Dataset Information

Comprehensive annotation of BRCA1 and BRCA2 missense variants by functionally validated sequence-based computational prediction models.

ABSTRACT: PURPOSE:To improve methods for predicting the impact of missense variants of uncertain significance (VUS) in BRCA1 and BRCA2 on protein function. METHODS:Functional data for 248 BRCA1 and 207 BRCA2 variants from assays with established high sensitivity and specificity for damaging variants were used to recalibrate 40 in silico algorithms predicting the impact of variants on protein activity. Additional random forest (RF) and naïve voting method (NVM) metapredictors for both BRCA1 and BRCA2 were developed to increase predictive accuracy. RESULTS:Optimized thresholds for in silico prediction models significantly improved the accuracy of predicted functional effects for BRCA1 and BRCA2 variants. In addition, new BRCA1-RF and BRCA2-RF metapredictors showed area under the curve (AUC) values of 0.92 (95% confidence interval [CI]: 0.88-0.96) and 0.90 (95% CI: 0.84-0.95), respectively. Similarly, the BRCA1-NVM and BRCA2-NVM models had AUCs of 0.93 and 0.90. The RF and NVM models were used to predict the pathogenicity of all possible missense variants in BRCA1 and BRCA2. CONCLUSION:The recalibrated algorithms and new metapredictors significantly improved upon current models for predicting the impact of variants in cancer risk-associated domains of BRCA1 and BRCA2. Prediction of the functional impact of all possible variants in BRCA1 and BRCA2 provides important information about the clinical relevance of variants in these genes.

SUBMITTER: Hart SN

PROVIDER: S-EPMC6287763 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Comprehensive annotation of BRCA1 and BRCA2 missense variants by functionally validated sequence-based computational prediction models.

Hart Steven N SN Hoskin Tanya T Shimelis Hermela H Moore Raymond M RM Feng Bingjian B Thomas Abigail A Lindor Noralane M NM Polley Eric C EC Goldgar David E DE Iversen Edwin E Monteiro Alvaro N A ANA Suman Vera J VJ Couch Fergus J FJ

Genetics in medicine : official journal of the American College of Medical Genetics 20180608 1

<h4>Purpose</h4>To improve methods for predicting the impact of missense variants of uncertain significance (VUS) in BRCA1 and BRCA2 on protein function.<h4>Methods</h4>Functional data for 248 BRCA1 and 207 BRCA2 variants from assays with established high sensitivity and specificity for damaging variants were used to recalibrate 40 in silico algorithms predicting the impact of variants on protein activity. Additional random forest (RF) and naïve voting method (NVM) metapredictors for both BRCA1 ...[more]

