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Background Endometrial cancer risk stratification may help target interventions, screening, or prophylactic hysterectomy to mitigate the rising burden of this cancer. However, existing prediction models have been developed in select cohorts and have not considered genetic factors. Methods We developed endometrial cancer risk prediction models using data on postmenopausal White women aged 45-85 years from 19 case-control studies in the Epidemiology of Endometrial Cancer Consortium (E2C2). Relative risk estimates for predictors were combined with age-specific endometrial cancer incidence rates and estimates for the underlying risk factor distribution. We externally validated the models in 3 cohorts: Nurses’ Health Study (NHS), NHS II, and the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. Results Area under the receiver operating characteristic curves for the epidemiologic model ranged from 0.64 (95% confidence interval [CI] = 0.62 to 0.67) to 0.69 (95% CI = 0.66 to 0.72). Improvements in discrimination from the addition of genetic factors were modest (no change in area under the receiver operating characteristic curves in NHS; PLCO = 0.64 to 0.66). The epidemiologic model was well calibrated in NHS II (overall expected-to-observed ratio [E/O] = 1.09, 95% CI = 0.98 to 1.22) and PLCO (overall E/O = 1.04, 95% CI = 0.95 to 1.13) but poorly calibrated in NHS (overall E/O = 0.55, 95% CI = 0.51 to 0.59). Conclusions Using data from the largest, most heterogeneous study population to date (to our knowledge), prediction models based on epidemiologic factors alone successfully identified women at high risk of endometrial cancer. Genetic factors offered limited improvements in discrimination. Further work is needed to refine this tool for clinical or public health practice and expand these models to multiethnic populations.

The aim of this study is to investigate the prognostic role of body mass index (BMI) and survival from head and neck cancer (HNC). We performed a pooled analysis of studies included in the International Head and Neck Cancer Epidemiology consortium in order to investigate the prognostic role of BMI and survival from HNC. We used Cox proportional hazards models to estimate the adjusted hazard ratios (HR) for overall survival and HNC-specific survival, by cancer site. The study included 10,177 patients from 10 studies worldwide. Underweight patients had lower overall survival (HR = 1.69, 95%CI: 1.31–2.19) respect to those having normal weight with consistent results across the HNC sites. Overweight and obese patients with oropharyngeal cancers had a favourable HNC-specific survival (HR = 0.50 (95%CI: 0.33–0.75) and HR = 0.51 (95%CI: 0.36–0.72), respectively). Among ever smokers overweight and obese patients showed a favourable HNC-specific survival (HR = 0.69 (95%CI: 0.56–0.86) and HR = 0.70 (95%CI: 0.61–0.80)). Our findings show that high BMI values at cancer diagnosis predict the survival rates in smoking patients with HNC. This association may be explained by residual confounding, reverse causation, and collider stratification bias, but may also suggest that a nutritional reserve may help patients survive HNC cancer.

Supplementary Tables 1-3 from A Sex-Specific Association between a 15q25 Variant and Upper Aerodigestive Tract Cancers

Supplementary Tables 1 and 2; legend for Supplementary Table 3 from TGF-β Signaling Pathway and Breast Cancer Susceptibility

Background: Genome-wide association studies (GWAS) have so far reported 12 loci associated with serous epithelial ovarian cancer (EOC) risk. We hypothesized that some of these loci function through nearby transcription factor (TF) genes and that putative target genes of these TFs as identified by coexpression may also be enriched for additional EOC risk associations.Methods: We selected TF genes within 1 Mb of the top signal at the 12 genome-wide significant risk loci. Mutual information, a form of correlation, was used to build networks of genes strongly coexpressed with each selected TF gene in the unified microarray dataset of 489 serous EOC tumors from The Cancer Genome Atlas. Genes represented in this dataset were subsequently ranked using a gene-level test based on results for germline SNPs from a serous EOC GWAS meta-analysis (2,196 cases/4,396 controls).Results: Gene set enrichment analysis identified six networks centered on TF genes (HOXB2, HOXB5, HOXB6, HOXB7 at 17q21.32 and HOXD1, HOXD3 at 2q31) that were significantly enriched for genes from the risk-associated end of the ranked list (P < 0.05 and FDR < 0.05). These results were replicated (P < 0.05) using an independent association study (7,035 cases/21,693 controls). Genes underlying enrichment in the six networks were pooled into a combined network.Conclusion: We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development.Impact: Network analysis integrating large, context-specific datasets has the potential to offer mechanistic insights into cancer susceptibility and prioritize genes for experimental characterization. Cancer Epidemiol Biomarkers Prev; 24(10); 1574–84. ©2015 AACR.

