNA chip kit, more preferably a Droplet Digital PCR kit or a MethylationEPIC BeadChip kit, but is not limited thereto as long as it is known in the art.

According to another embodiment of the present invention, it relates to a method for predicting drug reactivity using a probe represented by at least one of SEQ ID NOs: 1 to 27 with respect to a biological sample isolated from a target individual.

In the present invention, the "object of interest" refers to mammals including humans, for example, humans, rats, mice, mormots, hamsters, rabbits, monkeys, dogs, cats, cattle, horses, pigs, sheep and goats. It may be selected from the group consisting of, and preferably may be a human, but is not limited thereto.

In the present invention, the "human" is a patient who has or is suspected of developing cancer, and may mean a patient who needs or is expected to properly treat cancer, but is not limited thereto.

In the present invention, the "biological sample" refers to any material, biological body fluid, tissue or cell obtained from or derived from an individual, and more preferably, can mean any sample capable of confirming the patient's genetic information. have. Specifically, whole blood, leukocytes, peripheral blood mononuclear cells, leukocyte soft coat, plasma, serum, sputum, tears ), mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, ascites (ascites), cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, pancreatic fluid, lymph fluid, pleural fluid, nipple aspiration Nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cells, cell extract and cerebrospinal fluid) or the like, but is not limited thereto.

In one example of the present invention, the probe may include a probe represented by at least one of SEQ ID NOs: 1 to 27.

In another example of the present invention, the probe may include a probe represented by at least one of SEQ ID NOs: 1 to 6.

In another example of the present invention, the probe may include a probe represented by at least one of SEQ ID NOs: 1 to 6, and may include a probe represented by SEQ ID NO: 7.

In another example of the present invention, the probe may include a probe represented by at least one of probes represented by at least one of SEQ ID NOs: 1 to 6 and 8 to 27.

In another example of the present invention, the probe may include a probe represented by at least one of SEQ ID NOs: 1 to 6, and may include a probe represented by at least one of SEQ ID NOs: 8 to 27.

In another example of the present invention, the probe is a probe represented by SEQ ID NOs: 1 to 6, or a probe represented by SEQ ID NOs: 1 to 7, or a probe represented by SEQ ID NOs: 1 to 6, and SEQ ID NOs: 8 to 27. It may include a displayed probe.

The method of predicting drug reactivity of the present invention may include measuring the level of DNA methylation using the probe.

In the present invention, the measurement of the DNA methylation level is PCR, methylation specific PCR, real time methylation specific PCR (MSP), EpiTYPER ^® , methylation-sensitive single nucleotide primer extension (methylation-sensitive single PCR). -nucleotide primer extension; MS-SnuPE), combined bisulfite restriction analysis of DNA (COBRA), PCR using methylated DNA specific binding antibody or aptamer, quantitative PCR, digital PCR, DNA chip, It may be performed by at least one method selected from the group consisting of pyrosequencing and bisulfite-sequencing. More preferably, it may be performed by Droplet Digital PCR or MethylationEPIC BeadChip, but various methods known in the art may be used in addition to the method described above.

In the present invention, the methylated CpG can be detected through bisulfite-sequencing, and the change in the DNA methylation pattern can be confirmed by performing fatigue sequencing. In particular, bisulfite sequencing is performed by treating genomic DNA with bisulfite, amplifying it with a PCR primer corresponding to a site without a CpG sequence, and then performing a nucleotide sequence analysis using a nucleotide sequence analysis primer. By quantitatively analyzing the amount of cytosine and thymine at the site, the degree of methylation can be expressed as a methylation index.

When two or more probes are used in the present invention, the level of DNA methylation for each probe can be measured.

The method of predicting drug responsiveness of the present invention may include obtaining a beta value from the measured DNA methylation level.

In the present invention, the "beta value" refers to an estimate of the level of methylation using the ratio between methylated and non-methylated alleles. Beta values can be included in the range of 0 to 1, 0 means unmethylated, and 1 means fully methylated.