PMID: 29884841

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Project description:BACKGROUND: BRCA1 and BRCA2 germline mutations predispose heterozygous carriers to hereditary breast/ovarian cancer. However, unclassified variants (UVs) (variants with unknown clinical significance) and missense polymorphisms in BRCA1 and BRCA2 genes pose a problem in genetic counseling, as their impact on risk of breast and ovarian cancer is still unclear. The objective of our study was to identify UVs and missense polymorphisms in Algerian breast/ovarian cancer patients and relatives tested previously for BRCA1 and BRCA2 genes germline mutations analysis. METHODS: We analyzed 101 DNA samples from 79 breast/ovarian cancer families. The approach used is based on BRCA1 and BRCA2 sequence variants screening by SSCP or High-Resolution Melting (HRM) curve analysis followed by direct sequencing. In silico analyses have been performed using different bioinformatics programs to individualize genetics variations that can disrupt the BRCA1 and BRCA2 genes function. RESULTS: Among 80 UVs and polymorphisms detected in BRCA1/2 genes (33 BRCA1 and 47 BRCA2), 31 were new UVs (10 BRCA1 and 21 BRCA2), 7 were rare UVs (4 BRCA1 and 3 BRCA2) and 42 were polymorphic variants (19 BRCA1 and 23 BRCA2). Moreover, 8 new missense UVs identified in this study: two BRCA1 (c.4066C>A/p.Gln1356Lys, c.4901G>T/p.Arg1634Met) located respectively in exons 11 and 16, and six BRCA2 (c.1099G>A/p.Asp367Asn, c.2636C>A/p.Ser879Tyr, c.3868T>A/p.Cys1290Ser, c.5428G>T/p.Val1810Phe, c.6346C>G/p.His2116Asp and c.9256G>A/p.Gly3086Arg) located respectively in exons 10, 11 and 24, show a damaging PSIC score yielded by PolyPhen2 program and could be pathogenic. In addition, 5 new BRCA} missense UVs out of six that were found to be damaging by PolyPhen2 program, also were deleterious according to SIFT program. The rare BRCA1 UV c.5332G>A/p.Asp1778Asn was found here for the first time in co-occurrence in trans with the deleterious BRCA1 mutation c.798_799delTT/p.Ser267LysfsX19 in young breast cancer patient. Moreover, 10 new identified intronic variants with unknown clinical significance (3 BRCA1 and 7 BRCA2) in the present study, could be considered as benign, because GeneSplicer, SpliceSiteFinder and MaxEntScan prediction programs show no splice site alteration for these variants. Several missense polymorphisms of BRCA1 c.2612C>T/p.Pro871Leu, c.3548A>G/p.Lys1183Arg, c.4837A>G/p.Ser1613Gly and BRCA2 c.865A>C/p.Asn289His, c.1114A>C/p.Asn372His, c.2971A>G/p.Asn991Asp, c.7150C>A/p.Gly2384Lys have been identified with high frequency in patients who were tested negative for BRCA1 and BRCA2 mutations. These missense polymorphisms could have a role as susceptibility breast cancer markers in Algerian breast/ovarian cancer families where pathological BRCA1 and BRCA2 mutations were not present. CONCLUSIONS: For the first time, UVs and missense polymorphisms in BRCA1 and BRCA2 genes have been identified in Algerian breast/ovarian cancer families. Evaluation of breast/ovarian cancer risk induced by the eight new missense UVs and common polymorphisms detected in our present work is on going in a larger study.

Project description:Mutations in BRCA1 and BRCA2 are responsible for a large proportion of breast-ovarian cancer families. Protein-truncating mutations have been effectively used in the clinical management of familial breast cancer due to their deleterious impact on protein function. However, the majority of missense variants identified throughout the genes continue to pose an obstacle for predictive informative testing due to low frequency and lack of information on how they affect BRCA1/2 function. Phosphorylation of BRCA1 and BRCA2 play an important role in their function as regulators of DNA repair, transcription and cell cycle in response to DNA damage but whether missense variants of uncertain significance (VUS) are able to disrupt this important process is not known. Here we employed a novel approach using NetworKIN which predicts in vivo kinase-substrate relationship, and evolutionary conservation algorithms SIFT, PolyPhen and Align-GVGD. We evaluated whether 191 BRCA1 and 43 BRCA2 VUS from the Breast Cancer Information Core (BIC) database can functionally alter the consensus phosphorylation motifs and abolish kinase recognition and binding to sites known to be phosphorylated in vivo. Our results show that 13.09% (25/191) BRCA1 and 13.95% (6/43) BRCA2 VUS altered the phosphorylation of BRCA1 and BRCA2. We highlight six BRCA1 (K309T, S632N, S1143F, Q1144H, Q1281P, S1542C) and three BRCA2 (S196I, T207A, P3292L) VUS as potentially clinically significant. These occurred rarely (n<2 in BIC), mutated evolutionarily conserved residues and abolished kinase binding to motifs established in the literature involved in DNA repair, cell cycle regulation, transcription or response to DNA damage. Additionally in vivo phosphorylation sites identified via through-put methods are also affected by VUS and are attractive targets for studying their biological and functional significance. We propose that rare VUS affecting phosphorylation may be a novel and important mechanism for which BRCA1 and BRCA2 functions are disrupted in breast cancer.

Dataset Information

Comprehensive annotation of BRCA1 and BRCA2 missense variants by functionally validated sequence-based computational prediction models.

Publications

Comprehensive annotation of BRCA1 and BRCA2 missense variants by functionally validated sequence-based computational prediction models.

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