A therapeutic drug and drug target for KRAS mutation-positive high-risk colon adenocarcinoma

Abstract

The present invention discloses a therapeutic drug and drug target for KRAS mutation high-risk colon adenocarcinoma, belonging to the field of biomedicine. The present invention establishes a prognostic model based on KRAS mutation colon cancer genes, finds drug targets for patients with KRAS mutation high-risk colon adenocarcinoma, and analyzes the IC50 value of the drug to obtain the treatment of KRAS mutation high-risk colon adenocarcinoma by Dacinostat, Panobinostat or AZD5438. The present invention is the first to discover the effect of Dacinostat, Panobinostat or AZD5438 in treating KRAS mutation high-risk colon adenocarcinoma, and thus can be used as a potential drug for KRAS mutation high-risk colon adenocarcinoma for subsequent development.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine, and in particular to a therapeutic drug and a drug target for KRAS mutation high-risk colon adenocarcinoma. Background Art

[0002] Colon cancer is a common malignant tumor that occurs in the digestive tract. Nearly 100% of colorectal cancers (CRC) are diagnosed as colon adenocarcinoma (COAD). According to cancer statistics reported by the International Agency for Research on Cancer (IARC), there were more than 1.9 million new cases of CRC and 900,000 deaths worldwide in 2020, making CRC the second leading cause of cancer death worldwide.

[0003] The KRAS gene is a driver gene for many tumors, and the mutation rate in colon cancer is as high as more than 40%. However, KRAS is considered to be undruggable. However, after a lot of efforts, two drugs targeting the KRAS G12C mutation have finally been developed, one called Sotorasib and the other called adagrasib. However, the KRAS G12C mutation only accounts for 3-4% of the entire KRAS gene mutation, and there is still no drug available for the vast majority of KRAS mutations. Even the two drugs currently developed are not very effective in colon cancer. The phase 1 / 2 clinical trial of adagrasib's KRYSTAL-1 had a single-drug efficacy of only 22%, and mPFS (progression-free survival in Chinese) was 5.6 months. The effect of Sotorasib is even worse. Its phase II clinical study, the CodeBreak100 clinical trial results: the efficacy (ORR) is only 9.7%, the mPFS is 4 months, and the overall survival (OS) is 10.6 months. In short, drugs targeting KRAS G12C are still limited and cannot meet the clinical needs of current KRAS mutation patients. Therefore, it is necessary to develop new drugs!

[0004] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention

[0005] The purpose of the present invention is to provide the use of Dacinostat, Panobinostat or AZD5438 in the preparation of KRAS mutation high-risk colon adenocarcinoma drugs, which can determine the therapeutic drugs for KRAS mutant COAD high-risk patients and contribute to clinical precision treatment.

[0006] To achieve the above object, the present invention provides a drug for treating high-risk colon adenocarcinoma with KRAS mutation, wherein the active ingredient of the drug is Dacinostat, Panobinostat or AZD5438.

[0007] In one embodiment of the present invention, the drug for treating high-risk colon adenocarcinoma with KRAS mutation comprises a pharmaceutically acceptable carrier.

[0008] The present invention also provides the use of Dacinostat, Panobinostat or AZD5438 in preparing a drug for treating KRAS mutation high-risk colon adenocarcinoma.

[0009] The present invention also provides a drug target for KRAS mutation high-risk colon adenocarcinoma. The drug target for KRAS mutation high-risk colon adenocarcinoma is FMNL1, and its nucleotide sequence is shown in SEQ ID NO.1.

[0010] The present invention utilizes a variety of large medical databases: including 3 microarray data sets and 2 RNA-seq data sets, as well as the TCGA database, and uses LASSO-COX regression analysis to screen out 36 genes related to KRAS gene mutations in colon cancer from 211 genes related to KRAS prognosis. The risk score of the sample is calculated based on the expression level and difference of the selected genes, and a molecular prediction model is established to stratify colon cancer patients with KRAS gene mutations into high risk and low risk, which is conducive to clinical physicians to more accurately formulate reasonable and personalized treatment plans for colon cancer with KRAS mutations. This research result is of great significance to the precision treatment of colon cancer. In addition, the role of Dacinostat, Panobinostat or AZD5438 in the treatment of high-risk colon adenocarcinoma with KRAS mutations was discovered for the first time, and thus it can be used as a potential drug for high-risk colon adenocarcinoma with KRAS mutations for subsequent development. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic diagram of the process of establishing a prognostic model based on KRAS mutant colon cancer gene according to the present invention.

[0012] Figure 2A is a graph showing the IC50 values ​​of various drugs according to the present invention.

[0013] Figure 2B It is a schematic diagram of the IC50 estimation values ​​of the corresponding high-risk patient group and low-risk patient group of each drug according to the present invention. In the figure, the left side of each drug is the high-risk patient group and the right side is the low-risk patient group. DETAILED DESCRIPTION

[0014] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific implementation modes.

