Identifying genetic variants with pleiotropic associations can uncover common pathways influencing multiple cancers. We took a two-stage approach to conduct genome-wide association studies for lung, ovary, breast, prostate, and colorectal cancer from the GAME-ON/GECCO Network (61,851 cases, 61,820 controls) to identify pleiotropic loci. Findings were replicated in independent association studies (55,789 cases, 330,490 controls). We identified a novel pleiotropic association at 1q22 involving breast and lung squamous cell carcinoma, with eQTL analysis showing an association with ADAM15/THBS3 gene expression in lung. We also identified a known breast cancer locus CASP8/ALS2CR12 associated with prostate cancer, a known cancer locus at CDKN2B-AS1 with different variants associated with lung adenocarcinoma and prostate cancer, and confirmed the associations of a breast BRCA2 locus with lung and serous ovarian cancer. This is the largest study to date examining pleiotropy across multiple cancer-associated loci, identifying common mechanisms of cancer development and progression. Cancer Res; 76(17); 5103–14. ©2016 AACR.

Genome-wide association studies (GWAS) have identified hundreds of genetic variants that are associated with risk of specific cancers (1). It has been observed that some chromosomal regions show pleiotropic associations, where the same genetic loci are associated with different cancers. One of the first identified pleiotropic loci is at 8q24, where genetic variants are associated with breast, prostate, colorectal, and ovarian cancer risk, with some of the variants only associated with one cancer, while others are associated with multiple cancers (2). Similarly, genetic variants at the TERT-CLPTM1L region at 5p15.33 are associated with risk of lung, bladder, prostate, and other cancers (3).

The identification of pleiotropic loci is an important step in elucidating cancer etiology by understanding common pathways that influence carcinogenesis across different tumors, and in improving knowledge of cancer susceptibility. Furthermore, analyzing genomic data across multiple cancer sites might identify novel susceptibility loci, as variants that do not meet the stringent criteria for GWAS significance for any one cancer site might show significant associations when multiple cancers are analyzed together (4).

Our investigation used data from the Genetic Associations and Mechanisms in Oncology (GAME-ON) Network and the Genetic and Epidemiology of Colorectal Cancer Consortium (GECCO; ref. 5). The GAME-ON Network was launched by the NCI (Rockville, MD) to capitalize on the extensive investment in GWAS, with the overarching goal to integrate post-GWAS research and to facilitate analyses that address research questions that are common across multiple cancer sites. The GAME-ON Network is focused on tumors that currently represent a major public health burden and has assembled extensive genomic data from consortia investigating the cancer sites that constituted the basis of our cross-cancer analysis. We use these data and independent replication studies to investigate pleiotropic associations across lung, breast, colorectal, ovary, and prostate cancer using GWAS results for 61,851 cases and 61,820 controls, the largest investigation of pleiotropic associations to date.

Data and contributing consortia

This study used summary level data to perform cross-cancer GWAS analysis of lung, colorectal, prostate, breast, and ovarian cancers using a subset-based meta-analytical approach. Forty-six studies from North America and Europe organized into cancer site-specific consortia within the GAME-ON Network (http://epi.grants.cancer.gov/gameon/) or GECCO, participated. Table 1 provides details for contributing consortia and studies. Analyses also included the following subtypes: adenocarcinoma and squamous cell carcinoma (SqCC) of the lung; the aggressive form of prostate cancer; estrogen receptor–negative breast cancer, and serous and endometrioid cancers of the ovary (Table 1). All studies frequency matched cases and controls on at least age and gender. All subjects were of European descent.

Table 1.

Contributing consortia and characteristics of datasets

GAME-ON/GECCO analysisReplication stage (European ancestry)Generalizability (other ancestry)
Cancer site (consortium)No. of studiesCasesControlsaVariantsbGenotyping platformImputation thresholdcStudyCasesControlsaStudyCasesControlsa
Lung (TRICL; ref. 10) 12,160 16,838 8492272 Illumina R2 > 0.3 deCODE (10, 13) 3,865 196,658 Nanjing (18) 2,331 3,077 
       Harvard (14, 15) 984 970    
 Adenocarcinoma  3,718 15,871 8472145   deCODE 1,434 198,663 Nanjing 1,304 3,077 
       Harvard 597 970 Japan (17) 1,575 3,363 
 SqCC  3,422 16,015 8478230   deCODE 784 171,059 Nanjing 822 3,077 
       Harvard 216 970    
Colorectal (CORECT; ref. 11) 5,100 4,831 7229595 Affymetrix Axiom None deCODE 3,546 236,404    
Colorectal (GECCO; ref. 5) 13 10,314 12,857 9193926 Illumina, Affymetrix R2 > 0.3       
Prostate (ELLIPSE) 14,160 12,724 9084781 Illumina, Affymetrix R2 > 0.3 deCODE 4,858 83,103 LAPC/MEC (20) 1,034 1,046 
       iCOGS (16) 20,219 20,440 AAPC (23) 4,853 4,678 
          JAPC/MEC (20) 980 1,005 
 Prostate aggressive 4,450 12,724 9003304         
Breast (DRIVE; refs. 7, 9) 11 15,748 18,084 9331393 Illumina, Affymetrix R2 > 0.3 deCODE 5,318 280,808 Shanghai (19) 2,867 2,285 
       iCOGS (7) 46,785 42,892 LABC/MEC, SF (21) 1,497 3,213 
          AABC (22) 3,015 2,743 
 Breast ER (6) 4,939 13,128 9250406         
Ovary (FOCI; ref. 8) 4,369 9,123 9911464 Illumina R2 > 0.3 deCODE 716 111,373    
       iCOGS (8) 16,283 23,491    
 Ovary - serous 2,556 9,123 9911279   iCOGS 10,316 23,491    
 Ovary - endometrial 715 9,123 9910229   iCOGS 2,338 23,491    
Totala 45 61,851 61,820 9916564    55,789 330,490  18,152 21,410 
GAME-ON/GECCO analysisReplication stage (European ancestry)Generalizability (other ancestry)
Cancer site (consortium)No. of studiesCasesControlsaVariantsbGenotyping platformImputation thresholdcStudyCasesControlsaStudyCasesControlsa
Lung (TRICL; ref. 10) 12,160 16,838 8492272 Illumina R2 > 0.3 deCODE (10, 13) 3,865 196,658 Nanjing (18) 2,331 3,077 
       Harvard (14, 15) 984 970    
 Adenocarcinoma  3,718 15,871 8472145   deCODE 1,434 198,663 Nanjing 1,304 3,077 
       Harvard 597 970 Japan (17) 1,575 3,363 
 SqCC  3,422 16,015 8478230   deCODE 784 171,059 Nanjing 822 3,077 
       Harvard 216 970    
Colorectal (CORECT; ref. 11) 5,100 4,831 7229595 Affymetrix Axiom None deCODE 3,546 236,404    
Colorectal (GECCO; ref. 5) 13 10,314 12,857 9193926 Illumina, Affymetrix R2 > 0.3       
Prostate (ELLIPSE) 14,160 12,724 9084781 Illumina, Affymetrix R2 > 0.3 deCODE 4,858 83,103 LAPC/MEC (20) 1,034 1,046 
       iCOGS (16) 20,219 20,440 AAPC (23) 4,853 4,678 
          JAPC/MEC (20) 980 1,005 
 Prostate aggressive 4,450 12,724 9003304         
Breast (DRIVE; refs. 7, 9) 11 15,748 18,084 9331393 Illumina, Affymetrix R2 > 0.3 deCODE 5,318 280,808 Shanghai (19) 2,867 2,285 
       iCOGS (7) 46,785 42,892 LABC/MEC, SF (21) 1,497 3,213 
          AABC (22) 3,015 2,743 
 Breast ER (6) 4,939 13,128 9250406         
Ovary (FOCI; ref. 8) 4,369 9,123 9911464 Illumina R2 > 0.3 deCODE 716 111,373    
       iCOGS (8) 16,283 23,491    
 Ovary - serous 2,556 9,123 9911279   iCOGS 10,316 23,491    
 Ovary - endometrial 715 9,123 9910229   iCOGS 2,338 23,491    
Totala 45 61,851 61,820 9916564    55,789 330,490  18,152 21,410 

