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

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 probab...

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Autores: Parsons, M. T., Tudini, E., Li, H., Hahnen, E., Wappenschmidt, B., Feliubadaló, L., Aalfs, C. M., Agata, S., Aittomäki, K., Alducci, E., Alonso-Cerezo, M. C., Arnold, N., Auber, B., Austin, R., Azzollini, J., Balmaña, J., Barbieri, E., Bartram, C. R., Blanco Pérez, Ana, Blümcke, B., Bonache, S., Bonanni, B., Borg, Å, Bortesi, B., Brunet, J., Bruzzone, C., Bucksch, K., Cagnoli, G., Caldés, T., Caliebe, A., Caligo, M. A., Calvello, M., Capone, G. L., Caputo, S. M., Carnevali, I., Carrasco, E., Caux-Moncoutier, V., Cavalli, P., Cini, G., Clarke, E. M., Concolino, P., Cops, E. J., Cortesi, L., Couch, F. J., Darder, E., de la Hoya, M., Dean, M., Debatin, I., Del Valle, J., Delnatte, C., Derive, N., Diez, O., Ditsch, N., Domchek, S. M., Dutrannoy, V., Eccles, D. M., Ehrencrona, H., Enders, U., Evans, D. G., Farra, C., Faust, U., Felbor, U., Feroce, I., Fine, M., Foulkes, W. D., Galvao, H. C. R., Gambino, G., Gehrig, A., Gensini, F., Gerdes, A. M., Germani, A., Giesecke, J., Gismondi, V., Gómez, C., Gómez Garcia, E. B., González, S., Grau, E., Grill, S., Gross, E., Guerrieri-Gonzaga, A., Guillaud-Bataille, M., Gutiérrez-Enríquez, S., Haaf, T., Hackmann, K., Hansen, T. V. O., Harris, M., Hauke, J., Heinrich, T., Hellebrand, H., Herold, K. N., Honisch, E., Horvath, J., Houdayer, C., Hübbel, V., Iglesias, S., Izquierdo, A., James, P. A., Janssen, L. A. M., Jeschke, U., Kaulfuß, S., Keupp, K., Kiechle, M., Kölbl, A., Krieger, S., Kruse, T. A., Kvist, A., Lalloo, F., Larsen, M., Lattimore, V. L., Lautrup, C., Ledig, S., Leinert, E., Lewis, A. L., Lim, J., Loeffler, M., López-Fernández, A., Lucci-Cordisco, E., Maass, N., Manoukian, S., Marabelli, M., Matricardi, L., Meindl, A., Michelli, R. D., Moghadasi, S., Moles-Fernández, A., Montagna, M., Montalban, G., Monteiro, A. N., Montes, E., Mori, L., Moserle, L., Müller, C. R., Mundhenke, C., Naldi, N., Nathanson, K. L., Navarro, M., Nevanlinna, H., Nichols, C. B., Niederacher, D., Nielsen, H. R., Ong, K. R., Pachter, N., Palmero, E. I., Papi, L., Pedersen, I. S., Peissel, B., Perez-Segura, P., Pfeifer, K., Pineda, M., Pohl-Rescigno, E., Poplawski, N. K., Porfirio, B., Quante, A. S., Ramser, J., Reis, R. M., Revillion, F., Rhiem, K., Riboli, B., Ritter, J., Rivera, D., Rofes, P., Rump, A., Salinas, M., Sánchez de Abajo, A. M., Schmidt, G., Schoenwiese, U., Seggewiß, J., Solanes, A., Steinemann, D., Stiller, M., Stoppa-Lyonnet, D., Sullivan, K. J., Susman, R., Sutter, C., Tavtigian, S. V., Teo, S. H., Teulé, A., Thomassen, M., Tibiletti, M. G., Tischkowitz, M., Tognazzo, S., Toland, A. E., Tornero, E., Törngren, T., Torres-Esquius, S., Toss, A., Trainer, A. H., Tucker, K. M., van Asperen, C. J., van Mackelenbergh, M. T., Varesco, L., Vargas-Parra, G., Varon, R., Vega, A., Velasco, Á, Vesper, A. S., Viel, A., Vreeswijk, M. P. G., Wagner, S. A., Waha, A., Walker, L. C., Walters, R. J., Wang-Gohrke, S., Weber, B. H. F., Weichert, W., Wieland, K., Wiesmüller, L., Witzel, I., Wöckel, A., Woodward, E. R., Zachariae, S., Zampiga, V., Zeder-Göß, C., Investigators, K., Lázaro, C., De Nicolo, A., Radice, P., Engel, C., Schmutzler, R. K., Goldgar, D. E., Spurdle, A. B.
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Servizo Galego de Saúde (SERGAS)
Repositorio:RUNA. Repositorio da Consellería de Sanidade e Sergas
OAI Identifier:oai:runa.sergas.gal:20.500.11940/15792
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6772163/pdf/HUMU-40-1557.pdf
https://www.ncbi.nlm.nih.gov/pubmed/31131967
http://hdl.handle.net/20.500.11940/15792
Access Level:acceso abierto
Palabra clave:Mutation
Early Detection of Cancer
Computational Biology
Alternative Splicing
Likelihood Functions
Humans
BRCA1 Protein
Multifactorial Inheritance
BRCA2 Protein
Genetic Predisposition to Disease
Neoplasms
biología computacional
mutación
humanos
empalme alternativo
detección precoz del cáncer
neoplasias
proteína BRCA1
herencia multifactorial
funciones de verosimilitud
predisposición genética a la enfermedad
proteína BRCA2
FPGMX
IDIS
Descripción
Sumario: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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