KR20160073798A - Marker composition for predicting prognosis and chemo-sensitivity of cancer patients - Google Patents

Abstract

According to the present invention, a gene classifier TCA19 may be useful in predicting recurrence and metastasis of cancer and predicting sensitivity of an anti-cancer agent. Moreover, the present invention provides a method for sorting major genes related to occurrence and metastasis of cancer by statistically analyzing a gene expression aspect between normal tissue, cancer tissue, and metastasis cancer tissue, and selecting a gene capable of being used as a gene classifier by a prognostic value of a gene adjusted by the activity of the sorted genes. By using the method, a classifier for predicting a prognosis of cancer or sensitivity of an anti-cancer agent may be conveniently screened.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a marker composition for predicting the prognosis and anticancer drug susceptibility of cancer patients,

The present invention relates to a biomarker composition for predicting cancer susceptibility and anticancer drug susceptibility including a classifier TCA19, an agent for measuring the expression level of mRNA of the gene classifier TCA19 or a protein thereof, and prognosis and cancer susceptibility A kit for predicting cancer prognosis and anticancer drug susceptibility including the composition, a method for providing information for predicting cancer prognosis and anticancer drug sensitivity using the marker, and a method for screening a gene classifier for predicting cancer susceptibility and anticancer drug susceptibility .

Cancer is the leading cause of death in Korea, a major disease, and there have been many studies to conquer cancer, but it is still an incurable disease. Treatment of diagnosed cancers generally includes surgery, chemotherapy, and radiation therapy, but there are limitations to each method. In addition, cancer is highly likely to recur after treatment, and susceptibility to anticancer drugs varies widely among individuals. Therefore, predicting cancer prognosis and anticancer drug sensitivity is essential to determine the treatment direction of cancer patients.

On the other hand, colorectal cancer is a very common cancer worldwide, and pathologic stage is the standard diagnostic method of prognosis prediction, and it is a criterion for determining whether or not to receive chemotherapy (Hari et al., 2013). Patients with colorectal cancer classified as a pathologic stage are very frequent relapses, especially in patients with stage 3 colorectal cancer, recurrence occurs in about 50% of patients within 5 years after surgery (Carlsson et al., 1987; Midgley and Kerr , 1999). In colorectal cancer, metastases frequently occur in other organs with recurrence, mainly in liver and lung tissue (Cunningham et al., 2010), with poor prognosis and very heterogeneous clinical prognosis . Although most patients with stage 3 colorectal cancer undergo standard chemotherapy, patients with older age 3 colorectal cancer over 75 years old are not recommended for chemotherapy due to age-related physical weakness. Therefore, the prognosis of cancer and the susceptibility to anticancer drugs should be precisely predicted, and it is necessary to select whether or not to receive chemotherapy.

In addition, genomic studies based on gene expression quantification techniques have been performed in a variety of carcinomas, which demonstrate that the clinical diversity of cancer is reflected in gene expression. To date, a large variety of studies have reported subtypes of colorectal cancer, but few colon cancer classifications have demonstrated robust prognostic significance.

On the other hand, many anticancer drugs are being used as effective treatments, but the newly emerging problem is the resistance of cancer cells to anticancer drugs (Tsuruo et al., 1984). The resistance to anticancer drugs may be due to the long-term use of anticancer drugs, which may result in decreased intracellular accumulation of drug-exposed cells (Shen et al., 1986; Shen et al., 2000; Gottesman et al. (Urasaki et al., 2001) or by modifying the target protein (Schatz et al., 1996; Goto et al., 2002). This process is not only the biggest obstacle to cancer treatment, but also has a profound effect on treatment failure (Kang, 1996). When chemotherapy is tried in actual cancer patients, when some anticancer drugs are not effective, they are resistant to other anticancer drugs in the future. In the case of initial chemotherapy, several kinds of chemotherapeutic drugs Despite the attempted therapy, the phenomenon that has no therapeutic effect can often be observed. Therefore, the range of available anticancer drugs is very limited, which is pointed out as an important problem in cancer chemotherapy. However, the remarkable results are still weak due to the complex action of biologic reaction related factors, the diversity of therapeutic agents and dosage regimes, and the difficulty of securing large samples.

(CIM), chromosomal instability (CIN), and BRAF / KRAS mutations have been used as predictor of prognosis at the molecular level currently used in clinical practice. However, There is no methodology for predicting the sensitivity of chemotherapy. Therefore, it is urgent to develop a marker that can accurately predict the prognosis of colorectal cancer patients and predict the sensitivity of chemotherapy.

To solve this need, the present inventors analyzed the gene expression patterns between normal tissues, cancer tissues and liver metastatic cancer tissues statistically to select genes associated with cancer development and metastasis, By analyzing the literature, a group of genes for prediction of cancer prognosis and anticancer drug susceptibility was selected, and the present invention was completed.

Accordingly, an object of the present invention is to provide a biomarker composition for predicting cancer prognosis and anticancer drug susceptibility prediction including gene classifier TCA19.

It is still another object of the present invention to provide a composition for predicting cancer prognosis or anticancer drug sensitivity, which comprises an agent for measuring the expression level of mRNA of the gene classifier TCA19 or a protein thereof.

Still another object of the present invention is to provide a kit for predicting prognosis or anticancer drug susceptibility of cancer comprising the composition.

It is another object of the present invention to provide a method for providing information for predicting cancer prognosis or anticancer drug sensitivity using the gene classifier.

Another object of the present invention is to provide a method for predicting the prognosis of patients with stage 3 colorectal cancer and screening genes for anticancer drug sensitivity prediction.

In order to solve the above problems, the present invention provides a biomarker composition for predicting cancer prognosis and cancer susceptibility prediction including gene classifier TCA19.

The present invention also provides a composition for predicting cancer prognosis or anticancer drug sensitivity, which comprises an agent for measuring the expression level of mRNA of the gene classifier TCA19 or a protein thereof.

The present invention also provides a kit for predicting prognosis or anticancer drug susceptibility of cancer comprising the composition.

In addition, the present invention provides a method for providing information for predicting cancer prognosis or anticancer drug susceptibility using the gene classifier.

The present invention also provides a method of predicting the prognosis of patients with stage 3 colorectal cancer and screening genes for anticancer drug susceptibility prediction.

The gene classifier TCA19 can be used to predict the recurrence and metastasis of cancer and to predict the sensitivity of anticancer drugs. The present invention also provides a method for predicting the prognostic value of a gene regulated by the activity of the gene by statistically analyzing patterns of gene expression between normal tissues, cancer tissues and metastatic cancer tissues, value of the gene to be used as a gene classifier, it is possible to easily screen the classifier for cancer prognosis or anticancer drug susceptibility prediction by using the above method.

BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a schematic diagram of a workflow of the present invention.
Fig. 2 is a diagram showing a gene expression pattern of colon cancer in the AMC cohort (n = 54). Fig.
Figure 3 is a diagram showing comparative analysis of genes that are differentially expressed in tissue groups. FIG. 3A shows a Venn diagram of a gene selected by the GLM likelihood ratio using EdgeR software in another tissue, and FIG. 3B shows an expression pattern of a selected gene of the Venn diagram.
Fig. 4 is a diagram showing the result of concentration-analysis analysis of genes related to the generation or metastasis of cancer. Fig.
Figure 5 shows a TERM1 network enriched with genes associated with the generation (A) and metastasis (B) of cancer.
Figure 6 shows a CTGF network enriched with genes associated with the generation (A) and metastasis (B) of cancer.
Figure 7 shows a boxplot of TREM1 and CTGF in the original (RNA-sequencing data) and validation cohort (GSE14297, gene expression microarray).
Fig. 8 is a diagram showing a classification of colon cancer patients by the TCA66 predictor. Fig. FIG. 8A shows the risk score by TCA66 in the CIT cohort, FIG. 8B shows the Kaplan-Meier curve of the two subgroups of CIT classified by the TCA66 risk score, FIG. 8C shows the TCA66 risk derived from the CIT cohort Kaplan-Meier curves of two subgroups in the AUS cohort categorized by score. P value was obtained by log-rank test, and mDFS represents intermediate disease free survival rate.
FIG. 9 is a diagram showing a classification of colon cancer patients by the TCA19 predictor. FIG. Figure 9A shows the risk score by TCA19 in the CIT cohort, Figure 9B shows the Kaplan-Meier curve of two subgroups of CIT categorized by TCA19 risk score, Figure 9C shows the TCA19 risk from the CIT cohort FIG. 9D is a graph showing the Kaplan-Meier curves of two subgroups in the UAPC cohort classified by the TCA19 risk score derived from the CIT cohort; FIG. to be. P value was obtained by log-rank test, and mDFS represents intermediate disease free survival rate.
Figure 10 is a Kaplan-Meier plot of DFS (disease free survival) of patients classified by AJCC stage classification in the AUS and UPAC cohorts (n = 449). Figure 10A is an analysis of TCA19 risk score-based subset analysis of second stage colorectal cancer patients and Figure 10B is a TCA19 risk score-based subset analysis of stage 3 colorectal cancer patients. FIG. P value was obtained by log-rank test, and mDFS represents intermediate disease free survival rate.
11 is a graph showing the interaction of TCA19 with the stage of colon cancer patients in the CIT cohort (A) and the AUS and UAPC cohort (B).
Figure 12 is a Kaplan-Meier plot of DFS (disease free survival) of patients classified by the DNA mismatch repair (MMR) status in the CIT cohort. FIG. 12A is an analysis of TCA19 risk score-based subset analysis in dMMR (deficient MMR) patients, and FIG. 12B is an analysis of TCA19 risk score-based subset analysis in pMMR (proficient MMR) patients. P value was obtained by log-rank test, and mDFS represents intermediate disease free survival rate.
Figure 13 shows susceptibility predictions for chemotherapy in two subgroups based on the TCA classifier. 13A is a Kaplan-Meier plot of the DFS (disease free survival) of two subgroups (high TCA19 and low TCA19) in the AUS cohort, FIG. 13B shows DFS (disease free survival) FIG. 13C is a Kaplan-Meier plot of an AUS cohort of patients older than 75 years old with stage 3 colorectal cancer, and FIG. 13D is a Kaplan-Meier plot of a patient with stage 3 colorectal cancer in a high TCA19 subgroup FIG. 13E is a Kaplan-Meier plot of a third stage colorectal cancer patient in the low TCA19 subgroup. FIG. The data was plotted according to whether the patient received chemotherapy. P value was obtained by log-rank test, and mDFS represents intermediate disease free survival rate.
Figure 14 shows the interaction of the TCA 19 classifier in patients with colorectal cancer who received adjuvant chemotherapy in the AUS cohort.
Figure 15 shows the prediction of chemosensitivity susceptibility in the TCA19 high risk (A) and TCA19 low risk (B) subgroups classified by the TCA19 classifier in the AUC and CIT integrated cohort. The data was plotted according to whether the patient received chemotherapy. P value was obtained by log-rank test, and mDFS represents intermediate disease free survival rate.
Figure 16 is a Kaplan-Meier plot of DFS (disease free survival) in the AUS cohort classified by the risk level of TCA19 classifier (A), MDA114 classifier (B), and OncoDX classifier (C). P value was obtained by log-rank test, and mDFS represents intermediate disease free survival rate.
Figure 17 shows Kaplan-Meier plots of DFS (disease free survival) according to AJCC stage in the AUS cohort classified by TCA19 classifier (A), MDA114 classifier (B) and OncoDX classifier (C) to be. P value was obtained by log-rank test, and mDFS represents intermediate disease free survival rate.
Figure 18 shows Kaplan-Meier method for high-risk and low-risk subgroups of stage 3 colorectal cancer patients according to AJCC stage classified by risk level of TCA19 classifier (A), MDA114 classifier (B) and OncoDX classifier (C) Meier plot. The data was plotted according to whether the patient received chemotherapy. P value was obtained by log-rank test, and mDFS represents intermediate disease free survival rate.
19 is a diagram showing a comparison between the random classifier and the TCA19 classifier.
Figure 20 is an illustration of gene set enrichment analysis of 66 genes in the classifier. Classification enrichment was performed using the Ingenuity Analysis software and the significance threshold was P <0.05.
Figure 21 shows a comparison of subgroups classified by TCA19 classifier and 5 subtypes derived by CRCassigner.
Figure 22 shows a comparison of three subtypes derived by the TCA19 classifier and by the 146-gene classifier.
Figure 23 shows a comparison of subgroups classified by the TCA19 classifier and six subtypes derived by the 57-gene classifier.

Hereinafter, the present invention will be described in more detail.

The present invention provides a biomarker composition for predicting cancer prognosis or anticancer drug susceptibility including a gene classifier TCA19.

The gene classifier TCA19 may be selected from the group consisting of growth arrest and DNA-damage-inducible beta (GADD45B), sphingosine-1-phosphate receptor 3 (S1PR3), cyclin-dependent kinase inhibitor 2B (CDKN2B), early growth response 2 (RGS 16), RHOU (ras homolog family member), TIMP1 (metallopeptidase inhibitor 1), PHLDA1 (pleckstrin homology-like), serine protease inhibitor domain family A, member 1), IL36RN (interleukin 36 receptor antagonist), SLAMF7 (SLAM family member 7), E2F7 (transcription factor 7), DTL (denticleless E3 ubiquitin protein ligase homolog), CFB consisting of one or more genes selected from the group consisting of cyclin-dependent kinase 1), CXCL1 (chemokine (CXC motif) ligand 1), CXCL3 (chemokine (CXC motif) ligand 3), and CKS2 (CDC28 protein kinase regulatory subunit 2) Preferably GADD45B, S1PR3, CDKN2B, EGR2, CTGF, SERPINE1, RGS16, RHOU, TIM P1, PHDLA1, IL36RN, SLAMF7, E2F7, DTL, CFB, CDK1, CXCL1, CXCL3, and CKS2 genes.

The genes GADD45B, S1PR3, CDKN2B, EGR2, CTGF, SERPINE1, RGS16, RHOU, TIMP1, PHDLA1, IL36RN, SLAMF7, E2F7, DTL, CFB, CDK1, CXCL1, CXCL3, and CKS2 contained in the gene classifier TCA19 are TREM1 (TREM 1) and CTGF are two major regulators activated during the production and transduction of colorectal cancer. These TREM 1 and CTGF are regulated by the activity of CTGF (triggering receptor expressed on myeloid cells 1) and CTGF .

In the present invention, the terms "mutation", "variant" and "variant" include base substitution, deletion, insertion, amplification and rearrangement of the nucleotide and amino acid sequences of a gene of interest. A nucleotide variation refers to a change in the nucleotide sequence (e.g., insertion, deletion, inversion, or substitution of one or more nucleotides, such as a single nucleotide polymorphism (SNP)) relative to a reference sequence (e.g., a wild-type sequence). This term also includes corresponding changes in the complement of the nucleotide sequence, unless otherwise indicated. The nucleotide mutation may be a somatic mutation or a polymorphism.

In the present invention, the term " prognostic marker "refers to a substance capable of confirming the progress of disease, survival or cure after cancer therapy, and includes a polypeptide or nucleic acid (e.g., mRNA), lipid, Or sugars (monosaccharides, disaccharides, oligosaccharides, etc.), and the like. For the purpose of the present invention, the marker for prognosis of cancer of the present invention may be one or more of GADD45B, S1PR3, CDKN2B, EGR2, CTGF, SERPINE1, RGS16, RHOU, TIMP1, PHDLA1, IL36RN, SLAMF7, E2F7, DTL, CFB, CDK1, CXCL1, CXCL3, and CKS2 genes. The gene classifier TCA19 comprises at least one gene selected from the group consisting of CXCL3, CXCL3, and CKS2 genes.

In the present invention, "susceptibility" means whether a particular drug for cancer of an individual patient exhibits an effect.

For example, the specific drug is mainly an anticancer agent, and the anticancer agent may not exhibit the effect or the effect depending on the kind of cancer. Further, it is known that, even in the kind of cancer recognized as being effective, there are cases in which effects are exhibited depending on individual patients and cases where effects are not shown. The sensitivity of anticancer drugs to the cancer of individual patients is called anticancer drug susceptibility. Therefore, a chemotherapy with high efficacy and safety can be realized as long as the patient (the responder) whose effect can be expected before the initiation of treatment according to the present invention and the patient (the non-responder) whose effect can not be expected can be predicted. The anticancer agent of the present invention is selected from the group consisting of oxaplylatin, fluorouracil, levopolinate, and salts thereof.

