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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

基本信息

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
项目类别：
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

项目摘要

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.