Dörk, Thilo

Abstract Part of the substantial unexplained familial aggregation of breast cancer may be due to interactions between common variants, but few studies have had adequate statistical power to detect interactions of realistic magnitude. We aimed to assess all two-way interactions in breast cancer susceptibility between 70 917 single nucleotide polymorphisms (SNPs) selected primarily based on prior evidence of a marginal effect. Thirty-eight international studies contributed data for 46 450 breast cancer cases and 42 461 controls of European origin as part of a multi-consortium project (COGS). First, SNPs were preselected based on evidence (P < 0.01) of a per-allele main effect, and all two-way combinations of those were evaluated by a per-allele (1 d.f.) test for interaction using logistic regression. Second, all 2.5 billion possible two-SNP combinations were evaluated using Boolean operation-based screening and testing, and SNP pairs with the strongest evidence of interaction (P < 10−4) were selected for more careful assessment by logistic regression. Under the first approach, 3277 SNPs were preselected, but an evaluation of all possible two-SNP combinations (1 d.f.) identified no interactions at P < 10−8. Results from the second analytic approach were consistent with those from the first (P > 10−10). In summary, we observed little evidence of two-way SNP interactions in breast cancer susceptibility, despite the large number of SNPs with potential marginal effects considered and the very large sample size. This finding may have important implications for risk prediction, simplifying the modelling required. Further comprehensive, large-scale genome-wide interaction studies may identify novel interacting loci if the inherent logistic and computational challenges can be overcome.

The cytochrome P450 2B6 (CYP2B6) is involved in the metabolism of testosterone. Functional changes in this enzyme may influence endogenous hormone exposure, which has been associated with risk of breast cancer. To assess potential associations between two functional polymorphisms CYP2B6_516_G>T (rs3745274) and CYP2B6_785_A>G (rs2279343) and breast cancer risk, we established a specific matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry assay. The GENICA breast cancer case–control study showed associations between the variant genotypes CYP2B6_516_TT and CYP2B6_785_GG and breast cancer risk with odds ratios (ORs) of 1.34 (p = 0.001) and 1.31 (p = 0.002), respectively. A similar effect was observed for carriers of the CYP2B6_516_T allele in a validation study including four independent studies from Germany, Sweden and USA. In a pooled analysis of all five studies involving 4,638 breast cancer cases and 3,594 controls of European ancestry, carriers of the CYP2B6_516_G and the CYP2B6_785_G variant had an increased breast cancer risk with ORs of 1.10 (p = 0.027) and 1.10 (p = 0.031), respectively. We conclude that the genetic variants CYP2B6_516_G and CYP2B6_785_G (designated CYP2B6*6), which are known to decrease activity of the CYP2B6 enzyme, contribute to an increased breast cancer risk.

Abstract Background: The single-nucleotide polymorphism (SNP) 5p12-rs10941679 has been found to be associated with risk of breast cancer, particularly estrogen receptor (ER)-positive disease. We aimed to further explore this association overall, and by tumor histopathology, in the Breast Cancer Association Consortium. Methods: Data were combined from 37 studies, including 40,972 invasive cases, 1,398 cases of ductal carcinoma in situ (DCIS), and 46,334 controls, all of white European ancestry, as well as 3,007 invasive cases and 2,337 controls of Asian ancestry. Associations overall and by tumor invasiveness and histopathology were assessed using logistic regression. Results: For white Europeans, the per-allele OR associated with 5p12-rs10941679 was 1.11 (95% CI = 1.08–1.14, P = 7 × 10−18) for invasive breast cancer and 1.10 (95% CI = 1.01–1.21, P = 0.03) for DCIS. For Asian women, the estimated OR for invasive disease was similar (OR = 1.07, 95%CI = 0.99–1.15, P = 0.09). Further analyses suggested that the association in white Europeans was largely limited to progesterone receptor (PR)-positive disease (per-allele OR = 1.16, 95% CI = 1.12–1.20, P = 1 × 10−18 vs. OR = 1.03, 95% CI = 0.99–1.07, P = 0.2 for PR-negative disease; Pheterogeneity = 2 × 10−7); heterogeneity by ER status was not observed (P = 0.2) once PR status was accounted for. The association was also stronger for lower grade tumors [per-allele OR (95% CI) = 1.20 (1.14–1.25), 1.13 (1.09–1.16), and 1.04 (0.99–1.08) for grade 1, 2, and 3/4, respectively; Ptrend = 5 × 10−7]. Conclusion: 5p12 is a breast cancer susceptibility locus for PR-positive, lower grade breast cancer. Impact: Multicenter fine-mapping studies of this region are needed as a first step to identifying the causal variant or variants. Cancer Epidemiol Biomarkers Prev; 20(10); 2222–31. ©2011 AACR.