[0015] Unless explicitly stated otherwise, throughout the specification and claims, the term “comprise” or variations such as “include” or “comprising”, etc., will be understood to include the stated elements or components but not to exclude other elements or components.

[0016] In the present invention, a prognostic model for KRAS mutant colon cancer gene is first established, through which 36 genes with similar functions to KRAS gene can be screened, and then potential drugs for patients with high-risk colon adenocarcinoma with KRAS mutation can be determined.

[0017] In the present invention, the process of establishing the prognostic model adopted is as follows:

[0018] S1: Data collection and preprocessing: Obtain the sample matrix data and corresponding clinical information.

[0019] In this step, the matrix data includes 5 COAD datasets, including 3 microarray datasets and 2 RNA-seq datasets.

[0020] The three microarray datasets are GSE41258, GSE39582, and GSE17536. The two RNA-seq datasets include TCGA-COAD and CPTAC-COAD. The acquisition channels are as follows:

[0021] The data in the GSE39582 dataset were obtained from the GPL570 platform. Specifically, the matrix data and corresponding clinical information of 541 samples in GSE39582 were downloaded from the GEO database (https: / / www.ncbi.nlm.nih.gov / geo / ). The data in the GSE17536 dataset were obtained from the GPL570 platform, and the data in the GSE41258 dataset were obtained from the GPL96 platform. Specifically, the matrix data and corresponding clinical information of 177 samples in GSE17536 and 182 samples in GSE41258 were downloaded from the GEO database (https: / / www.ncbi.nlm.nih.gov / geo / ).

[0022] TCGA-COAD cohort: Transcriptome profiles, mutation data, and clinical follow-up information of 414 samples were downloaded from The Cancer Genome Atlas (TCGA) database (https: / / portal.gdc.cancer.gov), including 161 KRAS mutation samples (TCGA-COAD cohort).

[0023] CPTAC-COAD cohort: The colon cancer cohort prospectively collected by the Clinical Proteomics Tumor Analysis Consortium (CPTAC) is consistent with paired tumor and non-tumor colon tissues from 112 patients with colon cancer. RNA sequencing data, mutation data, and clinical data were all obtained from the cBioPortal database (https: / / www.cbioportal.org / study / summary?id=coad_cptac_2019). After excluding 6 samples without overall survival data, the remaining 106 samples were included in the CPTAC-COAD cohort, including 35 KRAS mutation samples.

[0024] After removing the batch effect, the 161 KRAS mutant samples in the TCGA-COAD cohort and the 35 KRAS mutant samples in the CPTAC-COAD cohort were combined into one RNA-seq cohort for further analysis.

[0025] S2: Prognostic model construction: Based on the acquired data, univariate Cox analysis was performed, and the prognostic model was established using the least absolute shrinkage and selection operators.

[0026] In this step, 216 samples with KRAS mutations in the GSE39582 data were used for model construction, and univariate Cox analysis was performed on the GSE39582 data to identify genes associated with the prognosis of KRAS mutant COAD.

[0027] Specifically, this step includes the following specific steps:

[0028] KRAS mutant samples in GSE39582 were randomized into training and test sets. The prognostic model was evaluated in the training set. The test set was used as internal validation, and KRAS mutant estimation samples in GSE39582, RNA-seq joint cohort, and GEO joint cohort were used as external validation;

[0029] The prognostic model was established using the least absolute shrinkage and selection operators, and the LASSO-COX regression was performed using the “glmnet” R package in the training set.

[0030] The least absolute shrinkage and selection operator (LASSO) is a linear regression method that uses l1-regularization to make the weights of some learned features zero. At the same time, it implements variable selection through regularization to avoid overfitting.

[0031] Calculate the risk score of the sample based on the expression level and coefficient of the selected genes:

[0032]

[0033] S3: Prediction of KRAS mutation status: A KRAS mutation status prediction model was constructed in the GSE39582 dataset using the random forest algorithm, and the KRAS mutation status in the GEO cohort was estimated.

[0034] The GEO cohort included two datasets without mutations (GSE17536 and GSE41258), and they were merged into one GEO cohort in order to obtain more reliable results with a larger sample size.

[0035] Random forest is an ensemble learning method based on decision trees for sample classification. Random forest analysis can account for the interrelationships among all potential predictor variables, including highly multi-level unordered categorical covariates, to build a tree-based prediction model.

[0036] Therefore, a KRAS mutation status prediction model was constructed in GSE39582 using random forest analysis, and the KRAS mutation status in the GEO cohort was estimated. Finally, 51 samples in the GEO cohort were predicted to be KRAS mutation samples, and these 51 estimated KRAS mutation samples were used as validation of the prognostic model.

[0037] S4: Prognostic model evaluation: The established prognostic model was evaluated using internal validation and external validation, respectively.

[0038] The test set of the GSE39582 dataset was used as internal validation, and the KRAS mutant estimation samples in the RNA-seq joint cohort and the GEO joint cohort were used as external validation.

