Statistical Methods for Analysis of Tumor Heterogeneity in Genetic Epidemiology

项目概要/摘要 癌症是全世界主要的发病率和死亡率负担。虽然取得了很大进展， 尽管如此，癌症的消除还没有实现。在目前资助的赠款中，我们开发了 用于癌症的全基因组关联分析的统计方法和通过起源部位研究的癌症。 然而，即使在一个位点内，癌症也可以在患者中具有不同的突变谱。合并所有癌症 在一个部位发生的病例作为一种疾病可能会错过重要的临床和病因学见解。最近 技术的进步使大量的体细胞突变的详细特征成为可能 这为研究肿瘤异质性提供了独特的机会。这次竞争的目的 更新是继续我们的统计方法的发展，肿瘤异质性的关联分析 临床结果，并研究潜在的遗传和环境病因。 在分析体细胞突变数据方面存在挑战。首先，体细胞突变可能只存在于 患者的肿瘤细胞的亚群，所谓的肿瘤内异质性。虽然我们的应用程序的重点是 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， 如果不考虑重要的洞察力，就可能错过它。目标1的目标是发展统计学 当评估体细胞突变与肿瘤内异质性的关联时， 临床结果。第二，发现生殖细胞-体细胞突变联系是非常有趣的;然而，尽管 由于技术的进步，肿瘤研究比以前大得多， 这种联系仍然是有限的，因为适度的遗传效应和负担，占多个 通过测试数百万种变体进行比较。目标2的目标是开发新的筛选策略， 在测试全基因组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，考虑到相关的遗传变异， 连续筛选统计。第三，常见的是，肿瘤块通常只能从 因此，病例子集和肿瘤测序数据仅可用于该子集。与此同时， 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的， 有效的方法，以纳入来自更大研究的汇总统计信息， 遗传和环境危险因素对发生具有特定肿瘤特征的癌症风险的影响。 该方法将应用于大肠癌遗传学和流行病学联盟 （GECCO，PI：Ulrike Peters;首席生物统计学家：Li Hsu），其中包括超过125，000例结直肠癌病例 对照组全部使用GWAS数据和另外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，我们将以计算效率高和用户友好的方式实现它们。 软件包，并通过R/CRAN，R/Bioconductor或Github向社区传播。

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.