Skip to main content

Advertisement

SpringerLink
Log in

Simplifying clinical use of the genetic risk prediction model BRCAPRO

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

Abstract

Health care providers need simple tools to identify patients at genetic risk of breast and ovarian cancers. Genetic risk prediction models such as BRCAPRO could fill this gap if incorporated into Electronic Medical Records or other Health Information Technology solutions. However, BRCAPRO requires potentially extensive information on the counselee and her family history. Thus, it may be useful to provide simplified version(s) of BRCAPRO for use in settings that do not require exhaustive genetic counseling. We explore four simplified versions of BRCAPRO, each using less complete information than the original model. BRCAPROLYTE uses information on affected relatives only up to second degree. It is in clinical use but has not been evaluated. BRCAPROLYTE-Plus extends BRCAPROLYTE by imputing the ages of unaffected relatives. BRCAPROLYTE-Simple reduces the data collection burden associated with BRCAPROLYTE and BRCAPROLYTE-Plus by not collecting the family structure. BRCAPRO-1Degree only uses first-degree affected relatives. We use data on 2,713 individuals from seven sites of the Cancer Genetics Network and MD Anderson Cancer Center to compare these simplified tools with the Family History Assessment Tool (FHAT) and BRCAPRO, with the latter serving as the benchmark. BRCAPROLYTE retains high discrimination; however, because it ignores information on unaffected relatives, it overestimates carrier probabilities. BRCAPROLYTE-Plus and BRCAPROLYTE-Simple provide better calibration than BRCAPROLYTE, so they have higher specificity for similar values of sensitivity. BRCAPROLYTE-Plus performs slightly better than BRCAPROLYTE-Simple. The Areas Under the ROC curve are 0.783 (BRCAPRO), 0.763 (BRCAPROLYTE), 0.772 (BRCAPROLYTE-Plus), 0.773 (BRCAPROLYTE-Simple), 0.728 (BRCAPRO-1Degree), and 0.745 (FHAT). The simpler versions, especially BRCAPROLYTE-Plus and BRCAPROLYTE-Simple, lead to only modest loss in overall discrimination compared to BRCAPRO in this dataset. Thus, we conclude that simplified implementations of BRCAPRO can be used for genetic risk prediction in settings where collection of complete pedigree information is impractical.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Antoniou A, Pharoah PD, Narod S, et al (2003) Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: a combined analysis of 22 studies. Am J Hum Genet 72:1117–1130

    Article  PubMed  CAS  Google Scholar 

  2. King MC, Marks JH, Mandell JB (2003) Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2. Science 302:643–646

    Article  PubMed  CAS  Google Scholar 

  3. Schwartz GF, Hughes KS, Lynch HT, Fabian CJ, Fentiman IS, Robson ME, Domchek SM, Hartmann LC, Holland R, Winchester DJ; Consensus Conference Committee The International Consensus Conference Committee. (2008) Proceedings of the international consensus conference on breast cancer risk, genetics, & risk management, April, 2007. Cancer 113:2627–2637

  4. Drohan B, Roche CA, Cusack JC, Hughes KS (2012) Hereditary breast and ovarian cancer and other hereditary syndromes: using technology to identify carriers. Ann Surg Oncol 19:1732–1737

    Article  PubMed  Google Scholar 

  5. Drohan B, Ozanne EM, Hughes KS (2009) Electronic health records and the management of women at high risk of hereditary breast and ovarian cancer. Breast J 15(Suppl 1):46–55

    Article  Google Scholar 

  6. Parmigiani G, Berry D, Aguilar O (1998) Determining carrier probabilities for breast cancer-susceptibility genes BRCA1 and BRCA2. Am J Hum Genet 62:145–158

    Article  PubMed  CAS  Google Scholar 

  7. Chen S, Wang W, Broman KW, et al (2004) BayesMendel: an R environment for Mendelian risk prediction. Stat Appl Genet Mol Biol 3:Article21

    PubMed  Google Scholar 

  8. Tai YC, Chen S, Parmigiani G, et al (2008) Incorporating tumor immunohistochemical markers in BRCA1 and BRCA2 carrier prediction. Breast Cancer Res 10:401