In the present invention, when two or more probes are used, a beta value can be obtained from the DNA methylation level measured for each probe.

In addition, the method of predicting drug reactivity of the present invention may include calculating a Drug Response Score (DRS) using the obtained beta value.

In the method for predicting drug reactivity of the present invention, descriptions of probes, drugs, and drug reactivity are overlapped with those described in the composition for predicting drug reactivity of the present invention, and detailed descriptions thereof will be omitted below.

In the case of using the probe provided in the present invention, it is possible to predict the treatment responsiveness to anticancer drugs for each cancer patient, and accordingly, it enables personalized precise treatment such as easily determining whether to use additional treatment methods or alternative drugs, and ultimately, cancer It can improve the patient's survival rate or duration of survival.

1 is a diagram showing a schematic diagram of a drug response score (DRS) prediction model based on organoids derived from colon cancer patients according to an embodiment of the present invention.
2 is a diagram showing a Lasso regression for checking prediction accuracy according to an embodiment of the present invention.
3A and 3B are diagrams illustrating a result of quantifying the DRS distribution of 40 training sets randomly selected through 7 CpG probes according to an embodiment of the present invention.
4 is a diagram illustrating a result of verifying a drug reactivity prediction model through seven CpG probes by quantifying DRS distribution with 17 test sets according to an embodiment of the present invention.
5A and 5B are diagrams illustrating a result of quantifying the DRS distribution of 40 training sets randomly selected through 26 CpG probes according to an embodiment of the present invention.
6 is a diagram showing a result of verifying a drug reactivity prediction model through 26 CpG probes by quantifying DRS distribution with 17 test sets according to an embodiment of the present invention.

Hereinafter, the present invention will be described in more detail through examples. These examples are only for describing the present invention in more detail, and it will be apparent to those of ordinary skill in the art that the scope of the present invention is not limited by these examples according to the gist of the present invention. .

Example 1: Preparation of colon cancer clinical database and patient-derived organoids

In order to establish a process for culturing patient-derived organoids, the entire process from surgical operation to patient samples (tissues) and organoid cultivation was optimized, and additionally approved freshly frozen tissues and organoids of stage 2/3 colorectal cancer patients. Secured. More specifically, specimens collected through the operating room or colonoscopy are used to establish organoids in a freshly frozen state, and at the same time, the clinical data of the patient is checked after surgery through electronic medical records (EMR). , It is used to collect information such as the progress of chemotherapy and recurrence. Through the primary culture of the collected tissues, 3D culture was performed using a medium exclusively for organoids. As a result of the homology test from the cultured organoids, it was confirmed whether it maintains its shape in the primary tumor tissue, and it was confirmed through immunofluorescence staining to determine whether it had a function, and finally, a genome test ( Whole exome sequencing (WES) was performed to confirm whether it reflects the genetic alteration of the tumor tissue. As described above, an organoid to be used in the experiment was finally established by performing a homology test, a functional test, and a genome test.

Example 2: Genetic DNA methylation mapping and discovery of epigenetic factors related to drug reactivity

2.1 Drug Reactivity Analysis

Reactivity to oxaliplatin, a cytotoxic anticancer agent, was analyzed from the colon cancer organoids obtained in Example 1. At this time, the drug reactivity of organoids was ^{double verified, such as cellTiter-Glo ®} Luminescent Cell Viability Assay and organoid morphology measurement using a microscope. In this case, as a method of quantifying the oxaliplatin drug response by treating the drug, for example, IC ₅₀ indicates the maximal concentration at the moment when the enzyme/protein activity of the cell drops by half when the drug is administered. It was used as an index to evaluate drug reactivity.

