新一代测序技术使得我们可用计算机在众多癌症患者中预测那些与癌症发生发展密切相关的体细胞突变，称驱动突变。然而，更多的体细胞突变与癌症的发生发展没有关系，这类体细胞突变被称为随机突变。我们面临的挑战问题是：如何从随机突变中通过高性能计算识别与癌症密切相关的驱动突变？预测驱动突变的常规方法是寻找在大量癌症基因组中突变频繁的基因。人们发现驱动突变会同时干扰多个信号通路，这对上述常规方法提出了严峻的挑战。所以，每个癌症患者可能会有不同的突变组合都足以干扰这些信号通路。这种突变的异质性对传统上仅从突变发生的频率预测其驱动突变提出了疑问。我们将利用覆盖率和互斥性这两个组合性质来研究识别驱动信号通路的方法。本项目研究癌症患者体细胞突变的内在机制，探索其理论问题，设计快速有效的算法及软件预测体细胞突变数据中的驱动突变及驱动突变信号通路，并运用我们的算法和软件对各类癌症患者的体细胞突变数据进行分析。

Next-generation DNA sequencing technologies are enabling genome-wide discovery of somatic mutations which are highly related to cancer development that are called driver mutations. However, most mutations involved in sequencing data are not related to cancer at all that are called passenger mutations. Therefore, the major challenging problem we have to face is to distinguish those functional ‘‘driver mutations’’ important for cancer development from random ‘‘passenger mutations’’.A common approach for identifying driver mutations is to find genes that are mutated at significant frequency in a large cohort of cancer genomes. This approach is confounded by the observation that driver mutations target multiple cellular signaling and regulatory pathways. Thus, each cancer patient may exhibit a different combination of mutations that are sufficient to perturb these pathways. This mutational heterogeneity presents a problem for predicting driver mutations solely from their frequency of occurrence. We employ two combinatorial properties, coverage and exclusivity, to distinguish driver pathways, or groups of genes containing driver mutations, from groups of genes with passenger mutations. In this project, we will develop an effective and efficient algorithm with its implementation to find driver pathways from somatic mutation data. We then apply our algorithm to analyze somatic mutation data from genes in lung adenocarcinoma patients, genes in glioblastoma patients, and known mutations in patients with various cancers.