Abstract Previous studies have suggested that minor alleles for ERCC4 rs744154, TNF rs361525, CASP10 rs13010627, PGR rs1042838, and BID rs8190315 may influence breast cancer risk, but the evidence is inconclusive due to their small sample size. These polymorphisms were genotyped in more than 30,000 breast cancer cases and 30,000 controls, primarily of European descent, from 30 studies in the Breast Cancer Association Consortium. We calculated odds ratios (OR) and 95% confidence intervals (95% CI) as a measure of association. We found that the minor alleles for these polymorphisms were not related to invasive breast cancer risk overall in women of European descent: ECCR4 per-allele OR (95% CI) = 0.99 (0.97-1.02), minor allele frequency = 27.5%; TNF 1.00 (0.95-1.06), 5.0%; CASP10 1.02 (0.98-1.07), 6.5%; PGR 1.02 (0.99-1.06), 15.3%; and BID 0.98 (0.86-1.12), 1.7%. However, we observed significant between-study heterogeneity for associations with risk for single-nucleotide polymorphisms (SNP) in CASP10, PGR, and BID. Estimates were imprecise for women of Asian and African descent due to small numbers and lower minor allele frequencies (with the exception of BID SNP). The ORs for each copy of the minor allele were not significantly different by estrogen or progesterone receptor status, nor were any significant interactions found between the polymorphisms and age or family history of breast cancer. In conclusion, our data provide persuasive evidence against an overall association between invasive breast cancer risk and ERCC4 rs744154, TNF rs361525, CASP10 rs13010627, PGR rs1042838, and BID rs8190315 genotypes among women of European descent. (Cancer Epidemiol Biomarkers Prev 2009;18(5):1610–6)

Abstract A recent genome-wide association study found that genetic variants on chromosomes 3, 6, 7, 10, 11, 19 and X were associated with prostate cancer risk. We evaluated the most significant single-nucleotide polymorphisms (SNP) in these loci using a worldwide consortium of 13 groups (PRACTICAL). Blood DNA from 7,370 prostate cancer cases and 5,742 male controls was analyzed by genotyping assays. Odds ratios (OR) associated with each genotype were estimated using unconditional logistic regression. Six of the seven SNPs showed clear evidence of association with prostate cancer (P = 0.0007-P = 10−17). For each of these six SNPs, the estimated per-allele OR was similar to those previously reported and ranged from 1.12 to 1.29. One SNP on 3p12 (rs2660753) showed a weaker association than previously reported [per-allele OR, 1.08 (95% confidence interval, 1.00-1.16; P = 0.06) versus 1.18 (95% confidence interval, 1.06-1.31)]. The combined risks associated with each pair of SNPs were consistent with a multiplicative risk model. Under this model, and in combination with previously reported SNPs on 8q and 17q, these loci explain 16% of the familial risk of the disease, and men in the top 10% of the risk distribution have a 2.1-fold increased risk relative to general population rates. This study provides strong confirmation of these susceptibility loci in multiple populations and shows that they make an important contribution to prostate cancer risk prediction. (Cancer Epidemiol Biomarkers Prev 2008;17(8):2052–61)