2.2 Genetic DNA methylation mapping

Changes in DNA methylation patterns according to individual differences in colorectal cancer organoids or treatment with anticancer drugs were analyzed using Illumina's Infinium MethylationEPIC BeadChip. In the case of Infinium MethylationEPIC BeadChip, probes exist at over 850,000 methylation sites, and it is possible to quantify methylated DNA directly from genomic DNA, allowing you to analyze samples very quickly. There is this.

Genomic DNA was extracted from colon cancer organoids using DNeasy Blood & Tissue kit (Qiagen). A 500 ng DNA sample quantified using Picogreen was prepared, treated with Bisulfite, and amplified by a whole genome amplification method. The amplified DNA was randomly fragmented by a chemical method to perform a purification process. Thereafter, the DNA sample was put into the chip pretreated with a buffer solution and incubated for about 16 hours, followed by staining, allele specific primer extension (ASPE), hybridization, and target. Target removal and washing were performed, and scanning was performed with an Illumina iScan.

The Infinium MethylationEPIC BeadChip experiment procedure enables you to check errors that may occur in each experimental step using an internal control material, helping to track the problem of the experiment result when it occurs, and the reliability of the experiment result. Was able to secure. Internal controls include sample independent controls that respond regardless of sample quality and sample dependent controls that are affected by sample quality. Image data import, Control Dashboard and the number of detected CpG probes were checked using GenomeStudio Software (GenomeStudio S/W). We investigated the DNA methylation pattern specific to each gene, and intensively searched and confirmed where these patterns showed differences according to drug reactivity. By performing pyrosequencing or bisulfite-sequencing for the sites that were primarily selected through epigenetic analysis, the DNA methylation pattern that appears specifically according to drug reactivity for each organoid is secondary. It was verified as.

Example 3: Discovery of probes related to oxaliplatin drug reactivity

The DNA methylation status of 866,895 CpG probes was measured using Infinium MethylationEPIC BeadChip for 57 organoids obtained in Example 1, and oxaliplatin ( Oxaliplatin) was carried out to obtain _{an IC 50 for oxaliplatin of each organoid.} After randomly classifying all 57 organoids into 40 training sets and 17 test sets, first, a drug response prediction model was obtained using 40 training sets, and then 17 test sets were used. This was to verify the prediction model. A schematic diagram of the entire process related to this is shown in FIG. 1.

Example 4: Oxaliplatin drug reactivity prediction model derivation

In order to secure a drug response prediction model, first, 1) a group with weak resistance to oxaliplatin (13 organoids) and a group with strong resistance based on _{IC 50 (oxaliplatin) = 8.5 for 40 organoids as a training set.} (27 organoids), 2) 3,559 Differently methylated CpG probes (DMPs) with statistically significant differences (p <0.01) between the two groups were selected. , 3) Using the Lasso regression algorithm, 27 CpG probes capable of distinguishing a group with resistance and a group without resistance were derived (see FIG. 2).

Through the above process, a model capable of predicting drug reactivity was derived using the finally selected CpG probe. Specifically, it is a value obtained by multiplying a beta value obtained from a DNA methylation level measured using a probe represented by any one of SEQ ID NOs: 1 to 27 and a weight coefficient value corresponding to each probe, or is used. In the case of multiple probes, a Drug Respose Score (DRS) corresponding to the total obtained by summing the multiplied values obtained for each probe was derived (Equation 1). By dividing into a training set and a test set through cross-validation, a weight coefficient value for each probe, which makes the error value the smallest, was derived (see Table 1 below). It was to evaluate and verify the stability as an optimal prediction model according to the probes selected through the above process.

[Equation 1]

Drug Reactivity Score (DRS) = (coefficient ₁ * b ₁ ) + (coefficient ₂ * b ₂ ) +… + (coefficient _n * b _n )

In Equation 1 above,

n is an integer from 1 to 27,

coefficient _n is a rational number of -3.50 to 4.35, and the coefficient for each probe represented by SEQ ID NO: n is as shown in Table 1 below,

b _n is a beta value obtained from the DNA methylation level measured using a probe represented by SEQ ID NO: n,

When n is an integer of 2 to 27, a plurality of coefficients _n may be the same or different from each other.

