KR101934107B1 - Composition for Predicting Survival and the Response of a Patient with Myelodysplastic Syndromes to Hypomethylating Agent - Google Patents

Abstract

The present invention relates to a composition, a gene panel and a kit for confirming the mutation of gene markers for prediction of survival prognosis and response to treatment of hypomethylated formulations in patients with myelodysplastic syndromes, responsiveness to hypomethylated formulations in patients with myelodysplastic syndromes And a method for providing information for predicting the survival prognosis. The composition for predicting the response and survival prognosis of a hypomethylated formulation of the present invention can provide a more accurate prediction of the prognosis than the existing composition and can provide useful information for determining the treatment direction of the patient with the myelodysplastic syndrome.

Description

TECHNICAL FIELD [0001] The present invention relates to a composition for predicting the response and survival prognosis of hypomethylated formulations in patients with myelodysplastic syndromes

The present invention relates to a composition for predicting the response and survival prognosis of hypomethylated formulations in patients suffering from myelodysplastic syndromes, and more particularly to a composition for predicting the reactivity and survival prognosis of a hypomethylated form of a patient with a myelodysplastic syndrome, And a method for providing information for predicting the reactivity and the survival prognosis of a hypomethylated agent in a patient with a bone marrow dysplasia syndrome using the gene panel and kit.

Myelodysplastic syndromes (MDS) are myeloid tumors due to clonal stem cell disorders that cause ineffective hematopoiesis and increase the risk of developing acute myeloid leukemia (AML). This syndrome represents a variety of life-threatening clinical symptoms, such as progression of AML or severe cytopenias, from lethargy. Treatment with hypomethylating agents (HMA) such as azacitidine or decitabine is the first treatment for high-risk MDS patients or low-risk MDS patients with hemodilution symptoms. However, predicting the response to HMT and the response to hypomethylating therapy (HMT) remains a problem to be solved.

To date, various prognostic score systems have been developed to assist in the selection of treatment modalities. Among them, the most recent system, Revised International Prognostic Scoring System (IPSS-R), is useful for predicting patient survival but not for predicting therapeutic response to HMT. Thus, identifying reliable molecular genetic markers may provide additional prognostic information for MDS.

In order to understand the pathogenesis of MDS, recent research on the relationship between somatic cell mutation and its pathological physiology has been actively conducted. With this effort, some mutation information has been added to the existing prognostic score system. Nevertheless, there is a problem in that sensitivity, specificity, and accuracy are significantly low when the existing system is applied to Asian or Korean MDS patients. Thus, there has been a need to develop a new prognostic score system that identifies and identifies minute-free electronic markers specifically identified in Asian or Korean patients with MDS.

U. S. Patent No. US2016 / 0083802 United States Patent US2014 / 0127690

 Greenberg, Peter L., et al. "Revised international prognostic scoring system for myelodysplastic syndromes." Blood 120.12 (2012): 2454-2465.  Traina, F., et al. "Impact of molecular mutations on treatment response to DNMT inhibitors in myelodysplasia and related neoplasms." Leukemia 28.1 (2014): 78-87.  Bejar, Rafael, et al. "TET2 mutations predict response to hypomethylating agents in myelodysplastic syndrome patients." Blood 124.17 (2014): 2705-2712.  Bejar, Rafael, et al. "Somatic mutations predict poor outcome in patients with myelodysplastic syndrome after hematopoietic stem-cell transplantation." Journal of Clinical Oncology 32.25 (2014): 2691-2698.  Haferlach, T., et al. "Landscape of genetic lesions in 944 patients with myelodysplastic syndromes." Leukemia 28.2 (2014): 241-247.  Papaemmanuil, Elli, et al. "Clinical and biological implications of driver mutations in myelodysplastic syndromes." Blood 122.22 (2013): 3616-3627.

Therefore, the present inventors have found mutations associated with the response to the survival and hypomethylation treatment in patients with myelodysplastic syndromes, and have found that through targeted deep sequencing, And confirmed the marker gene which can predict the survival prognosis, thereby completing the present invention.

Accordingly, a problem to be solved by the present invention is to provide a composition for predicting the reactivity and survival prognosis of a hypomethylated preparation in a patient suffering from a myelodysplastic syndrome.

Another problem to be solved by the present invention is to provide a gene panel for predicting the reactivity and survival prognosis of a hypomethylated agent in a patient suffering from a myelodysplastic syndrome.

Another object of the present invention is to provide a kit for predicting the reactivity and survival prognosis of a hypomethylated drug in a patient suffering from a myelodysplastic syndrome.

Another problem to be solved by the present invention is to provide a method for providing information for predicting the reactivity and survival prognosis of a hypomethylated drug in a patient suffering from a myelodysplastic syndrome.

To achieve the above object, the present invention U2AF1, DNMT1, DNMT3A, reactivity with the low methylation agent of myelodysplastic syndrome, comprising a detection reagent for the one or more biomarkers selected from the group consisting of RAS and the TP53 gene and A composition for predicting the survival prognosis is provided.

The invention also U2AF1, DNMT1, DNMT3A, the RAS and reactivity of the low methylation agent of the above syndrome, myelodysplastic comprising any one or more biomarkers selected from the group consisting of TP53 gene and survival prognostic gene panel for to provide.

