Literature DB >> 16288312

Improving the accuracy of BRCA1/2 mutation prediction: validation of the novel country-customized IC software.

Carlo Capalbo1, Enrico Ricevuto, Annarita Vestri, Tina Sidoni, Amelia Buffone, Enrico Cortesi, Paolo Marchetti, Giovanni Scambia, Silverio Tomao, Christian Rinaldi, Massimo Zani, Sergio Ferraro, Luigi Frati, Isabella Screpanti, Alberto Gulino, Giuseppe Giannini.   

Abstract

Inherited mutations of the BRCA1/2 genes confer a significantly increased risk for breast and/or ovarian cancer development. Several models were elaborated to help genetic counsellors in selecting individuals with high probability of being mutation carriers. The IC software, a country-customized version of the Brcapro model, was recently shown to be particularly accurate in the prediction of carrier probability status in the Italian population. Here, we used our independent series of 70 breast/ovarian cancer families to analyze the performances of the IC software and compare it to widely used models, such as Brcapro and the Myriad mutation prevalence tables. Analysis of the areas under the receiver operator characteristics (ROC) curves indicated that overall the models performed well. However, the IC software and Myriad tables were more efficient in predicting mutated cases, showing a higher sensitivity (94 and 88%, respectively) and negative predictive value (NPV, 94 and 92%, respectively) compared to Brcapro (sensitivity 71 and NPV 83%). IC software also appeared particularly accurate in the identification of families belonging the low mutation risk group (<10%). Finally, most Brcapro failures occurred in the hereditary breast cancer (HBC) family subset, and in 75% of the cases, the IC software corrected them. Our data suggest that the country-customized implementation operated on the Brcapro software generated a more accurate tool for the prediction of BRCA1/2 gene mutation. Whether the IC or other country-customized models might improve BRCA1/2 mutation prediction also in non-Italian families needs to be further explored.

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Year:  2006        PMID: 16288312     DOI: 10.1038/sj.ejhg.5201511

Source DB:  PubMed          Journal:  Eur J Hum Genet        ISSN: 1018-4813            Impact factor:   4.246


  7 in total

1.  The BRCAPRO 5.0 model is a useful tool in genetic counseling and clinical management of male breast cancer cases.

Authors:  Ines Zanna; Piera Rizzolo; Francesco Sera; Mario Falchetti; Paolo Aretini; Giuseppe Giannini; Giovanna Masala; Alberto Gulino; Domenico Palli; Laura Ottini
Journal:  Eur J Hum Genet       Date:  2010-03-17       Impact factor: 4.246

2.  Validity of models for predicting BRCA1 and BRCA2 mutations.

Authors:  Giovanni Parmigiani; Sining Chen; Edwin S Iversen; Tara M Friebel; Dianne M Finkelstein; Hoda Anton-Culver; Argyrios Ziogas; Barbara L Weber; Andrea Eisen; Kathleen E Malone; Janet R Daling; Li Hsu; Elaine A Ostrander; Leif E Peterson; Joellen M Schildkraut; Claudine Isaacs; Camille Corio; Leoni Leondaridis; Gail Tomlinson; Christopher I Amos; Louise C Strong; Donald A Berry; Jeffrey N Weitzel; Sharon Sand; Debra Dutson; Rich Kerber; Beth N Peshkin; David M Euhus
Journal:  Ann Intern Med       Date:  2007-10-02       Impact factor: 25.391

3.  Performance of BRCA1/2 mutation prediction models in Asian Americans.

Authors:  Allison W Kurian; Gail D Gong; Nicolette M Chun; Meredith A Mills; Ashley D Staton; Kerry E Kingham; Beth B Crawford; Robin Lee; Salina Chan; Susan S Donlon; Yolanda Ridge; Karen Panabaker; Dee W West; Alice S Whittemore; James M Ford
Journal:  J Clin Oncol       Date:  2008-09-08       Impact factor: 44.544

4.  Performance of prediction models for BRCA mutation carriage in three racial/ethnic groups: findings from the Northern California Breast Cancer Family Registry.

Authors:  Allison W Kurian; Gail D Gong; Esther M John; Alexander Miron; Anna Felberg; Amanda I Phipps; Dee W West; Alice S Whittemore
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2009-03-31       Impact factor: 4.254

5.  Optimizing the identification of risk-relevant mutations by multigene panel testing in selected hereditary breast/ovarian cancer families.

Authors:  Anna Coppa; Arianna Nicolussi; Sonia D'Inzeo; Carlo Capalbo; Francesca Belardinilli; Valeria Colicchia; Marialaura Petroni; Massimo Zani; Sergio Ferraro; Christian Rinaldi; Amelia Buffone; Armando Bartolazzi; Isabella Screpanti; Laura Ottini; Giuseppe Giannini
Journal:  Cancer Med       Date:  2017-12-22       Impact factor: 4.452

6.  Next-generation sequencing of BRCA1 and BRCA2 genes for rapid detection of germline mutations in hereditary breast/ovarian cancer.

Authors:  Arianna Nicolussi; Francesca Belardinilli; Yasaman Mahdavian; Valeria Colicchia; Sonia D'Inzeo; Marialaura Petroni; Massimo Zani; Sergio Ferraro; Virginia Valentini; Laura Ottini; Giuseppe Giannini; Carlo Capalbo; Anna Coppa
Journal:  PeerJ       Date:  2019-04-22       Impact factor: 2.984

7.  Identification of novel BRCA1 large genomic rearrangements by a computational algorithm of amplicon-based Next-Generation Sequencing data.

Authors:  Arianna Nicolussi; Francesca Belardinilli; Valentina Silvestri; Yasaman Mahdavian; Virginia Valentini; Sonia D'Inzeo; Marialaura Petroni; Massimo Zani; Sergio Ferraro; Stefano Di Giulio; Francesca Fabretti; Beatrice Fratini; Angela Gradilone; Laura Ottini; Giuseppe Giannini; Anna Coppa; Carlo Capalbo
Journal:  PeerJ       Date:  2019-11-15       Impact factor: 2.984

  7 in total

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