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Targeted massively parallel sequencing of a panel of putative breast cancer susceptibility genes in a large cohort of multiple-case breast and ovarian cancer families
  1. Jun Li1,
  2. Huong Meeks2,
  3. Bing-Jian Feng3,
  4. Sue Healey1,
  5. Heather Thorne4,5,
  6. Igor Makunin1,
  7. Jonathan Ellis1,
  8. kConFab Investigators4,
  9. Ian Campbell4,
  10. Melissa Southey6,
  11. Gillian Mitchell5,7,
  12. David Clouston8,
  13. Judy Kirk9,10,
  14. David Goldgar2,3,
  15. Georgia Chenevix-Trench1
  1. 1Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Australia
  2. 2Cancer Control and Population Sciences, Huntsman Cancer Institute, Salt Lake City, Utah, USA
  3. 3Department of Dermatology, University of Utah School of Medicine, Salt Lake City, Utah, USA
  4. 4kConFab, Research Department, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia
  5. 5The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Victoria, Australia
  6. 6Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Melbourne, Victoria, Australia
  7. 7Familial Cancer Centre, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia
  8. 8Tissupath, Mount Waverley, Victoria, Australia
  9. 9Centre for Cancer Research, Westmead Millennium Institute, University of Sydney, Sydney, Australia
  10. 10Westmead Hospital, Sydney, Australia
  1. Correspondence to Georgia Chenevix-Trench, Department of Genetics and Computational Biology, QIMR Berghofer, c/o Locked Bag 2000, RBH Post Office, Herston, QLD 4029, Australia; georgiat{at}qimr.edu.au

Abstract

Introduction Gene panel testing for breast cancer susceptibility has become relatively cheap and accessible. However, the breast cancer risks associated with mutations in many genes included in these panels are unknown.

Methods We performed custom-designed targeted sequencing covering the coding exons of 17 known and putative breast cancer susceptibility genes in 660 non-BRCA1/2 women with familial breast cancer. Putative deleterious mutations were genotyped in relevant family members to assess co-segregation of each variant with disease. We used maximum likelihood models to estimate the breast cancer risks associated with mutations in each of the genes.

Results We found 31 putative deleterious mutations in 7 known breast cancer susceptibility genes (TP53, PALB2, ATM, CHEK2, CDH1, PTEN and STK11) in 45 cases, and 22 potential deleterious mutations in 31 cases in 8 other genes (BARD1, BRIP1, MRE11, NBN, RAD50, RAD51C, RAD51D and CDK4). The relevant variants were then genotyped in 558 family members. Assuming a constant relative risk of breast cancer across age groups, only variants in CDH1, CHEK2, PALB2 and TP53 showed evidence of a significantly increased risk of breast cancer, with some supportive evidence that mutations in ATM confer moderate risk.

Conclusions Panel testing for these breast cancer families provided additional relevant clinical information for <2% of families. We demonstrated that segregation analysis has some potential to help estimate the breast cancer risks associated with mutations in breast cancer susceptibility genes, but very large case–control sequencing studies and/or larger family-based studies will be needed to define the risks more accurately.

  • Cancer: breast

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