In the present invention, "prediction" is used herein to refer to the likelihood that the subject patient will respond favorably or adversely to the drug or drug set. In one embodiment, the prediction is about the degree of such response. For example, the prediction relates to the likelihood and / or likelihood that a patient will survive after treatment, for example, without treatment of a particular therapeutic agent and / or after surgical removal of a primary tumor and / or chemotherapy for a specified period of time without cancer recurrence . The predictions of the present invention can be used clinically to determine treatment by selecting the most appropriate treatment regime for a cancer patient. The predictions of the present invention can be used to determine whether a patient will be beneficially responsive to a treatment procedure, such as a given therapeutic treatment, such as administration of a given therapeutic or combination, surgical intervention, chemotherapy, It is a useful tool in predicting whether or not

The types of cancer of the present invention include, but are not limited to, acute lymphocytic or lymphocytic leukemia, acute or chronic lymphocytic leukemia, acute non-lymphoid leukemia, bladder cancer, brain tumor, breast cancer, chronic myelogenous leukemia, Endometriosis, esophageal cancer, bile bladder cancer, Ewing's sarcoma, duodenum, stomach cancer, Hopkins lymphoma, caposic sarcoma, kidney cancer, liver cancer, lung cancer, mesothelioma, multiple myeloma, neuroblastoma, non- Wherein the cancer is selected from the group consisting of cancer, neuroblastoma, breast cancer, colorectal cancer, prostate cancer, pancreatic cancer, colon cancer, penis cancer, retinoblastoma, skin cancer, gastric cancer, thyroid cancer, uterine cancer, testicular cancer, Wilms' tumor, and tropoblastoma , Preferably, but not limited to, colorectal cancer, more preferably, third stage colorectal cancer.

The colon cancer includes rectal cancer, colon cancer, and anal cancer.

The present invention also relates to a method for the production of a compound of the present invention which is selected from the group consisting of GADD45B, S1PR3, CDKN2B, EGR2, CTGF, SERPINE1, RGS16, RHOU, TIMP1, PHLDA1, IL36RN, SLAMF7, E2F7, DTL, CFB, CDK1, CXCL1, CXCL3, A composition for predicting cancer prognosis or anticancer drug sensitivity, comprising an agent for measuring the expression level of mRNA of the above gene or its protein.

In order to predict cancer prognosis or anticancer drug susceptibility, the cancer-associated genes GADD45B, S1PR3, CDKN2B, EGR2, CTGF, SERPINE1, RGS16, RHOU, TIMP1, PHLDA1, IL36RN, SLAMF7, E2F7, DTL, CFB, CDK1, CXCL1, CXCL3, and CKS2 genes, and measuring the amount of mRNA. For example, RT-PCR, competitive RT-PCR, real-time RT-PCR, and RNase protection assay (RPA). RNase protection assay, Northern blotting, DNA chip, and the like. The agent for measuring the mRNA level of a gene in the present invention is preferably an antisense oligonucleotide, a primer pair or a probe.

In the present invention, "antisense" means that the antisense oligomer is hybridized with the target sequence in the RNA by Watson-Crick base pairing to form the sequence of the nucleotide base, typically within the target sequence, And an oligomer having a backbone between subunits. Oligomers may have an exact sequence complement or approximate complementarity to the target sequence. This antisense oligomer can alter the processing of mRNA that blocks or inhibits translation of mRNA and produces splice variants of mRNA.

"Primer" in the present invention means a short nucleic acid sequence capable of forming a base pair with a complementary template with a short free 3-terminal hydroxyl group and functioning as a starting point for template strand copying. Primers can initiate DNA synthesis in the presence of reagents and four different nucleoside triphosphates for polymerization reactions (i. E., DNA polymerase or reverse transcriptase) at appropriate buffer solutions and temperatures.

In the present invention, "probe" means a nucleic acid fragment such as RNA or DNA corresponding to a few nucleotides or hundreds of nucleotides that can specifically bind to mRNA, and is labeled to detect the presence or absence of a specific mRNA Can be confirmed. The probe can be produced in the form of an oligonucleotide probe, a single stranded DNA probe, a double stranded DNA probe, or an RNA probe. Selection of suitable probes and hybridization conditions can be modified based on what is known in the art.

Nucleic acid sequences of GADD45B, S1PR3, CDKN2B, EGR2, CTGF, SERPINE1, RGS16, RHOU, TIMP1, PHLDA1, IL36RN, SLAMF7, E2F7, DTL, CFB, CDK1, CXCL1, CXCL3 and CKS2 genes of the present invention A person skilled in the art can design an antisense oligonucleotide, a primer pair or a probe that specifically amplifies a specific region of these genes based on the above sequence.

The antisense oligonucleotides, primers or probes of the present invention can be chemically synthesized using the phosphoramidite solid support method, or other well-known methods. Such nucleic acid sequences may also be modified using many means known in the art. Non-limiting examples of such modifications include, but are not limited to, methylation, capping, substitution with one or more of the natural nucleotide analogs, and modifications between nucleotides, such as uncharged linkers (e.g., methylphosphonate, phosphotriester, Amidates, carbamates, etc.) or charged linkages (e.g., phosphorothioates, phosphorodithioates, etc.).

In order to predict cancer prognosis or anticancer drug susceptibility, the genes related to cancer, GADD45B, S1PR3, CDKN2B, EGR2, CTGF, SERPINE1, RGS16, RHOU, TIMP1, PHLDA1, IL36RN, SLAMF7, E2F7, DTL, CFB, CDK1, CXCL1, CXCL3, and CKS2, and measuring the amount of the protein. Examples of the assay methods include Western blotting, enzyme linked immunosorbent assay (ELISA), radioimmunoassay (RIA), radioimmunodiffusion, Ouchterlony immunodiffusion, rocket immunoelectrophoresis But are not limited to, tissue immuno staining, immunoprecipitation assays, complement fixation assays, fluorescence activated cell sorters (FACS), protein chips, and the like. The agent for measuring the protein expression level in the present invention is preferably an antibody.

In the present invention, &quot; antibody &quot; means a specific protein molecule directed against an antigenic site. For purposes of the present invention, an antibody refers to an antibody that specifically binds to a marker protein and includes both polyclonal antibodies, monoclonal antibodies, and recombinant antibodies.

There is provided a kit for predicting prognosis or anticancer drug susceptibility of cancer comprising the composition.

The kit of the present invention comprises a gene selected from the group consisting of GADD45B, S1PR3, CDKN2B, EGR2, CTGF, SERPINE1, RGS16, RHOU, TIMP1, PHLDA1, IL36RN, SLAMF7, E2F7, DTL, CFB, CDK1, CXCL1, CXCL3, The prognosis or anticancer drug sensitivity of cancer can be predicted by confirming the expression level of the mRNA or its protein. The kit of the present invention may further include one or more other component compositions, solutions, or devices suitable for the analysis method as well as primers, probes, antibodies, etc. for predicting cancer prognosis or anticancer drug susceptibility, preferably RT A PCR kit, a microarray chip kit, a DNA chip kit, and a protein chip kit.

As a specific example, in the present invention, the kit for measuring the mRNA expression level of the marker genes may be a kit containing essential elements necessary for performing RT-PCR. RT-PCR kits contain enzymes such as test tubes or other appropriate containers, reaction buffers, deoxynucleotides (dNTPs), Taq-polymerase and reverse transcriptase, DNase, RNase inhibitors, DEPC Water (DEPC-water), sterile water, and the like.

In addition, the kit of the present invention may be a kit including essential elements necessary for performing the microarray. The microarray kit may include a substrate to which a 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, Can be easily produced by a production method commonly used in the art. In order to fabricate a microarray, a micropipetting method using a piezo electric method or a micropipetting method using a pin-shaped method to immobilize the detected marker on a substrate of a DNA chip using the probe as a probe DNA molecule A method using a spotter or the like is preferably used, but the present invention is not limited thereto. The substrate of the microarray chip is preferably coated with an activator selected from the group consisting of amino-silane, poly-L-lysine and aldehyde, but is not limited thereto. In addition, the substrate is preferably selected from the group consisting of slide glass, plastic, metal, silicon, nylon film, and nitrocellulose membrane, but is not limited thereto.

In addition, the kit of the present invention may be a kit containing essential elements necessary for performing a DNA chip. The DNA chip kit may include a substrate on which a cDNA or an oligonucleotide corresponding to a gene or a fragment thereof is attached, and reagents, preparations, enzymes, and the like for producing a fluorescent-labeled probe. The substrate may also comprise a cDNA or oligonucleotide corresponding to a control gene or fragment thereof.

