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

项目总结/摘要 高通量分子谱分析技术使得分子驱动因子的系统鉴定成为可能 大多数主要肿瘤类型的癌症。临床和功能研究已经将这些驱动因素与患者 并帮助开发靶向治疗。然而，保持当前和全面的 对变体的临床意义的解释是一个主要的瓶颈。为了应付这一挑战， 癌症变异的临床解释知识库（CIViC; civicdb.org）的创建是为了提供 知识库和复杂的策展界面，用于专家众包 癌症变异重要的是，所有数据都是免费提供的公共领域许可证，每天和每月 数据冻结和公共API。这使得CIVIC变体解释被广泛采用， 用于变体注释以及商业和非商业报告生成工作流程的研究工具。 迄今为止，这些研究主要集中在通过靶向测序板检测到的小突变上， 并采用单一靶点对单一疗法的模式。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 大多数类型（大和小）的分子改变的无偏测定，并将同时取代许多 传统的细胞遗传学分析。它还将大大增加潜在的变异数量， 未知的临床意义。此外，我们的理解已经发展到认识到， 时间性肿瘤异质性导致协作突变的复杂肿瘤基因型， 更复杂的决策支持框架。为了应对这些挑战，CIViC数据模型将 扩展到支持：表示复杂肿瘤基因型的新框架; ACMG和AMP指南 用于生殖系和体细胞变异的治疗;以及用于评估体细胞变异的新证据代码 致瘤性将开发新的用户界面，以支持这些内容的管理、浏览和搜索。 功能.临床合作将扩大到：（a）开发可分发的临床级（CLIA认证） 用于使用全基因组测序对患者样品进行综合基因组谱分析的分析平台;（B） 通过ClinGen体细胞工作组支持标准化的体细胞变异体治疗;以及（c）整合 CIViC报告了个性化癌基因组学（POG）试验。该提案将解决几个关键问题， 挑战包括：1）理解将生殖系注释与体细胞癌整合的重要性 2）确定全基因组方法是否可以取代现有的靶向测序 面板和细胞遗传学检测; 3）评估公共变异解释知识库的影响 在分子肿瘤委员会会议上做出临床决定最后，社区外联和培训将 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下一代精准医学信息学研究人员。

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.