Cloud Enabled, Rigorous, Functional Assay Calibration (CERFAC)

支持云的严格功能测定校准 (CERFAC)

• 批准号: 10827690
• 负责人: Sean Vahram Tavtigian
• 金额: $ 22.04万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-13 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10827690
• 关键词: Acceleration; Adopted; Adoption; Attention; BIK gene; BRCA1 gene; Benchmarking; Benign; Biological Assay; Calibration; Classification; ClinVar; Clinical; Communities; Computer Analysis; Computer software; Data; Data Science; Data Set; Dependence; Dideoxy Chain Termination DNA Sequencing; Disease susceptibility; Educational workshop; Elements; Evaluation; Expert Opinion; Family; Frustration; Genes; Genetic Predisposition Testing; Goals; Guidelines; Healthcare; Individual; Laboratories; Left; Massive Parallel Sequencing; Measurement; Methods; Modernization; National Human Genome Research Institute; Oncogenes; Paper; Pathogenicity; Price; Process; Publishing; Research Personnel; Resources; Sampling; Scientist; Susceptibility Gene; System; Technology; Testing; Time; Traction; Universities; Utah; Variant; Visualization; Work; biobank; cancer predisposition; clinical sequencing; computerized tools; genetic panel test; genetic variant; genomics cloud; high risk; improved; large scale data; method development; migration; segregation; symposium; tool; user-friendly; variant of unknown significance

SUMMARY The use of gene sequencing to identify pathogenic sequence variants of high-risk disease susceptibility genes began in the mid-late 1990s. In about 2010, the technology used for clinical sequencing tests segued from Sanger sequencing to targeted capture massively parallel sequencing (i.e., multi-gene panel testing). This technological shift undoubtedly increased the clinical utility of genetic predisposition testing. But there was an unintended price: the shift to multi-gene panel testing also increased the rate at which sequence variants of uncertain significance (VUS) were observed. In part through two older NCI R01s (R01 CA121245 and R01 CA164944), my collaborators and I made important contributions to the development of methods for evaluation and classification of these VUS. Continuing that trajectory, the central goal of R01 CA264971 “Upgrading rigor and efficiency of germline cancer gene variant classification for the 2020s” is, as stated in the title, to improve both the rigor and the efficiency of classification of sequence variants observed during clinical multi-gene panel testing of cancer predisposition genes. The study has four Aims: (1) To place related ACMG data types into larger, logically consistent sets and then reduce or eliminate hidden dependencies between those sets; (2) To improve the rigor of calibration for key data types through empirical measurement; (3) To refine the quantitative Bayesian point-system for variant evaluation; and (4) To benchmark elements of sequence variant evaluation. With migration of the framework for VUS evaluation to the Bayesian points system that we pioneered, it is clear that a large fraction of individually rare missense substitutions initially classified as VUS can be reclassified to either Likely Benign or Likely Pathogenic on the basis of concordant evidence from computational tool analysis and high-throughput functional assay result. But there is a catch: both the computational tools and the functional assays need to be calibrated rigorously, with attention to independence between the two. Flowing from the second clause of R01 CA264971’s Aim 1 plus all of its Aim 2, the overall objective of our proposed Supplement is a proof-of-concept exploration of the use of a specific cloud resource – Terra workspaces – to better enable rigorous calibration of high-throughput / comprehensive functional assays for VUS evaluation by teams of investigators around the world. The proposed Supplement has three Aims: (1) To produce a template Terra workspace that investigators can clone to perform their own calibrations; (2) within the Terra workspace, To implement a workflow to generate a list of candidate sequence variants for evidence calibration; and (3) also within the Terra workspace, To develop a customizable Jupyter notebook that can generate an empirical functional assay calibration, given a set of calibration variants and the assay results. The proposed project should enhance the data science goal of leveraging access to modern computing to combine several different kinds of large-scale data, resulting in acceleration of sequence variant classification.

概括 利用基因测序鉴定高危疾病易感基因的致病序列变异 始于20世纪90年代中后期。大约在2010年，用于临床测序测试的技术从 桑格测序以靶向捕获大规模并行测序（即多基因面板测试）。这 技术转变无疑增加了遗传倾向检测的临床效用。但有一个 意想不到的代价：向多基因面板测试的转变也增加了序列变异的速度 观察到不确定的显着性（VUS）。部分通过两个较旧的 NCI R01（R01 CA121245 和 R01 CA164944），我和我的合作者为评估方法的开发做出了重要贡献 以及这些 VUS 的分类。延续这一轨迹，R01 CA264971 的中心目标“升级严谨性” 2020年代种系癌症基因变异分类的和效率”，如标题所述，是为了提高 临床多基因面板中观察到的序列变异分类的严格性和效率 癌症易感基因检测。该研究有四个目标：（1）将相关的 ACMG 数据类型放入 更大的、逻辑上一致的集合，然后减少或消除这些集合之间隐藏的依赖关系； (2) 至 通过经验测量提高关键数据类型校准的严谨性； （三）细化定量 用于变量评估的贝叶斯点系统； (4) 对序列变异评估的要素进行基准测试。 随着 VUS 评估框架迁移到我们首创的贝叶斯积分系统，很明显 最初分类为 VUS 的大部分单独罕见的错义替换可以重新分类为 根据计算工具分析的一致证据，可能良性或可能致病 和高通量功能测定结果。但有一个问题：计算工具和 功能测定需要严格校准，并注意两者之间的独立性。流动的 从 R01 CA264971 的目标 1 的第二个条款加上所有目标 2，我们提出的总体目标 补充是对特定云资源（Terra 工作区）的使用的概念验证探索 更好地对 VUS 评估的高通量/综合功能测定进行严格校准 世界各地的调查小组。拟议的补充有三个目标： (1) 制作模板 研究人员可以克隆 Terra 工作区以执行自己的校准； (2) 在 Terra 工作空间内， 实施工作流程来生成候选序列变体列表以进行证据校准；和（3） 也在 Terra 工作区中，开发一个可定制的 Jupyter 笔记本，可以生成经验 功能测定校准，给定一组校准变体和测定结果。 拟议的项目应加强利用现代计算的数据科学目标 结合几种不同类型的大规模数据，从而加速序列变异分类。