Statistical Methods for Analysis of Tumor Heterogeneity in Genetic Epidemiology

遗传流行病学中肿瘤异质性分析的统计方法

• 批准号: 9817026
• 负责人: Li Hsu
• 金额: $ 48.55万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9817026
• 关键词: Accounting; Address; Algorithms; Antineoplastic Agents; Bioconductor; Bioinformatics; Candidate Disease Gene; Clinical; Colorectal Cancer; Common Neoplasm; Communities; DNA; DNA Repair; DNA biosynthesis; Data; Development; Disease; Environment; Environmental Risk Factor; Etiology; Event; Funding; Genes; Genetic; Genetic Risk; Genetic Variation; Germ-Line Mutation; Goals; Grant; Harvest; Individual; Lead; Link; Linkage Disequilibrium; Malignant Neoplasms; Methods; Mismatch Repair; Morbidity - disease rate; Mutation; Normal Cell; Outcome; Outcome Study; Paraffin Embedding; Pathway interactions; Patients; Play; Point Mutation; Research Personnel; Risk; Risk Factors; Role; Sample Size; Sampling; Site; Smoking; Somatic Mutation; Statistical Data Interpretation; Statistical Methods; Technology; Testing; Uncertainty; Variant; cancer epidemiology; cancer genome; cancer prevention; cancer risk; cancer site; cancer therapy; case control; cost; gene discovery; gene repair; genetic association; genetic epidemiology; genetic variant; genome wide association study; genome-wide; improved; insight; interest; method development; mortality; neoplastic cell; novel; response; screening; statistics; tumor; tumor heterogeneity; tumor progression; tumorigenesis; user friendly software

PROJECT SUMMARY/ABSTRACT Cancer is a major morbidity and mortality burden throughout the world. While much progress has been made, the elimination of cancer has not yet been achieved. In the currently funded grant, we have developed statistical methods for genome-wide association analysis of cancer and studied cancer by the site of origin. However, even within a site, cancer can have distinct mutational profiles across patients. Pooling all cancer cases occurring at one site as one disease may miss important clinical and etiological insights. Recently technology advances have made it possible to characterize somatic mutations at great detail in large numbers of tumors, providing a unique opportunity to study tumor heterogeneity. The objective of this competitive renewal is to continue our statistical methods development for association analyses of tumor heterogeneity with clinical outcomes, and for studying the underlying genetic and environmental etiology. There are challenges in analyzing the somatic mutation data. First, somatic mutation may only exist in a subset of tumor cells of a patient, so called intra-tumor heterogeneity. While our application is focused on tumor heterogeneity across patients, because intra-tumor heterogeneity can also impact clinical outcomes, important insight could be missed if it were not accounted for. The goal of Aim 1 is to develop statistical methods to account for intra-tumor heterogeneity when assessing the association of somatic mutations with clinical outcomes. Second, it is of great interest to discover germline-somatic mutation link; however, despite that tumor studies are considerably larger than before due to technology advances, the power for discovering such links remains limited because of moderate genetic effects and the burden of accounting for multiple comparison from testing millions of variants. The goal of Aim 2 is to develop novel screening strategies for prioritizing genetic variants in testing genome-wide association with tumor heterogeneity. We will achieve optimal power by using the weighted hypothesis testing framework, allowing for correlated genetic variants and continuous screening statistics. Third, it is common that tumor blocks can usually only be retrieved from a subset of cases and tumor sequencing data are thus only available for this subset. Meanwhile, extensive risk factor information has already been collected for the larger study. The goal of Aim 3 is to develop a robust and efficient approach to incorporate the summary statistics information from the larger study for characterizing the effects of genetic and environmental risk factors on risk of developing cancer with specific tumor feature. The methods will be applied to the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO, PI: Ulrike Peters; Lead Biostatistician: Li Hsu), which includes over 125,000 colorectal cancer cases and controls all with GWAS data and additionally 7,000 tumors sequencing data. As our methods are also applicable to other cancer studies, we will implement them in computationally efficient and user-friendly software packages and disseminate them to the community through R/CRAN, R/Bioconductor, or Github.

项目总结/文摘