Comprehensive assessment of computational algorithms in predicting cancer driver mutations

• Main Authors: Hu Chen, Jun Li, Yumeng Wang, Patrick Kwok-Shing Ng, Yiu Huen Tsang, Kenna R. Shaw, Gordon B. Mills, Han Liang
• Format: Article
• Language: English
• Published: BMC 2020-02-01
• Series: Genome Biology
• Subjects: The Cancer Genome Atlas; Driver mutations; Passenger mutations; 3D clustering; TP53 mutations; Tumor transformation
• Online Access: http://link.springer.com/article/10.1186/s13059-020-01954-z

Abstract Background The initiation and subsequent evolution of cancer are largely driven by a relatively small number of somatic mutations with critical functional impacts, so-called driver mutations. Identifying driver mutations in a patient’s tumor cells is a central task in the era of precision cancer medicine. Over the decade, many computational algorithms have been developed to predict the effects of missense single-nucleotide variants, and they are frequently employed to prioritize mutation candidates. These algorithms employ diverse molecular features to build predictive models, and while some algorithms are cancer-specific, others are not. However, the relative performance of these algorithms has not been rigorously assessed. Results We construct five complementary benchmark datasets: mutation clustering patterns in the protein 3D structures, literature annotation based on OncoKB, TP53 mutations based on their effects on target-gene transactivation, effects of cancer mutations on tumor formation in xenograft experiments, and functional annotation based on in vitro cell viability assays we developed including a new dataset of ~ 200 mutations. We evaluate the performance of 33 algorithms and found that CHASM, CTAT-cancer, DEOGEN2, and PrimateAI show consistently better performance than the other algorithms. Moreover, cancer-specific algorithms show much better performance than those designed for a general purpose. Conclusions Our study is a comprehensive assessment of the performance of different algorithms in predicting cancer driver mutations and provides deep insights into the best practice of computationally prioritizing cancer mutation candidates for end-users and for the future development of new algorithms.