In addition, the present invention provides a kit for predicting the reactivity and survival prognosis of a hypomethylated agent in a patient with a myelodysplastic syndrome including the composition or the gene panel.

In addition, the low methylation agent of the present invention is a gene in a sample, U2AF1, DNMT1, DNMT3A, RAS and more TP53 form the bone marrow, comprising the step to determine mutations in any one or more genes which genes selected from the group consisting of a syndrome obtained from the patient And to provide information for predicting survival prognosis.

The present invention provides a composition for predicting the response and survival prognosis of a hypomethylated drug of a patient suffering from a bone marrow dysplasia syndrome (MDS), which can predict the prognosis with higher accuracy than existing compositions, And can provide useful information.

In particular, the conventional method of predicting the prognosis for MDS is not only low in accuracy, but also takes a long time, and it takes about 7 to 10 days to detect a prognostic factor in one patient. Therefore, it takes 15 to 20 days to determine the treatment method of the patient after obtaining the patient sample, determining the prognostic factors, determining the information processing and diagnostic findings, and so on. However, applying the prognosis prediction method of the MDS patient according to the present invention not only increases the accuracy but also saves time, compared with the existing composition and the conventional prognosis prediction method using the same. However, when the method of the present invention is applied, it can be completed within 4 to 6 days for the detection of the prognostic factors. Therefore, in order to determine the treatment method of the patient, 10 days may be required, which is effective in that the accuracy of the prognostic prediction is improved as compared with the conventional method and the time required for diagnosis is also saved.

Figure 1 shows a mutation of 26 candidate genes in the genome of 107 patients with myelodysplastic syndromes (each row represents a mutant gene, each row represents patients).
FIG. 2 is a graph showing the profiles of the mutation gene between the reactive group and the non-reactive group in the genome of 107 patients with abnormal myelodysplastic syndromes.
Figure 3 shows a diagram (A) of the SETBP1 mutation and U2AF1 (B) of the mutation.
FIG. 4 is a graph showing the total survival rate according to the Kaplan-Meier method according to DNMT3A, RAS, TP53 or DNMT1 mutation status.
FIG. 5 is a graph estimated by the Kaplan-Meier method for survival rate of no-AML according to DNMT3A, RAS or TP53 mutation status.
FIG. 6 is a graph showing the total survival rate (A) and the non-AML survival rate (B) of the four risk groups estimated by the Kaplan-Meier method.
FIG. 7 is a graph comparing the overall survival rate between the prognostic prediction model proposed in the present invention and the existing prognostic prediction model.
FIG. 8 is a graph comparing the survival rates of the prognostic prediction model proposed in the present invention and the existing prognostic prediction model without the onset of acute myelogenous leukemia.

The inventors of the present invention have conducted intensive studies to discover mutations associated with the survival and response to the treatment of hypomethylation in patients with myelodysplastic syndromes and have found that the gene markers capable of predicting the survival rate of patients with myelodysplastic syndromes and the response to hypomethylation treatment The present invention has been accomplished by discovering predictable gene markers.

Thus, the present invention is predictive of the responsiveness and survival prognosis for low methylation agent of myelodysplastic syndrome, comprising a detection reagent for the one or more biomarkers selected from the group consisting of U2AF1, DNMT1, DNMT3A, RAS and TP53 gene ≪ / RTI >

&Quot; Myelodysplastic syndromes " as a disease to be treated in the present invention refers to blood diseases derived from abnormal hematopoietic stem cells characterized by proliferation of bone marrow, dysregulation of constituent cells, and inefficient hematopoiesis.

As used herein, the term " Acute myeloid leukemia " refers to a condition in which a gene mutation occurs in hematopoietic stem cells to stop the differentiation of the myeloid progenitor cells at various stages, and the immature myeloid leukemia is monoclonal It is a proliferating hematopoietic tumor.

The term " low methylation treatment " as used in the present invention means treatment for hypomethylation of DNA.

The term " diagnosis " as used in the present invention means, in a broad sense, judging all aspects of the actual condition of a patient. The contents of the judgment are the pathology, etiology, pathology, severity, detailed aspects of the disease, and the presence or absence of complications. In the present invention, the diagnosis may be to determine the onset of the myelodysplastic syndrome and the progression level or the like.

As used herein, the term " prognosis " refers to a prediction of the progression or recovery of a disease, and refers to an outlook or a preliminary evaluation. The prognosis in the present invention may refer to the relapse, overall survival, or disease-free survival of the myelodysplastic syndrome.

The "detection reagent" of the present invention may be a reagent detectable at the nucleic acid level. Specifically, the detection reagent for nucleic acid level is used for polymerase chain reaction, reverse transcription polymerase chain reaction, competitive polymerase chain reaction, Nuclease protection assay (RNase, S1 nuclease assay), in situ hybridization, nucleic acid microarray or Northern blot Lt; / RTI >

The " patient " of the present invention may be a patient preferably Asian, and more preferably a Korean patient.

The "low-methylation agent" of the present invention may preferably be azacitidine or decitabine, more preferably azacytidine.

