Genome-wide association study identifies tumor anatomical site-specific risk variants for colorectal cancer survival
Julia Labadie, Sevtap Savas, Tabitha A. Harrison, Barb Banbury, Yu‐Han Huang, Daniel D. Buchanan, Peter T. Campbell, Steven Gallinger, Graham G. Giles, Marc J. Gunter, Michael Hoffmeister, Li Hsu, Mark A. Jenkins, Yi Lin, Shuji Ogino, Amanda I. Phipps, Martha L. Slattery, Robert S. Steinfelder, Wei Sun, Bethany Van Guelpen, Xinwei Hua, Jane C. Figuieredo, Rish K. Pai, Rami Nassir, Lihong Qi, Andrew T. Chan, Ulrike Peters, Polly A. Newcomb
Abstract
Abstract Identification of new genetic markers may improve the prediction of colorectal cancer prognosis. Our objective was to examine genome-wide associations of germline genetic variants with disease-specific survival in an analysis of 16,964 cases of colorectal cancer. We analyzed genotype and colorectal cancer-specific survival data from a consortium of 15 studies. Approximately 7.5 million SNPs were examined under the log-additive model using Cox proportional hazards models, adjusting for clinical factors and principal components. Additionally, we ran secondary analyses stratifying by tumor site and disease stage. We used a genome-wide p-value threshold of 5 × 10 –8 to assess statistical significance. No variants were statistically significantly associated with disease-specific survival in the full case analysis or in the stage-stratified analyses. Three SNPs were statistically significantly associated with disease-specific survival for cases with tumors located in the distal colon (rs698022, HR = 1.48, CI 1.30–1.69, p = 8.47 × 10 –9 ) and the proximal colon (rs189655236, HR = 2.14, 95% CI 1.65–2.77, p = 9.19 × 10 –9 and rs144717887, HR = 2.01, 95% CI 1.57–2.58, p = 3.14 × 10 –8 ), whereas no associations were detected for rectal tumors. Findings from this large genome-wide association study highlight the potential for anatomical-site-stratified genome-wide studies to identify germline genetic risk variants associated with colorectal cancer-specific survival. Larger sample sizes and further replication efforts are needed to more fully interpret these findings.