In addition,

(a) selected from the group consisting of GADD45B, S1PR3, CDKN2B, EGR2, CTGF, SERPINE1, RGS16, RHOU, TIMP1, PHLDA1, IL36RN, SLAMF7, E2F7, DTL, CFB, CDK1, CXCL1, CXCL3 and CKS2 genes in the biological sample Measuring an expression level of mRNA of the gene or a protein thereof; And

(b) comparing the expression level of the mRNA of the gene or its protein measured in the biological sample in step (a) with the expression level of mRNA of the gene or a protein thereof in a normal control sample; and Or a method for providing information for predicting anticancer drug susceptibility.

In the present invention, the term "biological sample" includes whole blood, serum, plasma, saliva, urine, sputum, lymphatic fluid, tissue, cells and the like isolated from an individual, preferably cancer tissue or cancer cells.

Methods for measuring mRNA expression levels in the present invention include RT-PCR, competitive RT-PCR, real-time RT-PCR, RNase protection assay (RPA RNase protection assay, Northern blotting, DNA chip, and the like.

Methods for measuring the expression level of a protein in the present invention include Western blotting, enzyme linked immunosorbent assay (ELISA), radioimmunoassay (RIA), radioimmunodiffusion, Ouchterlony immunodiffusion, Immunoprecipitation Assay, Complement Fixation Assay, Fluorescence Activated Cell Sorter (FACS), and Protein Chip are examples of the immunoassay method. It is not limited.

The gene selected from the group consisting of GADD45B, S1PR3, CDKN2B, EGR2, CTGF, SERPINE1, RGS16, RHOU, TIMP1, PHLDA1, IL36RN, SLAMF7, E2F7, DTL, CFB, CDK1, CXCL1, CXCL3, The prognosis of cancer is poor or the sensitivity of the anticancer drug is low. Therefore, the prognosis of the cancer or the susceptibility of the cancer drug can be accurately and quickly predicted.

In addition,

(a) statistically analyzing gene expression patterns between normal tissues, cancer tissues and metastatic cancer tissues to select major genes associated with cancer development and metastasis; And

(b) selecting a gene that can be used as a gene classifier by prognostic value of a gene regulated by the activity of a major gene associated with the development and metastasis of cancer in the step (a). A gene classifier screening method for predicting cancer prognosis or anticancer drug susceptibility.

The major genes involved in the development and metastasis of the cancer are TREM1 (triggering receptor expressed on myeloid cells 1) and CTGF (connective tissue growth factor), but are not limited thereto.

The prognostic value of the gene can be estimated from the risk score (Σ Cox coefficient of gene Gi × expression value of gene Gi) derived by multiplying the coefficient corresponding to the expression level of the gene.

Hereinafter, the present invention will be described more specifically based on examples. It will be apparent to those skilled in the art that the embodiments are only for describing the present invention in more detail and that the scope of the invention is not limited by these embodiments in accordance with the gist of the present invention.

Experimental Example 1. Preparation of tumor sample

Tumor samples used in the following experiments were obtained from 18 patients who were treated at Asan Medical Center from May 2011 to February 2012 and were treated with primary colorectal cancer tissue (PC), hepatocellular tissue (MC) ) And normal colonic epithelial tissue (NC) (AMC cohort). All patients in the AMC cohort showed microsatellite stable (MSS) status except for one patient with high-frequency MSI (MSI-H). All tumor samples were obtained with patient tissue sample donation and experimental consent. This study protocol was approved by the Institutional Review Board for Human Genetic and Genomic Research (Registration No. 2009-0091) for human genetic and genomic research under the Helsinki Declaration.

Experimental Example 2. RNA Extraction and RNA Sequencing

RNA was isolated from the sample obtained in Experimental Example 1 using an RNeasy Mini Kit (Qiagen, CA) according to the manufacturer's protocol. The quality and integrity of the RNA was confirmed by ultraviolet light after performing agarose gel electrophoresis and ethidium bromide staining. The sequencing library was prepared using the TruSeq RNA Sample Preparation kit v2 (Illumina, Calif.) According to the manufacturer's instructions. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads, fragmented, and converted into cDNA. Adapters were ligated into the cDNA and the fragments were amplified using PCR. Sequencing was performed in paired-end reads (2 x 100 bp) using Hiseq-2000 (Illumina).

Experimental Example 3. RNA Sequence Data Processing

Reference genome sequence data from Homo sapiens was obtained from the University of California Santa Cruz Genome Browser Gateway (assembly ID: hg19). The reference genomic index was constructed using the Bowtie2-build component of Bowtie2 (ver. 2.0) and SAMtools (ver. 0.1.18). Tophat2 was applied to tissue samples for mapping reads to the reference genome (ver. 2.0). The statistics of the mapping activity of Tophat2 are shown in Table 1 below. The data set generated by RNA sequencing can be used in the gene expression omnibus public database under data series accession number GSE 50760.

[Table 1] Mapping ratio of Tophat2 in the AMC cohort to Homo sapiens-based genome

Experimental example 4. Open gene expression data set and study design

The Hubrecht Institute gene expression data set was used as a validation data set (HI cohort, GSE 14297, n = 48) to confirm expression differences of selected genes in NC, PC, and MC tissue groups. In the HI cohort, primary colorectal cancer tissues (PC) and corresponding liver metastases (MC) from the same patient were analyzed by 18 pairs of gene expression profiling. In addition, expression data of normal bowel epithelium (NC) and normal liver tissue are present in the data set (Stange et al., 2010). In order to develop a risk score classifier for predicting the prognosis of colorectal cancer, data collected for the Cartes d'Identité des Tumeurs (CIT) program from the French Ligue Nationale Contre le Cancer, data set) (CIT cohort, GSE39582, n = 566). Patients who underwent preoperative chemotherapy and / or radiation therapy and those with primary colorectal cancer were excluded from the CIT cohort. Clinical and pathologic data were extracted from the medical records and the patient's stage was classified according to the American Joint Committee on Cancer (AJCC) staging system and monitored for recurrence (distal and / or local recurrence) (Marisa et al. , 2013). To validate the classifier of the present invention, a set of gene expression data generated from fresh frozen tumor specimens obtained from the Royal Melbourne Hospital, Western Hospital, and Peter McCallum Cancer Center in Australia and tissue banks from the H. Lee Moffitt Cancer Center in the United States (AUS cohort, GSE 14333, n = 229). In the AUS cohort, tumor-derived total RNA with a sample of RNA integrity number (RIN) &lt; 6 &gt; was excluded for patients undergoing preoperative chemotherapy and / or radiation therapy or for microarray analysis. Of these, 22 out of 94 patients with colorectal cancer and 64 out of 91 patients with colorectal cancer were treated with standard adjuvant chemotherapy (5-fluorouracil or a single preparation of capecitabine Or combined administration of 5-fluorouracil and oxaliplatin) or received concurrent chemoradiotherapy after surgery. Additional clinical data, including follow-up for AUS cohorts of stage 2 and stage 3 patients and patient gender and cancer staging (TNM staging), were reported by Australian patients to Bio-grid Australia and US patients to Moffitt Cancer Center Collected by the Tumor Registry (Jorissen et al., 2009). Two data sets from the Academic Medical Center at the University of Amsterdam (GSE33113, n = 90) and the Institut Paoli-Calmettes in France (GSE37892, n = 130) were combined for classifier validation (UAPC cohort, n = 220). In GSE 33113, patients were treated between 1997 and 2006, extensive medical records were kept and long-term clinical follow-up was possible for the majority. Paraffin-embedded tissues and fresh frozen tissues were available to obtain the gene expression profiles of all patients with GSE33113 (de Sousa et al., 2011). In GSE37892, a series of 130 patients with stage 2 and stage 3 colorectal cancer were maintained and expression profiles were generated in the oligonucleotide microarray. The primary end-point was disease-free survival (DFS), defined as the time since surgery for the first identified recurrence. DFS was available in the CIT, AUS and UAPC cohorts. All gene expression data were constructed using the Affymetrix Human Genome U133 Plus 2.0 platform. The experimental design and verification strategy for the present invention followed the REMARK guideline (McShane et al., 2005) and is shown in FIG.

Experimental Example 5. Transcriptomic profiling and significance test

To obtain the mRNA expression pattern in tissue samples, we applied a hierarchical clustering algorithm using a centre correlation coefficient as a measure of similarity and complete binding clustering. For cluster analysis, the FPKM (fragments per kilobase of transcript per million fragments mapped) of each sample was used to estimate the expression level of each gene. The FPKM data were normalized to the quantile method, log ₂ - transform and median values across genes and samples.

