An integrative model for the comprehensive classification of <i>BRCA1</i> and <i>BRCA2</i> variants of uncertain clinical significance

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Abstract

Loss-of-function variants in the BRCA1 and BRCA2 susceptibility genes predispose carriers to breast and/or ovarian cancer. The use of germline testing panels containing these genes has grown dramatically, but the interpretation of the results has been complicated by the identification of many sequence variants of undefined cancer relevance, termed “Variants of Uncertain Significance (VUS).” We have developed functional assays and a statistical model called VarCall for classifying BRCA1 and BRCA2 VUS. Here we describe a multifactorial extension of VarCall, called VarCall XT, that allows for co–analysis of multiple forms of genetic evidence. We evaluated the accuracy of models defined by the combinations of functional, in silico protein predictors, and family data for VUS classification. VarCall XT classified variants of known pathogenicity status with high sensitivity and specificity, with the functional assays contributing the greatest predictive power. This approach could be used to identify more patients that would benefit from personalized cancer risk assessment and management.

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Most cited references 27

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Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

Sue Richards, Nazneen Aziz, Sherri Bale … (2015)

The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.

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Assessing the performance of prediction models: a framework for traditional and novel measures.

Ewout W. Steyerberg, Andrew J Vickers, Nancy R Cook … (2010)

The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic [ROC] curve), and goodness-of-fit statistics for calibration.Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision-analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation).We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.

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dbNSFP v3.0: A One-Stop Database of Functional Predictions and Annotations for Human Nonsynonymous and Splice-Site SNVs.

Xiaoming Liu, Chunlei Wu, Chang Li … (2016)

The purpose of the dbNSFP is to provide a one-stop resource for functional predictions and annotations for human nonsynonymous single-nucleotide variants (nsSNVs) and splice-site variants (ssSNVs), and to facilitate the steps of filtering and prioritizing SNVs from a large list of SNVs discovered in an exome-sequencing study. A list of all potential nsSNVs and ssSNVs based on the human reference sequence were created and functional predictions and annotations were curated and compiled for each SNV. Here, we report a recent major update of the database to version 3.0. The SNV list has been rebuilt based on GENCODE 22 and currently the database includes 82,832,027 nsSNVs and ssSNVs. An attached database dbscSNV, which compiled all potential human SNVs within splicing consensus regions and their deleteriousness predictions, add another 15,030,459 potentially functional SNVs. Eleven prediction scores (MetaSVM, MetaLR, CADD, VEST3, PROVEAN, 4× fitCons, fathmm-MKL, and DANN) and allele frequencies from the UK10K cohorts and the Exome Aggregation Consortium (ExAC), among others, have been added. The original seven prediction scores in v2.0 (SIFT, 2× Polyphen2, LRT, MutationTaster, MutationAssessor, and FATHMM) as well as many SNV and gene functional annotations have been updated. dbNSFP v3.0 is freely available at http://sites.google.com/site/jpopgen/dbNSFP.

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Author and article information

Contributors

Edwin S. Iversen Jr.:

ORCID: http://orcid.org/0000-0002-0066-2763

iversen@duke.edu

Alvaro N. A. Monteiro:

ORCID: http://orcid.org/0000-0002-8448-4801

alvaro.monteiro@moffitt.org

Journal

Journal ID (nlm-ta): NPJ Genom Med

Journal ID (iso-abbrev): NPJ Genom Med

Title: NPJ Genomic Medicine

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2056-7944

Publication date (Electronic): 3 June 2022

Publication date PMC-release: 3 June 2022

Publication date Collection: 2022

Volume: 7

Electronic Location Identifier: 35

Affiliations

[1 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Department of Statistical Science, , Duke University, ; Durham, NC 27708 USA

[2 ]GRID grid.66875.3a, ISNI 0000 0004 0459 167X, Department of Health Sciences Research, Mayo Clinic, ; Rochester, MN 55901 USA

[3 ]GRID grid.66875.3a, ISNI 0000 0004 0459 167X, Department of Laboratory Medicine and Pathology, Mayo Clinic, ; Rochester, MN 55902 USA

[4 ]GRID grid.465138.d, ISNI 0000 0004 0455 211X, Ambry Genetics Corporation, ; Aliso Viejo, CA 92656 USA

[5 ]GRID grid.223827.e, ISNI 0000 0001 2193 0096, Department of Dermatology, , University of Utah School of Medicine, ; Salt Lake City, UT 84132 USA

[6 ]GRID grid.468198.a, ISNI 0000 0000 9891 5233, Cancer Epidemiology Program, , H. Lee Moffitt Cancer Center & Research Institute, ; Tampa, FL 33612 USA

Author information

Edwin S. Iversen Jr. http://orcid.org/0000-0002-0066-2763

Steven N. Hart http://orcid.org/0000-0001-7714-2734

Kun Y. Lee http://orcid.org/0000-0002-0684-8112

Tina Pesaran http://orcid.org/0000-0001-9807-4304

Rachid Karam http://orcid.org/0000-0002-5645-498X

Alvaro N. A. Monteiro http://orcid.org/0000-0002-8448-4801

Article

Publisher ID: 302

DOI: 10.1038/s41525-022-00302-3

PMC ID: 9166814

PubMed ID: 35665744

SO-VID: 761ec571-3a47-4e48-b0be-86cf6f314ae3

License:

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

Date received : 26 February 2021

Date accepted : 4 May 2022

Funding

Funded by: FundRef https://doi.org/10.13039/100000054, U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI);

Award ID: R01CA116167

Award ID: P50CA116201

Award ID: R01CA116167

Award ID: P50CA116201

Award ID: R01CA116167

Award Recipient : Edwin S. Iversen Jr. Gary Lipton Steven N. Hart Kun Y. Lee Chunling Hu Eric C. Polley David E. Goldgar Alvaro N. A. Monteiro

Funded by: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)

Funded by: FundRef https://doi.org/10.13039/501100001922, DH | NIHR | Efficacy and Mechanism Evaluation Programme (NIHR Efficacy and Mechanism Evaluation Programme);

Award ID: R35CA253187

Award Recipient : Fergus J. Couch