Abbreviations: AABC, African American Breast Cancer GWAS Consortium; AAPC, African Ancestry Prostate Cancer GWAS Consortium, SqCC, squamous cell carcinoma; ER, estrogen receptor negative.

aThe number of unique individuals after accounting for cancer subtypes and overlapping controls. Breast iCOGS included only 1q22 variants, so total for replicates without breast iCOGS is shown.

bSubset meta-analyses were performed for a specific variant if at least 3 sites (i.e., three of lung, colorectal, prostate, breast, or ovary) contributed data.

cImputation performed using the 1000 genome reference panel. Exclusion threshold is shown.

Genotyping and imputation

Genotyping was performed on Affymetrix or Illumina platforms (Table 1). Standard marker exclusion criteria were applied in each cancer consortium (5–11), and we have previously reported the summary of quality control details in the study by Hung and colleagues (12). Genotype imputation was conducted for each cancer site using IMPUTE, BEAGLE, MACH, and Minimac (imputation threshold of R2 > 0.3) using the 1000 genome reference panel.

Statistical analysis

Logistic regression analysis using a log additive model was performed previously to test variant associations with cancer risk for the 45 studies (5–11), providing per-allele ORs adjusted for age, principal components, and gender where applicable. Study-specific results were then combined for each cancer site using a fixed effects model.

A subset-based meta-analysis approach developed by Bhattacharjee and colleagues (ASSET) was used to investigate pleiotropic effects across cancer sites (4). The method generalizes the standard fixed effect meta-analysis by examining the association between a genetic variant and multiple subsets of cancers, allowing opposing direction of effects and null associations. Associations are summarized with an overall two-sided P value. A multiple testing adjustment procedure (based on statistical theory for tail-probability approximation) maintains appropriate type I error rates. Analysis was performed when at least three cancer sites had data.

Accounting for subsets of studies with no effects and/or effects in opposing directions (i.e., both increased and reduced risk subsets) is an advantage of the subset-based meta-analysis approach. However, when a large majority of underlying associations are in one direction, subset meta-analysis can have lower power than standard fixed effects analysis. We therefore explored results using standard fixed effects meta-analysis (when data was present for all cancer sites), again using ASSET.

Subjects appearing in several studies with different cancer subtypes (e.g., overlapping controls for lung adenocarcinoma and lung SqCC) and across cancer types (e.g., overlapping controls from the Welcome Trust Case Control Consortium for UK ovary and UK breast GWAS) were accounted for in the covariance matrix when estimating SEs for subset-based and standard meta-analyses.

To find robust pleiotropic associations, we first identified variants that showed evidence for pleiotropy and then sought to validate the individual variant cancer site associations in replication datasets. To prioritize variants for replication, we selected those with P < 5 × 10−7 based on the two-sided subset analysis test, the positive and negative associations that contributed to the two-sided subset-analysis test signal and the fixed effect meta-analysis. We excluded variants where associations were obviously driven by a single cancer site. We also selected variants associated with at least two cancers (including subtypes) at P < 5 × 10−3. Among variants selected, we further prioritized for validation those showing the strongest pleiotropic association in a region (based on subset or standard cross-cancer meta-analysis), as well as those representing association peaks in site-specific analyses. We then sought to validate these specific variant–cancer site associations using independent study populations of European descent with all five cancers from deCODE (10, 13), lung cancer from Harvard (14, 15), breast (region 1q22 only) from iCOGS, ovarian from OCAC/iCOGS, and prostate from PRACTICAL/iCOGS (7, 8, 16). For cross-ethnicity generalizability, we examined results for the selected variants in different race/ethnicities using data from Japan [lung (17)], Nanjing [lung (18)], Shanghai [breast (19)], MEC, African American Breast Cancer GWAS Consortium, African Ancestry Prostate Cancer GWAS Consortium, and San Francisco (breast for Latinas; refs. 20–23). An outline of analysis steps is presented in Supplementary Fig. S1.

Further investigation of pleiotropic effects

We further investigated pleiotropy between cancer sites using conditional Q-Q plots to examine enrichment of association signals in one cancer when conditioning on the significance of P values of a second cancer. Enrichment is reflected in a leftward deflection of the Q-Q plot with decreasing P value categories of the conditioning cancer (24), indicating higher pleiotropy between cancer sites than expected by chance.

Functional significance

eQTL analysis

We obtained nontumor lung eQTL data of 1,111 patients from three studies: Laval University (n = 409), The University of British Columbia (UBC; n = 339), and the University of Groningen (n = 363). Gene expression profiles were obtained using an Affymetrix array (GEO platform GPL10379). Genotyping was performed using the Illumina Human1M-Duo genotyping BeadChip. Analyses were adjusted for age, sex, and smoking status. eQTL data are deposited in the Gene Expression Omnibus (GEO) database with accession numbers GSE23352, GSE23529, and GSE23545 for the three studies, respectively. Further details of this study are published elsewhere (25). We investigated validated lung cancer–associated variants from our study. A statistically significant eQTL for a variant was declared if 2 of 3 studies showed a significant P value after Bonferroni correction for multiple comparisons. We also evaluated eQTL data for variants in linkage disequilibrium (LD; R2 > 0.7) with our validated variants. We also obtained TCGA eQTL data for 402 high-grade serous ovarian cases and 145 prostate tumor samples, again investigating variants with validated associations and those in high LD (R2 > 0.7) with them. Gene expression values for high-grade serous ovarian cases were assessed by P value. Gene expression values for prostate cancer were adjusted for somatic copy number and CpG methylation as described previously (26). Significant associations were defined as those having both P value and FDR (Benjamini–Hochberg method) of less than 0.05.

Function/prediction

We used FuncPred from SNPinfo (http://snpinfo.niehs.nih.gov/) to assist with variant function prediction. The software evaluates a variant's potential function in splicing regulation, TFBS prediction, miRNA-binding site prediction, and regulatory potential using in-house algorithms and tools developed elsewhere. In addition, we used RegulomeDB to further assess regulatory potential for variants of interest (http://www.regulomedb.org/). This tool includes high-throughput, experimental datasets from ENCODE and other sources, as well as computational predictions and manual annotations to identify putative regulatory potential and functional variants. We further examined ENCODE data using the UCSC genome browser (https://genome.ucsc.edu/; see Supplementary Materials: Bioinformatics Tools).