To estimate the significance of differences in the gene expression of the subgroup of samples, the EdgeR package using a negative binomial model was used to detect genes that were differentially expressed from count data (Robinson et &lt; RTI ID = 0.0 &gt; al., 2010). The gene count dispersion was estimated by the Cox-Reid profile-adjusted likelihood method. After model fitting and variance estimation, differentially expressed genes were selected using the generalized linear model (GLM) likelihood ration test, which specifies the probability distribution according to the mean variance relationship. The general linear model likelihood ratio test is based on the principle of fitting a negative binomial GLM with a Cox-Reid variance estimate. Differences in gene expression were considered statistically significant when the P value was less than 0.001 and the difference in the number of expressions between two sample groups was greater than 2. When liver metastasis was compared to primary colorectal cancer, liver-specific genes (309 genes) were filtered using the TiGER database (Liu et al., 2008) and other expressed genes were selected from the remaining genes.

SAMtools and VarScan (ver 2.3.4) were used to identify somatic mutations from RNA-sequencing data. First, SAMITools used the pileup command to create a pileup file from each arrayed bam file. Subsequently, VarScan's somatic commands were applied to identify somatic mutations from two conditions: tumor-versus-normal or metastatic vs. primary tumor. DbNSFP (ver. 2.04), an integrated database of functional predictions for different single-nucleotide polymorphisms (non-synonymous SNPs), was used to select functionally significant and non-synonymous mutations from the VarScan output. Multiple prediction scores were considered from three prediction algorithms (MA, sorting intolerant from tolerant (SIFT), and Polyphen2 (PP2)) to determine significant deformation.

Experimental Example 6. Analysis of gene set and upstream regulator

Gene set enrichment analysis was performed to identify the most important gene sets associated with disease processes, molecular and cellular functions, and physiological and developmental processes. The significance of the over-represented gene set was estimated using Fisher's exact test. Upper regulator analysis was performed to determine the number of known targets of each Adjuster that were present in the data set to identify the major upstream regulators that accounted for observed gene expression changes, They compared the direction of their change to what was expected from previously reported literature. For each potential adjuster, an overlap P-value and an active Z-score were estimated. The overlap P-value estimated by Fisher's exact test measures whether there is a statistically significant overlap between the gene in the data set and the gene regulated by the regulator. The active Z-score is used to infer the active state of the upper regulator based on a comparison with a model that assigns a random control direction. A positive or negative active Z-score indicates that the potential topadressors were each activated or inhibited. Gene set enrichment and upstream regulator analyzes were performed using the Ingenuity Pathway Analysis (IPA) Tool.

Experimental Example 7. Risk score development

Adopted a previously developed strategy using the Cox regression coefficient for the genes in the signature from the CIT cohort to develop a convenient risk score (Kim et al., 2012a Paik et al., 2004). The risk score for each patient was calculated as the sum of the scores of each gene, which was derived by multiplying the coefficient corresponding to the expression level of the gene (Risk score = Σ Cox coefficient of gene Gi × expression value of gene Gi). Patients were then categorized into two groups: high risk of recurrence or low risk of recurrence, using the intermediate cut-off of the risk score as a threshold. The coefficients and thresholds obtained from the CIT cohort were applied directly to the data from the AUS and UAPC cohorts in order to divide the patient into high-risk and low-risk groups. A time-dependent receiver operating characteristic (ROC) curve of the DFS (disease free survival) using a nearest neighbor estimation method with a 36-month cut-off value is used to estimate the prognostic value of a small number (Heagerty et al., 2000). Based on the area under curve (AUC) value derived from the ROC analysis, the top gene with the highest or lowest AUC value was selected to generate the optimal signature.

Experimental Example 8. Random classifiers generation [

In Experimental Example 7, as many genes as possible as a signature were randomly selected, and the regression coefficients of genes having an intermediate cut-off risk score in the CIT cohort were obtained. The session coefficients were then applied directly to the data from the AUS cohort to classify them into high-risk and low-risk groups of patients. Prognostic value was estimated in all patients, ie, stage 2, stage 3 colorectal cancer or stage 3 colorectal cancer elderly patients. Predictive values for chemotherapy treatment were assessed in high risk or low risk groups of stage 3 colorectal cancer induced from randomized classifiers. To estimate the performance of the randomly generated classifier, we used a 1000 bootstrap method (1000 resampling) to calculate 95% confidence for -log ₁₀ (log-rank P-value).

Experimental Example 9. Other statistical analyzes

To estimate the significance of differences in gene expression between subgroups, two sample tests were performed for each gene. The Kaplan-Meier method was used to calculate time to disease free survival (DFS), and the difference in survival rates between the two groups was assessed using log-rank statistics. The prognostic link between signatures and potential risk factors was assessed using multivariate Cox proportional hazard regression models. Backward-forward step procedures (R package stats) were applied to optimize multivariate models as informative variables (Venables et al., 2002). Cramer's V statistics and two-way contingency-table analysis were applied to assess the strength of the association between predicted results by other classifiers. Statistical analysis was performed using R language environment software (ver. 3.0.1).

Example 1. Baseline characteristics [

The baseline characteristics of patients with colorectal cancer in the AMC and HI cohorts are shown in Table 2 below.

[Table 2] Baseline characteristics of patients with colorectal cancer in the AMC and HI cohorts

The baseline characteristics of the other three groups of patients for developing a prognostic classifier are shown in Table 3 below. Supplementary chemotherapy data among three classifier-developed cohorts were available for the CIT and AUS cohorts. Of the 795 patients in these cohorts, 320 received regular adjuvant chemotherapy, while the remaining 475 received no chemotherapy.

[Table 3] Baseline characteristics of patients with colorectal cancer of Sekorch to develop and confirm genetic predictors

Example 2. Differential molecular characterization among patients with colorectal cancer with different phenotypes (CRC patient samples with distinct phenotypes)

To identify a set of genes that are significantly associated with the development and progression of colorectal cancer, we applied a variety of analytical methods to three samples (NC, PC, and MC) of AMC Cocht. Hierarchical clustering analysis was applied early in the gene expression data to evaluate the molecular characteristics of different sample groups. Autonomous hierarchical clustering analysis of gene expression data provided three primary clusters of primary colorectal cancer (PC), simultaneously histologically adenocarcinoma-identified liver metastasis (MC) and normal colonic epithelial tissue (NC) groups 2). Thus, gene expression patterns were easily distinguishable from PC, MC, and NC tissue groups.

The genes differentially expressed in the three tissue groups were then identified. When comparing PC and MC tissues, liver-specific genes (309 genes) were filtered using the TiGER database. Genes associated with the generation and metastasis of cancer were identified by a van diagram comparison between the two gene lists generated using the GLM likelihood ratio test (Figure 3A, P < 0.001). The gene list "A " represents genes that are differentially expressed between the NC and PC groups and the gene list" B " represents the genes that are differentially expressed between PC and MC groups. A comparison of the two gene lists revealed three different patterns: A (2018 gene), A and B (843 genes), and B (1003 genes) (Fig. 3B). The genes in the A category had an expression pattern associated with cancer generation, while the genes in the B category had an expression pattern associated with liver metastasis. Genes in the A and B categories were common to the generation and metastasis of colorectal cancer.

We also identified genes with important somatic cell sequence changes between different tissue groups from RNA-sequencing data. 2) somatic mutation of the same gene was observed in two or more patients; and 3) somatic mutation had functional significance (MA score> 1.5, SIFT score <0.05, or PP2 score> 0.9 Based on the cut-off values of the three scores of the three scores). Based on these criteria, we obtained a list of two genes with significant sequence changes between NC and PC groups with 36 genes identified or between PC and MC groups with 57 genes identified. These are shown in Tables 4 and 5 below.