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Background: Basal-like breast cancer (BLBC) is a rare aggressive subtype that is less likely to be detected through mammographic screening. Identification of circulating markers associated with BLBC could have promise in detecting and managing this deadly disease.Methods: Using samples from the Polish Breast Cancer study, a high-quality population-based case–control study of breast cancer, we screened 10,000 antigens on protein arrays using 45 BLBC patients and 45 controls, and identified 748 promising plasma autoantibodies (AAbs) associated with BLBC. ELISA assays of promising markers were performed on a total of 145 BLBC cases and 145 age-matched controls. Sensitivities at 98% specificity were calculated and a BLBC classifier was constructed.Results: We identified 13 AAbs (CTAG1B, CTAG2, TP53, RNF216, PPHLN1, PIP4K2C, ZBTB16, TAS2R8, WBP2NL, DOK2, PSRC1, MN1, TRIM21) that distinguished BLBC from controls with 33% sensitivity and 98% specificity. We also discovered a strong association of TP53 AAb with its protein expression (P = 0.009) in BLBC patients. In addition, MN1 and TP53 AAbs were associated with worse survival [MN1 AAb marker HR = 2.25, 95% confidence interval (CI), 1.03–4.91; P = 0.04; TP53, HR = 2.02, 95% CI, 1.06–3.85; P = 0.03]. We found limited evidence that AAb levels differed by demographic characteristics.Conclusions: These AAbs warrant further investigation in clinical studies to determine their value for further understanding the biology of BLBC and possible detection.Impact: Our study identifies 13 AAb markers associated specifically with BLBC and may improve detection or management of this deadly disease. Cancer Epidemiol Biomarkers Prev; 24(9); 1332–40. ©2015 AACR.

Supplementary Tables S1-S6. Table S1. Number and percentage of cases and control with exposure to each occupational agent. Table S2. SNP-level significant occupational exposure interactions for known occupational lung carcinogens. Table S3. SNP-level significant occupational exposure interactions for occupational exposures with increased risk for lung cancer not clear. Table S4. Gene-level significant occupational exposure interactions for occupational exposures with increased risk for lung cancer not clear. Table S5. Significant genomic regions for occupational exposure interactions and the significant genes identified in these regions. Table S6. Main categories of proteins coded by the significant genes identified by the gene-occupational interaction analyses.

Supplementary Table 5 - PDF file 46K, Clinicopathologic characteristics by the Active/Inactive extratumoral microenvironment subtype

Improved understanding of the etiology of estrogen receptor-α (ERα)–negative and progesterone receptor (PR)–negative breast cancers may permit improved risk prediction. In vitro studies implicate DNA hypermethylation of the ERα and PR promoters in the pathogenesis of ERα-negative and PR-negative tumors, but results are not definitive. We evaluated 200 invasive breast cancers selected from a population-based case-control study. DNA extracted from fixed tumor tissue cores was tested using MethyLight to assess DNA methylation at four CpG islands: ESR1 promoters A and B; PGR promoters A and B; and a CpG shore, ESR1 promoter C. DNA methylation results were compared with levels of ERα and PR, tumor characteristics, and breast cancer risk factors. We observed mild to moderate DNA methylation levels in most tumors for ESR1 promoters A and B and PGR promoter B, and a few tumors showed mild methylation in PGR promoter A. In contrast, ESR1 promoter C showed a wide range of methylation and was weakly correlated with lower expression levels of ERα (β = −0.26; P < 0.0001) and PR (β = −0.25; P < 0.0001). The percentage of tumors with methylated PGR promoters A and B was significantly higher for tumors with low ERα (A, Fisher's test P = 0.0001; B, P = 0.033) and PR levels (A, P = 0.0006; B, P = 0.001). Our data suggest that the relationships between DNA methylation of ESR1 and PGR promoters and protein expression are weak and unlikely to represent a predominant mechanism of receptor silencing. In contrast to CpG islands, ESR1 promoter C showed a wider range of methylation levels and inverse associations with ERα and PR expression. (Cancer Epidemiol Biomarkers Prev 2009;18(11):3036–43)

Supplementary Table 3 - PDF file 23K, Overlaps in genes among biologically relevant signatures

PDF file, 120KB, Supplemental Table 1. Study Descriptions, Design and Sample Size for 19 DENSNP Studies. Caucasian Women Only. Supplemental Table 2. Source and Timing of Reproductive and Anthropometric Variable Collection. Supplemental Table 3. Description of Mammogram View, Side, Film Digitizer and Density Estimation Software for DENSNP Studies. Supplemental Table 4. Genotyping Platform and SNP Call Rates Performed by Each Study.

