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

生物网络中癌症驱动突变模式的统计检测和生化分类

• 批准号: 9101334
• 负责人: Hunter Nathaniel Moseley
• 金额: $ 19.54万
• 依托单位: UNIVERSITY OF KENTUCKY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9101334
• 关键词: Accounting; Agonist; Biochemical; Biological; Classification; Computing Methodologies; Data; Detection; Development; Drug Targeting; Effectiveness; Epigenetic Process; Evaluation; Gene Mutation; Genes; Genetic; Goals; Heterogeneity; Human; Individual; International; Knowledge; Malignant Neoplasms; Maps; Markov chain Monte Carlo methodology; Metabolic; Methods; Mutate; Mutation; Outcome; Pathway interactions; Patients; Pattern; Process; Role; Sampling; Signal Pathway; Somatic Mutation; Statistical Methods; System; Systems Biology; Testing; The Cancer Genome Atlas; Therapeutic; Variant; actionable mutation; antitumor drug; base; biological systems; cancer classification; cancer genome; cancer therapy; cancer type; drug development; exome sequencing; genome-wide analysis; genomic profiles; innovation; insight; interest; novel; public health relevance; screening; success; tool; tumor; tumor progression; whole genome

 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.

 描述(申请人提供)：癌症源于躯体获得性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合会(ICGC)这样的大联盟已经基于全基因组/外显子组测序分析了来自不同癌症类型的数千个肿瘤样本的基因组体细胞突变，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司机突变和乘客突变，前者与癌症发生有因果关系，后者是随机发生的，影响是中性的。另一项关键任务是绘制、追踪和解释DRIVR突变在生物网络中的功能影响。在网络环境中，与一条通路中的基因相关的驱动程序突变通常显示出一种相互排斥的模式，这意味着每个患者在该通路中恰好携带一个突变，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会同时发生，因为肿瘤的形成需要多个途径的扰动。筛选相互排他性和共现模式可以极大地促进识别新的相关驱动基因突变集，它们相关的驱动通路，以及这些驱动通路之间的功能关系。尽管在过去的几年里已经提出了几种从头开始的驱动器突变基因集发现方法，但由于计算的可行性、无法处理患者之间的突变异质性以及缺乏生化解释，这些方法有很大的局限性。这项建议的总体目标是发展和结合先进的序列变异分析和互补的生物网络分析，形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/信号通路中的相关突变集合，ii)根据这些通路在肿瘤发展过程中的作用将这些通路分类为刺激、抑制或混合，以及iii)预测这些扰动通路的直接代谢结果。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程序突变集；以及2)开发相关驱动程序突变集的路径映射和分类方法。从驱动突变基因集到抑制/刺激通路到扰乱的生物网络，对聚集的肿瘤突变的鉴定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。同样有了这些信息，检测到的驱动通路中的潜在药物靶点可以被归类为需要激动剂或拮抗剂药物开发，使药物靶点评估和优先排序更加有效。此外，识别特定基因和途径之间的共同出现可能有助于开发多种治疗癌症的治疗方法，这些治疗方法针对表现出相同的共同发生突变模式的患者群体进行优化。