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: 10602853
• 负责人: Li Hsu
• 金额: $ 27.5万
• 依托单位: FRED HUTCHINSON CANCER 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/10602853
• 关键词: Statistical; Methods; Analysis; Tumor; Heterogeneity

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.

项目概要/摘要 癌症是全世界主要的发病率和死亡率负担。虽然已经取得了很大进展 但癌症的消除尚未实现。在目前资助的赠款中，我们开发了 用于癌症全基因组关联分析和按起源部位研究癌症的统计方法。 然而，即使在一个部位内，癌症患者之间也可能具有不同的突变特征。汇集所有癌症 作为一种疾病在一个地点发生的病例可能会错过重要的临床和病因学见解。最近 技术进步使得详细描述大量体细胞突变成为可能 肿瘤的异质性，为研究肿瘤异质性提供了独特的机会。本次竞赛的目的 更新是为了继续开发肿瘤异质性关联分析的统计方法 与临床结果，并用于研究潜在的遗传和环境病因学。 分析体细胞突变数据存在挑战。首先，体细胞突变可能只存在于 患者肿瘤细胞的子集，即所谓的肿瘤内异质性。虽然我们的应用程序专注于 患者之间的肿瘤异质性，因为肿瘤内异质性也会影响临床结果， 如果不加以考虑，可能会错过重要的见解。目标 1 的目标是开发统计数据 在评估体细胞突变与肿瘤之间的关联时解释肿瘤内异质性的方法 临床结果。其次，发现种系-体细胞突变联系具有重大意义；然而，尽管 由于技术的进步，肿瘤研究的规模比以前大得多，发现的力量 由于适度的遗传效应和解释多重因素的负担，这种联系仍然有限。 通过测试数百万个变体进行比较。目标 2 的目标是开发新颖的筛选策略 在测试全基因组范围内与肿瘤异质性的关联时优先考虑遗传变异。我们将实现 通过使用加权假设检验框架来实现最佳功效，允许相关的遗传变异和 连续筛选统计。第三，肿瘤块通常只能从 因此，病例子集和肿瘤测序数据仅适用于该子集。同时，广泛的风险 已经为更大规模的研究收集了因素信息。目标 3 的目标是开发一个强大且 整合来自大型研究的汇总统计信息以描述特征的有效方法 遗传和环境风险因素对患具有特定肿瘤特征的癌症风险的影响。 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 并利用 GWAS 数据和另外 7,000 个肿瘤测序数据来控制所有数据。由于我们的方法也是 适用于其他癌症研究，我们将以计算效率高且用户友好的方式实施它们 软件包并通过 R/CRAN、R/Bioconductor 或 Github 向社区传播。