CpG probe
(SEQ ID NO:) Probe
Bonding site Probe
Binding gene By probe used
Weight factor value Coef26 Coef7 cg24401656
(SEQ ID NO: 1) Body WIPF1 3.10 1.31 cg13562675
(SEQ ID NO: 2) Body TOPAZ1 -2.76 -0.68 cg27087956
(SEQ ID NO: 3) Body PRDM6 -2.61 -0.40 cg26469483
(SEQ ID NO: 4) Body CSMD3 -0.67 -0.06 cg09555818
(SEQ ID NO: 5) 5'UTR APOC2 2.85 0.91 cg08161337
(SEQ ID NO: 6) 1 ^st Exon APOC2 3.93 0.10 cg02637031
(SEQ ID NO: 7) Intergenic FEM1C, TICAM2 0 -0.65 cg04292976
(SEQ ID NO: 8) TSS1500 SLC26A9 -0.43 cg26879736
(SEQ ID NO: 9) Intergenic CPO, KLF7 1.11 cg09847528
(SEQ ID NO: 10) Intergenic PDCD6IP, LOC101928135 -0.21 cg08522426
(SEQ ID NO: 11) Intergenic ZIC4, LINC02010 -1.77 cg01791935
(SEQ ID NO: 12) Body C4orf22 -0.06 cg14627781
(SEQ ID NO: 13) 3'UTR KPNA5 -0.06 cg02858652
(SEQ ID NO: 14) Body ULBP2 0.16 cg07859489
(SEQ ID NO: 15) Body PTPRN2 0.28 cg12954529
(SEQ ID NO: 16) Body, TSS200 NRG1 -0.45 cg25496417
(SEQ ID NO: 17) 1 ^st Exon, 5'UTR GPIHBP1 0.49 cg03665520
(SEQ ID NO: 18) Body FRMD3 -1.06 cg15998725
(SEQ ID NO: 19) Intergenic PTPN5, MRGPRX1 -0.15 cg20210559
(SEQ ID NO: 20) TSS1500 KRT2 0.52 cg11090582
(SEQ ID NO: 21) Intergenic FOXA1, TTC6 -0.24 cg12352124
(SEQ ID NO: 22) Intergenic ONECUT1, LINC02490 -0.54 cg08176914
(SEQ ID NO: 23) Intergenic LOC102724596, PHB -1.31 cg23713742
(SEQ ID NO: 24) TSS1500 SPAG4 -0.03 cg12885728
(SEQ ID NO: 25) Intergenic LOC101928126, SLC5A3 -0.06 cg26757722
(SEQ ID NO: 26) 5'UTR, 1 ^st Exon CACNG2 0.76 cg18209489
(SEQ ID NO: 27) Body MTSS1 1.07

Example 5: Oxaliplatin drug reactivity prediction model validation

The present inventors verified the n CpG probe prediction models with 17 test samples in order to confirm the stability of the confidence prediction value (prediction accuracy). The results of verifying each model using 7 CpG probes and 26 CpG probes are as follows.

5.1 Model verification using 7 CpG probes

3A and 3B, the results of using 40 training sets randomly selected as a drug reactivity prediction model using 7 CpG probes were confirmed, and the results of verification with 17 test samples were shown (see FIG. 4). . As shown in FIG. 4, when the CpG probes of SEQ ID NOs: 1 to 7 according to an embodiment of the present invention are used, the area under the curve as a result of a receiver manipulation characteristic (ROC) graph for the drug-resistant group and the drug-sensitive group ( area under the curve; AUC) value of 0.764 showed very high diagnostic accuracy. The drug reactivity score (DRS) by the probe is as shown in Formula 2 below.

