Wang Haoxuan, Wang Tao, Zhao Xiaolu, Wu Honghu, You Mingcong, Sun Zhongsheng, Mao Fengbiao
Center of Basic Medical Research, Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing 100191, China.
Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410083, China.
NAR Genom Bioinform. 2020 Oct 13;2(4):lqaa084. doi: 10.1093/nargab/lqaa084. eCollection 2020 Dec.
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.
癌症研究当前面临的挑战是提高驱动因素预测的分辨率，从基因水平提升到突变水平，这与精准癌症医学的目标更为契合。迫切需要改进区分驱动因素和乘客因素的方法，以便从越来越多的外显子组测序研究中挖掘出驱动突变。在此，我们开发了一种集成方法AI-Driver（基于人工智能的驱动因素分类器，https://github.com/hatchetProject/AI-Driver），用于基于23种致病性特征预测体细胞错义突变的驱动状态。与任何单一工具以及两种癌症特异性驱动因素预测方法相比，AI-Driver具有最佳的整体性能。我们使用四个独立的基准测试证明了我们模型的卓越性和稳定性。我们为所有可能的人类错义变体提供了预先计算的AI-Driver分数（http://aidriver.maolab.org/），以便在个人癌症测序发现的体细胞突变海洋中识别驱动突变。我们相信，AI-Driver连同预先计算的数据库将在人类癌症研究中发挥至关重要的作用，例如在个人癌症基因组中识别驱动突变、发现癌症治疗的靶向位点以及通过液体活检预测早期诊断的肿瘤生物标志物。
