Genetic variants for mammographic density
Breast cancer is the most common cancer in Australian women and is highly heritable. Mammographic density (MD) is one of the strongest predictors of breast cancer risk, second only to carrying a mutation in the BRCA1 or BRCA2 genes, and is also highly heritable. Women with extensive MD are 4-6 times more likely to develop breast cancer than women of similar age with little or no mammographic density.
But what is mammographic density? MD is a measure of the proportion of different tissue types in the breast. Dense tissues include epithelial and stromal tissue, as compared to less-dense fatty tissue. Density can be seen in the white area on a mammogram, which represents the radiographic appearance of epithelial and stromal tissue. By comparison, fatty tissue appears dark on a mammogram. Unfortunately, mammographic density cannot be determined by feel or touch.
Mammographic density can be measured in anyone willing to undergo mammographic screening. It therefore has enormous potential as an intermediate phenotype in which to identify novel genetic variants associated with breast cancer risk, particularly in pedigree-based studies. Being able to identify genetic risk variants will increase our understanding of the biological pathways involved in the development of breast cancer, and could be used to identify and target women at high risk of the disease. We aim to obtain and measure mammographic images from thousands of women from high-risk families with existing questionnaire and genomic data to identify informative families for future genetic research.
Building capacity to identify variants associated with mammographic density will facilitate increased understanding of etiology, improved disease prediction and, since mammographic density is a modifiable risk factor, targeted prevention.
This work has been supported by the Breast Cancer Research Centre WA, the Royal Perth Hospital’s Medical Research Foundation and the National Breast Cancer Foundation.
- NBCF Career Development Fellowship [2017-2020] “Towards better breast screening for Australian women” and [2013-2016] “Understanding mammographic density & making it a clinically useful predictor of breast cancer risk”
- Stone, J., D. J. Thompson, I. Dos Santos Silva, C. Scott, R. M. Tamimi, S. Lindstrom, P. Kraft, A. Hazra, J. Li, L. Eriksson, K. Czene, P. Hall, M. Jensen, J. Cunningham, J. E. Olson, K. Purrington, F. J. Couch, J. Brown, J. Leyland, R. M. Warren, R. N. Luben, K. T. Khaw, P. Smith, N. J. Wareham, S. M. Jud, K. Heusinger, M. W. Beckmann, J. A. Douglas, K. P. Shah, H. P. Chan, M. A. Helvie, L. Le Marchand, L. N. Kolonel, C. Woolcott, G. Maskarinec, C. Haiman, G. G. Giles, L. Baglietto, K. Krishnan, M. C. Southey, C. Apicella, I. L. Andrulis, J. A. Knight, G. Ursin, G. I. Alnaes, V. N. Kristensen, A. L. Borresen-Dale, I. T. Gram, M. K. Bolla, Q. Wang, K. Michailidou, J. Dennis, J. Simard, P. Pharoah, A. M. Dunning, D. F. Easton, P. A. Fasching, V. S. Pankratz, J. L. Hopper and C. M. Vachon (2015). “Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures.” Cancer Res 75(12): 2457-2467. [pubmed]