[Equation 2]

Drug Reactivity Score (DRS) = (coefficient ₁ * b ₁ ) + (coefficient ₂ * b ₂ ) + (coefficient ₃ * b ₃ ) + (coefficient ₄ * b ₄ ) + (coefficient ₅ * b ₅ ) + (coefficient ₆ * b ₆ ) + (coefficient ₇ * b ₇ )

In Equation 2 above,

The coefficient ₁ is 1.31;

The coefficient ₂ is -0.68;

The coefficient ₃ is -0.40;

The coefficient ₄ is -0.06;

The coefficient ₅ is 0.91;

The coefficient ₆ is 0.10;

The coefficient ₇ is -0.65;

B ₁ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 1;

B ₂ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 2;

B ₃ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 3;

B ₄ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 4;

B ₅ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 5;

B ₆ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 6;

The b ₇ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 7.

When the DRS value of Equation 2 is 0.35 to 0.65, preferably 0.50 as a cut off value, when the DRS value calculated in each individual is less than the cut off value, the treatment responsiveness of oxaliplatin is It can be predicted to be good, and if the calculated DRS value is greater than or equal to the cut-off value, it can be predicted that the treatment responsiveness of oxaliplatin will be poor.

5.2 Model validation using 26 CpG probes

5A and 5B, the results of using 40 training sets randomly selected as a drug reactivity prediction model using 26 CpG probes were confirmed, and the verification results were shown with 17 test samples (see FIG. 6). . As shown in Figure 6, when using the CpG probe of SEQ ID NOs: 1 to 6 and the CpG probes of SEQ ID NOs: 8 to 27 according to an embodiment of the present invention, receiver manipulation characteristics (ROC) for the drug-resistant group and the drug-sensitive group As a result of the graph, the area under the curve (AUC) value was 0.917, showing very high diagnostic accuracy. The drug reactivity score (DRS) by the probe is as shown in Formula 3.

[Equation 3]

Drug Reactivity Score (DRS) = (coefficient ₁ * b ₁ ) + (coefficient ₂ * b ₂ ) + (coefficient ₃ * b ₃ ) + (coefficient ₄ * b ₄ ) + (coefficient ₅ * b ₅ ) + (coefficient ₆ * b ₆ ) + (coefficient ₈ * b ₈ ) + (coefficient ₉ * b ₉ ) + (coefficient ₁₀ * b ₁₀ ) + (coefficient ₁₁ * b ₁₁ ) + (coefficient ₁₂ * b ₁₂ ) + (coefficient ₁₃ * b ₁₃ ) + (coefficient ₁₄ * b ₁₄ ) + (coefficient ₁₅ * b ₁₅ ) + (coefficient ₁₆ * b ₁₆ ) + (coefficient ₁₇ * b ₁₇ ) + (coefficient ₁₈ * b ₁₈ ) + (coefficient ₁₉ * b ₁₉ ) + (coefficient ₂₀ * b ₂₀ ) + (coefficient ₂₁ * b ₂₁ ) + (coefficient ₂₂ * b ₂₂ ) + (coefficient ₂₃ * b ₂₃ ) + (coefficient ₂₄ * b ₂₄ ) + (coefficient ₂₅ * b ₂₅ ) + ( coefficient ₂₆ * b ₂₆ ) + (coefficient ₂₇ * b ₂₇ )

In Equation 3 above,

The coefficient ₁ is 3.10;

The coefficient ₂ is -2.76;

The coefficient ₃ is -2.61;

The coefficient ₄ is -0.67;

The coefficient ₅ is 2.85:

The coefficient ₆ is 3.93;

The coefficient ₈ is -0.43;

The coefficient ₉ is 1.11;

The coefficient ₁₀ is -0.21;

The coefficient ₁₁ is -1.77;

The coefficient ₁₂ is -0.06;

The coefficient ₁₃ is -0.06;

The coefficient ₁₄ is 0.16:

The coefficient ₁₅ is 0.28;