[Table 4] Significant genes with different parameters between normal and first cancer groups

[Table 5] Significant genes with different parameters between primary cancer and metastatic cancer group

Example 3. Activated Adjuvant in the Generation and Liver Metastasis of Colorectal Cancer

Gene set enrichment tests were performed using IPA software to identify the biological characteristics of the genes involved in cancer production and metastasis. From the 843 genes in the A and B categories (Figure 3A), 224 genes that did not continuously increase or decrease between the PC group and the NC or MC group were excluded. The set of genes involved in cancer development is a combination of 2018 genes, 36 variants of the A category (Figure 3A) and 619 genes of the A and B categories (2671 genes, including two overlapping genes) . Similarly, the set of genes involved in the metastasis includes 1003 genes, 57 variant genes in the B category (FIG. 3A) and 619 genes in the A and B categories (a total of 1673 genes, including six overlapping genes) Lt; / RTI &gt; Using IPA, 2671 and 1673 genes were analyzed and enriched in two sets of genes related to cancer, gastrointestinal disease, cell growth and proliferation, and apoptosis and survival. In addition, genes involved in inflammatory reactions, immune cell trafficking, and inflammatory diseases were significantly enhanced (FIG. 4). These results indicate that the two selected gene lists reflect common biological characteristics and that pathological processes involved in the production of colon cancer and liver metastasis share many biological functions.

The enriched gene contained several important regulators. Among them, TREM1 and CTGF were two major regulators activated during the generation and metastasis of colon cancer (Table 6, Figures 5 and 6).

[Table 6] Prediction of Upper Regulators Activated upon Cancer Generation or Transition

Expression levels of TREM1 were significantly higher in the PC and MC groups than in the NC group (two sample t-tests, P = 8.5 × 10 ^-7 and 2.68 × 10 ^-7 , respectively) May be a major event related to the aggressiveness of colorectal cancer. Although no significant difference in expression of CTGF was observed between tissue groups (P = 0.17 (NC vs. PC) and 0.27 (NC vs. MC); FIG. 7B), activation scores for CTGF were different (Figure 6), CTGF is strongly associated with colon cancer formation and metastasis (Table 6), since it is estimated by variant molecules expressed or directly linked to CTGF (Figure 6).

Expression levels of the two genes were analyzed using gene expression data from the HI cohort to verify differences in TREM1 and CTGF between different tissue groups (NC, PC, and MC). TREM1 expression levels were significantly higher in the PC and MC groups than in the NC group (two sample t-tests, P = 0.01 and 3.5 × 10 ^-4 , respectively), providing confidence in the efficacy of this molecule as a colon cancer marker. Furthermore, there was a significant difference in TREM1 expression between PC and MC groups (two sample t-test, P = 0.02; FIG. 7C). In contrast, CTGF expression did not differ between these three groups (Fig. 7D), consistent with the results of RNA-sequencing data in the AMC cohort.

Example 4. Development of a risk score using the TREM1 and CTGF regulator networks and their validation in an independent cohort (TREM1 and CTGF regulatory networks and their validation in independent cohortsDevelopment of a risk score using TREM1 and CTGF regulatory networks and its validation in independent cohorts.

The prognostic value of the set of genes regulated by TREM1 or CTGF was assessed in additional colon cancer cohorts. A total of 66 genes (Figures 5 and 6) regulated by TREM1 or CTGF from the RNA-sequencing data were used to generate the risk score classifier defined by TREM1 or CTGF activation (TCA66) and this score was calculated from the CIT cohort We used it as a risk assessment model for colorectal cancer prognosis. In the CIT cohort, the risk score for each patient was calculated using the regression coefficients for each of the 66 unique genes (130 unique probes) (Table 7).

[Table 7] From the multivariate Cox regression analysis, the regression coefficients and AUC values of the 66 genes (130 unique probes) regulated by TREM1 and CTGF

The middle cut-off of risk scores (8.410) was used to classify colorectal cancer samples in the CIT cohort into two groups with high or low TCA66 scores (Fig. 8A). DFS (disease free survival) rates were significantly different between the two groups in the log-rank test analysis ( P = 5.62 × 10 ^-7 ; FIG. 8B). To validate the TCA66 scoring system, the coefficient values and the intermediate cut-off values derived from the CIT cohort were applied directly to the gene expression data of the AUS cohort to bisect the patient into high risk and low risk groups. The Kaplan-Meier estimates showed significant differences in DFS (disease-free survival) between the two subgroups (log-rank test, P = 2.14 × 10 ^-4 ; FIG.

Time-dependent ROC analysis based on 3-year survival rates was performed to select a small number of genes with a significant impact on prognosis. 19 unique genes (32 unique probes) ( P <0.05, Cox regression analysis) with the highest or lowest AUC score (AUC <0.45 or AUC> 0.55) among 66 genes in TCA66 were empirically selected 7). The risk score classifier was generated based on 19 genes in the CIT cohort (TCA19). When applied to CIT cohort with off (8.053), DFS (disease free survival) cycle is shorter in high-risk patient group than in the low-risk group of patients (log-character TCA19 classification Medium Cut ranking test, P = 4.42 × 10 ^{- 8} ; FIG. 9B). When the coefficients and the median cut-off values were directly applied to the AUS curve, the recurrence rate of high-risk patients was significantly higher than the recurrence rate of low-risk patients (log-rank test, P = 7.52 × 10 ^-4 ; The relationship between DFS (disease free survival) and the TCA19 classifier was further evaluated because DFS (disease free survival) data was also available in two different cohorts (GSE33113 and GSE37892). For this test, gene expression data (UAPC cohort, n = 220) were entered from both groups and the same procedure was applied. The recurrence rate of high-risk subgroups by TCA19 was significantly higher than the recurrence rate of low-risk subgroups (log-rank test, P = 0.005; Figure 9D).

Example 5. The TCA19 classifier is an independent risk factor for DFS (disease free survival) in CRC, an independent risk factor for DFS (disease free survival) in colorectal cancer.

To assess the independence of the TCA19 predictor, gene expression data were collected from two valid cohorts, AUS and UAPC cohorts (n = 449), graded by patients' AJCC stages (Hari et al., 2013). In this analysis, patients in stage I were excluded due to lack of DFS (disease free survival) events and DFS (disease free survival) data from patients in stage IV were not available. TCA19-based grading showed a significantly worse DFS (disease free survival) of patients with high-risk patients with III disease when applied to the input Cochl's II-III patients ( P = 0.026 ; FIG. 10B), there was no significant risk difference between DFS (disease free survival) and high risk and low risk patient groups with bipolar disorder ( P = 0.326; FIG. 10A). The results indicate that TCA19 classifier is a potential predictor of DFS (disease free survival) in patients with advanced colorectal cancer. The prognostic association between signatures and other potential risk factors for DFS (disease free survival) in the input population was assessed by multivariate Cox regression analysis. TCA19 has been found to be an independent risk factor for DFS (HR = 1.894, 95% CI = 1.227-2.809, P = 0.002; Table 8) even after applying the variable selection procedure. Another multivariate analysis was performed in the AUS group and TCA19 was still found to have statistical significance for disease free survival (HR = 2.24, 95% CI = 1.22-4.114, P = 0.009; Table 8 ).

[Table 8] Univariate and multivariate Cox regression analyzes for DFS (disease free survival) predictors

The Cox proportional hazard regression model was also used to analyze the interaction between the TCA19 classifier and the AJCC stage. The interaction of TCA19 with the stage at CIT Coht was significantly higher ( P <2.0 × 10 ^-16 ) with estimated HRs for TCA19 in stage II (95% CI 1.103-3.119, P = 0.019) It had reached 1.55, significant levels having the HRs estimate for TCA19 from group III; reached the (95% CI 1.503-4..08, P = 3.693 × 10 -4 Figure 11A) 2.477. Strong interactions between TCA19 and the stage were also observed at AUS and UAPC cohorts ( P = 1.185 × 10 ^-11 ). The HR for TCA19 in groups II and III was 2.038 (95% CI 1.092-3.802, P = 0.025) and 1.778 (95% CI 1.063-2.972, P = 0.028) (FIG. 11B). These results show that the TCA19 classifier is strongly independent of the current staging system.

We also assessed the association between TCA19 and MSI status. The DNA repair (MMR) mismatch repair status data in the patient cohort was used in CIT coht (deficient MMR) or pMMR (proficient MMR). Multivariable Cox regression analysis in the CIT cohort was performed in the MMR category because cancer with dMMR showed MSI-H and pMMR had cancer at low frequency MSI (MSI-L) or MSS (Sinicrope et al., 2011) . TCA19 was an independent risk factor for stage and DFS (disease free survival) with MMR status (HR = 1.952, 95% CI = 1.407-2.708, P = 6.155 × 10 ^-5 ) (Table 9). Patients were classified according to the MMR status and the prognostic value of each subgroup was estimated. Groups of high-risk patients were successfully identified from both MMR conditions (log-rank test, P <0.05; FIG. 12). This finding strongly suggests that the TCA19 classifier is independent of the current MMR (or MSI) category.

