Population-based estimates of breast cancer risk for carriers of pathogenic variants identified by gene-panel testing.
Melissa C SoutheyJames G DowtyMoeen RiazJason A SteenAnne-Laure RenaultKatherine TuckerJudy KirkPaul A JamesIngrid WinshipNicholas PachterNicola K PoplawskiScott GristDaniel J ParkBernard J PopeKhalid MahmoodFleur HammetMaryam MahmoodiHelen TsimiklisDerrick TheysAmanda RewseAmanda M WillisApril MorrowCatherine SpeechlyRebecca HarrisRobert SebraEric SchadtPaul LacazeJohn J McNeilGraham G GilesRoger L MilneJohn L HopperTú Nguyen-DumontPublished in: NPJ breast cancer (2021)
Population-based estimates of breast cancer risk for carriers of pathogenic variants identified by gene-panel testing are urgently required. Most prior research has been based on women selected for high-risk features and more data is needed to make inference about breast cancer risk for women unselected for family history, an important consideration of population screening. We tested 1464 women diagnosed with breast cancer and 862 age-matched controls participating in the Australian Breast Cancer Family Study (ABCFS), and 6549 healthy, older Australian women enroled in the ASPirin in Reducing Events in the Elderly (ASPREE) study for rare germline variants using a 24-gene-panel. Odds ratios (ORs) were estimated using unconditional logistic regression adjusted for age and other potential confounders. We identified pathogenic variants in 11.1% of the ABCFS cases, 3.7% of the ABCFS controls and 2.2% of the ASPREE (control) participants. The estimated breast cancer OR [95% confidence interval] was 5.3 [2.1-16.2] for BRCA1, 4.0 [1.9-9.1] for BRCA2, 3.4 [1.4-8.4] for ATM and 4.3 [1.0-17.0] for PALB2. Our findings provide a population-based perspective to gene-panel testing for breast cancer predisposition and opportunities to improve predictors for identifying women who carry pathogenic variants in breast cancer predisposition genes.
Keyphrases
- breast cancer risk
- copy number
- genome wide
- genome wide identification
- dna methylation
- cardiovascular disease
- middle aged
- machine learning
- gene expression
- adipose tissue
- dna damage
- risk assessment
- metabolic syndrome
- cardiovascular events
- oxidative stress
- physical activity
- big data
- skeletal muscle
- young adults
- insulin resistance