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1. Breast and Prostate Cancer Risks for Male BRCA1 and BRCA2 Pathogenic Variant Carriers Using Polygenic Risk Scores.

Author

Barnes DR, Silvestri V, Leslie G, McGuffog L, Dennis J, Yang X, Adlard J, Agnarsson BA, Ahmed M, Aittomäki K, Andrulis IL, Arason A, Arnold N, Auber B, Azzollini J, Balmaña J, Barkardottir RB, Barrowdale D, Barwell J, Belotti M, Benitez J, Berthet P, Boonen SE, Borg Å, Bozsik A, Brady AF, Brennan P, Brewer C, Brunet J, Bucalo A, Buys SS, Caldés T, Caligo MA, Campbell I, Cassingham H, Christensen LL, Cini G, Claes KBM, Cook J, Coppa A, Cortesi L, Damante G, Darder E, Davidson R, de la Hoya M, De Leeneer K, de Putter R, Del Valle J, Diez O, Ding YC, Domchek SM, Donaldson A, Eason J, Eeles R, Engel C, Evans DG, Feliubadaló L, Fostira F, Frone M, Frost D, Gallagher D, Gehrig A, Giraud S, Glendon G, Godwin AK, Goldgar DE, Greene MH, Gregory H, Gross E, Hahnen E, Hamann U, Hansen TVO, Hanson H, Hentschel J, Horvath J, Izatt L, Izquierdo A, James PA, Janavicius R, Jensen UB, Johannsson OT, John EM, Kramer G, Kroeldrup L, Kruse TA, Lautrup C, Lazaro C, Lesueur F, Lopez-Fernández A, Mai PL, Manoukian S, Matrai Z, Matricardi L, Maxwell KN, Mebirouk N, Meindl A, Montagna M, Monteiro AN, Morrison PJ, Muranen TA, Murray A, Nathanson KL, Neuhausen SL, Nevanlinna H, Nguyen-Dumont T, Niederacher D, Olah E, Olopade OI, Palli D, Parsons MT, Pedersen IS, Peissel B, Perez-Segura P, Peterlongo P, Petersen AH, Pinto P, Porteous ME, Pottinger C, Pujana MA, Radice P, Ramser J, Rantala J, Robson M, Rogers MT, Rønlund K, Rump A, Sánchez de Abajo AM, Shah PD, Sharif S, Side LE, Singer CF, Stadler Z, Steele L, Stoppa-Lyonnet D, Sutter C, Tan YY, Teixeira MR, Teulé A, Thull DL, Tischkowitz M, Toland AE, Tommasi S, Toss A, Trainer AH, Tripathi V, Valentini V, van Asperen CJ, Venturelli M, Viel A, Vijai J, Walker L, Wang-Gohrke S, Wappenschmidt B, Whaite A, Zanna I, Offit K, Thomassen M, Couch FJ, Schmutzler RK, Simard J, Easton DF, Chenevix-Trench G, Antoniou AC, and Ottini L

Subjects
Aged, 80 and over, BRCA1 Protein genetics, BRCA2 Protein genetics, Genetic Predisposition to Disease, Heterozygote, Humans, Male, Mutation, Polymorphism, Single Nucleotide, Risk Assessment, Risk Factors, Breast Neoplasms epidemiology, Breast Neoplasms genetics, Prostatic Neoplasms epidemiology, Prostatic Neoplasms genetics
Abstract

Background: Recent population-based female breast cancer and prostate cancer polygenic risk scores (PRS) have been developed. We assessed the associations of these PRS with breast and prostate cancer risks for male BRCA1 and BRCA2 pathogenic variant carriers., Methods: 483 BRCA1 and 1318 BRCA2 European ancestry male carriers were available from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). A 147-single nucleotide polymorphism (SNP) prostate cancer PRS (PRSPC) and a 313-SNP breast cancer PRS were evaluated. There were 3 versions of the breast cancer PRS, optimized to predict overall (PRSBC), estrogen receptor (ER)-negative (PRSER-), or ER-positive (PRSER+) breast cancer risk., Results: PRSER+ yielded the strongest association with breast cancer risk. The odds ratios (ORs) per PRSER+ standard deviation estimates were 1.40 (95% confidence interval [CI] =1.07 to 1.83) for BRCA1 and 1.33 (95% CI = 1.16 to 1.52) for BRCA2 carriers. PRSPC was associated with prostate cancer risk for BRCA1 (OR = 1.73, 95% CI = 1.28 to 2.33) and BRCA2 (OR = 1.60, 95% CI = 1.34 to 1.91) carriers. The estimated breast cancer odds ratios were larger after adjusting for female relative breast cancer family history. By age 85 years, for BRCA2 carriers, the breast cancer risk varied from 7.7% to 18.4% and prostate cancer risk from 34.1% to 87.6% between the 5th and 95th percentiles of the PRS distributions., Conclusions: Population-based prostate and female breast cancer PRS are associated with a wide range of absolute breast and prostate cancer risks for male BRCA1 and BRCA2 carriers. These findings warrant further investigation aimed at providing personalized cancer risks for male carriers and informing clinical management., (© The Author(s) 2021. Published by Oxford University Press.)

