Since approximately 2010, the scale of clinical cancer predisposition genetic testing has increased
dramatically. While a large fraction of sequence variants observed during testing are easily classified as
benign or pathogenic, many others – principally missense substitutions, in-frame indels, and splice junction
variants – are not easily placed on a spectrum from benign to clearly pathogenic. These are termed Variants
of Uncertain Significance (VUS), and the clinical management of families in which they segregate would be
improved if they could actually be classified. Consortium efforts to develop methods for evaluation and
classification of VUS in BRCA1 and BRCA2 date back to a Breast Cancer Information Core satellite meeting
held at the ASHG annual meeting in 2000; and methods that had been developing separately within the breast
cancer, colorectal cancer, melanoma, and Li Fraumeni-syndrome genetics communities were cross-pollinated
at a 2008 International Agency for Research on Cancer (IARC) working group meeting on VUS in cancer
susceptibility genes. However, neither the qualitative nor the quantitative methods that sprouted from that
meeting produced a generalizable overall approach. In 2015, the American College of Medical Genetics
(ACMG) published guidelines for evaluating VUS across all Mendelian disease susceptibility genes. These
guidelines produced a practical VUS evaluation framework that has been adopted by testing labs and
organizations around the country. However, the ACMG system is entirely qualitative, with evidence weighted
by expert opinion rather than by empirical evidence. Subsequently, we fitted the ACMG system into a
quantitative Bayesian framework, providing a route to replacing qualitative evidence criteria from the ACMG
system with empirically measured counterparts. Indeed, we hypothesize that there will be clear instances
where strength accorded to current ACMG evidence criteria is contradicted by empirical measurement;
correcting these will self-evidently improve the rigor of VUS evaluation. Aim 1 will place related ACMG data
types into larger, logically consistent sets and then reduce or eliminate hidden dependencies between those
sets. Noting that the ACMG variant classification guidelines were almost entirely qualitative, Aim 2 will
improve the rigor of calibration for key data types through empirical measurement. Recently, we derived a
quantitative Bayesian point-system for VUS evaluation and classification, which is back compatible with its
parent quantitative Bayesian framework and the qualitative ACMG variant classification guidelines. Thus Aim
3 will refine this Bayesian point-system, taking advantage of the improved outputs from Aims 1 and 2. Finally,
Aim 4 will benchmark elements of VUS evaluation and classification. Successful completion of these Aims will
improve rigor in the system used for evaluation of VUS in cancer susceptibility genes, enabling higher
throughput VUS evaluation and improving confidence in the resulting classifications.
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