Citations

Abstract Recent studies have shown that associations between common genetic variation and gene expression in breast tumors provide insight into the functional mechanisms underlying breast cancer risk loci. However, the relationship between genetic variation, gene expression, and risk of triple negative (TN) breast cancer remains largely unexplored. We performed a genome-wide expression quantitative trait locus (eQTL) analysis of TN breast cancer using 668 formalin-fixed paraffin embedded TN tumors from the Triple Negative Breast Cancer Consortium (TNBCC) to identify novel genes relevant to TN breast cancer risk and to further explore the biology underlying known TN risk loci. Cis-eQTLs were defined as correlations between single nucleotide polymorphisms (SNP) and expression of genes within 1Mb, and trans-eQTLs were defined as the remainder of all SNP-gene correlations. In a genome-wide analysis, we identified 68,012 cis-eQTLs, representing correlations between 42,193 SNPs and 2,092 genes that were significant at a 10% false discovery rate (FDR). We also identified 70 trans-eQTLs across two unique genes at the same FDR threshold. Five cis-eQTLs involved SNPs previously associated with TN breast cancer risk (p<5.0x10-5) in 3,700 TN cases and 4,700 controls from the TNBCC. Three of these, located on chromosomes 5p (cis-eQTL p=4.8x10-34; risk p=1.1x10-7), 7p (cis-eQTL p=6.1x10-10; risk p=1.3x10-7), and 17q (cis-eQTL p=1.8x10-15; risk p=2.9x10-7), have previously been reported in public databases of normal tissue. The remaining two TN breast cancer cis-eQTLs, located on chromosomes 8q (cis-eQTL p=6.3x10-6, risk p=8.3x10-6) and 14q (cis-eQTL p=3.1x10-6, risk p=1.2x10-5), have not been reported as eQTLs in other tissues. In addition, 12 of 25 known TN breast cancer risk loci (PEX14, MDM4, 2q31.1, ESR1, 11q13.1, 11q24.3, 12p13.1, PTHLH, NTN4, 12q24, 19p13.1, MLK1), contained at least one significant cis-eQTL. In summary, we have identified five cis-eQTLs that may identify novel risk loci for TN breast cancer. Citation Format: Kristen S. Purrington, Drakoulis Yannoukakos, Jane Carpenter, Heli Nevanlinna, Angela Cox, Gianluca Severi, Christine Ambrosone, Amanda Ewart Toland, Andrew K. Godwin, Hiltrud Brauch, Peter A. Fasching, Penelope Miron, Jenny Chang-Claude, Nicholas G. Martin, Grant W. Montgomery, Vessela Kristensen, Hoda Anton-Culver, Paul Goodfellow, Janet E. Olson, Hugues Sicotte, Naresh Prodduturi, Daniel W. Visscher, Jeanette E. Eckel-Passow, S. Keith Anderson, Seth Slettedahl, Curtis Olswold, Xianshu Wang, V. Shane Pankratz, Susan Slager, Wei Zheng, Arto Mannermaa, Ute Hamann, Diana M. Eccles, Celine M. Vachon, Fergus J. Couch. Expression quantitative trait locus analysis of triple negative breast cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3266. doi:10.1158/1538-7445.AM2014-3266

Abstract Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk, but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1), and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all P < 10−5). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and nondense areas, and between rs17356907 (NTN4) and adjusted absolute nondense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiologic pathways implicated in how mammographic density predicts breast cancer risk. Cancer Res; 75(12); 2457–67. ©2015 AACR.

ABSTRACTGenes that alter disease risk only in combination with certain environmental exposures may not be detected in genetic association analysis. By using methods accounting for gene‐environment (G × E) interaction, we aimed to identify novel genetic loci associated with breast cancer risk. Up to 34,475 cases and 34,786 controls of European ancestry from up to 23 studies in the Breast Cancer Association Consortium were included. Overall, 71,527 single nucleotide polymorphisms (SNPs), enriched for association with breast cancer, were tested for interaction with 10 environmental risk factors using three recently proposed hybrid methods and a joint test of association and interaction. Analyses were adjusted for age, study, population stratification, and confounding factors as applicable. Three SNPs in two independent loci showed statistically significant association: SNPs rs10483028 and rs2242714 in perfect linkage disequilibrium on chromosome 21 and rs12197388 in ARID1B on chromosome 6. While rs12197388 was identified using the joint test with parity and with age at menarche (P‐values = 3 × 10−07), the variants on chromosome 21 q22.12, which showed interaction with adult body mass index (BMI) in 8,891 postmenopausal women, were identified by all methods applied. SNP rs10483028 was associated with breast cancer in women with a BMI below 25 kg/m2 (OR = 1.26, 95% CI 1.15–1.38) but not in women with a BMI of 30 kg/m2 or higher (OR = 0.89, 95% CI 0.72–1.11, P for interaction = 3.2 × 10−05). Our findings confirm comparable power of the recent methods for detecting G × E interaction and the utility of using G × E interaction analyses to identify new susceptibility loci.

