ArticlePLoS computational biology2007
Functional impact of missense variants in BRCA1 predicted by supervised learning.
Article in PLoS computational biology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Domain-Specific Computational, Functional and Structural Methods Enable Interpretation ofCurrent oncology (Toronto, Ont.) · 2026Article
- Assessing the pathogenicity ofFrontiers in oncology · 2022Article
- Structural bioinformatics enhances mechanistic interpretation of genomic variation, demonstrated through the analyses of 935 distinct RAS family mutations.Bioinformatics (Oxford, England) · 2021Article
- Screening of BRCA1 variants c.190T>C, 1307delT, g.5331G>A and c.2612C>T in breast cancer patients from North India.Genetics and molecular biology · 2020Article
- Reinterpretation of BRCA1 and BRCA2 variants of uncertain significance in patients with hereditary breast/ovarian cancer using the ACMG/AMP 2015 guidelines.Breast cancer (Tokyo, Japan) · 2019Article
- Impact of amino acid substitutions at secondary structures in the BRCT domains of the tumor suppressor BRCA1: Implications for clinical annotation.The Journal of biological chemistry · 2019Article
- Incorporating computational resources in a cancer research program.Human genetics · 2015Review
- Splicing analysis of 14 BRCA1 missense variants classifies nine variants as pathogenic.Breast cancer research and treatment · 2015Article
- BRCA1 point mutations in premenopausal breast cancer patients from Central Sudan.Familial cancer · 2014Article
- Evaluation of ultra-deep targeted sequencing for personalized breast cancer care.Breast cancer research : BCR · 2013Article
- Lessons from postgenome-wide association studies: functional analysis of cancer predisposition loci.Journal of internal medicine · 2013Review
- MuPIT interactive: webserver for mapping variant positions to annotated, interactive 3D structures.Human genetics · 2013Article
- Article
- Inferring causality and functional significance of human coding DNA variants.Human molecular genetics · 2012Review
- Two Missense Mutations in the Primary Autosomal Recessive Microcephaly Gene MCPH1 Disrupt the Function of the Highly Conserved N-Terminal BRCT Domain of Microcephalin.Molecular syndromology · 2012Article
- Description and analysis of genetic variants in French hereditary breast and ovarian cancer families recorded in the UMD-BRCA1/BRCA2 databases.Nucleic acids research · 2012Article
- Impact of BRCA1 BRCT domain missense substitutions on phosphopeptide recognition.Biochemistry · 2011Article
- Functional hot spots in human ATP-binding cassette transporter nucleotide binding domains.Protein science : a publication of the Protein Society · 2010Article
- Toward classification of BRCA1 missense variants using a biophysical approach.The Journal of biological chemistry · 2010Article
- Comprehensive analysis of missense variations in the BRCT domain of BRCA1 by structural and functional assays.Cancer research · 2010Article
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Authors and funding
5 authors.
Funding
Abstract
Many individuals tested for inherited cancer susceptibility at the BRCA1 gene locus are discovered to have variants of unknown clinical significance (UCVs). Most UCVs cause a single amino acid residue (missense) change in the BRCA1 protein. They can be biochemically assayed, but such evaluations are time-consuming and labor-intensive. Computational methods that classify and suggest explanations for UCV impact on protein function can complement functional tests. Here we describe a supervised learning approach to classification of BRCA1 UCVs. Using a novel combination of 16 predictive features, the algorithms were applied to retrospectively classify the impact of 36 BRCA1 C-terminal (BRCT) domain UCVs biochemically assayed to measure transactivation function and to blindly classify 54 documented UCVs. Majority vote of three supervised learning algorithms is in agreement with the assay for more than 94% of the UCVs. Two UCVs found deleterious by both the assay and the classifiers reveal a previously uncharacterized putative binding site. Clinicians may soon be able to use computational classifiers such as those described here to better inform patients. These classifiers can be adapted to other cancer susceptibility genes and systematically applied to prioritize the growing number of potential causative loci and variants found by large-scale disease association studies.
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