After applying quality control filters, 9,916,564 variants were analyzed for pleiotropic associations using 61,851 cases and 61,820 controls of European ancestry across five common cancer sites in the GAME-ON Network and GECCO (GAME-ON/GECCO; Table 1). Supplementary Fig. S2 displays an overview of the association test results for each of the five main cancer sites investigated.

A total of 190 variants in 33 regions (13 to 22 for each cancer site) were prioritized for follow-up for replication in Caucasian populations (55,789 cases and 330,490 controls) and generalizability (18,152 cancer cases and 21,410 controls) after the initial analysis (see Table 1 and Supplementary Table S1). An additional 46,785 cases and 42,892 controls from iCOGS breast cancer were used for validation of the novel pleiotropic locus at 1q22 (see below). We replicated associations at 4 of the 33 regions selected for follow-up in Europeans. Variants with replicated pleiotropic associations were not associated with cancer risk in other ethnic populations, and therefore, these data are not presented. The main findings in each region are described below (see Supplementary Tables S1 and S2 for summary).

Novel region: 1q22 for lung SqCC and breast cancer

Standard meta-analysis of GAME-ON/GECCO discovery data identified an association between rs1057941 located at 1q22 and overall risk of cancer (P = 1.74 × 10−7; Fig. 1). Overall lung, lung squamous cell carcinoma (lung SqCC) and breast cancer were strongly associated with this variant in GAME-ON/GECCO data [lung: OR = 1.08; 95% confidence interval (CI), 1.05–1.12; P = 9.2 × 10−6; lung SqCC: OR = 1.10; 95% CI, 1.04–1.16; P = 0.001; breast: OR = 1.07; 95% CI, 1.04–1.11; P = 6.08 × 10−5]. The association for lung SqCC was replicated in deCODE and Harvard studies combined, with OR of 1.12 (95% CI, 1.01–1.23; P = 0.03). The association with breast cancer was replicated in deCODE and iCOGS combined (OR = 1.03; 95% CI, 1.01–1.05; P = 0.004). The overall P value for association from both stages for lung SqCC and breast cancer approached genome-wide significance (P = 7.9 × 10−8; Fig. 1).

Figure 1.

Result for rs1057941. Forest plot showing per allele ORs for risk allele A (of A/G). Standard fixed effects meta-analysis (also indicated by dashed line) and subset meta-analysis results (two-sided, one-sided, and positive and negative subset associations) are shown. CRC, colorectal; ERNEG, estrogen receptor negative; endomet, endometrioid; aggrsv, aggressive; adeno, adenocarcinoma.

Figure 1.

Result for rs1057941. Forest plot showing per allele ORs for risk allele A (of A/G). Standard fixed effects meta-analysis (also indicated by dashed line) and subset meta-analysis results (two-sided, one-sided, and positive and negative subset associations) are shown. CRC, colorectal; ERNEG, estrogen receptor negative; endomet, endometrioid; aggrsv, aggressive; adeno, adenocarcinoma.

Close modal

The aggressive form of prostate cancer was also selected by ASSET as part of the subset of cancers associated with rs1057941. We have no replication data for this subtype and did not replicate the association with overall prostate cancer (deCODE and PRACTICAL/iCOGS combined: OR = 1.02; 95% CI, 1.00–1.05; P = 0.08).

Regional plots constructed from GAME-ON/GECCO results show a distinct peak in P values represented by rs1057941 in a 40-kb region of LD at 1q22 that includes KRTCAP2, GBA, MTX1, MUC1, TRIM46, THBS3, ADAM15, and ASH1L for the cross-cancer analysis (Fig. 2A), while for individual sites MUC1 variant rs4072037 was most significant for lung SqCC (P = 3.21 × 10−4) and the TRIM46 variant, rs3814316, for breast cancer (P = 3.06 × 10−6). These 3 variants are in high to complete LD with each other with pair-wise D′ of 0.86 (rs1057941-rs3814316) or 1.00 (rs1057941-rs4072037 and rs3814316-rs4072037).

Figure 2.

A, results for rs1057941. Regional plot showing P values from overall meta-analysis at region 1q22 using GAME-ON/GECCO discovery set data. The top breast cancer hit (rs3814316) and top SqCC hit (rs4072037) are also highlighted. B, boxplots of gene expression levels in normal lung tissue for ADAM15 and THBS3 by study (Laval, UBC, Groningen). GRNG, Groningen.

Figure 2.

A, results for rs1057941. Regional plot showing P values from overall meta-analysis at region 1q22 using GAME-ON/GECCO discovery set data. The top breast cancer hit (rs3814316) and top SqCC hit (rs4072037) are also highlighted. B, boxplots of gene expression levels in normal lung tissue for ADAM15 and THBS3 by study (Laval, UBC, Groningen). GRNG, Groningen.

Close modal

eQTL analyses indicated that rs4072037 acted as a normal lung tissue eQTL for two genes in this region, ADAM15 and THBS3, with two studies showing significant associations after adjustment for multiple comparisons and the third showing a nominally significant association consistent in direction with the other two (ADAM15: Laval P = 2.39 × 10−7, UBC P = 4.09 × 10−5, University of Groningen P = 0.08; THBS3: Laval P = 1.71 × 10−5, UBC P = 4.15 × 10−6, University of Groningen P = 0.004; Fig. 2B). ADAM15 is of primary interest as it was shown to be overexpressed in lung and breast cancer (27). The risk allele for rs4072037, A, was consistently associated with increased ADAM15 gene expression in all three studies. In the UBC discovery set, rs4072037 was the strongest eQTL among 33 variants.

Previously known cancer loci with pleiotropic associations

2q33.1(known breast and melanoma locus) and prostate cancer

We identified a pleiotropic association between rs13016963 located in 2q33.1 and prostate (OR = 1.08; 95% CI, 1.04–1.13; P = 3.05 × 10−5) and breast cancer (OR = 0.93; 95% CI, 0.90–0.96; P = 5.75 × 10−5; Fig. 3A) in GAME-ON/GECCO. This variant is in intron 5 of ALS2CR12, adjacent to CASP8 (Fig. 3B), and was associated with melanoma in a previous GWAS (28). The region was previously identified as harboring variants associated with breast cancer risk (29–31), and the association found with breast cancer can be explained through LD (R2 = 0.74) with one of these (rs1830298, P value for breast cancer association = 1.02 × 10−7). The association with prostate cancer has not been previously reported, and we replicated it in deCODE and iCOGS (OR = 1.05; 95% CI, 1.03–1.08; P = 7.6 × 10−5; Fig. 3A). The combined GAME-ON/GECCO, deCODE, and iCOGS P value was 1.9 × 10−8.

Figure 3.