Supplementary Figures 1-5, Tables 1-8. Supplementary Figure 1: Overview of the analytic approach. Supplementary Figure 2: QQ plots of SNPs with MAF ≥0.02 and imputation r2 ≥0.9 associated with Overall Survival in A. Supplementary Figure 3: ‘All OCAC’ histology-adjusted analysis. Supplementary Figure 4: Forest plots of promising SNPs. Supplementary Figure 5: KM-Plotter graphs of significant associations with outcome. Supplementary Table 1: All studies (OCAC & TCGA) eligible for analyses according to first-line chemotherapy. Supplementary Table 2: Description of individual OCAC studies included in the secondary 'all OCAC' analysis. Supplementary Table 3a: Overall Survival estimates for selected SNPs (MAF ≥0.02) comparing iCOGS imputed (r2 ≥0.3) and iPLEX genotyped samples. Supplementary Table 3b: Progression-free Survival estimates for selected SNPs (MAF ≥0.02) comparing iCOGS imputed (r2 ≥0.3) and iPLEX genotyped samples. Supplementary Table 4: SNPs with imputation r2 ≥0.9, EAF ≥0.02 and p ≤ 1E-05 for at least one of four outcomes analyzed in cases selected according to first-line chemotherapy. Supplementary Table 5: Meta-analysis of largest possible sample (TCGA, non-overlapping iCOGS and iplex genotyped) for selected promising SNPs analyzed in cases selected according to first-line chemotherapy. Supplementary Table 6: Significant association between protein-coding genes within 1Mb of promising SNPs and ovarian cancer outcomes. Supplementary Table 7: SNPs with imputation r-sq≥0.9 and p ≤ 1E-05 for Overall Survival in histology-adj 'all OCAC' analysis. Supplementary Table 8: iCOGS estimates for SNPs previously identified to be associated with response to chemotherapy as reported in the aNHGRI GWAS catalog.

Background: Occupational exposures are known risk factors for lung cancer. Role of genetically determined host factors in occupational exposure–related lung cancer is unclear.Methods: We used genome-wide association (GWA) data from a case–control study conducted in 6 European countries from 1998 to 2002 to identify gene–occupation interactions and related pathways for lung cancer risk. GWA analysis was performed for each exposure using logistic regression and interaction term for genotypes, and exposure was included in this model. Both SNP-based and gene-based interaction P values were calculated. Pathway analysis was performed using three complementary methods, and analyses were adjusted for multiple comparisons. We analyzed 312,605 SNPs and occupational exposure to 70 agents from 1,802 lung cancer cases and 1,725 cancer-free controls.Results: Mean age of study participants was 60.1 ± 9.1 years and 75% were male. Largest number of significant associations (P ≤ 1 × 10−5) at SNP level was demonstrated for nickel, brick dust, concrete dust, and cement dust, and for brick dust and cement dust at the gene-level (P ≤ 1 × 10−4). Approximately 14 occupational exposures showed significant gene–occupation interactions with pathways related to response to environmental information processing via signal transduction (P < 0.001 and FDR < 0.05). Other pathways that showed significant enrichment were related to immune processes and xenobiotic metabolism.Conclusion: Our findings suggest that pathways related to signal transduction, immune process, and xenobiotic metabolism may be involved in occupational exposure–related lung carcinogenesis.Impact: Our study exemplifies an integrative approach using pathway-based analysis to demonstrate the role of genetic variants in occupational exposure–related lung cancer susceptibility. Cancer Epidemiol Biomarkers Prev; 24(3); 570–9. ©2015 AACR.

