METHODS: We conducted a meta-analysis of four NPC GWAS among Chinese individuals (2,152 cases; 3,740 controls). Forty-three noteworthy findings outside the MHC region were identified and targeted for replication in a pooled analysis of four independent case-control studies across three regions in Asia (4,716 cases; 5,379 controls). A meta-analysis that combined results from the initial GWA and replication studies was performed.

RESULTS: In the combined meta-analysis, rs31489, located within the CLPTM1L/TERT region on chromosome 5p15.33, was strongly associated with NPC (OR = 0.81; P value 6.3 × 10(-13)). Our results also provide support for associations reported from published NPC GWAS-rs6774494 (P = 1.5 × 10(-12); located in the MECOM gene region), rs9510787 (P = 5.0 × 10(-10); located in the TNFRSF19 gene region), and rs1412829/rs4977756/rs1063192 (P = 2.8 × 10(-8), P = 7.0 × 10(-7), and P = 8.4 × 10(-7), respectively; located in the CDKN2A/B gene region).

CONCLUSIONS: We have identified a novel association between genetic variation in the CLPTM1L/TERT region and NPC. Supporting our finding, rs31489 and other SNPs in this region have been reported to be associated with multiple cancer sites, candidate-based studies have reported associations between polymorphisms in this region and NPC, the TERT gene has been shown to be important for telomere maintenance and has been reported to be overexpressed in NPC, and an EBV protein expressed in NPC (LMP1) has been reported to modulate TERT expression/telomerase activity.

METHODS: We conducted a fine-mapping analysis in 55,540 breast cancer cases and 51,168 controls from the Breast Cancer Association Consortium.

RESULTS: Conditional analyses identified two independent association signals among women of European ancestry, represented by rs9790517 [conditional P = 2.51 × 10(-4); OR, 1.04; 95% confidence interval (CI), 1.02-1.07] and rs77928427 (P = 1.86 × 10(-4); OR, 1.04; 95% CI, 1.02-1.07). Functional annotation using data from the Encyclopedia of DNA Elements (ENCODE) project revealed two putative functional variants, rs62331150 and rs73838678 in linkage disequilibrium (LD) with rs9790517 (r(2) ≥ 0.90) residing in the active promoter or enhancer, respectively, of the nearest gene, TET2. Both variants are located in DNase I hypersensitivity and transcription factor-binding sites. Using data from both The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), we showed that rs62331150 was associated with level of expression of TET2 in breast normal and tumor tissue.

CONCLUSION: Our study identified two independent association signals at 4q24 in relation to breast cancer risk and suggested that observed association in this locus may be mediated through the regulation of TET2.

METHODS: We conducted a nested case-control study in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort to evaluate C-reactive protein (CRP), IL6, and EOC risk by tumor characteristics. A total of 754 eligible EOC cases were identified; two controls (n = 1,497) were matched per case. We used multivariable conditional logistic regression to assess associations.

RESULTS: CRP and IL6 were not associated with overall EOC risk. However, consistent with prior research, CRP >10 versus CRP ≤1 mg/L was associated with higher overall EOC risk [OR, 1.67 (1.03-2.70)]. We did not observe significant associations or heterogeneity in analyses by tumor characteristics. In analyses stratified by waist circumference, inflammatory markers were associated with higher risk among women with higher waist circumference; no association was observed for women with normal waist circumference [e.g., IL6: waist ≤80: ORlog2, 0.97 (0.81-1.16); waist >88: ORlog2, 1.78 (1.28-2.48), Pheterogeneity ≤ 0.01].

CONCLUSIONS: Our data suggest that high CRP is associated with increased risk of overall EOC, and that IL6 and CRP may be associated with EOC risk among women with higher adiposity.

METHODS: Individual participant data meta-analysis included 362,114 participants (43% women), from seven prospective cohort studies, free from cancer at enrollment. The WCRF/AICR diet score was based on: (i) energy-dense foods and sugary drinks, (ii) plant foods, (iii) red and processed meat, and (iv) alcoholic drinks. Cox proportional hazards regression was used to examine the association between the diet score and cancer risks. Adjusted, cohort-specific HRs were pooled using random-effects meta-analysis. Risk advancement periods (RAP) were calculated to quantify the time period by which the risk of cancer was postponed among those adhering to the recommendations.

RESULTS: After a median follow-up of 11 to 15 years across cohorts, 70,877 cancer cases were identified. Each one-point increase in the WCRF/AICR diet score [range, 0 (no) to 4 (complete adherence)] was significantly associated with a lower risk of total cancer [HR, 0.94; 95% confidence interval (CI), 0.92-0.97], cancers of the colorectum (HR, 0.84; 95% CI, 0.80-0.89) and prostate (HR, 0.94; 95% CI, 0.92-0.97), but not breast or lung. Adherence to an additional component of the WCRF/AICR diet score significantly postponed the incidence of cancer at any site by 1.6 years (RAP, -1.6; 95% CI, -4.09 to -2.16).

CONCLUSIONS: Adherence to WCRF/AICR dietary recommendations is associated with lower risk of cancer among older adults.

METHODS: We conducted a gene-environment interaction (GxE) analysis including 8,255 cases and 11,900 controls from four pancreatic cancer genome-wide association study (GWAS) datasets (Pancreatic Cancer Cohort Consortium I-III and Pancreatic Cancer Case Control Consortium). Obesity (body mass index ≥30 kg/m2) and diabetes (duration ≥3 years) were the environmental variables of interest. Approximately 870,000 SNPs (minor allele frequency ≥0.005, genotyped in at least one dataset) were analyzed. Case-control (CC), case-only (CO), and joint-effect test methods were used for SNP-level GxE analysis. As a complementary approach, gene-based GxE analysis was also performed. Age, sex, study site, and principal components accounting for population substructure were included as covariates. Meta-analysis was applied to combine individual GWAS summary statistics.

RESULTS: No genome-wide significant interactions (departures from a log-additive odds model) with diabetes or obesity were detected at the SNP level by the CC or CO approaches. The joint-effect test detected numerous genome-wide significant GxE signals in the GWAS main effects top hit regions, but the significance diminished after adjusting for the GWAS top hits. In the gene-based analysis, a significant interaction of diabetes with variants in the FAM63A (family with sequence similarity 63 member A) gene (significance threshold P < 1.25 × 10-6) was observed in the meta-analysis (P GxE = 1.2 ×10-6, P Joint = 4.2 ×10-7).

CONCLUSIONS: This analysis did not find significant GxE interactions at the SNP level but found one significant interaction with diabetes at the gene level. A larger sample size might unveil additional genetic factors via GxE scans.

METHODS: We applied a multiplex serology assay to simultaneously measure antibody responses to 11 F. nucleatum antigens in prediagnostic serum samples from 485 colorectal cancer cases and 485 matched controls. Conditional logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CI).

RESULTS: We observed neither a statistically significant colorectal cancer risk association for antibodies to individual F. nucleatum proteins nor for combined positivity to any of the 11 proteins (OR, 0.81; 95% CI, 0.62-1.06).

CONCLUSIONS: Antibody responses to F. nucleatum proteins in prediagnostic serum samples from a subset of colorectal cancer cases and matched controls within the EPIC study were not associated with colorectal cancer risk.

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.