Results for rs13016963. A, forest plot showing per allele ORs for risk allele G (of A/G). Standard fixed effects meta-analysis (also indicated by dashed line) and subset meta-analysis results (two-sided, one-sided, and positive and negative subset associations) are shown. B, regional plot showing P values for GAME-ON prostate cancer GWAS at region 2q33.1 using GAME-ON/GECCO discovery set data. Peak is at ALS2CR12. C, boxplot of BZW1 gene expression levels in prostate tumor and normal tissue for rs1035142/rs13016963. rs1035142 is presented as a surrogate for rs13016963, with which it shows perfect LD in eQTL data. CRC, colorectal; ERNEG, estrogen receptor negative; Prost, prostate; endomet, endometrioid; Aggrsv, aggressive; Adeno, adenocarcinoma.

Figure 3.

Results for rs13016963. A, forest plot showing per allele ORs for risk allele G (of A/G). Standard fixed effects meta-analysis (also indicated by dashed line) and subset meta-analysis results (two-sided, one-sided, and positive and negative subset associations) are shown. B, regional plot showing P values for GAME-ON prostate cancer GWAS at region 2q33.1 using GAME-ON/GECCO discovery set data. Peak is at ALS2CR12. C, boxplot of BZW1 gene expression levels in prostate tumor and normal tissue for rs1035142/rs13016963. rs1035142 is presented as a surrogate for rs13016963, with which it shows perfect LD in eQTL data. CRC, colorectal; ERNEG, estrogen receptor negative; Prost, prostate; endomet, endometrioid; Aggrsv, aggressive; Adeno, adenocarcinoma.

Close modal

In Fig. 3C, we show eQTL analysis results for rs1035142, which was in perfect LD with rs13016963 in the eQTL dataset and was associated with BZW1 (P = 0.001, FDR = 0.04). This variant had the strongest association signal among those investigated. We do note that there were variants with more significant associations with BZW1 expression that fell outside of the LD range of R2 ≥ 0.70 with rs13016963 (e.g., rs13113, P = 4.9 × 10−6, R2 = 0.62). This suggests there are other potential cis-eQTL candidates for BZW1 in the region.

9p21.3 (known lung SqCC and breast locus) and lung adenocarcinoma

Similar to 8q24, the 9p21.3 region displays a complex nature of associations with multiple cancer sites. It was previously known to be associated with lung SqCC and breast cancer. In our analysis based on GAME-ON/GECCO discovery data, we observed suggestive evidence of association between the variant rs62560775 at CDKN2B-AS1 and lung adenocarcinoma (OR = 1.19; 95% CI, 1.08–1.31; P = 2.77 × 10−4), as well as breast cancer (OR = 1.11; 95% CI, 1.05–1.17; P = 5.30 × 10−4). Although this region was previously reported as a lung SqCC susceptibility locus, this is the first time that we observed an association with lung adenocarcinoma. The association was replicated based on deCODE and Harvard data (OR = 1.16; 95% CI, 1.03–1.30; P = 0.01). The combined (both stages) P value was 1.0 × 10−5 (Fig. 4A). The association of this variant with lung adenocarcinoma was the most significant in the region (Fig. 4B). Our lung eQTL investigation of this variant showed no significant association with gene expression in this region.

Figure 4.

A, results for rs62560775. Forest plot showing per allele ORs for risk allele G (of A/G). Standard fixed effects meta-analysis (also indicated by dashed line) and subset meta-analysis results (two-sided, one-sided, and positive and negative subset associations) are shown. B, regional plot showing P values for GAME-ON lung adenocarcinoma GWAS at region 9p21.3, using GAME-ON/GECCO discovery set data. Peak is at CDKN2B-AS1. C, results for rs1011970. Forest plot showing per allele ORs for risk allele T (of T/G). Standard fixed effects meta-analysis (also indicated by dashed line) and subset meta-analysis results (two-sided, one-sided, and positive and negative subset associations) are shown. D, regional plot showing P values for GAME-ON prostate cancer GWAS at region 9p21.3, using GAME-ON/GECCO discovery set data. Peak is at CDKN2B-AS1. CRC, colorectal; ERNEG, estrogen receptor negative; Aggrsv, aggressive; endomet, endometrioid; Prost, prostate; Adeno, adenocarcinoma.

Figure 4.

A, results for rs62560775. Forest plot showing per allele ORs for risk allele G (of A/G). Standard fixed effects meta-analysis (also indicated by dashed line) and subset meta-analysis results (two-sided, one-sided, and positive and negative subset associations) are shown. B, regional plot showing P values for GAME-ON lung adenocarcinoma GWAS at region 9p21.3, using GAME-ON/GECCO discovery set data. Peak is at CDKN2B-AS1. C, results for rs1011970. Forest plot showing per allele ORs for risk allele T (of T/G). Standard fixed effects meta-analysis (also indicated by dashed line) and subset meta-analysis results (two-sided, one-sided, and positive and negative subset associations) are shown. D, regional plot showing P values for GAME-ON prostate cancer GWAS at region 9p21.3, using GAME-ON/GECCO discovery set data. Peak is at CDKN2B-AS1. CRC, colorectal; ERNEG, estrogen receptor negative; Aggrsv, aggressive; endomet, endometrioid; Prost, prostate; Adeno, adenocarcinoma.

Close modal

Another variant in the same region, rs1011970, was shown to be associated with prostate cancer (OR = 1.10; 95% CI = 1.05–1.15; P = 7.3 × 10−5). This variant was found to be associated with breast cancer in a previous GWAS (32). The association with prostate cancer was replicated in deCODE and iCOGS combined (P = 0.001). The combined P value based on both stages was 9.5 × 10−7 (Fig. 4C). The regional plots shows that this variant had the second most significant association with prostate cancer in the region [the peak association for prostate cancer is represented by rs72652411, a variant not associated with any cancer other than prostate (Fig. 4D)].

13q13.1 (known breast and lung locus) confirmed for serous ovarian cancer

We observed a significant pleiotropic association for the BRCA2 variant rs11571833 (ASSET two-sided P = 6.14 × 10−10). This variant (Supplementary Fig. S3) is potentially functional, and genome-wide significant associations between this variant and breast and lung cancer (driven by SqCC) were previously reported, with the latter study using a subset of the lung cancer data used here (7, 10). More recently, a study focusing specifically on rs11571833 reported an association with serous ovarian cancer using the iCOGS data, which formed our replication dataset (P = 3 × 10−5). Our results from the GAME-ON dataset confirm the association with serous ovarian cancer (GAME-ON OR = 1.76; 95% CI, 1.30–2.39; P = 2.49 × 10−4; with the rare allele associated with increased risk) and reached genome-wide significance when we combined the two datasets (P = 3.95 × 10−8; Supplementary Fig. S3). We did not replicate the association with breast cancer; however, this might be due to a lack of power to detect an association for this rare variant in our sample.

Other evidence for pleiotropic associations

As demonstrated by the leftward deflection of Q-Q plots with decreasing P value category, there is evidence of pleiotropic associations for breast and ovarian cancer (Supplementary Fig. S4A), breast and prostate cancer (Supplementary Fig. S4B), and prostate and colorectal cancer (Supplementary Fig. S4C). There was no evidence of pleiotropic associations for prostate and ovary cancer (Supplementary Fig. S4D), or lung cancer with any of the other four cancer sites.

