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

Authors:
Dr. Sandrine Caputo, PhD
Dr. Sandrine Caputo, PhD
Institut Curie
Dr
Paris | France
Michael T Parsons Emma Tudini Hongyan Li Eric Hahnen Barbara Wappenschmidt Lidia Feliubadaló Cora M Aalfs Simona Agata Kristiina Aittomäki Elisa Alducci María Concepción Alonso-Cerezo Norbert Arnold Bernd Auber Rachel Austin Jacopo Azzollini Judith Balmaña Elena Barbieri Claus R Bartram Ana Blanco Britta Blümcke Sandra Bonache Bernardo Bonanni Åke Borg Beatrice Bortesi Joan Brunet Carla Bruzzone Karolin Bucksch Giulia Cagnoli Trinidad Caldés Almuth Caliebe Maria A Caligo Mariarosaria Calvello Gabriele L Capone Sandrine M Caputo Ileana Carnevali Estela Carrasco Virginie Caux-Moncoutier Pietro Cavalli Giulia Cini Edward M Clarke Paola Concolino Elisa J Cops Laura Cortesi Fergus J Couch Esther Darder Miguel de la Hoya Michael Dean Irmgard Debatin Jesús Del Valle Capucine Delnatte Nicolas Derive Orland Diez Nina Ditsch Susan M Domchek Véronique Dutrannoy Diana M Eccles Hans Ehrencrona Ute Enders D Gareth Evans Chantal Farra Ulrike Faust Ute Felbor Irene Feroce Miriam Fine William D Foulkes Henrique C R Galvao Gaetana Gambino Andrea Gehrig Francesca Gensini Anne-Marie Gerdes Aldo Germani Jutta Giesecke Viviana Gismondi Carolina Gómez Encarna B Gómez Garcia Sara González Elia Grau Sabine Grill Eva Gross Aliana Guerrieri-Gonzaga Marine Guillaud-Bataille Sara Gutiérrez-Enríquez Thomas Haaf Karl Hackmann Thomas V O Hansen Marion Harris Jan Hauke Tilman Heinrich Heide Hellebrand Karen N Herold Ellen Honisch Judit Horvath Claude Houdayer Verena Hübbel Silvia Iglesias Angel Izquierdo Paul A James Linda A M Janssen Udo Jeschke Silke Kaulfuß Katharina Keupp Marion Kiechle Alexandra Kölbl Sophie Krieger Torben A Kruse Anders Kvist Fiona Lalloo Mirjam Larsen Vanessa L Lattimore Charlotte Lautrup Susanne Ledig Elena Leinert Alexandra L Lewis Joanna Lim Markus Loeffler Adrià López-Fernández Emanuela Lucci-Cordisco Nicolai Maass Siranoush Manoukian Monica Marabelli Laura Matricardi Alfons Meindl Rodrigo D Michelli Setareh Moghadasi Alejandro Moles-Fernández Marco Montagna Gemma Montalban Alvaro N Monteiro Eva Montes Luigi Mori Lidia Moserle Clemens R Müller Christoph Mundhenke Nadia Naldi Katherine L Nathanson Matilde Navarro Heli Nevanlinna Cassandra B Nichols Dieter Niederacher Henriette R Nielsen Kai-Ren Ong Nicholas Pachter Edenir I Palmero Laura Papi Inge Sokilde Pedersen Bernard Peissel Pedro Perez-Segura Katharina Pfeifer Marta Pineda Esther Pohl-Rescigno Nicola K Poplawski Berardino Porfirio Anne S Quante Juliane Ramser Rui M Reis Françoise Revillion Kerstin Rhiem Barbara Riboli Julia Ritter Daniela Rivera Paula Rofes Andreas Rump Monica Salinas Ana María Sánchez de Abajo Gunnar Schmidt Ulrike Schoenwiese Jochen Seggewiß Ares Solanes Doris Steinemann Mathias Stiller Dominique Stoppa-Lyonnet Kelly J Sullivan Rachel Susman Christian Sutter Sean V Tavtigian Soo H Teo Alex Teulé Mads Thomassen Maria Grazia Tibiletti Marc Tischkowitz Silvia Tognazzo Amanda E Toland Eva Tornero Therese Törngren Sara Torres-Esquius Angela Toss Alison H Trainer Katherine M Tucker Christi J van Asperen Marion T van Mackelenbergh Liliana Varesco Gardenia Vargas-Parra Raymonda Varon Ana Vega Ángela Velasco Anne-Sophie Vesper Alessandra Viel Maaike P G Vreeswijk Sebastian A Wagner Anke Waha Logan C Walker Rhiannon J Walters Shan Wang-Gohrke Bernhard H F Weber Wilko Weichert Kerstin Wieland Lisa Wiesmüller Isabell Witzel Achim Wöckel Emma R Woodward Silke Zachariae Valentina Zampiga Christine Zeder-Göß KConFab Investigators Conxi Lázaro Arcangela De Nicolo Paolo Radice Christoph Engel Rita K Schmutzler David E Goldgar Amanda B Spurdle

Hum Mutat 2019 09;40(9):1557-1578

Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.

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.

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Source
http://dx.doi.org/10.1002/humu.23818DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6772163PMC
September 2019
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