Abstract 2265: An integrated analysis of gene mutations and gene sets for predicting paclitaxel response in lung adenocarcinoma

Abstract

Abstract Lung cancer is the leading cause of cancer death worldwide. A prevalent histological subtype of lung cancer is adenocarcinoma. Chemotherapies and targeted therapies have been developed to treat such malignancy. However, due to the heterogeneity of cancer genomes, drug responses vary considerably among patients and the average survival rate remains quite unsatisfactory. Therefore, integrated biomarkers for predicting drug responses are greatly needed. Addressing this, in the present study we aimed to develop a prediction model based on an integrated analysis of gene mutations and gene sets. Briefly, the two-tailed Student's t-test was performed to identify the gene mutations and gene sets of which activities were associated with drug sensitivity, and classification trees were derived from these genomic features. We applied the analysis to genomic datasets and drug sensitivity data from the Cancer Cell Line Encyclopedia (CCLE) and gene sets defined in the Molecular Signatures Database (MSigDB), and constructed a prediction model for response to paclitaxel, a widely used drug for cancers, in lung adenocarcinoma. Taking KRAS mutation as an example, we identified 20 and 15 drug response-associated gene sets in KRAS-mutant and KRAS-wild type cell lines, respectively. The two lists of gene sets were mutually exclusive, suggesting the need of building individual prediction models for groups of cancer subtypes. We then built a classification tree for each of the two groups and tested their prediction performance by leave one out cross-validation tests; ∼64% and ∼81% accuracy was achieved for KRAS-mutant and KRAS-wild type cell lines, respectively. Gene sets of “SIG_CHEMOTAXIS” and “PID_ERB_GENOMIC_PATHWAY” served as crucial nodes for the trees of KRAS-mutant and KRAS-wild type cells, respectively. In conclusion, we developed a novel method that integrates gene mutations and gene sets for predicting drug responses and demonstrated its high performance in lung adenocarcinoma. Our model is widely applicable to identify potent biomarkers for anticancer drugs in cancers and accelerate the realization of precision medicine. Citation Format: Chia-Yu Huang, Yu-Chiao Chiu, Tzu-Pin Lu, Liang-Chuan Lai, Mong-Hsun Tsai, Tzu-Hung Hsiao, Eric Y. Chuang. An integrated analysis of gene mutations and gene sets for predicting paclitaxel response in lung adenocarcinoma. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2265.