Background: While numerous susceptibility loci for epithelial ovarian cancer (EOC) have been identified, few associations have been reported with overall survival. In the absence of common prognostic genetic markers, we hypothesize that rare coding variants may be associated with overall EOC survival and assessed their contribution in two exome-based genotyping projects of the Ovarian Cancer Association Consortium (OCAC).Methods: The primary patient set (Set 1) included 14 independent EOC studies (4,293 patients) and 227,892 variants, and a secondary patient set (Set 2) included six additional EOC studies (1,744 patients) and 114,620 variants. Because power to detect rare variants individually is reduced, gene-level tests were conducted. Sets were analyzed separately at individual variants and by gene, and then combined with meta-analyses (73,203 variants and 13,163 genes overlapped).Results: No individual variant reached genome-wide statistical significance. A SNP previously implicated to be associated with EOC risk and, to a lesser extent, survival, rs8170, showed the strongest evidence of association with survival and similar effect size estimates across sets (Pmeta = 1.1E−6, HRSet1 = 1.17, HRSet2 = 1.14). Rare variants in ATG2B, an autophagy gene important for apoptosis, were significantly associated with survival after multiple testing correction (Pmeta = 1.1E−6; Pcorrected = 0.01).Conclusions: Common variant rs8170 and rare variants in ATG2B may be associated with EOC overall survival, although further study is needed.Impact: This study represents the first exome-wide association study of EOC survival to include rare variant analyses, and suggests that complementary single variant and gene-level analyses in large studies are needed to identify rare variants that warrant follow-up study. Cancer Epidemiol Biomarkers Prev; 25(3); 446–54. ©2016 AACR.

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Purpose: Chemotherapy resistance remains a major challenge in the treatment of ovarian cancer. We hypothesize that germline polymorphisms might be associated with clinical outcome.Experimental Design: We analyzed approximately 2.8 million genotyped and imputed SNPs from the iCOGS experiment for progression-free survival (PFS) and overall survival (OS) in 2,901 European epithelial ovarian cancer (EOC) patients who underwent first-line treatment of cytoreductive surgery and chemotherapy regardless of regimen, and in a subset of 1,098 patients treated with ≥4 cycles of paclitaxel and carboplatin at standard doses. We evaluated the top SNPs in 4,434 EOC patients, including patients from The Cancer Genome Atlas. In addition, we conducted pathway analysis of all intragenic SNPs and tested their association with PFS and OS using gene set enrichment analysis.Results: Five SNPs were significantly associated (P ≤ 1.0 × 10−5) with poorer outcomes in at least one of the four analyses, three of which, rs4910232 (11p15.3), rs2549714 (16q23), and rs6674079 (1q22), were located in long noncoding RNAs (lncRNAs) RP11-179A10.1, RP11-314O13.1, and RP11-284F21.8, respectively (P ≤ 7.1 × 10−6). ENCODE ChIP-seq data at 1q22 for normal ovary show evidence of histone modification around RP11-284F21.8, and rs6674079 is perfectly correlated with another SNP within the super-enhancer MEF2D, expression levels of which were reportedly associated with prognosis in another solid tumor. YAP1- and WWTR1 (TAZ)-stimulated gene expression and high-density lipoprotein (HDL)-mediated lipid transport pathways were associated with PFS and OS, respectively, in the cohort who had standard chemotherapy (pGSEA ≤6 × 10−3).Conclusions: We have identified SNPs in three lncRNAs that might be important targets for novel EOC therapies. Clin Cancer Res; 21(23); 5264–76. ©2015 AACR.

PDF file, 20KB, Supplemental Table 5a. Associations of Common Breast Cancer Susceptibility Variants with Adjusted Percent Mammographic Density. Supplemental Table 5b. Associations of Common Breast Cancer Susceptibility Variants with Adjusted Mammographic Dense Area. Supplemental Table 5c. Associations of Common Breast Cancer Susceptibility Variants with Adjusted Mammographic Non Dense Area.

