Reconstructing mechanisms of somatic variation in diverse cellular lineages

项目摘要 基因组技术使以不断扩大的范围和细节重建景观成为可能 不同个体、组织和时间的遗传变异。这带来的一个后果就是人民币升值。 体细胞遗传变异的重要性。体细胞变异和高度变异性缺陷 在癌症的背景下进行了最广泛的研究，但它们与 各种其他疾病，既有生殖系的，也有散发性的，以及在基础研究中对发育和 再生。然而，我们才刚刚开始了解基因变异积累的过程。 在癌症和一些明确定义的癌前状态下。到目前为止，我们对体细胞变异的了解很少 突起作用于潜在的癌变但无症状或纯粹的健康组织。了解体细胞 更广泛地说，变异是开发癌症风险的知情模型的关键问题，寻求选择 在癌症发作前识别和潜在地治疗癌症，以及诊断和治疗其他疾病 由非癌症的体细胞高度变异性或自发的遗传嵌合体引起。 重建癌症细胞谱系的经验为建立可比较的 癌症、癌前病变和非癌的体细胞遗传变异的更广泛的图景的方法 癌变组织。癌症细胞谱系重建的一个重要教训是，做可复制的 科学，以及计划这样做所需的实验数据生成研究，需要首先理解 要生成的数据以及将用来分析这些数据的数学/统计模型和算法。 肿瘤系统发育学领域为重建细胞谱系提供了理论基础和工具。 在存在各种体细胞突变过程的情况下，这些过程可以被适应来解决 更广泛地重建体细胞变异过程。它还为BEST提供了至关重要的经验 设计变异研究的实践和陷阱，这将是指导新的大规模实验所需的 非癌性体细胞变异的研究。绘制人类体细胞变异的全景图和 产生它的机制将是无数实验和计算小组的大量努力，这些小组将 如果不清楚地把握它所涉及的数据科学问题，就不会成功。 拟议的工作将有助于建立体细胞变异研究所需的信息学基础设施 通过四个特定的目标，旨在利用几十年来重建体细胞进化的经验 癌症病史。它将推动变种调用和细胞谱系追踪工具的最先进水平，以 处理体细胞结构变异(SVS)、拷贝数偏差(CNA)和 单核苷酸变异(SNV)。它将通过模拟研究使用这些方法来评估数据需求和 用于识别不同体细胞变异机制的研究设计。最后，它将把它们应用于一项初步研究 现有的变异资源，以验证它们在癌症背景下和之外的使用，并执行新的 发现癌、非癌和癌前病变的体细胞变异机制。

Project Summary Genomic technologies have made it possible to reconstruct, with ever-growing scope and detail, the landscape of genetic variations across individuals, tissues, and time. One consequence of this is a growing appreciation for the importance of somatic genetic variation. Somatic variations, and the hypermutability defects that can produce them, have been most extensively studied in the context of cancers, but they have relevance to a variety of other disorders, both germline and sporadic, as well as value in basic research into development and regeneration. Yet we are only beginning to understand the processes by which genetic variations accumulate in cancers and a few well-defined precancerous conditions. We so far know little about how somatic variation processes act in potentially cancerous but asymptomatic or in purely healthy tissues. Understanding somatic variation more broadly is a crucial question for developing informed models of cancer risk, pursuing options to identify and potentially treat cancers before they start, as well as for diagnosing and treating other illnesses that arise from non-cancerous somatic hypermutability or spontaneous genetic mosaicism. Experience reconstructing cell lineages in cancers provides the foundation for building comparable methods for the broader landscape of somatic genetic variability across cancerous, pre-cancerous, and non- cancerous tissues. One crucial lesson of cell lineage reconstruction in cancers is that doing reproducible science, and planning the experimental data-generation studies needed to do so, requires first understanding the data to be generated and the mathematical/statistical models and algorithms by which it will be analyzed. The field of tumor phylogenetics has provided a groundwork of theory and tools for reconstructing cell lineages in the presence of diverse somatic mutation processes that can be adapted to address the problem of reconstructing somatic variation processes more broadly. It has also provided crucial experience into best practices and pitfalls in designing variation studies, which will be needed to guide new large-scale experimental studies into non-cancerous somatic variation. Mapping the full landscape of human somatic variation and the mechanisms that produce it will be a large effort of numerous experimental and computational groups that will not succeed without a clear grasp on the data science problems it entails. The proposed work will help to build the informatics infrastructure needed for somatic variation study through four specific aims intended to leverage decades of experience with reconstructing somatic evolutionary histories in cancers. It will advance the state-of-the-art of tools for variant calling and cell lineage tracing to handle diverse mechanisms of somatic structural variations (SVs), copy number aberrations (CNAs), and single nucleotide variations (SNVs). It will use these methods via simulation study to assess data needs and study designs for identifying diverse somatic variation mechanisms. Finally, it will apply them in a pilot study of existing variation resources, both to validate their use in and beyond the cancer context and to perform novel discovery of cancerous, non-cancerous, and pre-cancerous somatic variation mechanisms.