||3R01CA264971-02S1 Interpret this number
||University Of Utah
||Cloud Enabled, Rigorous, Functional Assay Calibration (CERFAC)
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.