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

基本信息

批准号：
10656385
负责人：
Li Hsu
金额：
$ 40.76万
依托单位：
FRED HUTCHINSON CANCER CENTER
依托单位国家：
美国
项目类别：
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/10656385
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 Epidemiology Etiology Event Funding Genes Genetic 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 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 cost gene discovery gene repair genetic association genetic epidemiology genetic risk factor 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

项目摘要

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.

项目摘要/摘要癌症是全世界的主要发病率和死亡率负担。虽然进展很大制作，尚未消除癌症。在当前资助的赠款中，我们已经开发了基因组全基因组关联分析的统计方法，对原产地点研究了癌症。但是，即使在一个站点内，癌症也可以在患者之间具有不同的突变特征。汇集所有癌症在一个部位发生的病例可能会错过重要的临床和病因见解。最近技术的进步使得大量详细详细描述体细胞突变成为可能肿瘤，为研究肿瘤异质性提供了独特的机会。这个竞争激烈的目标更新将继续我们的统计方法开发以进行肿瘤异质性的关联分析具有临床结果，并用于研究潜在的遗传和环境病因。分析体突变数据存在挑战。首先，体细胞突变可能仅存在于患者的肿瘤细胞子集，所谓的肿瘤内异质性。而我们的应用集中于患者的肿瘤异质性，因为肿瘤内异质性也会影响临床结果，所以如果不考虑重要的洞察力，可能会错过。目标1的目标是开发统计评估体细胞突变与临床结果。其次，发现种系及其性突变链接是引起极大的兴趣。但是，尽管如此由于技术进步，肿瘤研究比以前大得多，这是发现的力量由于遗传效应中等和计算多个的负担，此类联系仍然受到限制测试数百万变体的比较。目标2的目标是制定新颖的筛选策略优先在测试全基因组与肿瘤异质性结合的遗传变异方面的优先级。我们将实现通过使用加权假设检验框架，最佳功率，允许相关的遗传变异和连续筛选统计。第三，通常只能从一个因此，病例和肿瘤测序数据的子集仅适用于该子集。同时，风险很大已经为大型研究收集了因素信息。 AIM 3的目标是开发一个健壮的和有效地纳入较大研究的摘要统计信息以表征的有效方法遗传和环境风险因素对具有特定肿瘤特征发展癌症风险的影响。该方法将应用于结直肠癌联盟的遗传学和流行病学（Gecco，PI：Ulrike Peters；首席生物统计学家：Li HSU），其中包括超过125,000个结直肠癌病例并用GWAS数据控制所有内容，并另外7,000个肿瘤测序数据。因为我们的方法也是适用于其他癌症研究，我们将在计算高效且用户友好的情况下实施它们软件包并通过R/Cran，R/Bioconductor或GitHub将它们传播到社区。