Using data from the GAME-ON Network and GECCO, we conducted a cross-cancer GWAS analysis investigating pleiotropic associations for five cancer sites (lung, breast, colorectal, ovary, and prostate), including histology and subtypes. We identified three new pleiotropic associations that were supported by results in GAME-ON/GECCO data and our independent replication datasets. We identified a novel pleiotropic association at the 1q22 region involving breast cancer and lung SqCC, neither of which was previously known to be associated with genetic variation in this region. The association with lung SqCC was further supported by its functional significance as demonstrated by eQTL analysis. Further novel results include a locus at CASP8/ALS2CR12, known to be associated with breast cancer and melanoma, which was found to be associated with prostate cancer, while genetic variation at the 9p21.3 region, known to be associated with breast cancer and lung SqCC, appears to be associated with lung adenocarcinoma and prostate cancer. We also confirmed an association between a known lung and breast cancer locus at BRCA2 with serous ovarian cancer risk.

The locus at 1q22 represented by rs1057941, rs3814316, and rs4072037 lies in a region of LD that includes KRTCAP2, MTX1, TRIM46, MUC1, GBA, THBS3, ADAM15, and ASH1L. Of these variants, rs4072037 at MUC1 might be functional as it was shown to regulate alternative splicing of the second exon in MUC1 and modifies the gene's transcriptional activity (33). Aberrantly glycosylated MUC1 is overexpressed in most epithelial cancers and is known to have an oncogenic effect. It mediates the production of growth factors, such as connective tissue growth factor (CTGF) and platelet-derived growth factor A and B (PDGF-A and PDGF-B) that promote activation of the MAPK and PI3k/Akt pathways potentiating proliferation and survival of tumor cells (34). It also plays a critical role in EGFR signaling, promoting survival of NSCLC cells (35).

Our lung eQTL investigation found no association with MUC1 expression but the risk allele, A, of rs4072037 was associated with increased expression of two other genes in the region (ADAM15 and THBS3). This result suggests other mechanisms by which this variant could influence cancer risk. ADAM15 is of particular interest as it was shown to be overexpressed in both lung and breast cancer (27), consistent with our finding of pleiotropic associations of these two cancer sites. Overexpression in breast cancer is associated with Her2/neu expression and evidence from breast cancer cell lines indicates that ADAM15 catalyzes the cleavage of E-cadherin, which in turn binds to and enhances ErbB receptor signaling (36). A recent meta-analysis provided support for an association of this variant with gastric cancer in Asian populations (37), and a recent GWAS in the Icelandic population also suggested an association between this region and gastric cancer in European populations. These results lend further support for the etiologic role of this region on cancer susceptibility in general (38).

For 2q33.1, we found evidence for a pleiotropic effect for rs13016963 (at ALS2CR12) on breast and prostate cancer. This region is known to harbor breast cancer susceptibility loci: rs1045485, encoding the missense alteration D302H in CASP8 (adjacent to ALS2CR12) (29), rs1830298 (mentioned above) at ALS2CR12, and rs1045494 at CASP8 (30, 31). Of these three variants, rs1830298 was found to have the most significant association with breast cancer (P = 1.02 × 10−7) in our study and was associated with prostate cancer risk (P = 5.2 × 10−4), whereas rs1045485 and rs1045494 were not. For prostate cancer, rs13016963 represented the peak association, although we point out strong LD between rs13016963 and rs1830298 (R2 = 0.74). Interestingly, rs13016963 was also found to be associated with risk of melanoma in a previous GWAS in subjects of European descent (28), indicating this variant may be associated with both prostate cancer and melanoma in Caucasian populations. It was also found to be associated with esophageal SqCC in Han Chinese (39).

Previous research has examined associations between other genetic variants in this region and prostate cancer risk. A possible association between the CASP8 histidine variant D302H and the more aggressive form of prostate cancer in European populations was reported (40). This variant was however not associated with aggressive prostate cancer (P = 0.11) or overall prostate cancer (P = 0.14) in GAME-ON/GECCO.

Our prostate eQTL analysis suggested that rs13016963 influences the expression of BZW1. Previous studies indicate a role for BZW1 in carcinogenesis. BZW1 can activate histone H4 gene transcription and serves as a coregulator of other transcription factors involved in cell cycling. It has been implicated in promoting mucoepidermoid carcinoma tumor growth (41). We also found two potential functional variants in the region (rs700636 and rs1035142). These variants are in strong LD with rs13016963 (R2 ≥ 0.93) and have associations with prostate cancer similar to rs13016963 in strength and are predicted to sit in miRNA-binding sites.

The 9q21.3 region encoding CDKN2B-AS1 has been much studied in cancer research. We observed a pleiotropic association of rs62560775 (located in the intronic region of CDKN2B-AS1) involving lung adenocarcinoma and breast cancer. The association with breast cancer might to be due to LD (R2 = 0.38) with a previously identified breast cancer susceptibility variant, rs1011970. Interestingly, we did replicate an association between rs1011970 and prostate cancer, suggesting this specific variant or variants in LD with it contribute to risk for both breast and prostate cancer. Timofeeva and colleagues found an association between this region and lung SqCC, represented by rs1333040 (42), but this variant was not associated with adenocarcinoma in our dataset (P = 0.62). Previous GWASs also report associations between this region and risk of glioma (43), melanoma (28), and basal cell carcinoma (44) and a recent pleiotropy study indicated an association with esophageal SqCC (45). LD between variants reported in these studies and either rs62560775 or rs1011970 range from R2 = 0.21 to R2 = 0.60, indicating that multiple variants in this region contribute to cancer risk. Despite that associations for different cancers were exerted from multiple variants, our results represent an important pleiotropic finding, as all variants are located in the same gene, CDKN2B-AS1.

Modification of CDKN2B-AS1 activity could be the mechanism through which this locus influences cancer risk. CDKN2B-AS1, also known as ANRIL (antisense noncoding RNA in the INK4 locus) is known to recruit a polycomb repression complex (PRC2) that silences CDKN2B but not CDKN2A (46). Although there is no known function for rs62660775 or rs1011970, a variant with which rs62660775 is in strong LD, rs3217986 (at R2 = 0.75), was identified to be located in an miRNA-binding site (47) and classified as likely to affect binding by regulome.

Our pleiotropy Q-Q plots (Supplementary Fig. S4A–S4C) suggest pleiotropy between some sites: breast and ovarian cancer, breast and prostate cancer, and prostate and lung cancer, with some evidence provided for pleiotropic associations involving prostate and lung and colorectal and ovarian cancer. There was little evidence for pleiotropic associations involving other site combinations using this approach.

Although we have built-in studies from non-European descendants, including Asians, African Americans and Hispanics, we did not observe an association with any of the loci in other ethnic groups. It is likely that the smaller sample sizes for the non-European descendants restricted our power to detect associations. Variation in LD across ethnicities could also have reduced our power to detect associations. More research is needed to determine whether associations in these regions can be generalized to different ethnic groups based on larger sample size. These efforts could make meaningful contributions to addressing disparities in health between populations.

