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

Jun Li, Huong Meeks, Bing Jian Feng, Sue Healey, Heather Thorne, Igor Makunin, Jonathan Ellis, kConFab Investigators, Ian Campbell, Melissa Southey, Gillian Mitchell, David Clouston, Judy Kirk, David Goldgar, Georgia Chenevix-Trench

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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.

Original languageEnglish
Pages (from-to)34-42
Number of pages9
JournalJournal of Medical Genetics
Volume53
Issue number1
DOIs
Publication statusPublished - 1 Jan 2016
Externally publishedYes

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