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Machine learning based anti-cancer drug response prediction and search for predictor genes using cancer cell line gene expression

Genomics & Informatics 2021년 19권 1호 p.10 ~ 10
Qiu Kexin, 이중호, 김한별, 윤석현, 강근수,
소속 상세정보
 ( Qiu Kexin ) - Dankook University Department of Computer Science
이중호 ( Lee Joong-Ho ) - Dankook University Department of Computer Science
김한별 ( Kim Han-Byeol ) - Dankook University Department of Computer Science
윤석현 ( Yoon Seok-Hyun ) - Dankook University Department of Computer Science
강근수 ( Kang Keun-Soo ) - Dankook University Department of Microbiology

Abstract


Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for completing oncology precision medicine. In this paper, based on the cancer cell line gene expression and the drug response data, we established a reliable and accurate drug response prediction model and found predictor genes for some drugs of interest. To this end, we first performed pre-selection of genes based on the Pearson correlation coefficient and then used ElasticNet regression model for drug response prediction and fine gene selection. To find more reliable set of predictor genes, we performed regression twice for each drug, one with IC50 and the other with area under the curve (AUC) (or activity area). For the 12 drugs we tested, the predictive performance in terms of Pearson correlation coefficient exceeded 0.6 and the highest one was 17-AAG for which Pearson correlation coefficient was 0.811 for IC50 and 0.81 for AUC. We identify common predictor genes for IC50 and AUC, with which the performance was similar to those with genes separately found for IC50 and AUC, but with much smaller number of predictor genes. By using only common predictor genes, the highest performance was AZD6244 (0.8016 for IC50, 0.7945 for AUC) with 321 predictor genes.

키워드

cell line gene expression data; drug response prediction; machine learning; predictor genes

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