Research On The Acquired Drug Resistance To Cancer Kinase Inhibitors For Cancer Treatment Based On Transcriptomics Data

Cancer and related malignant diseases have always been a primary concern of medical science.An important type of cancer treatment drugs,kinase inhibitors have been increasingly proscribed for treatment in the last 20 years,and new kinase inhibitors are being developed and approved.Due to high adaptability to cancer heterogeneity,kinase inhibitors can be used as a drug treatment for many cancer patients who cannot undergo surgery or who are resistant to chemotherapy.However,with the long-term use of kinase inhibitors,acquired drug resistance is becoming more and more common,which makes the treatment of patients more difficult and cost expensive.Researchers have tried to explore the internal mechanism,and proscribe a compound to reverse drug resistance and restore patient sensitivity to kinase inhibitors with in vitro drug resistance models.However,experimental methods often focus on the mechanism of resistance to a certain kinase inhibitor,and fail to provide overall analysis of the mechanism of resistance to all kinase inhibitors.Therefore,this paper focuses on the following two aspects.First is to analyze the mechanism of resistance in existing patients;second is to provide reliable compounds which can reverse resistance.The four parts of this paper all focus on these two aspects.The first part of this paper was to collect and classify transcriptome data on acquired drug resistance in cancer patients from public databases,and to conduct cluster analysis and pattern division of these cases.In this work,signature gene sets of drug resistance cases were extracted from transcriptome data,similarity networks of drug resistance cases were determined,and hierarchical clustering was performed.The cases were divided into four patterns: "same drug,same cancer","same drug,different cancers","different drugs,same cancer",and "different drugs,different cancers".These patterns provide the basis for the subsequent systematic research.Based on the results of the first part,the second part of this paper determined the subnets of up-down signature genes in the protein-protein interaction network.Then,the hub genes were classified according to the degree of nodes in the subnetwork.Functional annotation was used to analyze the hub genes and the biological processes of drug resistance in clusters with different patterns,which further confirmed the rationality.In this part of the study,the protein-protein interaction network was used to analyze the mechanisms of drug resistance.Based on the previous results,the third part of this paper used LINCS transcriptomic omics data to construct a prediction framework of drug resistance for hub genes.In this framework,GSEA enrichment analysis is used to calculate the enrichment score of signature gene sets from the transcriptome data on LINCS overexpression and silencing,and the top 50 overexpressed genes or silencing genes with positive enrichment scores were selected as the hub genes to predict drug resistance.For instance,in cluster 1,there is literature supporting that the BCR-ABL gene in the prediction list can reverse the acquired resistance to BTK inhibitors for diffuse large Blymphoma.The framework provides prediction of drug resistance for hub genes and recommends drugs based on patient genetics.Building on the former results,the fourth part of this paper constructed a prediction framework for a strategy to reverse drug resistance by using LINCS transcriptomic omics data.The GSEA enrichment analysis was used to calculate the enrichment score of signature gene sets in the LINCS compound-disturbed transcriptomic data.The 50 compounds with lowest enrichment score were selected as the predictable compounds with potential to reverse drug resistance.For instance,in cluster 5,there are multiple publications supporting that the use of inhibitors such as NFk B,Rho kinase,ERK on the prediction list can all reverse acquired resistance to BRAF inhibitors for melanoma.The framework can quickly and efficiently predict strategies that can reverse drug resistance.This paper is most unique in the following three aspects.First,based on the expression profiles in the GEO database,more systematic computational research is carried out on the problem of acquired resistance to cancer kinase inhibitors,four patterns of drug resistance are classified,and the drug resistance mechanism of each cluster is studied.Secondly,a framework is proposed for predicting hub genes that trigger kinase inhibitor resistance based on LINCS transcriptomic omics data,which provides a basis for further study of the mechanisms of drug resistance.Third,a framework is proposed for predicting effective drug treatments for drug resistant patients based on the indications of LINCS transcriptomic omics data.