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.

Abstract Triple-negative breast cancers (TNBC), defined by the absence of estrogen receptor, progesterone receptor, and HER-2 expression, account for 12% to 24% of all breast cancers. TNBC is associated with early recurrence of disease and poor outcome. Germline mutations in the BRCA1 and BRCA2 breast cancer susceptibility genes have been associated with up to 15% of TNBC, and TNBC accounts for 70% of breast tumors arising in BRCA1 mutation carriers and 16% to 23% of breast tumors in BRCA2 carriers. Whether germline mutations in other breast cancer susceptibility genes also predispose to TNBC remains to be determined. Common variation in a subset of the 72 known breast cancer susceptibility loci identified through genome-wide association studies and other large-scale genotyping efforts have also been associated with risk of TNBC (TOX3, ESR1, RAD51L1, TERT, 19p13.1, 20q11, MDM4, 2p24.1, and FTO). Furthermore, variation in the 19p13.1 locus and the MDM4 locus has been associated with TNBC, but not other forms of breast cancer, suggesting that these are TNBC-specific loci. Thus, TNBC can be distinguished from other breast cancer subtypes by a unique pattern of common and rare germline predisposition alleles. Additional efforts to combine genetic and epidemiologic data are needed to better understand the etiology of this aggressive form of breast cancer, to identify prevention and therapeutic targets, and to impact clinical practice through the development of risk prediction models. Cancer Res; 73(7); 2025–30. ©2012 AACR.

Abstract Background: Overexpression of mitotic kinases has been associated with prognosis, histologic grade, and clinical stage in ovarian cancer, but the relationship between inherited variation in these genes and ovarian cancer risk has not been well defined. Methods: We measured associations between 397 single nucleotide polymorphisms (SNPs) from 67 mitotic kinases and invasive epithelial ovarian cancer risk in two case–control studies (n = 671 cases; n = 939 controls). Thirty-six candidate SNPs (P < 0.05) were assessed in a replication analysis consisting of three additional studies (n = 1,094 cases; n = 829 controls). Results: In initial analysis, thirty-six SNPs were suggestive of association with risk of serous ovarian cancer, all subtypes of ovarian cancer, or both (P < 0.05). Replication analyses suggested an association between rs2125846 in the Nemo-like kinase (NLK) gene and ovarian cancer (serous OR = 1.36, 95% CI: 1.11–1.67, P = 1.77 × 10−3; all subtypes OR = 1.30, 95% CI: 1.08–1.56, P = 2.97 × 10−3). Furthermore, rs2125846 was associated with risk in the combined discovery and replication sets (serous OR = 1.33, 95% CI: 1.15–1.54; all subtypes OR = 1.27, 95% CI: 1.12–1.45). Conclusions: Variation in NLK may be associated with risk of invasive epithelial ovarian cancer. Further studies are needed to confirm and understand the biologic relationship between this mitotic kinase and ovarian cancer risk. Impact: An association between SNPs in NLK and ovarian cancer may provide biologic insight into the development of this disease. Cancer Epidemiol Biomarkers Prev; 21(3); 523–8. ©2012 AACR.

Abstract The 19p13.1 breast cancer susceptibility locus is a modifier of breast cancer risk in BRCA1 mutation carriers and is also associated with the risk of ovarian cancer. Here, we investigated 19p13.1 variation and risk of breast cancer subtypes, defined by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2) status, using 48,869 breast cancer cases and 49,787 controls from the Breast Cancer Association Consortium (BCAC). Variants from 19p13.1 were not associated with breast cancer overall or with ER-positive breast cancer but were significantly associated with ER-negative breast cancer risk [rs8170 OR, 1.10; 95% confidence interval (CI), 1.05–1.15; P = 3.49 × 10−5] and triple-negative (ER-, PR-, and HER2-negative) breast cancer (rs8170: OR, 1.22; 95% CI, 1.13–1.31; P = 2.22 × 10−7). However, rs8170 was no longer associated with ER-negative breast cancer risk when triple-negative cases were excluded (OR, 0.98; 95% CI, 0.89–1.07; P = 0.62). In addition, a combined analysis of triple-negative cases from BCAC and the Triple Negative Breast Cancer Consortium (TNBCC; N = 3,566) identified a genome-wide significant association between rs8170 and triple-negative breast cancer risk (OR, 1.25; 95% CI, 1.18–1.33; P = 3.31 × 10−13]. Thus, 19p13.1 is the first triple-negative–specific breast cancer risk locus and the first locus specific to a histologic subtype defined by ER, PR, and HER2 to be identified. These findings provide convincing evidence that genetic susceptibility to breast cancer varies by tumor subtype and that triple-negative tumors and other subtypes likely arise through distinct etiologic pathways. Cancer Res; 72(7); 1795–803. ©2012 AACR.