Triple Negative Breast Cancer: Is it Genetic? — ASN Events

Triple Negative Breast Cancer: Is it Genetic? (#84)

Nicola K Poplawski 1 2
  1. University of Adelaide, Adelaide, South Australia, Australia
  2. Adult Genetics Unit, SA Pathology at the Women's and Children's Hospital, North Adelaide, SA, Australia

Background: Only 20% of familial breast cancer is explained by germline mutations in the BRCA1 and BRCA2 genes. As BRCA1/2 genetic testing is expensive it should be targeted at the group of women most likely to have a mutation. There is international agreement that women with ≥10% chance of a BRCA1/2 mutation should be offered testing. However, mutation prediction tools have relatively poor positive predictive value for a pathogenic mutation, largely because of reliance on family cancer history. A family history is only as useful as a patient’s knowledge of that history, and their willingness to report it. Also, some women have few female relatives to “express” the family’s genetic risk, and mutation prediction tools underestimate the chance of identifying a mutation in these women. Triple negative breast cancer (TNB) refers to breast carcinoma that tests negative for oestrogen and progesterone receptors, and does not over-express HER2. TNB is more common in younger onset breast cancer and in women with germline BRCA1 mutations. Early data suggests BRCA1 mutations are identified in ~12% of unselected TNB. Mutations are more frequent in the presence of a significant family history of breast cancer (~33%) or young age at diagnosis (15-25% of women <40 years, depending on family history). When there is no significant family history of breast cancer, less than 10% of women with TNB over the age of 40 have a BRCA1 mutation.
Conclusion: It is appropriate to offer germline testing for BRCA1 mutations to women with TNB and a family history of breast and/or ovarian cancer, and to all women with TNB under the age of 40 years regardless of their family cancer history. Mutation prediction tools should incorporate breast cancer pathology into the variables in their underlying statistical model as preliminary evidence suggests this would increase their accuracy.