Standardized and Genome-Wide Clinical Interpretation of Complex Genotypes for Cancer Precision Medicine

项目摘要/摘要 高通量分子图谱技术使得对分子驱动因素的系统识别成为可能 对于大多数主要的肿瘤类型来说，这是一种癌症。临床和功能研究已经将这些司机与患者联系起来 结果，并帮助开发有针对性的治疗方法。但是，保持时代性和全面性 对变异的临床意义的解释是一个主要的瓶颈。为了应对这一挑战， 癌症变异的临床解释知识库(CIRICAL；​CICVICDB.org​)是为了提供 用于专家的知识库和复杂的管理界面--众包可操作的管理 癌症变种。重要的是，所有数据都可以通过公共领域许可证免费获得，无论是每天还是每月 数据冻结，以及公共API。这使得公民的不同解释在许多情况下得以广泛采用 用于不同注释以及商业和非商业报告生成工作流的研究工具。 到目前为止，这些研究主要集中在通过靶向测序小组检测到的小突变上， 并假设了一种单一靶点对单一治疗的范例。随着测序成本的降低，整个基因组， 转录组和表观基因组方法将取代这些靶向方法。这将使越来越多的 对大多数类型(大小)的分子变化进行公正的分析，并将同时取代许多 传统的细胞遗传学分析。它还将显著增加潜在和变种的数量 临床意义不明。此外，我们的理解已经进化到认识到空间和 颞叶肿瘤的异质性导致复杂的肿瘤基因类型的协同突变，这将需要 更完善的决策支持框架。为了应对这些挑战，公民数据模型将是 扩展到支持：代表复杂肿瘤基因类型的新框架；ACMG和AMP指南 用于生殖系和体细胞变异治疗；以及用于评估体细胞变异的新证据代码 致癌性。将开发新的用户界面以支持对这些内容的管理、浏览和搜索 功能。临床协作将扩展到：(A)开发可分发的临床级别(CLIA认证) 使用全基因组测序对患者样本进行全面基因组图谱分析的分析平台；(B) 通过Clingen躯体工作组支持标准化的躯体变异治疗；以及(C)整合 公民对个性化肿瘤基因组学(POG)试验的报道。该提案将涉及几个关键问题 挑战包括：1)了解将生殖系注释与体细胞癌整合的重要性 不同的解释；2)确定全基因组方法是否可以取代现有的靶向测序 小组和细胞遗传学分析；以及3)评估公共变体解释知识库的影响 在分子肿瘤委员会会议上做出临床决定。最后，社区外展和培训将 开展在线研讨会，改善实习机会，并增加与 医学遗传学研究员培训精准医学信息学的下一代研究人员。

Project Summary/Abstract High-throughput molecular profiling technologies have allowed the systematic identification of molecular drivers of cancer for most major tumor types. Clinical and functional studies have correlated these drivers with patient outcomes and helped develop targeted therapies. However, maintaining current and comprehensive interpretations of the clinical significance of variants represents a major bottleneck. To address this challenge, the Clinical Interpretations of Variants in Cancer knowledgebase (CIViC; ​civicdb.org​) was created to provide a knowledge repository and sophisticated curation interface for expert-crowdsourcing the curation of actionable cancer variants. Importantly, all data are made freely available with a public domain license, daily and monthly data freezes, and public API. This has allowed widespread adoption of CIViC variant interpretations into many research tools for variant annotation as well as commercial and non-commercial report generation workflows. To date, these have focused predominantly on small mutations, detected through targeted sequencing panels, and assumed a single-target-to-single-therapy paradigm. As sequencing costs decrease, whole genome, transcriptome, and epigenome approaches will replace these targeted methods. This will allow increasingly unbiased assay of molecular alterations of most types (large and small) and will simultaneously replace many traditional cytogenetic assays. It will also dramatically increase the number of variants of potential and unknown clinical significance. Furthermore, our understanding has evolved to recognize that spatial and temporal tumor heterogeneity result in complex tumor genotypes of collaborating mutations that will require a more sophisticated decision support framework. To address these challenges, the CIViC data model will be extended to support: new frameworks for representing complex tumor genotypes; ACMG and AMP guidelines for both germline and somatic variant curation; and new evidence codes for assessing somatic variant oncogenicity. New user interfaces will be developed to support curation, browsing and searching of these features. Clinical collaborations will be extended to: (a) develop a distributable clinical-grade (CLIA-certified) analysis platform for comprehensive genomic profiling of patient samples using whole-genome sequencing; (b) support standardized somatic variant curation through the ClinGen Somatic Working group; and (c) integrate CIViC reports into the Personalized Oncogenomics (POG) trial. The proposal will address several key challenges including: 1) understanding the importance of integrating germline annotations with somatic cancer variant interpretations; 2) determining if a whole genome approach can replace existing targeted sequencing panels and cytogenetic assays; and 3) assessing the impact of a public variant interpretation knowledgebase on clinical decisions at molecular tumor board meetings. Finally, community outreach and training will be performed to develop online workshops, improve internship opportunities, and increase interaction with medical genetics fellows to train the next-generation of researchers in precision medicine informatics.