Multigene assessment of genetic risk for women for two or more breast cancers



The prevalence, penetrance, and spectrum of pathogenic variants that predispose women to two or more breast cancers is largely unknown.


We queried clinical and genetic data from women with one or more breast cancer diagnosis who received multigene panel testing between 2013 and 2018. Clinical data were obtained from provider-completed test request forms. For each gene on the panel, a multivariable logistic regression model was constructed to test for association with risk of multiple breast cancer diagnoses. Models accounted for age of diagnosis, personal and family cancer history, and ancestry. Results are reported as odds ratios (ORs) with 95% confidence intervals (CIs).


This study included 98,979 patients: 88,759 (89.7%) with a single breast cancer and 10,220 (10.3%) with ≥ 2 breast cancers. Of women with two or more breast cancers, 13.2% had a pathogenic variant in a cancer predisposition gene compared to 9.4% with a single breast cancer. BRCA1, BRCA2, CDH1, CHEK2, MSH6, PALB2, PTEN, and TP53 were significantly associated with two or more breast cancers, with ORs ranging from 1.35 for CHEK2 to 3.80 for PTEN. Overall, pathogenic variants in all breast cancer risk genes combined were associated with both metachronous (OR 1.65, 95% CI 1.53–1.79, p = 7.2 × 10–33) and synchronous (OR 1.33, 95% CI 1.19–1.50, p = 2.4 × 10–6) breast cancers.


This study demonstrated that several high and moderate penetrance breast cancer susceptibility genes are associated with ≥ 2 breast cancers, affirming the association of two or more breast cancers with diverse genetic etiologies.

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Data availability

The data generated during clinical testing and analyzed during the current study are not publicly available in order to protect the privacy of patients/tested individuals, but are available from the corresponding author on reasonable request.

Code Availability

All analyses were conducted using R version 3.5.2. or SAS version 9.2 or higher. Request for code availability can be made to the authors and will be provided upon reasonable request.


  1. 1.

    Polednak AP (2003) Bilateral synchronous breast cancer: a population-based study of characteristics, method of detection, and survival. Surgery 133(4):383–389.

    Article  PubMed  Google Scholar 

  2. 2.

    Pan B, Xu Y, Zhou YD et al (2019) The prognostic comparison among unilateral, bilateral, synchronous bilateral, and metachronous bilateral breast cancer: a meta-analysis of studies from recent decade (2008–2018). Cancer Med 8(6):2908–2918.

    Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Mavaddat N, Peock S, Frost D et al (2013) Cancer risks for BRCA1 and BRCA2 mutation carriers: results from prospective analysis of EMBRACE. J Natl Cancer Inst 105(11):812–822.

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Ford D, Easton DF, Bishop DT, Narod SA, Goldgar DE (1994) Risks of cancer in BRCA1-mutation carriers. Breast Cancer Linkage Consortium Lancet 343(8899):692–695

    CAS  PubMed  Google Scholar 

  5. 5.

    Hauke J, Horvath J, Gross E et al (2018) Gene panel testing of 5589 BRCA1/2-negative index patients with breast cancer in a routine diagnostic setting: results of the German Consortium for hereditary breast and ovarian cancer. Cancer Med 7(4):1349–1358.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Castera L, Harter V, Muller E et al (2018) Landscape of pathogenic variations in a panel of 34 genes and cancer risk estimation from 5131 HBOC families. Genet Med 20(12):1677–1686.

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Maxwell KN, Wenz BM, Kulkarni A et al (2020) Mutation rates in cancer susceptibility genes in patients with breast cancer with multiple primary cancers. JCO Precis Oncol 4:916–925.

    Article  Google Scholar 

  8. 8.

    Daly MB, Pilarski R, Berry M et al. (2020) NCCN Guidelines Version 1.2021 Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic National Comprehensive Cancer Network (NCCN).

  9. 9.