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Background: Increased risk of renal cell carcinoma (RCC) with milk consumption has been reported from observational studies. Whether this represents a causal association or is a result of confounding or bias is unclear. We assessed the potential for using genetic variation in lactase persistence as a tool for the study of this relationship.Methods: Using a large, hospital-based case-control study, we used observational, phenotypic, and genetic data to determine whether the MCM6 −13910 C/T(rs4988235) variant may be used as a nonconfounded and unbiased marker for milk consumption.Results: Consumption of milk during adulthood was associated with increased risk of RCC [odds ratio (OR), 1.35; 95% confidence interval (95% CI), 1.03-1.76; P = 0.03]. Among controls, consumption of milk was associated with the lactase persistence genotype at rs4988235 (OR, 2.39; 95% CI, 1.81-3.15; P = 6.9 × 10−10); however, the same genotype was not associated with RCC (OR, 1.01; 95% CI, 0.83-1.22; P = 0.9). In controls, milk consumption was associated with confounding factors, including smoking and educational attainment, whereas genotypes at rs4988235 showed negligible association with confounding factors.Conclusion: The absence of an association between the MCM6 genotype and RCC suggests that observational associations between milk consumption and RCC may be due to confounding or bias.Impact: Although these data suggest that associations between milk consumption and RCC may be spurious, if the association between genotype and behavioral exposure is weak, then the power of this test may be low. The nature of intermediate risk factor instrumentation is an important consideration in the undertaking and interpretation of this type of causal analysis experiment. Cancer Epidemiol Biomarkers Prev; 19(5); 1341–8. ©2010 AACR.

Supplementary Table 7

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Supplemental Figures and tables

Purpose:Ovarian cancer is a heterogeneous disease that can be divided into multiple subtypes with variable etiology, pathogenesis, and prognosis. We analyzed DNA methylation profiling data to identify biologic subgroups of ovarian cancer and study their relationship with histologic subtypes, copy number variation, RNA expression data, and outcomes.Experimental Design:A total of 162 paraffin-embedded ovarian epithelial tumor tissues, including the five major epithelial ovarian tumor subtypes (high- and low-grade serous, endometrioid, mucinous, and clear cell) and tumors of low malignant potential were selected from two different sources: The Polish Ovarian Cancer study, and the Surveillance, Epidemiology, and End Results Residual Tissue Repository (SEER RTR). Analyses were restricted to Caucasian women. Methylation profiling was conducted using the Illumina 450K methylation array. For 45 tumors array copy number data were available. NanoString gene expression data for 39 genes were available for 61 high-grade serous carcinomas (HGSC).Results:Consensus nonnegative matrix factorization clustering of the 1,000 most variable CpG sites showed four major clusters among all epithelial ovarian cancers. We observed statistically significant differences in survival (log-rank test, P = 9.1 × 10−7) and genomic instability across these clusters. Within HGSC, clustering showed three subgroups with survival differences (log-rank test, P = 0.002). Comparing models with and without methylation subgroups in addition to previously identified gene expression subtypes suggested that the methylation subgroups added significant survival information (P = 0.007).Conclusions:DNA methylation profiling of ovarian cancer identified novel molecular subgroups that had significant survival difference and provided insights into the molecular underpinnings of ovarian cancer.See related commentary by Ishak et al., p. 5729

Supplementary Figure 1 - PDF file 385K, Representative pictures of breast composition by digital pathological image analysis

supplementary figures 1-10. Supplementary Figure 1 - Representative array images for "ring" structure surrounding spots. Supplementary Figure 2 - Array screening reproducibility. Supplementary Figure 3 - AAb response counts per sample are similar between BLBC and controls. Supplementary Figure 4 - GSEA analysis of AAb targets identified by either visual analysis. Supplementary Figure 5 - GSEA analysis of AAb protein targets against their biochemical properties. Supplementary Figure 6 - GO cellular component analysis. Supplementary Figure 7 - Heatmap of plasma AAb responses against selected proteins based on quantitative analysis in sample set 1 (basal-like, n=45; controls, n=45). Supplementary Figure 8 - Heatmap of plasma AAb responses against selected proteins based on visual image analysis in sample set 1 (basal-like, n=45; controls, n=45). Supplementary Figure 9 - mRNA expression levels of candidate biomarkers in breast invasive carcinoma grouped by subtypes (TCGA). Supplementary Figure 10 - Overall survival of BLBC patients stratified by AAb responses.

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.

