Unraveling the topological architecture and phenotypic contexture of structural variation

摘要 全基因组测序（WGS）在基因组医学中的应用越来越多， 精确肿瘤学加速了患者结构变异（SV）的发现， 癌症基因组然而，虽然人类癌症类型通常以广泛的癌症为特征， 基因组不稳定性是大多数结构和拷贝数变异（CNV）的功能后果 仍然知之甚少。关键的是，我们不知道这成百上千的 通常在患者肿瘤中观察到的基因组重排是致病性的， 功能性基因疤痕因为SV改变了基因组的结构（线性序列）， 拓扑（三维组织）和表型水平（表观遗传景观）， 需要综合和多尺度数据集来正确预测其影响。这种缺乏 综合资源和工具严重限制了对患者遗传数据的医学解释。 现有的大规模基因组和蛋白基因组癌症表征工作，包括 共同基金（CF）Gabriella米勒儿童优先（GMKF）数据资源提供丰富的数据链接 遗传信息，包括SV及其表型结果，如基因表达。 然而，这些数据集本身不足以提供深入的机制和功能见解。 CF数据集，特别是4D Nucleome（4DN），Epigenomics（Roadmap）和GTEx提供了 将种系变异、基因组拓扑结构和染色质结构与基因表达联系起来的蓝图。 因此，我们建议整合来自患者肿瘤样品的基因组数据（GMKF）， 空间和功能数据（4DN，路线图，GTEx），这将使我们能够阐明和预测 结构变异的致病机制： 目标1：创建TopVar数据资源，以增强我们对以下因素之间相互作用的理解： 基因组拓扑学和结构变异。整合的TopVar资源将提供 表型背景需要在遗传和生物学方面解释SV，这将产生可检验的 关于其下游影响的假设。 目的2：建立和评估SV在多种人类癌症中致病性的预测模型。 使用结构化的TopVar数据资源，我们将实现一个可解释的统计模型， 利用多层整合数据预测哪些SV对基因表达有影响。 这两个目标的实现将代表TopVar用于预测的效用的原理证明。 精确肿瘤学背景下的SV建模。虽然我们的研究将集中在 对GMKF（儿科癌症）生成的综合基因组数据进行了询问， CPTAC（成人癌症），它将作为其在实时测序中使用的基础 项目，如MI-OncoSeq和Peds-MI-OncoSeq，专注于难治性和转移性肿瘤。

Abstract The increasing adoption of whole-genome sequencing (WGS) in the context of genomic medicine and precision oncology has resulted in the accelerated discovery of structural variants (SVs) in patient cancer genomes. However, while human cancer types are generally characterized by widespread genomic instability the functional consequences of most structural and copy number variants (CNV) remain poorly understood. Critically, it is unknown which of the hundreds to thousands of genomic rearrangements typically observed in a patient tumor are pathogenic and which are non- functional genomic scars. Because SVs alter the genome at the structural (linear sequence), topological (three-dimensional organization), and phenotypic levels (epigenetic landscape), integrative and multiscale datasets are necessary to correctly predict their impact. This dearth of integrative resources and tools critically limits the medical interpretation of patient genetic data. Existing large-scale genomic and proteogenomic cancer characterization efforts, including the Common Fund (CF) Gabriella Miller Kids First (GMKF) data resource provide rich data to link genetic information including SVs with their phenotypic consequences, such as gene expression. However, these datasets alone are insufficient to provide deep mechanistic and functional insights. CF data sets, specifically 4D Nucleome (4DN), Epigenomics (Roadmap), and GTEx provide the blueprint to link germline variation, genome topology, and chromatin architecture to gene expression. Therefore, we propose the integration of genomic data from patient tumor samples (GMKF), with spatial and functional data (4DN, Roadmap, GTEx), which will allow us to elucidate and predict the pathogenic mechanisms of structural variants: Aim 1: To create TopVar a data resource to enhance our understanding of the interplay between genome TOPology and structural VARiation. The integrative TopVar resource will provide the phenotypic context required to interpret SVs in genetic and biological terms, which will yield testable hypotheses regarding their downstream effects. Aim 2: To develop and evaluate a predictive model of SV pathogenicity across multiple human cancers. Using the structured TopVar data resource, we will implement an interpretable statistical model to predict which SVs have an impact on gene expression, utilizing multiple layers of the integrated data. The realization of both aims will represent a proof-of-principle for the utility of TopVar for predictive modeling of SVs in the context of precision oncology. While our proposed study will focus on interrogating the comprehensive genomic data generated by GMKF (pediatric cancer) and CPTAC (adult cancer), it will serve as the foundation for their use within real-time sequencing programs, such as MI-OncoSeq and Peds-MI-OncoSeq, focusing on refractory and metastatic tumors.