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

  • Epidemiology
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

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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Abbreviations

AJ:

Ashkenazi Jewish

AUC:

Area under the ROC curve

CI:

Confidence interval

CK:

Cytokeratin

ER:

Estrogen receptor

FISH:

Fluorescence in situ hybridization

Her-2/neu:

Human epidermal growth factor receptor 2

IHC:

Immunohistochemistry

IDI:

Integrated discrimination improvement

MDACC:

MD Anderson Cancer Center

NRI:

Net reclassification improvement

PR:

Progesterone receptor

PVN:

Predictive value negative

PVP:

Predictive value positive

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Acknowledgments

This study was supported in part by the Susan G Komen for the Cure Grant KG081303, an Intramural Seed Grant from the University of North Texas Health Science Center, and the Program in Human and Computational Genomics at MDACC.

Conflict of interest

All authors declare that they have no conflict of interest.

Author information

Authors and Affiliations

  1. Department of Biostatistics, School of Public Health, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107-2699, USA

    Swati Biswas & Neelam Tankhiwale

  2. Division of Biostatistics, Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA

    Amanda Blackford

  3. Department of Breast Medical Oncology and Clinical Cancer Genetics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA

    Angelica M. Gutierrez Barrera, Kaylene Ready & Banu Arun

  4. Department of Gynecologic Oncology and Clinical Cancer Genetics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA

    Karen Lu

  5. Department of Genetics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA

    Christopher I. Amos

  6. Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute, Boston, MA, USA

    Giovanni Parmigiani

  7. Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA

    Giovanni Parmigiani

Authors
  1. Swati Biswas
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  2. Neelam Tankhiwale
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  3. Amanda Blackford
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  4. Angelica M. Gutierrez Barrera
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  5. Kaylene Ready
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  6. Karen Lu
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  7. Christopher I. Amos
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  8. Giovanni Parmigiani
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  9. Banu Arun
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Corresponding author

Correspondence to Swati Biswas.

Additional information

Giovanni Parmigiani and Banu Arun have contributed equally to this study.

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Biswas, S., Tankhiwale, N., Blackford, A. et al. Assessing the added value of breast tumor markers in genetic risk prediction model BRCAPRO. Breast Cancer Res Treat 133, 347–355 (2012). https://doi.org/10.1007/s10549-012-1958-z

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  • DOI: https://doi.org/10.1007/s10549-012-1958-z

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