The present invention provides a U2AF1, DNMT1, DNMT3A, RAS and reactivity prediction or survival biomarker for prognosis of the TP53 gene to that of methylated formulation syndrome patients bone marrow formation. Specifically, it can be predicted the U2AF1, DNMT1, DNMT3A, RAS and TP53 reactivity prediction or the prognosis for survival over a pattern having a mutation in the gene for low-methylated preparation of the above patient myelodysplastic syndrome.

In one embodiment of the present invention, the genotype (wild type or mutant type) of the biomarker is identified to predict reactivity to a low methylated drug in patients with myelodysplastic syndromes or to predict the survival prognosis. More specifically, for example, if the U2AF1 gene in a patient with a myelodysplastic syndrome is a wild-type, hypomethylation therapeutic response may be expected to be positive. Conversely, if the U2AF1 gene is mutated, the hypomethylation therapeutic response may be expected to be negative.

The present invention provides compositions comprising a detection reagent which can determine the U2AF1, DNMT1, DNMT3A, type of the RAS or TP53 gene (wild-type or mutant type).

The detection reagent may include a primer set specific for the marker gene of the invention, an appropriate amount of a DNA polymerase, a dNTP mixture, a PCR buffer solution and water to perform PCR using a patient gene. The PCR buffer may contain KCl, Tris-HCl and MgCl _2. In addition, it may contain components necessary for performing electrophoresis to confirm whether the PCR product is amplified.

The present invention is of the reactive and survival prognosis for low methylation agent of the patient U2AF1, DNMT1, DNMT3A, RAS and TP53 to check for mutations if the one or more biomarkers selected from the group consisting of genes, myelodysplastic syndrome Provides a gene panel for prediction.

In addition, the present invention provides a kit for predicting the reactivity and survival prognosis of a hypomethylated agent in a patient suffering from a myelodysplastic syndrome including the composition or the gene panel described above.

In the present invention, the kit may include one or more other component compositions, solutions or devices suitable for the analysis method as well as polynucleotides, polypeptides, cDNAs, etc. of the present invention. In one embodiment, the kit of the present invention may be a kit containing the necessary elements necessary to perform PCR, and may be a test tube or other suitable container, a reaction buffer (pH and magnesium concentrations vary), deoxynucleotides (dNTPs) Enzymes such as Taq polymerase and reverse transcriptase, DNase, RNAse inhibitor, DEPC-water and sterile water. In another embodiment, the kit of the present invention may be a kit for predicting the survival prognosis of abnormal myelodysplastic syndromes comprising essential elements necessary for carrying out a DNA chip, and the DNA chip kit may comprise a polynucleotide, a primer, A substrate to which a probe is attached, and the substrate may include a nucleic acid corresponding to a quantitative control gene or a fragment thereof.

In addition, the low methylation agent of the present invention is a gene in a sample, U2AF1, DNMT1, DNMT3A, RAS and more TP53 form the bone marrow, comprising the step to determine mutations in any one or more genes which genes selected from the group consisting of a syndrome obtained from the patient And a method for providing information for predicting the survival prognosis.

As used herein, the term "method for providing information for predicting prognosis of myelodysplastic syndromes" is a preliminary step for predicting prognosis and provides objective basic information necessary for predicting prognosis of myelodysplastic syndromes. Judgments or observations are excluded.

The " patient " of the present invention may be a patient preferably Asian, and more preferably a Korean patient.

The "low-methylation agent" of the present invention may preferably be azacitidine or decitabine, more preferably azacytidine.

The present inventors have found that the survival rate of the pathological grade and patient U2AF1, DNMT1, DNMT3A, RAS and above the mutated form of the TP53 gene myelodysplastic syndrome using the genetic information and (or) a pathological data of the patient-derived than myelodysplastic syndrome and And that there is a close relationship between them.

In one embodiment of the invention, U2AF1, DNMT1, DNMT3A, when any one or more genes selected from the group consisting of the RAS and the TP53 gene is mutated, the reactivity of the low methylation agent is low, or verify that the survival prognosis is bad Respectively.

In the step of confirming the mutation of the present invention, the presence of a mutation in the U2AF1 gene may include a step of predicting that there will be no response to the hypomethylated agent.

In one embodiment of the present invention, mutation of the U2AF1 gene was confirmed in 12.3% of the non-responsive patients and 28% of the non-reactive patients, and the P-value was 0.052. Thus, in the presence of mutations in the U2AF1 gene, it is predicted that there will be no response to hypomethylated agents. More preferably, the mutation of the U2AF1 gene as well as the platelet count and hemoglobin level can be considered together to predict reactivity to the hypomethylated agent. Specifically, when the U2AF1 gene is mutated, it can be predicted that when the platelet count is 50,000 / ul or less, the hemoglobin level of 10 g / dl or less will not respond to the hypomethylated agent.

In the steps to determine the mutations of the present invention, when there are mutations in any one or more genes selected from the group consisting of DNMT1, NDMT3A, RAS and TP53 gene, it may include the step of predicting that a survival prognosis bad.

According to one embodiment of the invention, when the mutation present in one or more genes selected from the group consisting of DNMT1, NDMT3A, RAS and TP53 gene, it can be predicted that a survival prognosis bad. Preferably, the presence of one or more gene mutations in the group consisting of the DNMT1 , RAS and TP53 genes predicts a poor survival prognosis. More preferably, the presence of a mutation in the DNMT1 , RAS and TP53 genes can be expected to have a poor survival prognosis.