Background: Genome-wide association studies (GWAS) have identified more than 100 genetic loci for various cancers. However, only one is for endometrial cancer.Methods: We conducted a three-stage GWAS including 8,492 endometrial cancer cases and 16,596 controls. After analyzing 585,963 single-nucleotide polymorphisms (SNP) in 832 cases and 2,682 controls (stage I) from the Shanghai Endometrial Cancer Genetics Study, we selected the top 106 SNPs for in silico replication among 1,265 cases and 5,190 controls from the Australian/British Endometrial Cancer GWAS (stage II). Nine SNPs showed results consistent in direction with stage I with P < 0.1. These nine SNPs were investigated among 459 cases and 558 controls (stage IIIa) and six SNPs showed a direction of association consistent with stages I and II. These six SNPs, plus two additional SNPs selected on the basis of linkage disequilibrium and P values in stage II, were investigated among 5,936 cases and 8,166 controls from an additional 11 studies (stage IIIb).Results: SNP rs1202524, near the CAPN9 gene on chromosome 1q42.2, showed a consistent association with endometrial cancer risk across all three stages, with ORs of 1.09 [95% confidence interval (CI), 1.03–1.16] for the A/G genotype and 1.17 (95% CI, 1.05–1.30) for the G/G genotype (P = 1.6 × 10−4 in combined analyses of all samples). The association was stronger when limited to the endometrioid subtype, with ORs (95% CI) of 1.11 (1.04–1.18) and 1.21 (1.08–1.35), respectively (P = 2.4 × 10−5).Conclusions: Chromosome 1q42.2 may host an endometrial cancer susceptibility locus.Impact: This study identified a potential genetic locus for endometrial cancer risk. Cancer Epidemiol Biomarkers Prev; 21(6); 980–7. ©2012 AACR.

Supplemental Table S1: Number of invasive EOC patients by study site. Supplemental Table S2: Results of Set 1 single variant analysis, for variants with P

Supplementary Table 8 - PDF file 43K, The association of breast composition percentage with Active/Inactive subtype and mammographic density

Supplementary Table 3 from TGF-β Signaling Pathway and Breast Cancer Susceptibility

Background: TGF-β acts as a suppressor of primary tumor initiation but has been implicated as a promoter of the later malignant stages. Here associations with risk of invasive breast cancer are assessed for single-nucleotide polymorphisms (SNP) tagging 17 genes in the canonical TGF-β ALK5/SMADs 2&3 and ALK1/SMADs 1&5 signaling pathways: LTBP1, LTBP2, LTBP4, TGFB1, TGFB2, TGFB3, TGFBR1(ALK5), ALK1, TGFBR2, Endoglin, SMAD1, SMAD2, SMAD3, SMAD4, SMAD5, SMAD6, and SMAD7 [Approved Human Gene Nomenclature Committee gene names: ACVRL1 (for ALK1) and ENG (for Endoglin)].Methods: Three-hundred-fifty-four tag SNPs (minor allele frequency > 0.05) were selected for genotyping in a staged study design using 6,703 cases and 6,840 controls from the Studies of Epidemiology and Risk Factors in Cancer Heredity (SEARCH) study. Significant associations were meta-analyzed with data from the NCI Polish Breast Cancer Study (PBCS; 1,966 cases and 2,347 controls) and published data from the Breast Cancer Association Consortium (BCAC).Results: Associations of three SNPs, tagging TGFB1 (rs1982073), TGFBR1 (rs10512263), and TGFBR2 (rs4522809), were detected in SEARCH; however, associations became weaker in meta-analyses including data from PBCS and BCAC. Tumor subtype analyses indicated that the TGFB1 rs1982073 association may be confined to increased risk of developing progesterone receptor negative (PR−) tumors [1.18 (95% CI: 1.09–1.28), 4.1 × 10−5 (P value for heterogeneity of ORs by PR status = 2.3 × 10−4)]. There was no evidence for breast cancer risk associations with SNPs in the endothelial-specific pathway utilizing ALK1/SMADs 1&5 that promotes angiogenesis.Conclusion: Common variation in the TGF-β ALK5/SMADs 2&3 signaling pathway, which initiates signaling at the cell surface to inhibit cell proliferation, might be related to risk of specific tumor subtypes.Impact: The subtype specific associations require very large studies to be confirmed. Cancer Epidemiol Biomarkers Prev; 20(6); 1112–9. ©2011 AACR.

PDF file, 133KB, Supplemental Table 6a. Per-Allele Association (SE) of Breast Cancer Susceptibility Variants with Mammographic Percent Density Stratifying by Case Status, BMI, Menopausal Status and PMH Use. Supplemental Table 6b. Per-Allele Associations (SE) of Breast Cancer Susceptibility Variants with Adjusted Mammographic Dense Area Stratifying by Case Status, BMI Menopausal Status and PMH Use. Supplemental Table 6c. Per-Allele Associations (SE) of Breast Cancer Susceptibility Variants with Adjusted Mammographic Non Dense Area Stratifying by Case Status, BMI Menopausal Status and PMH Use.