    Article  PubMed  Google Scholar 

  9. Katki HA, Blackford A, Chen S, et al (2008) Multiple diseases in carrier probability estimation: accounting for surviving all cancers other than breast and ovary in BRCAPRO. Stat Med 27:4532–4548

    Article  PubMed  Google Scholar 

  10. Katki HA (2007) Incorporating medical interventions into Mendelian mutation prediction models. BMC Med Genet 8:13

    Article  PubMed  Google Scholar 

  11. Chen S, Blackford AL, Parmigiani G (2009) Tailoring BRCAPRO to Asian-Americans. J Clin Oncol 27:642–643

    Article  PubMed  Google Scholar 

  12. Biswas S, Tankhiwale N, Blackford A, et al (2012) Assessing the added value of breast tumor markers in genetic risk prediction model BRCAPRO. Breast Cancer Res Treat 133:347–355

    Article  PubMed  CAS  Google Scholar 

  13. Ozanne EM, Loberg A, Hughes S, et al (2009) Identification and management of women at high risk for hereditary breast/ovarian cancer syndrome. Breast J 15:155–162

    Article  PubMed  Google Scholar 

  14. Gilpin CA, Carson N, Hunter AG (2000) A preliminary validation of a family history assessment form to select women at risk for breast or ovarian cancer for referral to a genetics center. Clin Genet 58:299–308

    Article  PubMed  CAS  Google Scholar 

  15. Parmigiani G, Chen S, Iversen ES, et al (2007) Validity of models for predicting BRCA1 and BRCA2 mutations. Ann Intern Med 147:441–450

    Article  PubMed  Google Scholar 

  16. Chen S, Wang W, Lee S, et al (2006) Prediction of germline mutations and cancer risk in the Lynch syndrome. J Am Med Assoc 296:1479–1487

    Article  CAS  Google Scholar 

  17. Pencina MJ, D’Agostino RB, D’Agostino RB, Vasan RS. (2008) Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 27:157–172

    Article  PubMed  Google Scholar 

  18. Efron B, Tibshirani R (1994) An introduction to the bootstrap. Chapman and Hall/CRC, Boca Raton

    Google Scholar 

  19. Parmigiani G (2002) Modeling in medical decision making: a Bayesian approach. Wiley, Chichester

Download references

Acknowledgments

This work was supported in part by Susan G Komen Grant KG081303, National Cancer Institute Grants 1R03CA173834-01 and 2P30CA006516-47 and the Dana Farber Cancer Institute.

Conflict of interest

The BayesMendel and HRA softwares are free to nonprofit institutions. HRA may be licensed to commercial entities, which would generate royalties for Hughes and Parmigiani.

Author information

Authors and Affiliations

  1. Department of Mathematical Sciences, FO 35, University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75080-3021, USA

    Swati Biswas

  2. Department of Biostatistics, School of Public Health, University of North Texas Health Science Center, Fort Worth, TX, USA

    Philamer Atienza

  3. Alcon Research, Ltd., Fort Worth, TX, USA

    Philamer Atienza

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

    Jonathan Chipman & Giovanni Parmigiani

  5. Massachusetts General Hospital, Boston, MA, USA

    Kevin Hughes

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

    Angelica M. Gutierrez Barrera & Banu Arun

  7. Department of Community and Family Medicine, Geisel College of Medicine, Dartmouth College, Hanover, NH, USA

    Christopher I. Amos

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

    Giovanni Parmigiani

Authors
  1. Swati Biswas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Philamer Atienza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jonathan Chipman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kevin Hughes
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Angelica M. Gutierrez Barrera
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Christopher I. Amos
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Banu Arun
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Giovanni Parmigiani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Swati Biswas.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Biswas, S., Atienza, P., Chipman, J. et al. Simplifying clinical use of the genetic risk prediction model BRCAPRO. Breast Cancer Res Treat 139, 571–579 (2013). https://doi.org/10.1007/s10549-013-2564-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10549-013-2564-4

Keywords

Search

Navigation