Non-marker clinical information may be further used in the step of predicting the survival prognosis of the present invention. Specifically, the non-marker clinical information may be at least one selected from the group consisting of hemoglobin level, platelet count, sex information, and revised international prognostic scoring system (IPSS-R) information.

In the present invention, the method of classifying according to the risk group is classified into a low-risk group with a score of 0, a low-risk group with a score of 1, a high-risk group with a score of 2, and a high- It can be done; a) 1 for male and 0 for female; b) 1 if the IPSS-R value is higher than or equal to 0; c) 1 if the DNMT1 gene is mutated; 0 if not; d) 1 if the DNMT3A gene is mutated, 0 if not; e) 1 if the RAS gene is mutated; 0 if not; And f) 1 if the TP53 gene is mutated, 0 if not.

Hereinafter, the present invention will be described in more detail with reference to examples and comparative examples. However, the examples and comparative examples are provided only for the understanding of the present invention, and the scope of the present invention is not limited by these examples and comparative examples in any sense.

[ Experimental Example  One]

Selection of study subjects and characterization of patients

107 patients with myelodysplastic syndromes (MDS), including chronic myelogenous leukemia, participated in this study. Clinical and molecular variables were identified prior to hypomethylating therapy (HMT). One or two hypomethylating agents (HMA) were prescribed to all patients: azacytidine (n = 66) or decitabine (n = 41). The mean age of the cohort was 59 years (range: 23-76 years). All patients were recruited from Asan Medical Center (Seoul, Korea) and Seoul St. Mary's Hospital (Seoul, Korea). To avoid potential bias due to molecular aberrations that may occur after treatment, only patients who had obtained pre-HMT bone marrow samples were included. The existing standard value characteristics and therapeutic effects are shown in Table 1 below.

* IPSS and IPSS-R data of one patient in the non-response group can not be collected. N, number; WHO, World Health Organization; RCUD, single sequence formation abnormal refractory cytopenias; RCMD, multiple system formation abnormal refractory cytopenias; RAEB, increased maternal cell refractory anemia; CMML, chronic myeloid monocytic leukemia; IPSS, International Prognostic Scoring System; IPSS-R, revised IPSS; ANC, absolute neutrophil count; L, low; VL, very low; lnt, middle; H, high; VH, very high; HMA, low methylating agent; EPO, erythropoietin (erythropoietin); CS, cyclosporine; OXM, oxymetolone; CR, complete remission; mCR, bone marrow response; HI, hematologic improvement; SD, no change.

Reactive patients appeared more frequently in the group that prescribed azacitidine. Compared with baseline characteristics, there was no significant difference between the two groups, but the mean age of the patients was higher for patients prescribed azacitidine (see Table 2, below).

* WHO, World Health Organization; RCUD, single sequence formation abnormal refractory cytopenias; RCMD, multiple system formation abnormal refractory cytopenias; RAEB, increased maternal cell refractory anemia; CMML, chronic myeloid monocytic leukemia; IPSS, International Prognostic Scoring System; IPSS-R, revised IPSS; ANC, absolute neutrophil count; L, low; VL, very low; lnt, middle; H, high; VH, very high; BM, bone marrow; EPO, erythropoietin (erythropoietin); CS, cyclosporine; OXM, oxymetolone.

Response to treatment was assessed according to the modified International Working Group (IWG 2006) criteria. Stable disease with complete response, partial response, bone marrow response or hematologic improvement proved to be reactive (n = 57, 53.3%) and the rest (n = 50, 46.7% Were considered non-responsive. This study was approved by the judges of each institution. Patient consent was obtained from each patient.

[ Experimental Example  2]

Statistical analysis

Categorical variables were compared using Fisher exact test or Chi square test, and continuous variables were compared using Student's t test. For the survival analysis, the duration used in the time-to-event analysis was defined as the overall survival (OS), the overall survival rate, or the free-AML survival (no AML survival) ). Survivors in the univariate survival analysis were calculated according to the Kaplan-Meier method. Differences in survival curves were assessed using the log-rank method. For multivariate analysis, stepwise multiple logistic regression analysis and Cox proportional hazard analysis model were used. All statistical analyzes showed that only P values less than 0.05 were significant.

[ Experimental Example  3]

target  Performing deep sequencing and analyzing results

Experimental Example  3-1. target  Performing deep sequencing

Using targeted deep sequencing, the total associated with MDS 26 genes (DNMT3A, TET2, EZH2, RUNX1 , ASXL1, STAG2, CBL, TP53, SRSF2, SF3B1, U2AF1, LAMB4, DNMT1, ETV6, KRAS, NF1, NPM1, NRAS , PRPF8 , IDH1 , IDH2 , JAK2 , FLT3 , SETBP1 , ATRX And ZRSR2) were analyzed in the 107 MDS genome (57 responders and 50 non-responders). Briefly, sequencing libraries were generated according to the manufacturer's instructions using the AmpliSeq Library Kit 2.0 (Life Technologies, Carlsbad, Calif.) With customized target panels. This customized target contains 1,088 amplicons containing 98.4% of the coding region genes of 26 target genes. Sequencing was done using the P1 chip from Ion Torrent Proton (Life Technologies) as instructed. Using Torrent Suite 4.2, the gene variables and the sequencing reads as GRCh37 / hg19 are listed.

