Literature DB >> 22270937

Assessing the added value of breast tumor markers in genetic risk prediction model BRCAPRO.

Swati Biswas1, Neelam Tankhiwale, Amanda Blackford, Angelica M Gutierrez Barrera, Kaylene Ready, Karen Lu, Christopher I Amos, Giovanni Parmigiani, Banu Arun.   

Abstract

The BRCAPRO model estimates carrier probabilities for the BRCA1 and BRCA2 genes, and was recently enhanced to use estrogen receptor (ER) and progesterone receptor (PR) status of breast cancer. No independent assessment of the added value of these markers exists. Moreover, earlier versions of BRCAPRO did not use human epidermal growth factor receptor 2 (Her-2/neu) status of breast cancer. Here, we incorporate Her-2/neu in BRCAPRO and validate all the markers. We trained the enhanced model on 406 germline tested individuals, and validated on a separate clinical cohort of 796 individuals for whom test results and family history are available. For model-building, we estimated joint probabilities of ER, PR, and Her-2/neu status for carriers and non-carriers of BRCA1/2 mutations. For validation, we obtained BRCAPRO predictions with and without markers. We calculated area under the receiver operating characteristic curve (AUC), sensitivity, specificity, predictive values, and correct reclassification rates. The AUC for predicting BRCA1 status among individuals who are carriers of at least one mutation improved when ER and PR were used. The AUC for predicting the presence of either mutation improved when Her-2/neu was added. Use of markers also produced highly significant correct reclassification improvements in both cases. Breast tumor markers are useful for prediction of BRCA1/2 mutation status. ER and PR improve discrimination between BRCA1 and BRCA2 mutation carriers while Her-2/neu helps discriminate between carriers and non-carriers, particularly among women who are ER positive and Her-2/neu negative. These results support the use of the enhanced version of BRCAPRO in clinical settings.

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Year:  2012        PMID: 22270937     DOI: 10.1007/s10549-012-1958-z

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN: 0167-6806            Impact factor:   4.872


  13 in total

1.  Comparison between CaGene 5.1 and 6.0 for BRCA1/2 mutation prediction: a retrospective study of 150 BRCA1/2 genetic tests in 517 families with breast/ovarian cancer.

Authors:  Ivana Antonucci; Martina Provenzano; Luca Sorino; Michela Balsamo; Gitana Maria Aceto; Pasquale Battista; David Euhus; Ettore Cianchetti; Patrizia Ballerini; Clara Natoli; Giandomenico Palka; Liborio Stuppia
Journal:  J Hum Genet       Date:  2016-12-08       Impact factor: 3.172

2.  Reclassification of predictions for uncovering subgroup specific improvement.

Authors:  Swati Biswas; Banu Arun; Giovanni Parmigiani
Journal:  Stat Med       Date:  2013-12-18       Impact factor: 2.373

3.  A two-stage approach to genetic risk assessment in primary care.

Authors:  Swati Biswas; Philamer Atienza; Jonathan Chipman; Amanda L Blackford; Banu Arun; Kevin Hughes; Giovanni Parmigiani
Journal:  Breast Cancer Res Treat       Date:  2016-01-19       Impact factor: 4.872

4.  A Pragmatic Testing-Eligibility Framework for Population Mutation Screening: The Example of BRCA1/2.

Authors:  Ana F Best; Margaret A Tucker; Megan N Frone; Mark H Greene; June A Peters; Hormuzd A Katki
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2019-01-28       Impact factor: 4.254

5.  Providing access to risk prediction tools via the HL7 XML-formatted risk web service.

Authors:  Jonathan Chipman; Brian Drohan; Amanda Blackford; Giovanni Parmigiani; Kevin Hughes; Phil Bosinoff
Journal:  Breast Cancer Res Treat       Date:  2013-06-23       Impact factor: 4.872

Review 6.  Genetic risk assessments in individuals at high risk for inherited breast cancer in the breast oncology care setting.

Authors:  Tuya Pal; Susan T Vadaparampil
Journal:  Cancer Control       Date:  2012-10       Impact factor: 3.302

7.  Recent BRCAPRO upgrades significantly improve calibration.

Authors:  Emanuele Mazzola; Jonathan Chipman; Su-Chun Cheng; Giovanni Parmigiani
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2014-06-02       Impact factor: 4.254

8.  Simplifying clinical use of the genetic risk prediction model BRCAPRO.

Authors:  Swati Biswas; Philamer Atienza; Jonathan Chipman; Kevin Hughes; Angelica M Gutierrez Barrera; Christopher I Amos; Banu Arun; Giovanni Parmigiani
Journal:  Breast Cancer Res Treat       Date:  2013-05-21       Impact factor: 4.872

9.  KOHBRA BRCA risk calculator (KOHCal): a model for predicting BRCA1 and BRCA2 mutations in Korean breast cancer patients.

Authors:  Eunyoung Kang; Sue K Park; Jong Won Lee; Zisun Kim; Woo-Chul Noh; Yongsik Jung; Jung-Hyun Yang; Sung Hoo Jung; Sung-Won Kim
Journal:  J Hum Genet       Date:  2016-01-14       Impact factor: 3.172

Review 10.  Assessing Risk of Breast Cancer: A Review of Risk Prediction Models.

Authors:  Geunwon Kim; Manisha Bahl
Journal:  J Breast Imaging       Date:  2021-02-19
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