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 使用基因测序来鉴定高危疾病易感性基因的致病序列变体 从1990年代中期开始。大约在2010年，用于临床测序测试的技术 Sanger测序对靶向捕获大规模平行测序（即多基因面板测试）。这 技术转移无疑增加了遗传倾向测试的临床实用性。但是有一个 意外价格：向多基因面板测试的转变也提高了序列变体的速率 观察到不确定的显着性（VU）。部分通过两个较旧的NCI R01（R01 CA121245和R01） CA164944），我和我的合作者为开发评估做出了重要贡献 和这些VUS的分类。继续轨迹，R01 CA264971的核心目标“升级严格 标题中指出的是，2020年代种系癌基因变异分类的效率”是为了改善 在临床多基因面板期间观察到的序列变体的严格和分类效率 癌症易感基因的测试。该研究有四个目的：（1）将相关的ACMG数据类型置于 更大的，逻辑上一致的集合，然后减少或消除这些集合之间的隐藏依赖性； （2）至 通过经验测量来改善关键数据类型的校准严格； （3）优化定量 用于变体评估的贝叶斯点系统； （4）序列变体评估的基准元素。 随着VUS评估框架的迁移到我们开创的贝叶斯点系统，很明显 最初被归类为VU的大量的单独稀有错义替代可以重新分类为 根据计算工具分析的一致证据，可能是良性或可能的致病性 和高通量功能测定结果。但是有一个捕获：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 从R01 CA264971的AIM 1加上所有目标2的第二条，我们提议的总体目标 补充是对特定云资源（Terra Workspaces）使用的概念证明的探索 通过 世界各地的调查人员团队。拟议的补充剂具有三个目的：（1）产生模板 调查人员可以克隆进行自己的校准的Terra工作区； （2）在Terra工作区内， 实施工作流程以生成候选序列变体列表以进行证据校准； （3） 同样在Terra工作区内，开发可自定义的Jupyter笔记本，可以生成经验 给定一组校准变体和测定结果，功能测定校准。 拟议的项目应加强利用对现代计算的访问的数据科学目标 结合几种不同类型的大规模数据，从而导致序列变体分类的加速。