Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization

• Published in: BMC cancer Vol. 17; no. 1; pp. 513 - 12
• Main Authors: Wang, Lin, Li, Xiaozhong, Zhang, Louxin, Gao, Qiang
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 02.08.2017; BioMed Central; BMC
• Subjects: Algorithms; Anticancer drug response prediction; Antimitotic agents; Antineoplastic agents; Antineoplastic Agents - pharmacology; Cancer; Cell Line, Tumor; Computational Biology - methods; Databases, Factual; Dosage and administration; Dose-Response Relationship, Drug; Drug Repositioning; Drug Resistance, Neoplasm - genetics; Genetic aspects; Humans; Matrix factorization; Pharmacogenetics; Pharmacogenetics - methods; Pharmacogenomic Variants; Precision cancer medicines; Precision Medicine - methods; Reproducibility of Results; Drug repositioning; Anticancer drug response prediction; Matrix factorization; Precision cancer medicines
• DOI: 10.1186/s12885-017-3500-5
• ISSN: 1471-2407

Human cancer cell lines are used in research to study the biology of cancer and to test cancer treatments. Recently there are already some large panels of several hundred human cancer cell lines which are characterized with genomic and pharmacological data. The ability to predict drug responses using these pharmacogenomics data can facilitate the development of precision cancer medicines. Although several methods have been developed to address the drug response prediction, there are many challenges in obtaining accurate prediction. Based on the fact that similar cell lines and similar drugs exhibit similar drug responses, we adopted a similarity-regularized matrix factorization (SRMF) method to predict anticancer drug responses of cell lines using chemical structures of drugs and baseline gene expression levels in cell lines. Specifically, chemical structural similarity of drugs and gene expression profile similarity of cell lines were considered as regularization terms, which were incorporated to the drug response matrix factorization model. We first demonstrated the effectiveness of SRMF using a set of simulation data and compared it with two typical similarity-based methods. Furthermore, we applied it to the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets, and performance of SRMF exceeds three state-of-the-art methods. We also applied SRMF to estimate the missing drug response values in the GDSC dataset. Even though SRMF does not specifically model mutation information, it could correctly predict drug-cancer gene associations that are consistent with existing data, and identify novel drug-cancer gene associations that are not found in existing data as well. SRMF can also aid in drug repositioning. The newly predicted drug responses of GDSC dataset suggest that mTOR inhibitor rapamycin was sensitive to non-small cell lung cancer (NSCLC), and expression of AK1RC3 and HINT1 may be adjunct markers of cell line sensitivity to rapamycin. Our analysis showed that the proposed data integration method is able to improve the accuracy of prediction of anticancer drug responses in cell lines, and can identify consistent and novel drug-cancer gene associations compared to existing data as well as aid in drug repositioning.