Predicting Anticancer Drug Resistance Mediated by Mutations

Pharmaceuticals

卷:15

Chin-Sheng Yu¹  Wei Yi²  Yu-Feng Lin²  Hsien-Yuan Lane³  Jia-Jun Liu⁴  Chih-Hao Lu⁴  Yu-Jen Chang⁴ 

[1] Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40201, Taiwan;

[2] Department of Medical Laboratory Science and Biotechnology, Asia University, Taichung 41354, Taiwan;

[3] Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan;

[4] The Ph.D. Program of Biotechnology and Biomedical Industry, China Medical University, Taichung 40402, Taiwan;

关键词: cancer drug;    drug resistance;    single amino acid variation;    protein structure;    machine learning;    feature selection;   

DOI  :  10.3390/ph15020136

来源: DOAJ

【 摘 要 】

Cancer drug resistance presents a challenge for precision medicine. Drug-resistant mutations are always emerging. In this study, we explored the relationship between drug-resistant mutations and drug resistance from the perspective of protein structure. By combining data from previously identified drug-resistant mutations and information of protein structure and function, we used machine learning-based methods to build models to predict cancer drug resistance mutations. The performance of our combined model achieved an accuracy of 86%, a Matthews correlation coefficient score of 0.57, and an F1 score of 0.66. We have constructed a fast, reliable method that predicts and investigates cancer drug resistance in a protein structure. Nonetheless, more information is needed concerning drug resistance and, in particular, clarification is needed about the relationships between the drug and the drug resistance mutations in proteins. Highly accurate predictions regarding drug resistance mutations can be helpful for developing new strategies with personalized cancer treatments. Our novel concept, which combines protein structure information, has the potential to elucidate physiological mechanisms of cancer drug resistance.

【 授权许可】

Unknown