In order to find a significant mutation, a stringent post-filtering method was performed. Initially, functional parameters in the coding region gene were selected. Known polymorphic sites (> 1% of the frequency of minor genetic traits) were determined to be polymorphic, filtered, and removed through generic databases (dbSNP138, ESP6500, and the 1000 Genome Project). Variables with a frequency of> 1% minority genotypes were also filtered out in internal organ data (38 whole genomes and 2,283 total exome nucleotide sequences in Korean). The remaining variables were judged to be candidate somatic mutations.

Experimental Example  3-2. target  Deep Sequencing Results

Most of the MDS genomes (94/107, 87.9%) showed mutations in at least one target gene (see Figure 1). On average, 1.9 single nucleotide variants (SNVs) and indels per genome were identified (SD 1.4, range 0-7). There was no significant difference in the number and pattern of mutations between the reaction group (mean 1.7 mutations; 0-5) and the unreacted group (mean 2.2 mutations; 0-7) (see FIG. 2). Six of the mutated genes were detected in more than 10% of 107 MDS patients;U2AF1 (19.6%),ASXL1 (18.7%),TET2 (15.9%),TP53(12.1%),RUNX1 (11.2%) andSF3B1 (10.3%).

The mutation frequency identified in the present invention DNMT3A, DNMT1, SRSF2, IDH2, and NPM1 (See Table 3), except for some exceptions, which were similar to those identified in other MDS studies.

Experimental Example  3-3. Low methylation  Formulation therapy ( HMT ) Factors associated with reactivity

Although the gene mutations detected in Experimental Example 3-2 were largely similar to those identified in other MDS studies, these studies did not mention the hypomethylated drug therapy (HMT) reactivity, and even if used for predicting prognosis Only some prognoses were reported. In the present invention, in order to more specifically select the factors related to the HMT therapeutic reactivity of the MDS patients, the prognostic factors were selected again according to the reactivity of the detected mutants to HMT.

Through univariate analysis, SETBP1 Only mutations were found to be significantly associated with non-responsiveness to HMT (mutation frequency: 0% (0/57) versus 8% (4/50), P = 0.045) 5). all including p.G870S (n = 3) and p.A1193T (n = 1) The SETBP mutation appeared to be a missense mutation in the ciprofloxine gene provided in the COSMIC database (Fig. 3A).

* For variable analysis, multiple logistic regression analysis was performed for variables with a P value of less than 0.1 (hemoglobin, platelet, low methylating agent, mutations of TP53, SETBP1 and U2AF1 genes). OR, and the ratio of the low methylating agent.

* HMT, low methylation therapy; AML, acute myelogenous leukemia; ANC, absolute neutrophil count; Hb, hemoglobin; PLT, platelets; IPSS-R, revised international prognostic score system; VG, very good; G, good; INT, medium; P, bad; VP, very bad; LR, low risk; HR, high risk; HMA, low methylating agent; AZA, azacytidine; DAC, decitabine; Bold indicates P <0.05.

U2AF1 Mutations were more frequent in non-responders, but significance remained at the border (12.3% of responders vs. 28% of non-responders, P = 0.052) (see Table 5). All U2AF1 , including p.S34F (n = 12), p.S34Y (n = 7) and p.Q157R Mutations are error mutations provided in the COSMIC database (Figure 3B). In the clinical variables, low hemoglobin levels (<10 g / dL) and low platelet counts (<50,000 / μL) were found to be significantly associated with low reactivity (low hemoglobin level: 46.8% P = 0.029; low platelet count: 39.0% vs. 62.1%, P = 0.028). Multivariate analysis using only the P <0.1 values obtained from the Chi-square test revealed a hemoglobin level of 10 g / dL (OR 3.56, 95% CI 1.22-10.33, P = 0.020) and platelets <50,000 / % CI 1.05-5.93, P = 0.039) were associated with a positive response to HMT in MDS (see Table 4). Although TET2 The frequency of mutations was the same as in previous reports (see Table 3), but the mutation did not significantly affect the response of HMT in our study. Clinical and genetic variables associated with reactivity to HMT are shown in Table 5.

Next, we performed subgroup analyzes using HMA type (66 patients treated with azacytidine and 41 patients treated with decitabine). U2AF1 Mutations were significantly associated with the non-responsiveness of the azacytidine-receiving group (mutation frequency: 2.5% (1/40) versus 30.8% (8/26), P = 0.002) in the susceptible group Did not ( P = 0.507). Significantly, eight azacitidine-responsive patients had a pS34F / Y mutation known to be important for MDS ( Fig . 3B ). Frequently occurring mutations, including TET2 , ASXL1 , and RUNX1 , failed to show significant results in both azacitidine and decitabine treated groups. When multivariate analysis was performed as above, U2AF1 Only mutations (OR 14.96, 95% CI 1.67-134.18, P = 0.016) were independent prognostic factors for reactivity to azacytidine in MDS.