Power to detect associations is also relevant when considering our primary analyses involving subjects of European ancestry. Our initial investigation using the GAME-ON and GECCO dataset identified 33 regions and 190 variants that we further examined in replication datasets. We were able to replicate the associations for four of these regions. As our replication datasets' sample sizes were often smaller than those of our discovery (GAME-ON/GECCO) set (depending on cancer site), we may have insufficient power to replicate true associations particularly for less common variants, underlining the importance of sample size for investigations of pleiotropy.

In summary, using data from the GAME-ON initiative and GECCO, we have found four regions that show associations with multiple cancers, including a novel association between genetic variation at 1q22 and breast cancer and lung SqCC. This is the largest study to date examining pleiotropy across multiple cancer sites. There are likely more loci that are associated with multiple cancers, but these will require additional efforts with larger data series for detection. Our results provide important insights into common carcinogenesis across multiple major cancers and highlight the value of pleiotropy analysis.

K. Stefansson is the Chief Executive Officer at deCodeGenetics/Amgen Inc. No potential conflicts of interest were disclosed by other authors.

Conception and design: P. Kraft, P.D. Pharoah, N. Chatterjee, P. Brennan, H. Bickeböller, A. Risch, E.L. Giovannucci, F. Wiklund, W.C. Willett, E.L. Goode, H.A. Risch, H. Ahsan, P. Hall, J.L. Hopper, P.H. Peeters, M.J. Sanchez, M.C. Southey, D.D. Buchanan, M. Jenkins, L. Le Marchand, D. Seminara, J. Gong, U. Peters, C.I. Amos, D.F. Easton, R.J. Hung

Development of methodology: P. Kraft, N. Chatterjee, J.L. Hopper, M.C. Southey, R.J. Hung

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): G. Fehringer, P.D. Pharoah, R.A. Eeles, F.R. Schumacher, J.M. Schildkraut, S. Lindström, H. Bickeböller, R.S. Houlston, M.T. Landi, N. Caporaso, A. Risch, S.I. Berndt, E.L. Giovannucci, H. Grönberg, Z. Kote-Jarai, J. Ma, M.J. Stampfer, F. Wiklund, W.C. Willett, H.A. Risch, H. Brenner, A.T. Chan, J. Chang-Claude, T.J. Hudson, P.A. Newcomb, R.E. Schoen, M.L. Slattery, E. White, M.A. Adank, H. Ahsan, K. Aittomäki, C. Blomquist, F. Canzian, K. Czene, I. dos-Santos-Silva, J.D. Figueroa, D. Flesch-Janys, O. Fletcher, M. Garcia-Closas, M.M. Gaudet, N. Johnson, P. Hall, A. Hazra, R. Hein, J.L. Hopper, M. Johansson, R. Kaaks, M.G. Kibriya, P. Lichtner, J. Liu, E. Lund, A. Meindl, B. Müller-Myhsok, T.A. Muranen, H. Nevanlinna, P.H. Peeters, J. Peto, N. Rahman, M.J. Sanchez, R.K. Schmutzler, M.C. Southey, R. Tamimi, R.C. Travis, C. Turnbull, X.R. Yang, D.D. Buchanan, G. Casey, D.V. Conti, S. Gallinger, R.W. Haile, M. Jenkins, L. Le Marchand, L. Li, N.M. Lindor, S.N. Thibodeau, M.O. Woods, T. Rafnar, J. Gudmundsson, S.N. Stacey, K. Stefansson, P. Sulem, D.C. Christiani, H. Shen, Z. Hu, X.-O. Shu, Y. Bossé, M. Obeidat, D. Nickle, W. Timens, S.J. Chanock, J. Gong, U. Peters, S.B. Gruber, C.I. Amos, T.A. Sellers, D.F. Easton, B.E. Henderson, R.J. Hung

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): G. Fehringer, N. Chatterjee, F.R. Schumacher, P. Brennan, R.S. Houlston, A. Amin Al Olama, J. Ma, W.C. Willett, H.A. Risch, B.M. Reid, A.T. Chan, J. Chang-Claude, I. dos-Santos-Silva, J.D. Figueroa, M.M. Gaudet, A. Hazra, J.L. Hopper, A. Irwanto, B. Müller-Myhsok, D.F. Schmidt, M.C. Southey, Z. Wang, C.K. Edlund, S.L. Schmit, K. Stefansson, Y.A. Chen, J.P. Tyrer, Y. Wei, K. Shiraishi, A. Takahashi, Y. Bossé, M. Obeidat, M.L. Freedman, Q. Li, S.J. Chanock, U. Peters, C.I. Amos, D.F. Easton, D.J. Hunter, R.J. Hung

Writing, review, and/or revision of the manuscript: G. Fehringer, P. Kraft, P.D. Pharoah, R.A. Eeles, N. Chatterjee, F.R. Schumacher, J.M. Schildkraut, S. Lindström, H. Bickeböller, R.S. Houlston, M.T. Landi, N. Caporaso, A. Risch, A. Amin Al Olama, S.I. Berndt, E.L. Giovannucci, J. Ma, K. Muir, M.J. Stampfer, V.L. Stevens, F. Wiklund, W.C. Willett, J.B. Permuth, H.A. Risch, S. Bezieau, H. Brenner, A.T. Chan, J. Chang-Claude, T.J. Hudson, J.K. Kocarnik, R.E. Schoen, M.L. Slattery, E. White, K. Aittomäki, L. Baglietto, C. Blomquist, K. Czene, I. dos-Santos-Silva, A.H. Eliassen, J.D. Figueroa, M. Garcia-Closas, M.M. Gaudet, N. Johnson, A. Hazra, R. Hein, A. Hofman, J.L. Hopper, R. Kaaks, M.G. Kibriya, P. Lichtner, E. Lund, E. Makalic, T.A. Muranen, P.H. Peeters, J. Peto, R.L. Prentice, M.J. Sanchez, D.F. Schmidt, R.K. Schmutzler, M.C. Southey, R. Tamimi, R.C. Travis, A.G. Uitterlinden, Z. Wang, A.S. Whittemore, X.R. Yang, W. Zheng, D.D. Buchanan, C.K. Edlund, M. Jenkins, L. Le Marchand, L. Li, T. Rafnar, S.N. Stacey, J.P. Tyrer, D.C. Christiani, Y. Wei, Y. Bossé, W. Timens, M.L. Freedman, D. Seminara, J. Gong, U. Peters, S.B. Gruber, C.I. Amos, T.A. Sellers, D.F. Easton, D.J. Hunter, C.A. Haiman, R.J. Hung

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): G. Fehringer, N. Caporaso, Z. Kote-Jarai, J. Ma, K. Muir, E.L. Goode, P.A. Newcomb, L. Baglietto, P. Hall, J.L. Hopper, M. Johansson, A. Meindl, T.A. Muranen, P.H. Peeters, J. Peto, N. Rahman, R.K. Schmutzler, M.C. Southey, C.K. Edlund, L. Le Marchand, M.O. Woods, J. Gudmundsson, S.N. Stacey, P. Sulem, D.C. Christiani, H. Shen, D. Nickle, D. Seminara, S.B. Gruber, C.I. Amos

