Penetrances of breast and ovarian cancer in a large series of families tested for BRCA1/2 mutations View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2004-11

AUTHORS

Fabio Marroni, Paolo Aretini, Emma D'Andrea, Maria Adelaide Caligo, Laura Cortesi, Alessandra Viel, Enrico Ricevuto, Marco Montagna, Giovanna Cipollini, Massimo Federico, Manuela Santarosa, Paolo Marchetti, Joan E Bailey-Wilson, Generoso Bevilacqua, Giovanni Parmigiani, Silvano Presciuttini

ABSTRACT

Accurate estimates of breast and ovarian cancer penetrance in BRCA1/2 mutation carriers are crucial in genetic counseling. Estimation is difficult because of the low frequency of mutated alleles and the often-uncertain mechanisms of family ascertainment. We estimated the penetrances of breast and ovarian cancers in carriers of BRCA1/2 mutations by maximizing the retrospective likelihood of the genetic model, given the observed test results, in 568 Italian families screened for germline mutations. The software BRCAPRO was used as a probability calculation tool in a Markov Chain Monte Carlo approach. Breast cancer penetrances were 27% (95% CI 20-34%) at age 50 years and 39% (27-52%) at age 70 in BRCA1 carriers, and 26% (0.18-0.34%) at age 50 and 44% (29-58%) at age 70 in BRCA2 carriers, and ovarian cancer penetrances were 14% (7-22%) at age 50 and 43% (21-66%) at age 70 in BRCA1 carriers and 3% (0-7%) at age 50 and 15% (4-26%) at age 70 in BRCA2 carriers. The new model gave a better fit than the current default in BRCAPRO, the likelihood being 70 log units greater; in addition, the observed numbers of mutations in families stratified by gene and by cancer profile were not significantly different from those expected. Our new penetrance functions are appropriate for predicting breast cancer risk, and for determining the probability of carrying BRCA1/2 mutations, in people who are presently referred to genetic counseling in Italy. Our approach could lead to country-customized versions of the BRCAPRO software by providing appropriate population-specific estimates. More... »

PAGES

5201256

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/sj.ejhg.5201256

DOI

http://dx.doi.org/10.1038/sj.ejhg.5201256

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1044228342

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/15340362


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