Reconstructing mechanisms of somatic variation in diverse cellular lineages

项目摘要 基因组技术使我们有可能以不断增长的范围和细节重建景观 个体、组织和时间的遗传变异。其结果之一是， 体细胞遗传变异的重要性。体细胞变异和超变缺陷， 产生它们，在癌症的背景下进行了最广泛的研究，但它们与癌症相关， 各种其他疾病，包括生殖系和散发性疾病，以及在基础研究中的价值， 再生然而，我们才刚刚开始了解遗传变异积累的过程， 在癌症和一些明确的癌前病变中。到目前为止，我们对体细胞变异 过程在潜在的癌性但无症状的或纯健康的组织中起作用。了解躯体 更广泛的变化是发展癌症风险的知情模型的关键问题， 在癌症开始之前识别和潜在治疗癌症，以及诊断和治疗其他疾病 由非癌性体细胞超变或自发性遗传嵌合体引起。 在癌症中重建细胞谱系的经验为建立可比较的 用于癌性、癌前和非癌性的体细胞遗传变异性的更广泛景观的方法， 癌组织癌症细胞谱系重建的一个关键教训是， 科学，以及规划所需的实验数据生成研究，需要首先了解 要生成的数据以及分析数据的数学/统计模型和算法。 肿瘤遗传学领域为细胞谱系重建提供了理论基础和工具 在存在多种体细胞突变过程的情况下，这些过程可以适应于解决以下问题： 更广泛地重建体细胞变异过程。它还为最佳实践提供了重要经验， 设计变异研究的实践和陷阱，这将需要指导新的大规模实验 非癌性体细胞变异的研究绘制人类躯体变异的全貌， 产生它的机制将是许多实验和计算小组的巨大努力， 如果没有对数据科学问题的清晰把握，就不会成功。 该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 通过四个具体的目标，旨在利用数十年的经验， 癌症的历史它将推动变异识别和细胞谱系追踪工具的发展， 处理体细胞结构变异（SV）、拷贝数畸变（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， 研究设计，以确定不同的体细胞变异机制。最后，它将把它们应用于一项试点研究， 现有的变异资源，既要验证它们在癌症背景下的使用，又要进行新的 发现癌性、非癌性和癌前体细胞变异机制。

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.