Experimental Example  3-4. Of acute myelogenous leukemia Without an outbreak  Survival rate ( No AML ) And associated factors

Survival analysis was performed to identify prognostic factors. The median duration of treatment was 2.28 years (range: 0.07 to 6.24 years) after HMT initiation; 45 patients died and 28 patients developed Acute myeloid leukemia (AML). The overall 2 - year and AML - free survival rates were 62.4% and 71.3%, respectively. Univariate analysis showed a lower overall survival rate ( P < 0.05) for the six mutations ( DNMT1 , P = 0.012; DNMT3A , P = 0.001; TP53 , P = 0.003; NPM1 , P = 0.029; NRAS , P <0.001; KRAS , P = Overall Survival, OS) (see Table 5).

KRAS and NRAS When the mutation was incorporated into the RAS mutation, the OS of the patients with RAS mutation was significantly reduced ( P <0.001) (see Table 5) compared to the non-mutated patients. In addition to these mutations, five clinical variables (male sex, P = 0.006; age ≥60, P = 0.004; BM blast ≥5%, P = 0.029; low IPSS- (VP), P = 0.007; IPSS-R high (H) or very high (VH), P = 0.039) were associated with low OS in the univariate analysis (see Table 5). When multivariate analysis was performed using clinical variables that were significant in candidate mutation and univariate analysis, independent prognostic factors of OS were DNMT1 (Hazard ratio [HR] = 4.08, 95% CI 1.14-14.62, P = 0.031), DNMT3A (HR = 4.12, 95% CI 1.51-11.22, P = 0.006), RAS (HR = 2.76, 95% CI 1.03-7.37, P = 0.043), and TP53 (HR = 3.17, 95% CI 1.35-7.43, P P = 0.002 and IPSS-R H / VH, HR = 2.36, 95% CI 1.11-5.02 , P = 0.008) and two clinical variables (male to female, HR = 3.70, 95% CI 1.63-8.36 , 0.026) (Table 6). The presence or absence of mutations in the four genes was not associated with the type of HMA or frequency of treatment. Mutations that were significantly associated with OS are shown in FIG.

* Variability Survival analysis was performed by the Kaplan-Meier method.

a Cox proportional hazards model was established with variables with a P value of less than 0.1 in the univariate analysis.

IPSS-R, revised international prognostic score system; VL, very low; L, low; INT, medium; H, high; VH, very high; WT, wild type; MT, mutated.

Multivariate analysis showed that DNMT3A ( P < 0.001), STAG2 ( P < 0.001), NPM1 ( P = 0.042) and NRAS ( P <0.001) mutations showed low AML-free survival rates (Table 5). The RAS mutation was also associated with low AFS ( P <0.001). Two clinical variables (BM blast ≥5%, P = 0.015 and IPSS-R H / VH, P = 0.044) were associated with low AFS through multivariate analysis (Table 5). Through multivariate analysis, DNMT3A (HR = 12.81, 95% CI 4.04-40.63, P <0.001), TP53 (HR = 2.80, 95% CI 1.01-7.75, P = 0.047), and RAS (HR = 7.04, 95% CI 2.24-22.12, P = 0.001) and two clinical variables (male to female, HR = 2.85, 95% CI 1.15-7.09, P = 0.024 and IPSS-R H / P = 0.005) were found to be independent prognostic factors for low AFS (Table 6). Significant mutations associated with AFS are shown in FIG.

Risk assessment system to predict therapeutic response

To predict reactivity by azacytidine, we used clinical and genetic factors that were found to be associated with therapeutic reactivity ( U2AF1 Mutation, hemoglobin, and platelet count). In the case of OR obtained through multivariate analysis, patients were divided into four groups; Group 1 (hemoglobin ≥ 10 g / dL, platelet count ≥50,000 / μL, and U2AF1 Wild type), Group 2 (hemoglobin <10 g / dL or platelet count <50,000 / μL, and U2AF1 Wild type), Group 3 (hemoglobin <10 g / dL, platelet count <50,000 / μL, and U2AF1 Wild type), and Group 4 ( U2AF1 excluding clinical factors Mutation). Specific classification criteria are shown in Table 7 below.

As shown in FIG. 6A, the proportion for HMT-reactive patients was significantly different between groups: 85.7% (12/14) in Group 1, 70.0% (21/30) in Group 2, 46.2% 6/13), and 11.1% (1/9) in group 4 ( P = 0.002).

Risk assessment system to predict survival rate

A similar rating method was introduced to measure survival rate. Male gender and, IPSS-R H / VH and each of the four genes for mutation of the (DNMT1, DNMT3A, RAS, and TP53) were given each point for one, female gender, IPSS-R VL / L / Int and wild-type four kinds Gene was given zero points. Based on the total sum of the scores, the patients were divided into four groups; The risk is low (total sum = 0), mid-1 (total sum = 1), mid-2 (total sum = 2) and high (total sum ≥3). Specific classification criteria are shown in Table 8 below.

As shown in FIG. 6B, it was confirmed that the OS ( P <0.001) and AFS ( P <0.001) decreased in proportion to the score as the sum of the total scores increased.

[ Comparative Example  One]

Non-patent literature  002 Low methylation  Therapeutic Reactive Predictive Score System

The low-methylation therapeutic response predictive scoring system described in Non-Patent Document 002 (F. Traina, Impact of molecular mutations on treatment response to DNMT inhibitors in myelodysplasia and related neoplasms , Leukemia (2015) 28, 78-87) As shown above.