[Table 9] Univariate and multivariate Cox regression analysis for prediction of DFS (disease free survival) in the CIT cohort

Example 6. Association of TCA19 classifier and adjuvant chemotherapy with TCA19 classifier after adjuvant chemotherapy (TCA19 classifier is associated with DFS after adjuvant chemotherapy)

Among the verification cohorts for the TCA19 predictor, the adjuvant chemotherapy data were obtained from AUS Cocht. We analyzed the predictability of susceptibility of adjuvant chemotherapy using TCA19 classifier. This analysis was performed on patients with AJCC stage 3 (n = 91) who were known to have prolonged survival with adjuvant chemotherapy (Laurie et al., 1989; Moertel et al., 1990). The prognostic value was estimated in stage III patients and TCA19 identified high-risk patients for DFS (disease free survival) consistent with the evaluation including all patients as expected (FIGS. 13A and B). Interestingly, TCA19 also successfully identified high-risk patients (Figure 13C) in the evaluation of elderly patients with stage 3 colon cancer (> 75 years old, n = 23, recurrence of 8) It also indicates that patients with colorectal cancer have significant prognostic potential. Patients with colorectal cancer were classified as high- and low-risk subgroups based on TCA19 risk scores and evaluated independently for DFS (disease-free survival) to assess the predictors of TCA19 classifier in stage III patients. The adjuvant chemotherapy improved the patient's DFS (disease free survival) in the TCA19-classifier high-risk patient subgroup ( P = 0.009, Figure 13D), while in the low risk patient subgroup ( P = 0.704, Figure 13E) Not confirmed. In the Cox regression model, the interaction of TCA19 with adjuvant chemotherapy reached a significant level of 0.599 (Figure 14). However, HR estimated for adjuvant chemotherapy was 0.363 (95% CI = 0.163-0.805; P = 0.013) in the high-risk group of patients classified by TCA19 classifier consistent with the Kaplan-Meier plot and log- The HR for recurrence for adjuvant chemotherapy in the low-risk patient group was 0.758 (95% CI = 0.180-3.186; P = 0.705), while it had significant predictive value. This suggests that TCA19 classifier has a significant prognostic potential in patients with colorectal cancer, regardless of age, as well as having predictive value for patients with advanced colorectal cancer.

A further assessment of the predicted value of TCA19 was performed for older patients with stage 3 colorectal cancer. To prevent under-sampling, the AUS cohort was integrated with the CIT cohort that used the adjuvant chemotherapy data. The elderly patients (age 75 and over, n = 84) with stage 3 colorectal cancer were classified into high-risk and low-risk subgroups using the classifier and the differences in DFS (disease free survival) . Although we did not find statistical significance here, we found a trend toward benefit from adjuvant chemotherapy in TCA19-based high-risk patients, whereas no association was observed in low-risk patient groups (Figure 15). In the integrated cohort, colorectal cancer patients were classified into chemotherapeutic groups and non - chemotherapeutic groups, and multivariate analyzes were performed on these two groups. TCA19 classifier group that received chemotherapy (HR = 1.851, 95% CI = 1.283-2.671, P = 9.946 × 10 -4; Table 10) have not been and chemotherapy group (HR = 2.287, 95% CI = 1.478- 3.538, P = 2.401 × 10 ^-4 ; Table 10) as an independent risk factor for DFS (disease free survival). These results demonstrate that TCA19 predictors can be independent of adjuvant chemotherapy despite the presence of patient selection bias in the patient cohort used in the present study.

[Table 10] Single and multivariate Cox regression analysis for DFS prediction of chemotherapy in AUS and CIT integrated cohort

Example 7. Comparison of TCA19 classifier and other genetic predictors with the TCA19 classifier

(Oh et al., 2012) and OncoDX (7 gene oncotype DX recurrence score) (Clark-Langone et al., 2010) have identified poor prognosis in MDA114 Because of its good performance (Park et al., 2013), these two predictors were compared with TCA19 predictors. The original predictive methods and threshold values of the three classifiers (TCA19, MDA114, and OncoDX) were applied and the patients were classified according to the risk level predicted by each classifier in the AUS cohort. Kaplan-Meier plots showed significant differences in DFS (disease free survival) rates between the high-risk and low-risk groups categorized by each gene predictor (Fig. 16). Patients were classified by grade according to the AJCC stage and all classifiers were found to have high risk patients with stage 3 colorectal cancer successfully, whereas no classifier showed significant prognostic value in classifying patients with stage 2 colorectal cancer. TCA19 was the only predictor of high-risk patients, especially in older (> 75 years old) patients with stage 3 colorectal cancer, whereas MDA114 or OncoDX did not show such predictive ability (FIG. 17). Another subset analysis was performed to compare the association between the predicted results by the classifier and the susceptibility of adjuvant chemotherapy. The third group of high risk patients with colorectal cancer, classified by all gene predictors, had the potential effect of adjuvant chemotherapy, but not the low risk patient subgroup (Fig. 18). The characteristics of each classifier are shown in Table 11, in which all classifiers were able to predict the outcome of colorectal cancer, but only the TAC19 predictor showed prognostic potential in patients with advanced stage 3 colorectal cancer. Coincidence between the predicted results was assessed by comparing subgroups of patients predicted by each prediction model (Cramer's V statistics; Table 12). All correlations between predictors were statistically significant (χ ² test, r = 0.219-0.472; P <0.001), with the highest correlation being highest in TCA19 and OncoDX (χ ² test , r = 0.472; P = 1.209 × 10 -12).

[Table 11] Performance of the three classifiers in the AUS cohort (n = 229)

[Table 12] Matching of three classifiers in the AUS cohort

In addition, the prognostic value and the predicted value of TCA19 were verified by comparing with randomly generated gene predictors. A similar procedure for the development and validation of the TCA19 classifier was used to construct a classifier with 19 randomly selected genes and confirm their predictions. In the case of random classifiers, the mean P value by log-rank test in all subset categories did not reach statistical significance. The significance level of TCA19 for chemotherapy susceptibility in all patients, patients with colorectal cancer 3, patients with colorectal cancer 3, and patients with colorectal cancer 3, when compared with TCA 19 classifier, (Figure 19A), indicating that the TCA19 classifier was not accidentally generated. There were some random classifiers that outperform TCA19 in the subset analysis, but in the bootstrap resampling analysis (FIG. 19B), there was no random classifier that outperforms TCA19 across all sub-categories.

Example 8. Biological characteristics of the classifier and comparison of CRC subtypes

The gene set enrichment test was performed on the TCA66 gene to study the biological properties of the classifier (Table 7). The well-known active inflammatory disease of TREM1 and the genes involved in the response were enhanced. In addition, terms related to cell development, cell growth and proliferation, intercellular signaling and interaction, apoptosis and survival were identified (Fig. 20). These results indicate that 66 genes, including TREM1 and CTGF, can have significant activity associated with cancer progression beyond the inflammatory or immune response.

A comparative analysis of recently reported colorectal cancer subtypes and TCA19 classifiers was also performed. In the AUS cohort, the classification of patients with colorectal cancer between TCA19 and CRCassigner was compared (Sadanandam et al., 2013). We compared the five subtypes of CRCassigner (goblet-like, enterocyte, stem-like, inflammatory, and transit-amplifying) with the TCA19 classifier, (33 out of 38, 86.8%) were classified as high risk patients by TCA19. Of the five subtypes, 41.5% (17 out of 41) of the inflammatory subtypes that showed adequate efficacy from chemotherapy were classified as TCA19 high risk subgroups (Figure 21A). Patients were categorized by TCA19 risk score and most of the high-grade colorectal cancer patients were classified as stem-like subtypes (Fig. 21B), indicating that the high-risk subgroups classified by TCA19 had a poor prognosis It reflects well the characteristic biological subtype. There were 16 common genes between CRCassigner (786 gene) and the present classifier (66 gene). Among them, TREM1 and CTFG had the highest Prediction of Microarray Analysis (PAM) score in inflammatory and stem-like subtypes, respectively (Table 13) (Sadanandam et al., 2013).