Study supervision: J.L. Hopper, M.C. Southey, D.D. Buchanan, M. Jenkins, X.-O. Shu, U. Peters, R.J. Hung

This study was supported by a grant from the NIH (U19CA148127). Additional funding was obtained from NIH grants (5R01CA055769, 5R01CA127219, 5R01CA133996, and 5R01CA121197). See supplementary materials for additional acknowledgements.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Hindorff
LA
,
MacArthur
J
,
Morales
J
,
Junkins
HA
,
Hall
PN
,
Klemm
AK
, et al
A Catalog of Published Genome-Wide Association Studies
.
NIH
:
Bethesda, MD
. Available from: www.genome.gov/gwastudies.
2.
Ghoussaini
M
,
Song
H
,
Koessler
T
,
Al Olama
AA
,
Kote-Jarai
Z
,
Driver
KE
, et al
Multiple loci with different cancer specificities within the 8q24 gene desert
.
J Natl Cancer Inst
2008
;
100
:
962
6
.
3.
Rafnar
T
,
Sulem
P
,
Stacey
SN
,
Geller
F
,
Gudmundsson
J
,
Sigurdsson
A
, et al
Sequence variants at the TERT-CLPTM1L locus associate with many cancer types
.
Nat Genet
2009
;
41
:
221
7
.
4.
Bhattacharjee
S
,
Rajaraman
P
,
Jacobs
KB
,
Wheeler
WA
,
Melin
BS
,
Hartge
P
, et al
A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits
.
Am J Hum Genet
2012
;
90
:
821
35
.
5.
Peters
U
,
Jiao
S
,
Schumacher
FR
,
Hutter
CM
,
Aragaki
AK
,
Baron
JA
, et al
Identification of genetic susceptibility loci for colorectal tumors in a genome-wide meta-analysis
.
Gastroenterology
2013
;
144
:
799
807
.
e24
.
6.
Garcia-Closas
M
,
Couch
FJ
,
Lindstrom
S
,
Michailidou
K
,
Schmidt
MK
,
Brook
MN
, et al
Genome-wide association studies identify four ER negative-specific breast cancer risk loci
.
Nat Genet
2013
;
45
:
392
8
.
7.
Michailidou
K
,
Hall
P
,
Gonzalez-Neira
A
,
Ghoussaini
M
,
Dennis
J
,
Milne
RL
, et al
Large-scale genotyping identifies 41 new loci associated with breast cancer risk
.
Nat Genet
2013
;
45
:
353
61
.
8.
Pharoah
PD
,
Tsai
YY
,
Ramus
SJ
,
Phelan
CM
,
Goode
EL
,
Lawrenson
K
, et al
GWAS meta-analysis and replication identifies three new susceptibility loci for ovarian cancer
.
Nat Genet
2013
;
45
:
362
70
.
9.
Siddiq
A
,
Couch
FJ
,
Chen
GK
,
Lindstrom
S
,
Eccles
D
,
Millikan
RC
, et al
A meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11
.
Hum Mol Genet
2012
;
21
:
5373
84
.
10.
Wang
Y
,
McKay
JD
,
Rafnar
T
,
Wang
Z
,
Timofeeva
MN
,
Broderick
P
, et al
Rare variants of large effect in BRCA2 and CHEK2 affect risk of lung cancer
.
Nat Genet
2014
;
46
:
736
41
.
11.
Wang
H
,
Burnett
T
,
Kono
S
,
Haiman
CA
,
Iwasaki
M
,
Wilkens
LR
, et al
Trans-ethnic genome-wide association study of colorectal cancer identifies a new susceptibility locus in VTI1A
.
Nat Commun
2014
;
5
:
4613
.
12.
Hung
RJ
,
Ulrich
CM
,
Goode
EL
,
Brhane
Y
,
Muir
K
,
Chan
AT
, et al
Cross cancer genomic investigation of inflammation pathway for five common cancers: lung, ovary, prostate, breast, and colorectal cancer
.
J Natl Cancer Inst
2015
;
107
:
djv246
.
13.
Gudbjartsson
DF
,
Helgason
H
,
Gudjonsson
SA
,
Zink
F
,
Oddson
A
,
Gylfason
A
, et al
Large-scale whole-genome sequencing of the Icelandic population
.
Nat Genet
2015
;
47
:
435
44
.
14.
Asomaning
K
,
Miller
DP
,
Liu
G
,
Wain
JC
,
Lynch
TJ
,
Su
L
, et al
Second hand smoke, age of exposure and lung cancer risk
.
Lung Cancer
2008
;
61
:
13
20
.
15.
Liu
G
,
Wheatley-Price
P
,
Zhou
W
,
Park
S
,
Heist
RS
,
Asomaning
K
, et al
Genetic polymorphisms of MDM2, cumulative cigarette smoking and nonsmall cell lung cancer risk
.
Int J Cancer
2008
;
122
:
915
8
.
16.
Al Olama
AA
,
Kote-Jarai
Z
,
Berndt
SI
,
Conti
DV
,
Schumacher
F
,
Han
Y
, et al
A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer
.
Nat Genet
2014
;
46
:
1103
9
.
17.
Shiraishi
K
,
Kunitoh
H
,
Daigo
Y
,
Takahashi
A
,
Goto
K
,
Sakamoto
H
, et al
A genome-wide association study identifies two new susceptibility loci for lung adenocarcinoma in the Japanese population
.
Nat Genet
2012
;
44
:
900
3
.
18.
Hu
Z
,
Wu
C
,
Shi
Y
,
Guo
H
,
Zhao
X
,
Yin
Z
, et al
A genome-wide association study identifies two new lung cancer susceptibility loci at 13q12.12 and 22q12.2 in Han Chinese
.
Nat Genet
2011
;
43
:
792
6
.
19.
Cai
Q
,
Zhang
B
,
Sung
H
,
Low
SK
,
Kweon
SS
,
Lu
W
, et al
Genome-wide association analysis in East Asians identifies breast cancer susceptibility loci at 1q32.1, 5q14.3 and 15q26.1
.
Nat Genet
2014
;
46
:
886
90
.
20.
Cheng
I
,
Chen
GK
,
Nakagawa
H
,
He
J
,
Wan
P
,
Laurie
CC
, et al
Evaluating genetic risk for prostate cancer among Japanese and Latinos
.
Cancer Epidemiol Biomarkers Prev
2012
;
21
:
2048
58
.
21.
Fejerman
L
,
Ahmadiyeh
N
,
Hu
D
,
Huntsman
S
,
Beckman
KB
,
Caswell
JL
, et al
Genome-wide association study of breast cancer in Latinas identifies novel protective variants on 6q25
.
Nat Commun
2014
;
5
:
5260
.
22.
Chen
F
,
Chen
GK
,
Stram
DO
,
Millikan
RC
,
Ambrosone
CB
,
John
EM
, et al
A genome-wide association study of breast cancer in women of African ancestry
.
Hum Genet
2013
;
132
:
39
48
.
23.
Haiman
CA
,
Chen
GK
,
Blot
WJ
,
Strom
SS
,
Berndt
SI
,