    Hyder Z, Harkness EF, Woodward ER et al (2020) Risk of Contralateral Breast Cancer in Women with and without Pathogenic Variants in BRCA1, BRCA2, and TP53 Genes in Women with Very Early-Onset (<36 Years) Breast Cancer. Cancers (Basel).

    Article  Google Scholar 

  10. 10.

    Casadei S, Norquist BM, Walsh T et al (2011) Contribution of inherited mutations in the BRCA2-interacting protein PALB2 to familial breast cancer. Can Res 71(6):2222–2229.

    CAS  Article  Google Scholar 

  11. 11.

    Antoniou AC, Casadei S, Heikkinen T et al (2014) Breast-cancer risk in families with mutations in PALB2. N Engl J Med 371(6):497–506.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Masciari S, Dillon DA, Rath M et al (2012) Breast cancer phenotype in women with TP53 germline mutations: a Li-Fraumeni syndrome consortium effort. Breast Cancer Res Treat 133(3):1125–1130.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Xie ZM, Li LS, Laquet C et al (2011) Germline mutations of the E-cadherin gene in families with inherited invasive lobular breast carcinoma but no diffuse gastric cancer. Cancer 117(14):3112–3117.

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    Daly MB, Pilarski R, Berry MP et al. (2020) NCCN Clinical Practice Guidelines in Oncology: Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic (Version 1.2020). Accessed January 14, 2020

  15. 15.

    Dieterich M, Hartwig F, Stubert J et al (2014) Accompanying DCIS in breast cancer patients with invasive ductal carcinoma is predictive of improved local recurrence-free survival. Breast (Edinburgh, Scotland) 23(4):346–351.

    CAS  Article  Google Scholar 

  16. 16.

    Judkins T, Leclair B, Bowles K et al (2015) Development and analytical validation of a 25-gene next generation sequencing panel that includes the BRCA1 and BRCA2 genes to assess hereditary cancer risk. BMC Cancer 15(1):215

    Article  Google Scholar 

  17. 17.

    Mancini-DiNardo D, Judkins T, Kidd J et al (2019) Detection of large rearrangements in a hereditary pan-cancer panel using next-generation sequencing. BMC Med Genomics 12(1):138.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Richards S, Aziz N, Bale S et al (2015) Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American college of medical genetics and genomics and the association for molecular pathology. Genet Med 17(5):405–424.

    Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Eggington JM, Bowles KR, Moyes K et al (2014) A comprehensive laboratory-based program for classification of variants of uncertain significance in hereditary cancer genes. Clin Genet 86(3):229–237.

    CAS  Article  PubMed  Google Scholar 

  20. 20.

    Esterling L, Wijayatunge R, Brown K et al (2020) Impact of a cancer gene variant reclassification program over a 20-year period. JCO Precis Oncol.

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Hall MJ, Bernhisel R, Hughes E et al (2021) Germline pathogenic variants in the Ataxia Telangiectasia Mutated (ATM) gene are associated with high and moderate risks for multiple cancers. AACR Cancer Prevent Res.

    Article  Google Scholar 

  22. 22.

    Kurian AW, Hughes E, Handorf EA et al (2017) Breast and ovarian cancer penetrance estimates derived from germline multiple-gene sequencing results in women. JCO Precis Oncol 1:1–12.

    Article  Google Scholar 

  23. 23.

    Rainville I, Hatcher S, Rosenthal E et al (2020) High risk of breast cancer in women with biallelic pathogenic variants in CHEK2. Breast Cancer Res Treat 180(2):503–509.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Corredor J, Woodson AH, Gutierrez Barrera A, Arun B (2020) Multigene panel testing results in patients with multiple breast cancer primaries. Breast J.

    Article  PubMed  Google Scholar 

  25. 25.

    Lowstuter K, Espenschied CR, Sturgeon D et al (2017) Unexpected CDH1 mutations identified on multigene panels pose clinical management challenges. JCO Precis Oncol 1:1–12.

    Article  Google Scholar 

  26. 26.