The coefficient ₁₆ is -0.45;

The coefficient ₁₇ is 0.49;

The coefficient ₁₈ is -1.06;

The coefficient ₁₉ is -0.15;

The coefficient ₂₀ is 0.52;

The coefficient ₂₁ is -0.24;

The coefficient ₂₂ is -0.54;

The coefficient ₂₃ is -1.31;

The coefficient ₂₄ is -0.03:

The coefficient ₂₅ is -0.06;

The coefficient ₂₆ is 0.76;

The coefficient ₂₇ is 1.07;

B ₁ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 1;

B ₂ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 2;

B ₃ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 3;

B ₄ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 4;

B ₅ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 5;

B ₆ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 6;

B ₈ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 8;

B ₉ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 9;

B ₁₀ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 10;

B ₁₁ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 11;

B ₁₂ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 12;

B ₁₃ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 13;

B ₁₄ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 14;

B ₁₅ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 15;

B ₁₆ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 16;

B ₁₇ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 17;

B ₁₈ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 18;

B ₁₉ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 19;

B ₂₀ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 20;

B ₂₁ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 21;

B ₂₂ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 22;

B ₂₃ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 23;

B ₂₄ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 24;

B ₂₅ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 25;

B ₂₆ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 26;

The b ₂₇ is a beta value obtained from the DNA methylation level measured using the probe represented by SEQ ID NO: 27.

When the DRS value of Equation 3 is 4.20 to 5.50, preferably 4.80 as a cut off value, when the DRS value calculated in each individual is less than the cut off value, the treatment responsiveness of oxaliplatin is It can be predicted to be good, and if the calculated DRS value is greater than or equal to the cut-off value, it can be predicted that the treatment responsiveness of oxaliplatin will be poor.

As a result of investigating drug responsiveness by each CpG probe selected for the 17 organoids of the test set as described above, the predictive model using DRS can statistically significantly distinguish between resistant patients and non-resistant patients. Was able to confirm. As such, the present invention is expected to be utilized in a personalized treatment prediction system by discovering a novel epigenetic factor capable of predicting drug responsiveness to individual oxaliplatin.

As described above, specific parts of the present invention have been described in detail, and it is clear that these specific techniques are only preferred embodiments for those of ordinary skill in the art, and the scope of the present invention is not limited thereto. Therefore, it will be said that the practical scope of the present invention is defined by the appended claims and their equivalents.

