A multigenic approach to predict breast cancer risk

• Published in: Breast cancer research and treatment 2007-08, Vol.104 (2), p.159-164
• Main Authors: GERGER, Armin, LANGSENLEHNER, Uwe, RENNER, Wilfried, WEITZER, Werner, EDER, Tanja, YAZDANI-BIUKI, Babak, HOFMANN, Günter, SAMONIGG, Hellmut, KRIPPL, Peter
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Biomarkers, Tumor - genetics; Breast - metabolism; Breast cancer; Breast Neoplasms - genetics; Cancer research; Cancer therapies; Case-Control Studies; Classification; Cyclooxygenase-2; Epidemiology; Female; Genetic factors; Genetic Predisposition to Disease; Genetics; Genotype; Gynecology. Andrology. Obstetrics; Humans; Mammary gland diseases; Matrix metalloproteinase; Medical sciences; Metalloproteinase; Polymorphism; Polymorphism, Genetic; Risk assessment; Risk Factors; Stromelysin 1; Tumors; Vascular endothelial growth factor
• ISSN: 0167-6806; 1573-7217
• DOI: 10.1007/s10549-006-9408-4

In the biology of complex disorders, such as breast cancer, interactions among genetic factors may play an important role and theoretical considerations suggest that gene-gene interactions are quite common in such diseases. In this case-control study with 500 breast cancer patients and 500 population-based healthy sex- and age-matched control subjects, we applied a multigenic approach to examine the associations with breast cancer risk of a comprehensive panel of 16 selected polymorphisms in a variety of pathways using classification tree analysis (CART). Overall, 79.6% of all breast cancer patients and 80.6% of all control subjects were correctly classified on the basis of their individual genetic profile by the classification procedure. CART analysis of the data identified the heterozygous vascular endothelial growth factor (VEGF) and matrix metalloproteinase 3 (MMP3) genotype and homozygous cyclooxygenase-2 (PTGS2) mutant as the initial splits, indicating that these genotypes exert the greatest impact on the classification process. Breast cancer patients were primarily indicated by 30 distinct genetic profiles. The odds ratio of these genetic risk profiles for breast cancer was 16.12 (95% confidence interval 11.09-23.49). Five genetic profiles formed homogenous breast cancer subgroups and represented highest risk genetic profiles. This is the first comprehensive study to use a multigenic analysis for breast cancer and the data suggest that individuals with distinct genetic profiles are at an increased risk for breast cancer, confirming the importance of taking a multigenic approach for risk assessment.