Statistical Detection and Biochemical Classification of Cancer Driver Mutation Patterns in Biological Networks

 描述（由申请人提供）：癌症源于体细胞获得的遗传和表观遗传改变。虽然像癌症基因组图谱（TCGA）和国际癌症基因组联盟（ICGC）这样的大型联盟已经基于全基因组/外显子组测序对来自各种癌症类型的数千个肿瘤样本的基因组体细胞突变进行了分析，但对这些基因变异结果的有意义的机制解释仍然非常有限。一个基本但具有挑战性的任务是区分与癌症发展有因果关系的驱动突变和随机发生的中性效应的乘客突变。另一项关键任务是绘制、追踪和解释生物网络中驱动突变的功能影响。在网络背景下，与通路内基因相关的驱动突变通常显示出相互排斥的模式，这意味着每个患者在通路中携带恰好一个突变，这足以扰乱该通路的功能。另一个突出的模式是来自几个不同途径的基因的驱动突变可能同时发生，因为肿瘤形成需要多个途径的干扰。筛选互斥性和共现模式可以极大地促进识别相关驱动基因突变的新集合、其相关驱动途径以及这些驱动途径之间的功能关系。尽管在过去几年中已经提出了几种从头驱动突变基因集发现方法，但由于计算可行性，无法处理患者之间的突变异质性以及缺乏生化解释，它们具有重大局限性。 本提案的总体目标是开发并联合收割机高级序列变异分析与互补生物网络分析结合成一种高度新颖的系统生物学方法，该方法将：i）检测驱动调节/信号传导途径中的相关突变的集合，ii）关于这些途径在肿瘤发展过程中的作用，将这些途径分类为受刺激的、受抑制的或混合的，和iii）预测这些扰动途径的直接代谢结果。我们的具体目标是：1）开发一种统计方法，用于从头发现相互排斥和共存的驱动突变集;以及2）开发一种用于相关驱动突变集的途径映射和分类方法。从驱动突变基因集到抑制/刺激途径再到扰动生物网络的聚集肿瘤突变的识别和生化解释将在系统水平上为肿瘤进展提供新的机制见解。此外，利用这些信息，检测到的驱动途径中的潜在药物靶标可以被分类为需要激动剂或拮抗剂药物开发，从而使药物靶标评估和优先级排序更加有效。此外，鉴定特定基因和途径之间的共现可以有助于开发针对显示相同共现突变模式的患者组优化的多种治疗性癌症治疗。

 DESCRIPTION (provided by applicant): Cancer arises from somatically acquired genetic and epigenetic alterations. While large consortia like The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) have profiled genomic somatic mutations of thousands of tumor samples from various cancer types based on whole-genome/exome sequencing, meaningful mechanistic interpretation of these gene variation results are still very limited. One basic yet challenging task is to distinguish driver mutations, which are causally implicated in cancer development, from passenger mutations, which occur randomly with neutral effect. Another critical task is to map, trace, and interpret the functional impact of drivr mutations within biological networks. In a network context, driver mutations associated with genes within a pathway often show a mutually exclusive pattern, meaning that each patient carries exactly one mutation in the pathway, which is sufficient to perturb the function of that pathway. Another prominent pattern is that driver mutations of genes from several different pathways may co-occur, since perturbation of multiple pathways is required for tumor formation. Screening for mutual exclusivity and co-occurrence patterns can greatly facilitate the identification of novel sets of related driver gene mutations, their associated driver pathways, and functional relationships between these driver pathways. Although several de novo driver mutation gene set discovery methods have been proposed in the past few years, they have major limitations due to computational feasibility, an inability to deal with mutational heterogeneity across patients, and lack of biochemical interpretation. The overall goal of this proposal is to develop and combine advance sequence variation analyses with complementary biological network analyses into a highly novel systems biology approach that will: i) detect sets of related mutations in driver regulatory/signaling pathways, ii) classify these pathways as stimulated, inhibited, or mixed with respect to their role in the tumor development process, and iii) predict direct metabolic outcomes of these perturbed pathways. Our specific aims are: 1) to develop a statistical method for de novo discovery of mutually exclusive and co-occurrent sets of driver mutations; and 2) to develop a pathway mapping and classification method for related sets of driver mutations. The identification and biochemical interpretation of aggregated tumor mutations from driver mutation gene sets to inhibited/stimulated pathways to perturbed biological network will provide new mechanistic insights in tumor progression at a systems level. Also with this information, potential drug targets in the detected driver pathways can be classified as requiring agonist or antagonist drug development, making drug target evaluation and prioritization much more effective. Furthermore, identification of co-occurrence between specific genes and pathways may aid in the development of multi- therapeutic cancer treatments that are optimized to groups of patients showing the same mutational patterns of co-occurrence.