Cloud Enabled, Rigorous, Functional Assay Calibration (CERFAC)

总结 利用基因测序鉴定高危疾病易感基因的致病序列变异 始于上世纪90年代中后期。大约在2010年，用于临床测序测试的技术从 桑格测序到靶向捕获大规模平行测序（即，多基因面板测试）。这 技术的转变无疑增加了遗传倾向测试的临床效用。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。 不确定的意义（VUS）。部分通过两个较旧的NCI R 01（R 01 CA 121245和R 01 CA 164944），我和我的合作者对评估方法的发展做出了重要贡献 和这些VUS的分类。延续这一轨迹，R 01 CA 264971的中心目标“提升严谨性 2020年代生殖系癌症基因变异分类的效率”，正如标题所述， 在临床多基因面板期间观察到的序列变体分类的严格性和效率 检测癌症易感基因。本研究的目的有四：（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） 通过实证测量，提高关键数据类型校准的严谨性;（3）细化定量 变异评估的贝叶斯评分系统;（4）序列变异评估的基准要素。 随着VUS评估框架迁移到我们开创的贝叶斯积分系统， 最初被归类为VUS的很大一部分单独罕见的错义替换可以重新归类为 基于计算工具分析的一致证据，可能为良性或可能为致病性 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，注意两者之间的独立性。流动 从R 01 CA 264971的目标1的第二条加上其目标2的全部，我们提出的总体目标 Supplement是对使用特定云资源Terra Cloud的概念验证探索， 更好地实现用于VUS评估的高通量/综合功能测定的严格校准， 世界各地的调查团队。拟议补编有三个目的：（1）制作一个模板 研究人员可以克隆Terra工作区以执行自己的校准;（2）在Terra工作区内， 实施工作流程以产生用于证据校准的候选序列变体列表;以及（3） 同样在Terra工作空间中，为了开发一个可定制的可编辑笔记本， 功能测定校准，给定一组校准变体和测定结果。 拟议的项目应增强利用现代计算的数据科学目标， 联合收割机将几种不同类型的大规模数据结合起来，从而加速了序列变异体分类。

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.