[0039] In the above method, the genes in the GSE39582 dataset can be tested for prognosis. In the KRAS mutation samples, 211 genes were identified as prognosis-related genes (according to the p value obtained by the significance test method, p<0.01). The LASSO-COX method was used to select the optimal combination, and the prognostic model was constructed based on the prognostic genes in the training set. A total of 36 genes were selected for model construction, including APOL3, AUTS2, CD6, COG2, CUL1, CYP1B1, DBI, DHX29, FDFT1, GATA6, IFI27, LHPP, LONRF3, MEST, MMRN2, MRPS35, NLE1, NUP107, PLA2R1, POP4, PPP1R8, PSMG2, RHOBTB1, RHOF, S1PR2, SHC1, SLC2A5, SLCO2A1, SNRNP35, SSBP2, SYNGR1, TMEM159, TRIT1, VPS28, YTHDF2, and ZNF552.

[0040] The 36-gene model performed well in both actual and estimated KRAS-mutated COAD samples and was able to identify the FMNL1 gene as a potential drug target.

[0041] The GDSC database contains drug sensitivity data of more than 100 compounds in more than 1,000 cancer cell lines. Among them, drug response data of 13 KRAS mutant COAD cell lines were found. By molecular analysis of the drug sensitivity of these 13 KRAS mutant COAD cell lines, it was found that these 13 KRAS mutant COAD cell lines were sensitive to 12 drugs, including mitomycin c, Dacinostat, Belinostat, Omipalisib, Trichostatin A, Panobinostat, AZD5438, AZD7762, IGFR-3801, Trametinib, Sabutoclax, MG-132 ( Figure 2A The IC50 values ​​of various drugs are shown in the figure. The IC50 value refers to the concentration of the inhibitor when a certain enzyme catalytic reaction, antigen-antibody reaction, etc. is inhibited by half. In terms of apoptosis, it can be understood that a certain concentration of a certain drug induces 50% apoptosis of tumor cells. This concentration is called the 50% inhibitory concentration, that is, the concentration corresponding to the ratio of apoptotic cells to the total number of cells is equal to 50%). Then, further evaluation is made on whether these drugs are particularly effective for high-risk patients. Comparing the estimated IC50 values ​​of the high-risk patient group and the low-risk patient group, Figure 2BThe IC50 estimates in the high-risk group and the low-risk group are shown in . Comparing the IC50 estimates in the high-risk group and the low-risk group, not all of the 12 drugs are more effective in high-risk patients than in low-risk patients.

[0042] The IC50 values ​​of Dacinostat, Panobinostat, and AZD5438 were significantly reduced in high-risk patients (p<0.05), indicating that Dacinostat, Panobinostat, and AZD5438 may be potential drugs for high-risk patients with KRAS mutant COAD.

[0043] Through the above steps, it was found that high-risk patients were more sensitive to three drugs, including Dacinostat, Panobinostat and AZD5438.

[0044] The molecular formula of Dacinostat is C 22 H 25 N3O3, its chemical molecular structure is as follows:

[0045]

[0046] The molecular formula of Panobinostat is C 21 H 23 N3O2, its chemical molecular structure is as follows:

[0047]

[0048] The molecular formula of AZD5438 is C 18 H 21 N5O2S, its chemical molecular structure is as follows:

[0049]

[0050] Dacinostat and Panobinostat are two histone deacetylase inhibitors that have been used to treat blood diseases. In recent years, the effectiveness of these two histone deacetylase inhibitors in certain types of cancer has been studied. However, the role of these two histone deacetylase inhibitors in the treatment of colon cancer has not been studied. The present invention finds that Dacinostat and Panobinostat are effective drugs for patients with KRAS mutant colon cancer, especially patients with KRAS high-risk mutant COAD. AZD5438 is a pan-cyclin-dependent kinase (CDK) inhibitor that has been studied for the treatment of a variety of cancers. An existing study shows that AZD5438 can block the proliferation of p53-mutated colon cancer cells and induce apoptosis of colon cancer cells in a p53-independent manner. The present invention can further discover the application of AZD5438 in KRAS mutant colon cancer.

[0051] The present invention can identify the FMNL1 gene as a potential drug target, and use the three drugs as potential therapeutic drugs for KRAS mutant high-risk score COAD patients, providing a new idea for the precise treatment of KRAS mutant patients.

[0052] The nucleotide sequence of FMNL1 is shown in SEQ ID NO.1:

[0053]

[0054] The foregoing description of specific exemplary embodiments of the present invention is for the purpose of illustration and demonstration. These descriptions are not intended to limit the present invention to the precise form disclosed, and it is obvious that many changes and variations can be made in accordance with the above teachings. The purpose of selecting and describing the exemplary embodiments is to explain the specific principles of the present invention and its practical application, so that those skilled in the art can realize and utilize various different exemplary embodiments of the present invention and various different selections and changes.

[0055] It is intended that the scope of the invention be defined by the Claims appended hereto and their equivalents.

Claims

1. Use of Dacinostat, Panobinostat or AZD5438 in the preparation of drugs for KRAS mutation high-risk colon adenocarcinoma.

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