<110> Industry-Academic Cooperation Foundation, Yonsei University Seoul National University R&DB Foundation KNU-Industry Cooperation Foundation Ewha University-Industry Collaboration Foundation Sookmyung Women's university industry-academic <120> A composition for predicting drug responsibility and uses thereof <130> PDPB204179 <160> 27 <170> KoPatentIn 3.0 <210> 1 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg24401656 <400> 1 tcagccacat tccacccttg tactatccat cacacgagac cttaaacaaa caaaaccctt 60 cgtgttgttt gtatttgctg ggtttcagga tttggcttac cctgggcact gggaaagcct 120 gt 122 <210> 2 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg13562675 <400> 2 caaactgcca aactgtttcc tgaagaagct gcatcatttt atatttatat tcccaccagc 60 cgtgtacggg atttcctgtt tctttatatc ttcaccaaca cttgttactg tgttttttat 120 tt 122 <210> 3 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg27087956 <400> 3 gggagctttg gcttcagtgc gtgggctttt gtgtttctct ctctctctct ctctctctct 60 cgcgtgtgtg caagcccagc ctcatcccat aacacacatg atttagctat tttacatctt 120 tc 122 <210> 4 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg26469483 <400> 4 atacaaggaa aaaagccatt ctatgttctt cttctgtgct ctcataatgt gtaaccatgc 60 cgtttgttta aataattcca gaaactggcc ttaggagatt cgaaatcaaa ccaagtttgc 120 ag 122 <210> 5 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg09555818 <400> 5 gggttgtggc tgtggagcgg aagtgggtct caaccactat aaatcctctc tgtgcccgtc 60 cggagctggt gaggacagcc tgccagagtc tggtaagaaa gggactcagg gtgcggggac 120 ag 122 <210> 6 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg08161337 <400> 6 caatgacacc tcagtcatgt ggaaaacact ttccgtgtcg gggtcccagt tggatgccgg 60 cgtgcagagg taactcagaa tcggtccctg ccctcccagt ctgcggggaa ggtgaatggg 120 aa 122 <210> 7 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg02637031 <400> 7 gtcttatttc ctgaagttgt cagggcccta actctagctc tctagccaca ccctaagtga 60 cgaactctgg tcctgagttc cccatcacca cccagactga tgtcagatgg ggccaggttc 120 tt 122 <210> 8 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg04292976 <400> 8 tttgttttac ctggggagca gggctgtgtg atgggagctt ggcacttcct aaatccaccc 60 cgaagacaat aggtcacatt cagcggggct cctgccccac cggctccccc aaggcggccc 120 tg 122 <210> 9 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg26879736 <400> 9 ttgggatatt tgtgaagtga tggcccagga tttctctctc tgtttttgac acctgtttcc 60 cgaggacttt tatgttaaat gcaaaacacc tgaaaaatac agtactctat tggaaggatg 120 ca 122 <210> 10 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg09847528 <400> 10 tcggtggccc ctttaatggc catttcatag ggtgaagatt aaataagaca atgagcacaa 60 cgggcttagc atagcatctg aaacatctta agagctcaat gaatggtagc cattgtcatt 120 at 122 <210> 11 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg08522426 <400> 11 gcccctaagc caatagatga aactcgtagt ttggaggaag actgcgacag cttaggccac 60 cggaaaatga cccagaagtt caacttctgc gctctccggg gaggctgggt cagagctcca 120 gc 122 <210> 12 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg01791935 <400> 12 ttgagtttct gagcctttgg cttccctgta gcttccagga gtcccctggt acctttagcc 60 cgtggggtct tcagcattgg tggagagtca aagatggcca cggtgccccc ggtggcactg 120 gt 122 <210> 13 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg14627781 <400> 13 attattaaac ctttgttctg ggatttactg ttaaaaaaca aacaaaaaac aacaaaaaag 60 cggtgacagg aaacagttaa ggaaatgatt taactgaagg gaagcaagat ttttttaaag 120 ca 122 <210> 14 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg02858652 <400> 14 cctcgggaag gaaaaaccag agcccccgga accggagctg gggacaggac aggggtgaag 60 cgggattcca cagggacgcg atcacagaca cccccaccag ccacaggcct gctctgctct 120 ca 122 <210> 15 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg07859489 <400> 15 aacgtgttca gagtcaattt tccaaaacag atatggatga cacaagctgt gggccagacc 60 cgtcctgcaa gccatgttgc ccgccccatc catcagcttc agcagtagaa ttgtgaaatc 120 at 122 <210> 16 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg12954529 <400> 16 ccacctacac caccccccac cccatcccca tcccagcttt gttagattgc tagcatcgat 60 cgctggcttt ctctgaccca acagtgggtg aaggaaagag actgaaagct ggcaaggtgg 120 gg 122 <210> 17 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg25496417 <400> 17 gaggctggag ctggtggggg ccactaggaa gcccacggca atgcccttca tcccacttac 60 cgcagctcca gagccctgcg ggaggactca gagtcaggga cacagcagcg tccggcgaga 120 tg 122 <210> 18 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg03665520 <400> 18 ttagttcaat gtgggacttg ggcttcttca gagaaaccct agcactcttc agaaggaacc 60 cgtggcagct cgaggcatag acaatcctca ttaggtaccc aatatttatt aactaaactc 120 tt 122 <210> 19 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg15998725 <400> 19 agagattcta agattcatca attcacagta atggtactgt ggtttggcaa gaatatgtaa 60 cgatgatttt ggccagagac tcagggccct aaggggattt gtacatcaca gcgaaagcag 120 ct 122 <210> 20 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg20210559 <400> 20 tgaggctctg tggtcccagc tataaaatgg gaaattcgta tgaagtgttt tgaaaaccac 60 cgtgcacaat aaaatgtcag tatcattatt gtttggggtg agacaaagat aattcctttt 120 cc 122 <210> 21 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg11090582 <400> 21 catttttgtc cccatgacaa ccctatcagg tcaatagata ctaataaaag tcctgtctga 60 cgtggaggcc ttctagcttt agtgaagtta aaaaagattg gcacagtgtt ctggagctaa 120 tg 122 <210> 22 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg12352124 <400> 22 gggccttttg gggcgccttt taccccagag ctcagaaagc tccgggcagc ggggtggtgg 60 cgggatggcc agtagccagg gcccacctgg cggcccgccg ggatgtggag gcccactggg 120 tt 122 <210> 23 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg08176914 <400> 23 aaccccagca aggaggaagt ggactggctt cctgtttatt tatgaatctc agagcagcac 60 cgtgcagccg tgaacaccaa ccaagcacag caatcccagc ccaagccaag agccccaggg 120 ag 122 <210> 24 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg23713742 <400> 24 gatagaaact ggtatttgct aagtgcaatt ttagatgggg ttttctagga agtgctgtga 60 cgtcatcact tgctttaaaa gaggccaggc gcagtagctg actcctgtaa tcccagcact 120 tt 122 <210> 25 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg12885728 <400> 25 cctctgaggg cacaggggca gacaggacat cctcccacca ggggctgcct agtgactggc 60 cgaagattct gatcatttca ggcctccctt cacattctcc agcggcattt taaggtcttt 120 gc 122 <210> 26 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg26757722 <400> 26 ggatatatgt atgaatagag aatatggaga gttataaaaa aagggaggta agaaagctca 60 cggaaaagag tgtaaattat aaagatcaca cgggaagagg cttgcctttt gagatcagaa 120 ac 122 <210> 27 <211> 122 <212> DNA <213> Artificial Sequence <220> <223> cg18209489 <400> 27 ctttctgctg gttttgtaaa cacaagaaac attcctgtgg gtgagtgcat aggctggata 60 cggctggggg atgagcaaac cactgtgcct agaggaaagg attcacattt cacaggcttt 120 tt 122

