Extensive prediction of drug response in mutation-subtype-specific LUAD with machine learning approach

作     者：KEGANG JIA YAWEI WANG QI CAO YOUYU WANG 

作者机构：Department of Thoracic SurgerySichuan Provincial People’s HospitalUniversity of Electronic Science and Technology of ChinaChengduChina School of MedicineUniversity of Electronic Science and Technology of ChinaChengduChina Department of Assisted Reproductive MedicineSichuan Provincial Academy of Medical Sciences&Sichuan Provincial People’s HospitalChengduChina 

出 版 物：《Oncology Research》 (肿瘤学研究（英文）)

年 卷 期：2024年第32卷第2期

页      面：409-419页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

主　　题：Lung adenocarcinoma Drug resistance Machine learning Molecular features Personalized treatment 

摘      要：Lung cancer is the most prevalent cancer diagnosis and the leading cause of cancer death *** failure in lung cancer(LUAD)is heavily influenced by drug *** challenge stems from the diverse cell populations within the tumor,each having unique genetic,epigenetic,and phenotypic *** variations lead to varied therapeutic responses,thereby contributing to tumor relapse and disease ***:The Genomics of Drug Sensitivity in Cancer(GDSC)database was used in this investigation to obtain the mRNA expression dataset,genomic mutation profile,and drug sensitivity information of *** Learning(ML)methods,including Random Forest(RF),Artificial Neurol Network(ANN),and Support Vector Machine(SVM),were used to predict the response status of each compound based on the mRNA and mutation characteristics determined using statistical *** most suitable method for each drug was proposed by comparing the prediction accuracy of different ML methods,and the selected mRNA and mutation characteristics were identified as molecular features for the drug-responsive cancer ***,the prognostic influence of molecular features on the mutational subtype of LUAD in publicly available ***:Our analyses yielded 1,564 gene features and 45 mutational features for 46 *** the ML approach to predict the drug response for each medication revealed an upstanding performance for SVM in predicting Afuresertib drug response(area under the curve[AUC]0.875)using CIT,GAS2L3,STAG3L3,ATP2B4-mut,and IL15RA-mut as molecular ***,the ANN algorithm using 9 mRNA characteristics demonstrated the highest prediction performance(AUC 0.780)in Gefitinib with ***:This work extensively investigated the mRNA and mutation signatures associated with drug response in LUAD using a machine-learning approach and proposed a priority algorithm to predict drug response for different drugs.