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      Systematic misclassification of missense variants in BRCA1 and BRCA2 “coldspots”

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          Abstract

          Purpose

          Guidelines for variant interpretation incorporate variant hotspots in critical functional domains as evidence for pathogenicity (e.g., PM1 and PP2), but do not use “coldspots,” that is, regions without essential functions that tolerate variation, as evidence a variant is benign. To improve variant classification we evaluated BRCA1 and BRCA2 missense variants reported in ClinVar to identify regions where pathogenic missenses are extremely infrequent, defined as coldspots.

          Methods

          We used Bayesian approaches to model variant classification in these regions.

          Results

          BRCA1 exon 11 (~60% of the coding sequence), and BRCA2 exons 10 and 11 (~65% of the coding sequence), are coldspots. Of 89 pathogenic (P) or likely pathogenic (LP) missense variants in BRCA1, none are in exon 11 (odds <0.01, 95% confidence interval [CI] 0.0–0.01). Of 34 P or LP missense variants in BRCA2, none are in exons 10–11 (odds <0.01, 95% CI 0.0–0.01). More than half of reported missense variants of uncertain significance (VUS) in BRCA1 and BRCA2 are in coldspots (3115/5301 = 58.8%). Reclassifying these 3115 VUS as likely benign would substantially improve variant classification.

          Conclusion

          In BRCA1 and BRCA2 coldspots, missense variants are very unlikely to be pathogenic. Classification schemes that incorporate coldspots can reduce the number of VUS and mitigate risks from reporting benign variation as VUS.

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          Most cited references26

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          Accurate classification of BRCA1 variants with saturation genome editing

          Variants of uncertain significance (VUS) fundamentally limit the clinical utility of genetic information. The challenge they pose is epitomized by BRCA1, a tumor suppressor in which germline loss-of-function variants predispose women to breast and ovarian cancer. Although BRCA1 has been sequenced in millions of women, the risk associated with most newly observed variants cannot be definitively assigned. Here, we employ saturation genome editing to assay 96.5% of all possible single nucleotide variants (SNVs) in 13 exons encoding functionally critical domains of BRCA1. Functional effects for nearly 4,000 SNVs are bimodally distributed and almost perfectly concordant with established assessments of pathogenicity. Over 400 non-functional missense SNVs are identified, as well as ~300 SNVs that disrupt expression. We predict that these results will be immediately useful for clinical interpretation of BRCA1 variants, and that this paradigm can be extended to overcome the challenge of VUS in additional clinically actionable genes.
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            Modeling the ACMG/AMP Variant Classification Guidelines as a Bayesian Classification Framework

            Purpose We evaluated the ACMG/AMP variant pathogenicity guidelines for internal consistency and compatibility with Bayesian statistical reasoning. Methods The ACMG/AMP criteria were translated into a naïve Bayesian classifier, assuming four levels of evidence and exponentially scaled odds of pathogenicity. We tested this framework with a range of prior probabilities and odds of pathogenicity. Results We modeled the ACMG/AMP guidelines using biologically plausible assumptions. Most ACMG/AMP combining criteria were compatible. One ACMG/AMP likely pathogenic combination was mathematically equivalent to pathogenic and one ACMG/AMP pathogenic combination was actually likely pathogenic. We modeled combinations that include evidence for and against pathogenicity, showing that our approach scored some combinations as pathogenic or likely pathogenic that ACMG/AMP would designate as VUS. Conclusion By transforming the ACMG/AMP guidelines into a Bayesian framework, we provide a mathematical foundation for what was a qualitative heuristic. Only two of the 18 existing ACMG/AMP evidence combinations were mathematically inconsistent with the overall framework. Mixed combinations of pathogenic and benign evidence could yield a likely pathogenic, likely benign, or VUS result. This quantitative framework validates the approach adopted by the ACMG/AMP, provides opportunities to further refine evidence categories and combining rules, and supports efforts to automate components of variant pathogenicity assessments.
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              Massively Parallel Functional Analysis of BRCA1 RING Domain Variants.

              Interpreting variants of uncertain significance (VUS) is a central challenge in medical genetics. One approach is to experimentally measure the functional consequences of VUS, but to date this approach has been post hoc and low throughput. Here we use massively parallel assays to measure the effects of nearly 2000 missense substitutions in the RING domain of BRCA1 on its E3 ubiquitin ligase activity and its binding to the BARD1 RING domain. From the resulting scores, we generate a model to predict the capacities of full-length BRCA1 variants to support homology-directed DNA repair, the essential role of BRCA1 in tumor suppression, and show that it outperforms widely used biological-effect prediction algorithms. We envision that massively parallel functional assays may facilitate the prospective interpretation of variants observed in clinical sequencing.
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                Author and article information

                Contributors
                cpritch@uw.edu
                Journal
                Genet Med
                Genet. Med
                Genetics in Medicine
                Nature Publishing Group US (New York )
                1098-3600
                1530-0366
                8 January 2020
                8 January 2020
                2020
                : 22
                : 5
                : 825-830
                Affiliations
                [1 ]ISNI 0000000122986657, GRID grid.34477.33, Department of Medicine, Division of Medical Genetics, , University of Washington, ; Seattle, WA USA
                [2 ]ISNI 0000000122986657, GRID grid.34477.33, Department of Laboratory Medicine, , University of Washington, ; Seattle, WA USA
                [3 ]ISNI 0000 0004 0421 8357, GRID grid.410425.6, Department of Medical Oncology, , Division of Clinical Cancer Genomics, City of Hope, ; Duarte, CA USA
                [4 ]ISNI 0000000122986657, GRID grid.34477.33, Department of Genome Sciences, , University of Washington, ; Seattle, WA USA
                Author information
                http://orcid.org/0000-0002-2461-1557
                Article
                740
                10.1038/s41436-019-0740-6
                7200594
                31911673
                028cc28b-b8e8-453c-95d2-cea7a5da1c39
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 July 2019
                : 19 December 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: 1R35 CA197458
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/http://dx.doi.org/10.13039/100009634, Susan G. Komen;
                Award ID: SAC110020;
                Award Recipient :
                Categories
                Article
                Custom metadata
                © American College of Medical Genetics and Genomics 2020

                Genetics
                variant classification,vus,coldspot,acmg,brca1
                Genetics
                variant classification, vus, coldspot, acmg, brca1

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