[Table 13] TCA66 classifier and CRCassigner common gene and score by Prediction of Microarray Analysis (PAM)

We also compared the classification of patients with colorectal cancer between the TCA19 and 3 colon cancer subtypes induced by the 146-gene classifier (De Sousa et al., 2013) in the AUS cohort. It is characterized by a particularly unfavorable prognosis, epithelial-mesenchymal transition (EMT) and extracellular matrix remodeling, as compared to three subtypes of CRC (CCS1, CCS2, and CCS3) A large number of colorectal cancer patients (27 out of 36, 75%) of the CCS3 subtype were classified as high risk subgroups by TCA19 (Figure 22A). Most of the high-grade colorectal cancer patients classified by TCA19 were significantly associated with CCS3, suggesting that the high-risk subgroups identified by TCA19 are very similar to those of EMT and matrix remodeling showing poor prognosis. Interestingly, CCS3 is almost MSS (De Sousa et al., 2013), MMR state [pMMR (MSS or MSI-L) vs. dMMR (MSI-H), HR = 0.452, 95% CI = 0.238-0.858, P = 0.015; Table 9]. Only four genes are common between the 146-gene classifier (146 gene) and the inventive classifier (66 genes) (FIG. 22C).

Finally, a comparative analysis of six colon cancer subtypes (C1-C6) classified by the TCA19 and 57-gene centroid classifier (Marisa et al., 2013) Respectively. Of the 96.6% (57 out of 59) and C6 (normal-like) subtypes of colorectal cancer patients with C4 (stem cell phenotype-like) subtypes associated with shorter relapse-free survival 71.7% (43 out of 60) of patients were classified as high-risk subgroups by TCA19 (Figure 23A). When patients were classified by TCA19 risk score, the highest score of colorectal cancer patients was strongly associated with the C4 or C6 subtype (Figure 23B), suggesting that the high-risk subgroups based on TCA19 were consistent with CRCassigner and earlier comparisons of the present invention (CSC) characteristics of cancer stem cells. Interestingly, no common gene was found between the 57-gene centroid classifier (57 genes) and the classifier of the present invention (66 genes) (Figure 23C).

Claims (19)

A biomarker composition for predicting cancer prognosis or anticancer drug susceptibility including genetic classifier TCA19. 4. The method of claim 1, wherein the gene classifier TCA19 is selected from the group consisting of growth arrest and DNA-damage-inducible beta (SADD45B), sphingosine-1-phosphate receptor (S1PR3), cyclin-dependent kinase inhibitor 2B growth factor 2, CTGF (connective tissue growth factor), SERPINE1 (serpin peptidase inhibitor, clade E), RGS16 (regulator of G-protein signaling 16), RHOU (ras homolog family member U), TIMP1 (metallopeptidase inhibitor 1) (IL-6R), IL6RN (interleukin 36 receptor antagonist), SLAMF7 (SLAM family member 7), E2F7 (transcription factor 7), DTL (denticleless E3 ubiquitin protein ligase homolog), CFB factor B, CDK1 (cyclin-dependent kinase 1), CXCL1 (chemokine (CXC motif) ligand 1), CXCL3 (CXC motif) ligand 3 and CKS2 (CDC28 protein kinase regulatory subunit 2) Wherein the biomarker composition comprises one or more genes. 2. The method according to claim 1, wherein the gene classifier TCA19 is a gene regulated by the activity of TREM1 (triggering receptor expressed on myeloid cells 1) and CTGF (connective tissue growth factor) Biomarker composition. The method of claim 1, wherein the cancer is selected from the group consisting of acute lymphoblastic leukemia, lymphocytic leukemia, chronic lymphocytic leukemia, acute non-lymphoid leukemia, bladder cancer, brain tumor, breast cancer, chronic myelogenous leukemia, Endometriosis, esophageal cancer, bile bladder cancer, Ewing's sarcoma, dysplasia, Hopkins lymphoma, caposic sarcoma, kidney cancer, liver cancer, lung cancer, mesothelioma, multiple myeloma, neuroblastoma, non-hopskin lymphoma, osteosarcoma, ovarian cancer, neuroblastoma Wherein the cancer is selected from the group consisting of breast cancer, colorectal cancer, prostate cancer, pancreatic cancer, colon cancer, penis cancer, retinoblastoma, skin cancer, gastric cancer, thyroid cancer, uterine cancer, testicular cancer, Wilms' tumor, and tropoblastoma A biomarker composition for predicting prognosis or anticancer drug susceptibility. The biomarker composition according to claim 1, wherein the cancer is a colon cancer, for predicting cancer prognosis or anticancer drug susceptibility. 6. The biomarker composition according to claim 5, wherein the colorectal cancer comprises rectal cancer, colon cancer and anal cancer. The biomarker composition according to claim 5, wherein the large intestine is a third stage large intestine cancer. The biomarker composition according to claim 1, wherein the anticancer agent is selected from the group consisting of oxaplylatin, fluorouracil, levopolinate and salts thereof. MRNA of one or more genes selected from the group consisting of GADD45B, S1PR3, CDKN2B, EGR2, CTGF, SERPINE1, RGS16, RHOU, TIMP1, PHDLA1, IL36RN, SLAMF7, E2F7, DTL, CFB, CDK1, CXCL1, CXCL3, A composition for predicting cancer prognosis or anticancer drug susceptibility, comprising an agent for measuring the expression level of a protein. The method according to claim 9, wherein the agent for measuring mRNA level of the gene is selected from the group consisting of GADD45B, S1PR3, CDKN2B, EGR2, CTGF, SERPINE1, RGS16, RHOU, TIMP1, PHLDA1, IL36RN, SLAMF7, E2F7, DTL, CFB, CDK1, A primer pair or a probe that specifically binds to a gene selected from the group consisting of CXCL3, CXCL3, and CKS2. 10. The method of claim 9, wherein the agent is selected from the group consisting of GADD45B, S1PR3, CDKN2B, EGR2, CTGF, SERPINE1, RGS16, RHOU, TIMP1, PHLDA1, IL36RN, SLAMF7, E2F7, DTL, CFB, CDK1, CXCL1, CXCL3, and CKS2 in the presence of an antibody specific for the prognosis or cancer susceptibility of cancer. A kit for predicting prognosis or anticancer drug sensitivity of cancer comprising the composition of any one of claims 9 to 11. 13. The kit according to claim 12, wherein the kit is an RT-PCR kit, a microarray chip kit, a DNA kit or a protein chip kit. (a) a gene selected from the group consisting of GADD45B, S1PR3, CDKN2B, EGR2, CTGF, SERPINE1, RGS16, RHOU, TIMP1, PHLDA1, IL36RN, SLAMF7, E2F7, DTL, CFB, CDK1, CXCL1, CXCL3 and CKS2 in the biological sample Lt; RTI ID = 0.0 &gt; mRNA &lt; / RTI &gt; or a protein thereof; And
(b) comparing the expression level of the mRNA of the gene or its protein measured in the biological sample in step (a) with the expression level of mRNA of the gene or a protein thereof in a normal control sample; and Or a method for providing information for predicting anticancer drug susceptibility. The method according to claim 14, wherein in the step (a), the biological sample is at least one sample selected from the group consisting of whole blood, serum, plasma, saliva, urine, sputum, lymph, , A method of providing information for predicting cancer prognosis or anticancer drug susceptibility. The method according to claim 14, wherein the mRNA expression level is measured by RT-PCR, competitive RT-PCR, real-time RT-PCR, Wherein the cancer cell is one or more methods selected from the group consisting of DNA polymerase chain reaction (PCR), RNase protection assay (RPA), Northern blotting and DNA chip. Delivery method. The method according to claim 14, wherein the method for measuring the level of expression of the protein in step (b) includes Western blot, enzyme linked immunosorbent assay (ELISA), radioimmunoassay (RIA), radioimmunodiffusion Immunoprecipitation Assay, Complement Fixation Assay, Fluorescence Activated Cell Sorter (FACS), and Protein Immunoprecipitation Assay (FACS), as well as Ouchterlony immunodiffusion, rocket immunoelectrophoresis, Wherein the method is one or more methods selected from the group consisting of a protein chip, and a method of providing information for predicting the prognosis or cancer susceptibility of a cancer. (a) statistically analyzing gene expression patterns between normal tissues, cancer tissues and metastatic cancer tissues to select major genes associated with cancer development and metastasis; And
(b) selecting a gene that can be used as a gene classifier by prognostic value of a gene regulated by the activity of a major gene associated with the development and metastasis of cancer in the step (a). Screening method of gene classifier for prediction of cancer prognosis or anticancer drug susceptibility. 19. The method of claim 18, wherein the major genes associated with the development and metastasis of the cancer are TREM1 and CTGF.

Images