Claims (15)

A composition for predicting therapeutic responsiveness to oxaliplatin of a colon cancer patient comprising the probe represented by SEQ ID NO: 1. delete delete The method of claim 1,
The composition further comprises a probe represented by at least one of SEQ ID NOs: 2 to 27, composition. The method of claim 1,
The composition comprises a probe represented by SEQ ID NOs: 2 to 6; And, the composition further comprises a probe represented by SEQ ID NO: 7. The method of claim 1,
The composition comprises a probe represented by SEQ ID NOs: 2 to 6; And a probe represented by at least one of SEQ ID NOs: 8 to 27. Claims 1 and 4 to 6, comprising the composition of any one of claims, a kit for predicting the therapeutic response to oxaliplatin in colon cancer patients. A method for predicting the therapeutic responsiveness to oxaliplatin in colorectal cancer patients using a probe represented by SEQ ID NO: 1 with respect to a biological sample obtained from a subject of interest. The method of claim 8,
The method comprises a probe represented by SEQ ID NOs: 2 to 6; And a probe represented by SEQ ID NO: 7 is further used. The method of claim 8,
The method comprises a probe represented by SEQ ID NOs: 2 to 6; And the method further using a probe represented by at least one of SEQ ID NOs: 8 to 27. delete delete The method of claim 8,
The method further uses a probe represented by at least one of SEQ ID NOs: 2 to 27. delete delete

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