[ Comparative Example  2]

Non-patent literature  001 Low methylation  Therapeutic Reactive Predictive Score System

The prognostic prediction score system described in Non-Patent Document 001 is as shown in Table 10 below.

[ Comparative Example  3]

The prognostic predictor system described in Non-Patent Document 002 (F. Traina, Impact of molecular mutations on treatment response to DNMT inhibitors in myelodysplasia and related neoplasms , Leukemia (2015) 28, 78-87) .

[ Experimental Example  4]

Example  1 and Comparative Example  Comparison of prediction accuracy of 1

The present inventors have investigated how more accurate prediction of the low methylation therapeutic reactive score system presented in Example 1 of the present invention is in comparison with the conventional prediction method shown in Comparative Example 1.

The prognostic significance of the treatment response scoring system model of the myelodysplastic syndrome is described in Non-Patent Document 002 (F. Traina, Impact of molecular mutations on treatment response to DNMT inhibitors in myelodysplasia and related neoplasms , Leukemia (2015) 28, 78-87) The model is known and predicts therapeutic response based on platelet count, leukocyte count, and TET2 , DNMT3A mutations. In this model, risk is categorized into three groups based on risk scores (see Table 9).

In the therapeutic response score system of Example 1 presented in the present invention, the therapeutic response is predicted based on hemoglobin, platelet count and U2AF1 mutation, and classified into four groups according to the risk score (see Table 7).

The comparison between the two models was performed for all patients with osteoarthritic syndrome (107 cases) in Korea. The results of applying the comparative example 1 to the Korean sample set showed that the linear-by-linear association value was 0.891 (Table 12). This result means that the prediction model of Comparative Example 1 is not suitable for Koreans. As a result of applying the newly proposed therapeutic response prediction scoring system to the same sample set, the linear correlation value was less than 0.001 (Table 13).

The results of applying the Comparative Example 1 system to Korean osteoarthritic syndrome Total score A dangerous group Number of people( % ) Number of respondents ( % ) P ³ 0 or 1 Favorable 33 (30.8%) 19 (57.6%) 2 Intermediate 53 (49.5%) 26 (49.1%) 3 Unfavorable 21 (19.6%) 12 (57.1%) 0.891

The results of applying the system of Example 1 to Korean osteoarthritic syndrome Total score A dangerous group Number of people( % ) Number of respondents ( % ) P ³ 0 Favorable 16 (15.0%) 14 (87.5%) One Intermediate-1 48 (44.9%) 28 (58.3%) 2 Intermediate-2 22 (20.6%) 8 (36.4%) ≥ 5 Unfavorable 21 (19.6%) 7 (33.3%) <0.001

To compare the sensitivity, specificity, and accuracy between the two models, each model was divided into two groups (reactive and nonreactive). To this end, the 'favorable' group was assigned to the responding group, the 'unfaborable' group was the non-responding group, and the 'intermediate' group was not included in the analysis. In the scoring system according to the first embodiment of the present invention, 'favorable' and 'intermediate-1' groups were used as the reaction group, and 'intermediate-2' and 'unfavorable' groups were used as the non-responding group.

As a result of the analysis, as shown in the following Table 14, not only the sensitivity specificity accuracy of Example 1 was higher than that of Comparative Example 1 but also the application range of the 'intermediate' group was widened. Therefore, the treatment response prediction score system newly proposed in the present invention is more effective than the existing prediction model in that more accurate prediction can be segmented.

Comparison of Sensitivity, Specificity, and Accuracy between Two Systems in Korean Myelodysplastic Syndrome responsiveness Specificity accuracy Comparative Example 1
(Favorable 33 cases vs. Unfavorable 21 cases) 61.3% 39.1% 51.9% Example 1
(Favorable 16 cases, Int-1 48 cases, Int-2 22 cases, Unfavorable 21 cases) 73.7% 56.0% 65.4%

In summary, the gene U2AF1 presented in Example 1 of the present invention was significantly associated with the non-response to azacitidine. Thus, the system of the present invention comprising this gene can more accurately predict the therapeutic response to azacytidine. Indeed, sensitivity and specificity accuracy were significantly higher for patients receiving azaclitidine (66 cases) as a result of the scoring system (see Tables 15, 16 and 17).

The results of applying the system of Comparative Example 1 to the azacitidine administration group Total score A dangerous group Number of people( % ) Number of respondents ( % ) P ³ 0 or 1 Favorable 22 (33.3%) 14 (63.6%) 2 Intermediate 33 (50.0%) 20 (60.6%) 3 Unfavorable 11 (16.7%) 6 (54.5%) 0.717

The results of applying the system of Example 1 to the azacytidine-treated group Total score A dangerous group Number of people( % ) Number of respondents ( % ) P ³ 0 Favorable 14 (21.2%) 12 (85.7%) One Intermediate-1 30 (45.5%) 21 (70.0%) 2 Intermediate-2 13 (19.7%) 6 (46.2%) 3 Unfavorable 9 (13.6%) 1 (11.1%) <0.001

Comparison of sensitivity, specificity and accuracy between two systems in azacytidine-treated group responsiveness Specificity accuracy Comparative Example 1
(Favorable 22 cases vs. Unfavorable 11 cases) 70.0% 38.5% 57.6% Example 1
(Favorable 14 cases, Int-1 30 cases, etc.).
Int-2 13 cases, Unfavorable 9 cases) 82.5% 57.7% 72.7%

[ Experimental Example  5]

Example  2 and Comparative Example  Comparison of prediction accuracy of 2 and 3

The present inventors have investigated whether the second embodiment of the present invention can predict more accurately than the conventional prediction method. As a prognostic prediction model of the myelodysplastic syndrome, IPSS-R (Revised International Prognostic Scoring System) of Non-Patent Document 001 described in Comparative Example 2 and Non-Patent Document 002 (F. Traina, Impact of molecular the prognostic model of the mutations on treatment response to DNMT inhibitors in myelodysplasia and related neoplasms, Leukemia (2015) 28, 78-87) is known.

