SEMCM: A Self-Expressive Matrix Completion Model for Anti-cancer Drug Sensitivity Prediction

Background: Genomic data sets generated by several recent large scale high-throughput screening efforts pose a complex computational challenge for anticancer drug sensitivity prediction. Objective: We aimed to design an algorithm model that would predict missing elements in incomplete matrices and could be applicable to drug response prediction programs. Methods: We developed a novel self-expressive matrix completion model to improve the predictive performance of drug response prediction problems. The model is based on the idea of subspace clustering and as a convex problem, it can be solved by alternating direction method of multipliers. The original incomplete matrix can be filled through model training and parameters updated iteratively. Results: We applied SEMCM to Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets to predict unknown response values. A large number of experiments have proved that the algorithm has good prediction results and stability, which are better than several existing advanced drug sensitivity prediction and matrix completion algorithms. Without modeling mutation information, SEMCM could correctly predict cell line-drug associations for mutated cell lines and wild cell lines. SEMCM can also be used for drug repositioning. The newly predicted drug responses of GDSC dataset suggest that TI-73 was sensitive to Erlotinib. Moreover, the sensitivity of A172 and NCIH1437 to Paclitaxel was roughly the same. Conclusion: We report an efficient anticancer drug sensitivity prediction algorithm which is opensource and can predict the unknown responses of cancer cell lines to drugs. Experimental results prove that our method can not only improve the prediction accuracy but also can be applied to drug repositioning.