    Weitzel JN, Chao EC, Nehoray B et al (2018) Somatic TP53 variants frequently confound germ-line testing results. Genet Med 20(8):809–816.

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Rana HQ, Gelman R, LaDuca H et al (2018) Differences in TP53 mutation carrier phenotypes emerge from panel-based testing. J Natl Cancer Inst 110(8):863–870.

    Article  PubMed  Google Scholar 

  28. 28.

    Slavin TP, Maxwell KN, Lilyquist J et al (2017) The contribution of pathogenic variants in breast cancer susceptibility genes to familial breast cancer risk. NPJ Breast Cancer.

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Rosenthal ET, Bernhisel R, Brown K, Kidd J, Manley S (2017) Clinical testing with a panel of 25 genes associated with increased cancer risk results in a significant increase in clinically significant findings across a broad range of cancer histories. Cancer Genet 218–219:58–68.

    CAS  Article  PubMed  Google Scholar 

  30. 30.

    Weischer M, Nordestgaard BG, Pharoah P et al (2012) CHEK2*1100delC heterozygosity in women with breast cancer associated with early death, breast cancer-specific death, and increased risk of a second breast cancer. J Clin Oncol 30(35):4308–4316

    CAS  Article  Google Scholar 

  31. 31.

    Nagy R, Sweet K, Eng C (2004) Highly penetrant hereditary cancer syndromes. Oncogene 23(38):6445–6470.

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Kluijt I, Siemerink EJ, Ausems MG et al (2012) CDH1-related hereditary diffuse gastric cancer syndrome: clinical variations and implications for counseling. Int J Cancer J int du Cancer 131(2):367–376.

    CAS  Article  Google Scholar 

  33. 33.

    Yang X, Leslie G, Doroszuk A et al (2019) Cancer risks associated with germline palb2 pathogenic variants an international study of 524 families. J Clin Oncol.

    Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Stoll JR, Cummings E, Willmott S, Bernhisel J, Kupfer R (2020) No evidence of increased risk of breast cancer in women with Lynch syndrome identifed by multigene panel testing. JCO Precis Oncol 4:51–60

    Article  Google Scholar 

  35. 35.

    Dominguez-Valentin M, Sampson JR, Seppala TT et al (2020) Cancer risks by gene, age, and gender in 6350 carriers of pathogenic mismatch repair variants: findings from the prospective Lynch syndrome database. Genet Med 22(1):15–25.

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Tung N, Domchek SM, Stadler Z et al (2016) Counselling framework for moderate-penetrance cancer-susceptibility mutations. Nat Rev Clin Oncol 13(9):581–588.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Easton DF, Pharoah PD, Antoniou AC et al (2015) Gene-panel sequencing and the prediction of breast-cancer risk. N Engl J Med 372(23):2243–2257

    CAS  Article  Google Scholar 

  38. 38.

    Couch FJ, Shimelis H, Hu C et al (2017) Associations between cancer predisposition testing panel genes and breast cancer. JAMA Oncol 3(9):1190–1196.

    Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Kurian AW, Ward KC, Howlader N et al (2019) Genetic testing and results in a population-based cohort of breast cancer patients and ovarian cancer patients. J Clin Oncol 37(15):1305–1315.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Couch FJ, Wang X, McGuffog L et al (2013) Genome-wide association study in brca1 mutation carriers identifies novel loci associated with breast and ovarian cancer risk. PLoS Genet 9(3):e1003212.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Antoniou AC, Sinilnikova OM, Simard J et al (2007) RAD51 135G–>C modifies breast cancer risk among BRCA2 mutation carriers: results from a combined analysis of 19 studies. Am J Hum Genet 81(6):1186–1200.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Wang X, Pankratz VS, Fredericksen Z et al (2010) Common variants associated with breast cancer in genome-wide association studies are modifiers of breast cancer risk in BRCA1 and BRCA2 mutation carriers. Hum Mol Genet 19(14):2886–2897.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Antoniou AC, Beesley J, McGuffog L et al (2010) Common breast cancer susceptibility alleles and the risk of breast cancer for BRCA1 and BRCA2 mutation carriers: implications for risk prediction. Cancer Res 70(23):9742–9754.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Mavaddat N, Michailidou K, Dennis J et al (2019) Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. The American Journal of Human Genetics 104(1):21–34.