Kittles
RA
, et al
Genome-wide association study of prostate cancer in men of African ancestry identifies a susceptibility locus at 17q21
.
Nat Genet
2011
;
43
:
570
3
.
24.
Andreassen
OA
,
Zuber
V
,
Thompson
WK
,
Schork
AJ
,
Bettella
F
,
Djurovic
S
, et al
Shared common variants in prostate cancer and blood lipids
.
Int J Epidemiol
2014
;
43
:
1205
14
.
25.
Obeidat
M
,
Miller
S
,
Probert
K
,
Billington
CK
,
Henry
AP
,
Hodge
E
, et al
GSTCD and INTS12 regulation and expression in the human lung
.
PLoS One
2013
;
8
:
e74630
.
26.
Li
Q
,
Seo
JH
,
Stranger
B
,
McKenna
A
,
Pe'er
I
,
Laframboise
T
, et al
Integrative eQTL-based analyses reveal the biology of breast cancer risk loci
.
Cell
2013
;
152
:
633
41
.
27.
Kuefer
R
,
Day
KC
,
Kleer
CG
,
Sabel
MS
,
Hofer
MD
,
Varambally
S
, et al
ADAM15 disintegrin is associated with aggressive prostate and breast cancer disease
.
Neoplasia
2006
;
8
:
319
29
.
28.
Barrett
JH
,
Iles
MM
,
Harland
M
,
Taylor
JC
,
Aitken
JF
,
Andresen
PA
, et al
Genome-wide association study identifies three new melanoma susceptibility loci
.
Nat Genet
2011
;
43
:
1108
13
.
29.
Cox
A
,
Dunning
AM
,
Garcia-Closas
M
,
Balasubramanian
S
,
Reed
MW
,
Pooley
KA
, et al
A common coding variant in CASP8 is associated with breast cancer risk
.
Nat Genet
2007
;
39
:
352
8
.
30.
Khan
S
,
Greco
D
,
Michailidou
K
,
Milne
RL
,
Muranen
TA
,
Heikkinen
T
, et al
MicroRNA related polymorphisms and breast cancer risk
.
PLoS One
2014
;
9
:
e109973
.
31.
Lin
WY
,
Camp
NJ
,
Ghoussaini
M
,
Beesley
J
,
Michailidou
K
,
Hopper
JL
, et al
Identification and characterization of novel associations in the CASP8/ALS2CR12 region on chromosome 2 with breast cancer risk
.
Hum Mol Genet
2014
;
24
:
285
98
.
32.
Turnbull
C
,
Ahmed
S
,
Morrison
J
,
Pernet
D
,
Renwick
A
,
Maranian
M
, et al
Genome-wide association study identifies five new breast cancer susceptibility loci
.
Nat Genet
2010
;
42
:
504
7
.
33.
Saeki
N
,
Saito
A
,
Choi
IJ
,
Matsuo
K
,
Ohnami
S
,
Totsuka
H
, et al
A functional single nucleotide polymorphism in mucin 1, at chromosome 1q22, determines susceptibility to diffuse-type gastric cancer
.
Gastroenterology
2011
;
140
:
892
902
.
34.
Nath
S
,
Mukherjee
P
. 
MUC1: a multifaceted oncoprotein with a key role in cancer progression
.
Trends Mol Med
2014
;
20
:
332
42
.
35.
Kharbanda
A
,
Rajabi
H
,
Jin
C
,
Tchaicha
J
,
Kikuchi
E
,
Wong
K-K
, et al
Targeting the oncogenic MUC1-C protein inhibits mutant EGFR-mediated signaling and survival in non–small cell lung cancer cells
.
Clin Cancer Res
2014
;
20
:
5423
34
.
36.
Najy
AJ
,
Day
KC
,
Day
ML
. 
The ectodomain shedding of E-cadherin by ADAM15 supports ErbB receptor activation
.
J Biol Chem
2008
;
283
:
18393
401
.
37.
Liu
X
,
Wang
Z
,
Zhang
X
,
Chang
J
,
Tang
W
,
Gan
L
, et al
MUC1 gene polymorphism rs4072037 and susceptibility to gastric cancer: a meta-analysis
.
SpringerPlus
2014
;
3
:
599
.
38.
Helgason
H
,
Rafnar
T
,
Olafsdottir
HS
,
Jonasson
JG
,
Sigurdsson
A
,
Stacey
SN
, et al
Loss-of-function variants in ATM confer risk of gastric cancer
.
Nat Genet
2015
;
47
:
906
10
.
39.
Abnet
CC
,
Wang
Z
,
Song
X
,
Hu
N
,
Zhou
FY
,
Freedman
ND
, et al
Genotypic variants at 2q33 and risk of esophageal squamous cell carcinoma in China: a meta-analysis of genome-wide association studies
.
Hum Mol Genet
2012
;
21
:
2132
41
.
40.
Lubahn
J
,
Berndt
SI
,
Jin
CH
,
Klim
A
,
Luly
J
,
Wu
WS
, et al
Association of CASP8 D302H polymorphism with reduced risk of aggressive prostate carcinoma
.
Prostate
2010
;
70
:
646
53
.
41.
Li
S
,
Chai
Z
,
Li
Y
,
Liu
D
,
Bai
Z
,
Situ
Z
. 
BZW1, a novel proliferation regulator that promotes growth of salivary muocepodermoid carcinoma
.
Cancer Lett
2009
;
284
:
86
94
.
42.
Timofeeva
MN
,
Hung
RJ
,
Rafnar
T
,
Christiani
DC
,
Field
JK
,
Bickeboller
H
, et al
Influence of common genetic variation on lung cancer risk: meta-analysis of 14 900 cases and 29 485 controls
.
Hum Mol Genet
2012
;
21
:
4980
95
.
43.
Shete
S
,
Hosking
FJ
,
Robertson
LB
,
Dobbins
SE
,
Sanson
M
,
Malmer
B
, et al
Genome-wide association study identifies five susceptibility loci for glioma
.
Nat Genet
2009
;
41
:
899
904
.
44.
Stacey
SN
,
Sulem
P
,
Masson
G
,
Gudjonsson
SA
,
Thorleifsson
G
,
Jakobsdottir
M
, et al
New common variants affecting susceptibility to basal cell carcinoma
.
Nat Genet
2009
;
41
:
909
14
.
45.
Li
WQ
,
Pfeiffer
RM
,
Hyland
PL
,
Shi
J
,
Gu
F
,
Wang
Z
, et al
Genetic polymorphisms in the 9p21 region associated with risk of multiple cancers
.
Carcinogenesis
2014
;
35
:
2698
705
.
46.
Kotake
Y
,
Nakagawa
T
,
Kitagawa
K
,
Suzuki
S
,
Liu
N
,
Kitagawa
M
, et al
Long non-coding RNA ANRIL is required for the PRC2 recruitment to and silencing of p15(INK4B) tumor suppressor gene
.
Oncogene
2011
;
30
:
1956
62
.
47.
Xu
Z
,
Taylor
JA
. 
SNPinfo: integrating GWAS and candidate gene information into functional SNP selection for genetic association studies
.
Nucleic Acids Res
2009
;
37
(
Web Server issue
):
W600
5
.