Published
2022
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2. Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification.

Author

Parsons MT, Tudini E, Li H, Hahnen E, Wappenschmidt B, Feliubadaló L, Aalfs CM, Agata S, Aittomäki K, Alducci E, Alonso-Cerezo MC, Arnold N, Auber B, Austin R, Azzollini J, Balmaña J, Barbieri E, Bartram CR, Blanco A, Blümcke B, Bonache S, Bonanni B, Borg Å, Bortesi B, Brunet J, Bruzzone C, Bucksch K, Cagnoli G, Caldés T, Caliebe A, Caligo MA, Calvello M, Capone GL, Caputo SM, Carnevali I, Carrasco E, Caux-Moncoutier V, Cavalli P, Cini G, Clarke EM, Concolino P, Cops EJ, Cortesi L, Couch FJ, Darder E, de la Hoya M, Dean M, Debatin I, Del Valle J, Delnatte C, Derive N, Diez O, Ditsch N, Domchek SM, Dutrannoy V, Eccles DM, Ehrencrona H, Enders U, Evans DG, Farra C, Faust U, Felbor U, Feroce I, Fine M, Foulkes WD, Galvao HCR, Gambino G, Gehrig A, Gensini F, Gerdes AM, Germani A, Giesecke J, Gismondi V, Gómez C, Gómez Garcia EB, González S, Grau E, Grill S, Gross E, Guerrieri-Gonzaga A, Guillaud-Bataille M, Gutiérrez-Enríquez S, Haaf T, Hackmann K, Hansen TVO, Harris M, Hauke J, Heinrich T, Hellebrand H, Herold KN, Honisch E, Horvath J, Houdayer C, Hübbel V, Iglesias S, Izquierdo A, James PA, Janssen LAM, Jeschke U, Kaulfuß S, Keupp K, Kiechle M, Kölbl A, Krieger S, Kruse TA, Kvist A, Lalloo F, Larsen M, Lattimore VL, Lautrup C, Ledig S, Leinert E, Lewis AL, Lim J, Loeffler M, López-Fernández A, Lucci-Cordisco E, Maass N, Manoukian S, Marabelli M, Matricardi L, Meindl A, Michelli RD, Moghadasi S, Moles-Fernández A, Montagna M, Montalban G, Monteiro AN, Montes E, Mori L, Moserle L, Müller CR, Mundhenke C, Naldi N, Nathanson KL, Navarro M, Nevanlinna H, Nichols CB, Niederacher D, Nielsen HR, Ong KR, Pachter N, Palmero EI, Papi L, Pedersen IS, Peissel B, Perez-Segura P, Pfeifer K, Pineda M, Pohl-Rescigno E, Poplawski NK, Porfirio B, Quante AS, Ramser J, Reis RM, Revillion F, Rhiem K, Riboli B, Ritter J, Rivera D, Rofes P, Rump A, Salinas M, Sánchez de Abajo AM, Schmidt G, Schoenwiese U, Seggewiß J, Solanes A, Steinemann D, Stiller M, Stoppa-Lyonnet D, Sullivan KJ, Susman R, Sutter C, Tavtigian SV, Teo SH, Teulé A, Thomassen M, Tibiletti MG, Tischkowitz M, Tognazzo S, Toland AE, Tornero E, Törngren T, Torres-Esquius S, Toss A, Trainer AH, Tucker KM, van Asperen CJ, van Mackelenbergh MT, Varesco L, Vargas-Parra G, Varon R, Vega A, Velasco Á, Vesper AS, Viel A, Vreeswijk MPG, Wagner SA, Waha A, Walker LC, Walters RJ, Wang-Gohrke S, Weber BHF, Weichert W, Wieland K, Wiesmüller L, Witzel I, Wöckel A, Woodward ER, Zachariae S, Zampiga V, Zeder-Göß C, Lázaro C, De Nicolo A, Radice P, Engel C, Schmutzler RK, Goldgar DE, and Spurdle AB

Subjects
Alternative Splicing, Early Detection of Cancer, Female, Genetic Predisposition to Disease, Humans, Likelihood Functions, Male, Multifactorial Inheritance, Neoplasms genetics, BRCA1 Protein genetics, BRCA2 Protein genetics, Computational Biology methods, Mutation, Missense, Neoplasms diagnosis
Abstract

The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification., (© 2019 Wiley Periodicals, Inc.)

Published
2019
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