    CAS  Article  PubMed  Google Scholar 

  45. 45.

    Gallagher S, Hughes E, Wagner S, Tshiaba P, Rosenthal R, Roa BB, Kurian AW, Domchek SM, Garber J, Lancaster J, Weitzel JN, Gutin A, Lanchbury JS, Robson M (2020) Association of a polygenic risk score with breast cancer among women carriers of high and moderate risk breast cancer genes. JAMA Netw Open 3(7):e208501

    Article  Google Scholar 

  46. 46.

    Robson ME, Reiner AS, Brooks JD et al (2017) Association of common genetic variants with contralateral breast cancer risk in the WECARE study. J Natl Cancer Inst.

    Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Sawyer S, Mitchell G, McKinley J et al (2012) A role for common genomic variants in the assessment of familial breast cancer. J Clin Oncol 30(35):4330–4336.

    Article  PubMed  Google Scholar 

  48. 48.

    Kramer I, Hooning MJ, Mavaddat N et al (2020) Breast cancer polygenic risk score and contralateral breast cancer risk. Am J Hum Genet.

    Article  PubMed  Google Scholar 

  49. 49.

    Rothman KJ, Greenland S, Lash T (2008) Modern epidemiology, vol 3. Lippincott Williams & Wilkins, Philadelphia, PA

    Google Scholar 

  50. 50.

    RajamaniI S (2016) Eliminating bias in cancer risk estimates: a simulation study. Dissertation, University of Utah

  51. 51.

    Chavarri-Guerra Y, Marcum CA, Hendricks CB, Wilbur D, Cescon T, Hake C, Abugattas J, Rodriguez Y, Villarreal-Garza C, Yang K, Cervantes A, Sand S, Castillo D, Herzog J, Mokhnatkin J, Sedrak MS, Soto-Perez-de-Celis E, Weitzel JN (2020) Breast cancer associated pathogenic variants among women 61years and older with triple negative breast cancer. J Geriat Oncol.

    Article  Google Scholar 

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The authors would like to thank Kevin Tsang for his assistance with manuscript preparation, Brenda Rubalcaba for figure development, and Eric Rosenthal for his editorial contributions.


Dr. Weitzel was supported in part by the Breast Cancer Research Foundation, the American Society of Clinical Oncology Conquer Cancer® Research Professorship in Breast Cancer Disparities, the Dr. Norman & Melinda Payson Professorship in Medical Oncology, and NCI grant R01CA242218. Dr. Slavin was supported in part by NCI grant K08CA234394. The research reported in this publication also included work performed in the Biostatistical Core supported by the National Cancer Institute of the National Institutes of Health (NIH) under grant number P30CA033572. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Author information




All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by John Kidd, Ryan Bernhisel, and Elisha Hughes. The first draft of the manuscript was written by Jeffrey Weitzel and all authors commented on subsequent versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jeffrey N. Weitzel.

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Conflict of interest

Authors JK, RB, DT, KM, KS, KB, AG, EH, SC, JS and TS were employees of Myriad Genetics at the time of manuscript preparation and had stock options. JNW received speaker fees from AstraZeneca. No other authors have any conflict of interest to disclose.

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All individuals provided consent for clinical testing, and testing data were de-identified for analysis.

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All individuals provided consent for clinical testing, and testing data were de-identified for analysis.

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Weitzel, J.N., Kidd, J., Bernhisel, R. et al. Multigene assessment of genetic risk for women for two or more breast cancers. Breast Cancer Res Treat (2021).

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  • Breast cancer
  • BRCA1
  • BRCA2
  • Second breast cancer
  • Multiple breast cancers
  • Hereditary breast cancer