Expanding the criteria for BRCA mutation testing in breast cancer survivors.
OBJECTIVE
Every year approximately 25% of women diagnosed with breast cancer are younger than 50 years of age, and almost 10% of them have a BRCA mutation. Not all potential carriers are identified by existing criteria for BRCA testing. We estimated the costs and benefits of different BRCA testing criteria for women with breast cancer younger than 50 years.


METHODS
We developed a Markov Monte Carlo simulation to compare six criteria for BRCA mutation testing: (1) no testing (reference); (2) medullary breast cancer in patients younger than 50 years; (3) any breast cancer in patients younger than 40 years; (4) triple negative (TN) breast cancer in patients younger than 40 years; (5) TN breast cancer in patients younger than 50 years; (6) any breast cancer in patients younger than 50 years. Net health benefits were life expectancy and quality-adjusted life expectancy, and primary outcome was the incremental cost-effectiveness ratio (ICER). The model estimated the number of new breast and ovarian cancer cases.


RESULTS
BRCA mutation testing for all women with breast cancer who were younger than 50 years could prevent the highest number of breast and ovarian cancer cases, but with unfavorable ICERs. Testing women with TN breast cancers who were younger than 50 years was cost-effective with an ICER of $8,027 per year of life gained ($9,084 per quality-adjusted life-year), and could reduce subsequent breast and ovarian cancer risks by 23% and 41%, respectively, compared with the reference strategy.


CONCLUSIONS
Testing women with TN breast cancers who were younger than 50 years for BRCA mutations is a cost-effective strategy and should be adopted into current guidelines for genetic testing.
Kwon
Janice S
JS
Division of Gynecologic Oncology, University of British Columbia and British Columbia Cancer Agency, Vancouver, British Columbia, Canada. jkwon@post.harvard.edu
Gutierrez-Barrera
Angelica M
AM
Young
Diana
D
Sun
Charlotte C
CC
Daniels
Molly S
MS
Lu
Karen H
KH
Arun
Banu
B
D000328:Adult; D051017:Apoptosis Regulatory Proteins; D019313:BRCA1 Protein / Q000235:genetics*; D024682:BRCA2 Protein / Q000235:genetics*; D001943:Breast Neoplasms / Q000191:economics / Q000235:genetics* / Q000401:mortality / Q000628:therapy; D003198:Computer Simulation; D003362:Cost-Benefit Analysis; D005260:Female; D005820:Genetic Testing / Q000191:economics; D017048:Health Care Costs; D006801:Humans; D008017:Life Expectancy; D008875:Middle Aged; D018803:Models, Economic; D009010:Monte Carlo Method; D009154:Mutation; D018579:Patient Selection; D017410:Practice Guidelines as Topic; D011237:Predictive Value of Tests; D019057:Quality-Adjusted Life Years; D013997:Time Factors; D016896:Treatment Outcome
D051017:Apoptosis Regulatory Proteins; C118024:BLID protein, human; D019313:BRCA1 Protein; C492913:BRCA1 protein, human; D024682:BRCA2 Protein; C551750:BRCA2 protein, human