Skip to main content
An official website of the United States government
Grant Details

Grant Number: 3R01CA264971-02S1 Interpret this number
Primary Investigator: Tavtigian, Sean
Organization: University Of Utah
Project Title: Cloud Enabled, Rigorous, Functional Assay Calibration (CERFAC)
Fiscal Year: 2023


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.


None. See parent grant details.

Back to Top