Published in:
Open Access
01-08-2005 | Research article
CYP17genetic polymorphism, breast cancer, and breast cancer risk factors: Australian Breast Cancer Family Study
Authors:
Jiun-Horng Chang, Dorota M Gertig, Xiaoqing Chen, Gillian S Dite, Mark A Jenkins, Roger L Milne, Melissa C Southey, Margaret RE McCredie, Graham G Giles, Georgia Chenevix-Trench, John L Hopper, Amanda B Spurdle
Published in:
Breast Cancer Research
|
Issue 4/2005
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Abstract
Introduction
Because CYP17 can influence the degree of exposure of breast tissues to oestrogen, the interaction between polymorphisms in this gene and hormonal risk factors is of particular interest. We attempted to replicate the findings of studies assessing such interactions with the -34T→C polymorphism.
Methods
Risk factor and CYP17 genotyping data were derived from a large Australian population-based case-control-family study of 1,284 breast cancer cases and 679 controls. Crude and adjusted odds ratio (OR) estimates and 95% confidence intervals (CIs) were calculated by unconditional logistic regression analyses.
Results
We found no associations between the CYP17 genotype and breast cancer overall. Premenopausal controls with A
2/A
2 genotype had a later age at menarche (P < 0.01). The only associations near statistical significance were that postmenopausal women with A
1/A
1 (wild-type) genotype had an increased risk of breast cancer if they had ever used hormone replacement therapy (OR 2.40, 95% CI 1.0 to 5.7; P = 0.05) and if they had menopause after age 47 years (OR 2.59, 95% CI 1.0 to 7.0; P = 0.06). We found no associations in common with any other studies, and no evidence for interactions.
Conclusion
We observed no evidence of effect modification of reproductive risk factors by CYP17 genotype, although the experiment did not have sufficient statistical power to detect small main effects and modest effects in subgroups. Associations found only in subgroup analyses based on relatively small numbers require cautious interpretation without confirmation by other studies. This emphasizes the need for replication in multiple and large population-based studies to provide convincing evidence for gene–environment interactions.