Comparisons between Example 2 of the present invention and the conventional prediction models (Comparative Examples 2 and 3) were performed in 107 Korean patients with myelodysplastic syndromes and all survival rates and / or survival rates without acute myelogenous leukemia (AML-free survival).

In the model of Comparative Example 2, prognosis was predicted based on cytogenetic factors, BM blast, hemoglobin, platelet and ANC, and classified into five groups according to the risk score (see Table 10). In the comparative example 3 model, cytogenetic risk, age, hemoglobin, SF3B1 And ASXL1 mutations, and classified into two groups based on 12 risk points (see Table 11).

In the second embodiment of the present invention model predicting the prognosis as gender, IPSS-R, DNMT1, DNMT3A , RAS , and based on the presence or absence and TP53 mutations are classified into four groups based risk score (see Table 8).

As a result of comparing the overall survival rate between the respective models, the model of Example 2 showed the most significant result ( P = 1.8x10 ^-12 ), while the model of Example 3 did not show statistical significance P = 0.078 ). The model of Comparative Example 2 showed statistical significance in predicting the overall survival rate ( P = 0.032 ), but the significance was significantly lower than that of the Example 2 model. These results indicate that the model according to the second embodiment of the present invention is most excellent in predicting the prognosis of Korean myelodysplastic syndromes.

Next, the possibility of survival (AML-free survival) without acute myelogenous leukemia was analyzed. 8, was significantly predict the probability of survival without disease of acute myelogenous leukemia in Example 2. As shown in the model (P = 4.0x10 ^-12). The statistical significance of the model ( P = 0.941 ) and the model of the comparative example 2 ( P = 0.194 ) in the analysis of the possibility of survival without acute myelogenous leukemia could not be confirmed. This means that the prognostic prediction model of Example 2 presented in the present invention can accurately predict not only the overall survival rate but also the possibility of survival without the onset of acute myelogenous leukemia.

Claims (13)

DNMT1, DNMT3A, RAS and predicted composition of the survival prognosis of myelodysplastic syndrome, comprising a reagent that can detect whether or not mutations in the TP53 biomarker in the nucleic acid level. delete The method according to claim 1,
Reagents capable of detecting the mutation of the biomarker at the nucleic acid level include polymerase chain reaction, reverse transcription polymerase chain reaction, competitive polymerase chain reaction, Nuclease protection assay (RNase, S1 nuclease assay), in situ hybridization, A composition for predicting the survival prognosis of a patient suffering from a myelodysplastic syndrome characterized by being a reagent for use in a microarray or Northern blot. DNMT1, DNMT3A, RAS and a gene panel to check for mutations in the TP53 gene, DNMT1, DNMT3A, RAS and TP53 myelodysplastic predicted gene panel for survival prognosis in a patient's syndrome containing the gene. A kit for predicting the survival prognosis of a patient with a myelodysplastic syndrome comprising the composition of claim 1 or the gene panel of claim 4. In gene samples from patients,
DNMT1, DNMT3A, RAS and TP53 to provide information for predicting the survival outcome of myelodysplastic syndrome, comprising the step of identifying a mutation in the gene. The method according to claim 6,
Wherein said patient is an Asian patient. &Lt; RTI ID = 0.0 &gt; 18. &lt; / RTI &gt; delete delete The method according to claim 6,
In the step of confirming the mutation, DNMT1, DNMT3A, RAS, and if there is mutated in any one or more genes selected from the group consisting of TP53 gene, survival prognosis survival of myelodysplastic syndrome, comprising the step of predicting that bad A method of providing information for predicting prognosis. 11. The method of claim 10,
Wherein the non-marker clinical information is further used in the predicting step. &Lt; RTI ID = 0.0 &gt; 8. &lt; / RTI &gt; 12. The method of claim 11,
Wherein the non-marker clinical information is at least one selected from the group consisting of hemoglobin level, platelet count, sex information, and revised international prognostic scoring system (IPSS-R) information. To provide information for predicting survival prognosis. 11. The method according to claim 10, wherein the survival prognosis is calculated by summing the following risk scores. If the score is 0, it is classified as a low-risk group. If the score is 1, The present invention provides a method for predicting survival prognosis of a patient suffering from a myelodysplastic syndrome,
a) 1 for male and 0 for female;
b) 1 if the IPSS-R value is higher than or equal to 0;
c) 1 if the DNMT1 gene is mutated; 0 if not;
d) 1 if the DNMT3A gene is mutated, 0 if not;
e) 1 if the RAS gene is mutated; 0 if not; And
f) 1 if the TP53 gene is mutated, 0 if not.

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