AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes

Haoxuan Wang; Tao Wang; Xiaolu Zhao; Honghu Wu; Mingcong You; Zhongsheng Sun; Fengbiao Mao

doi:10.1093/nargab/lqaa084

AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes

NAR Genom Bioinform. 2020 Oct 13;2(4):lqaa084. doi: 10.1093/nargab/lqaa084. eCollection 2020 Dec.

Authors

Haoxuan Wang¹, Tao Wang², Xiaolu Zhao³, Honghu Wu⁴, Mingcong You⁵, Zhongsheng Sun⁶, Fengbiao Mao¹

Affiliations

¹ Center of Basic Medical Research, Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing 100191, China.
² Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410083, China.
³ Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.
⁴ Department of Science and Technology, Second Affiliated Hospital of Nanchang University, Nanchang 330006, China.
⁵ Baiyining Medicine, Beijing 102200, China.
⁶ Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China.

Abstract

The current challenge in cancer research is to increase the resolution of driver prediction from gene-level to mutation-level, which is more closely aligned with the goal of precision cancer medicine. Improved methods to distinguish drivers from passengers are urgently needed to dig out driver mutations from increasing exome sequencing studies. Here, we developed an ensemble method, AI-Driver (AI-based driver classifier, https://github.com/hatchetProject/AI-Driver), to predict the driver status of somatic missense mutations based on 23 pathogenicity features. AI-Driver has the best overall performance compared with any individual tool and two cancer-specific driver predicting methods. We demonstrate the superior and stable performance of our model using four independent benchmarks. We provide pre-computed AI-Driver scores for all possible human missense variants (http://aidriver.maolab.org/) to identify driver mutations in the sea of somatic mutations discovered by personal cancer sequencing. We believe that AI-Driver together with pre-computed database will play vital important roles in the human cancer studies, such as identification of driver mutation in personal cancer genomes, discovery of targeting sites for cancer therapeutic treatments and prediction of tumor biomarkers for early diagnosis by liquid biopsy.