Abstract 45: Examining genetic markers of obesity and metabolism against prostate cancer risk using Metabochip.
Authors: Edwards Todd , Giri Ayush , Motley Saundra , Duong Wynne , Fowke Jay H. 
Published: August 2013

Background and Purpose: Examining genetic markers of metabolism may inform the link between obesity and prostate cancer (PC). Our purpose is three-fold: 1) identify novel genetic metabolic loci associated with PC; 2) examine the mediating effect of metabolic loci on BMI or WHR with PC, and 3) better define obesity subgroups associated with PC.

Methods: Metabochip provides an efficient platform to investigate over 200,000 single nucleotide polymorphisms (SNPs) from past studies of 23 metabolic traits, including body mass index (BMI) and waist-hip ratio (WHR). Using the Metabochip, we genotyped 899 PC cases and 941 biopsy-negative controls. There were 176,520 SNPs in 871 cases and 906 controls passing QC, including 427 high-grade cases with Gleason≥7. Logistic regression controlling for age was used to examine the association between SNPs with PC and high-grade PC. With an interest in exploring potentially new mechanisms, we did not correct for multiple testing. Instead, SNPs with p-values &lt;10-4 were subjected to pathway identification (KEGG) analysis and literature review for biologic plausibility. A risk-difference approach was used to evaluate genetic mediation of the association between BMI or WHR with PC. Finally, genetic susceptibility scores for obesity risk were created by summing the number of alleles associated with greater BMI or WHR within each participant.

Results: We identified 12 loci associated with PC. Noteworthy SNPs (Chromosome {CHR}: Position) included CHR12:111540303 near the region including the gene PTPN11 (Odds Ratio {OR]: 1.34, p-value 1.5*10-5), which has been previously associated with hematological parameters, esophageal carcinoma, blood pressure, and metabolic traits; CHR12:109231826 on ATP2A2 and near ANAPC7 (OR: 0.40, p-value 2.3*10-5), which has been associated with protein levels of alpha-1-globulin; and CHR16:52732814 near the well-known obesity gene FTO (OR: 2.54, p-value 4.5*10-5). For high grade PC, SNPs near PTPN11, TECRL which has been associated with Kawasaki disease, and FTO had p-values with a magnitude of 10-5. Additional loci associated with high-grade PC involved CABYR (p-value= 8.24*10-6), CADM2 which is associated with BMI, ZNF57, SPI1 near several Type 2 diabetes loci, and DCC which has been associated with gallbladder cancer (all p-values &lt; 10-4). Exploration of aggressive vs. low-grade PC found loci near ROBO1&2, ZNF536 which is near the bladder cancer gene CCNE1, and ASB7 (p-value &lt; 10-4) in a region previously linked to glioma and T2D, suggesting markers of an aggressive phenotype. WHR and BMI were not significantly associated with PC or high-grade PC vs. controls. However, both WHR ((OR: 1.20 (95% confidence interval {CI}: 0.99, 1.46) per 0.1 unit increase) and BMI (OR: 1.15 (95%CI: 0.99, 1.33), per 5 unit increase) were marginally associated with high-grade vs. low-grade PC. Evaluation of WHR genetic risk scores was stronger, with each additional WHR-increasing allele associated with a 1.06 (95% CI: 1.01, 1.12) increased odds of high-grade vs. low-grade PC. In contrast, the BMI allele burden was not significantly associated with PC or high-grade PC.

Conclusions: Using the Metabochip platform, we identified several novel loci associated with PC and high-grade PC independent of BMI and WHR, including an association with FTO, suggesting sources of metabolic dysregulation aside from body adiposity are involved. Additionally, genetic markers were useful in refining obesity measures, with genetic risk of WHR representing centralized adiposity significantly associated with PC progression to high-grade disease. With validation, our results may suggest new pathways to prevent or treat PC.

Citation Format: Todd Edwards, Ayush Giri, Saundra Motley, Wynne Duong, Jay H. Fowke. Examining genetic markers of obesity and metabolism against prostate cancer risk using Metabochip. [abstract]. In: Proceedings of the AACR Special Conference on Post-GWAS Horizons in Molecular Epidemiology: Digging Deeper into the Environment; 2012 Nov 11-14; Hollywood, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2012;21(11 Suppl):Abstract nr 45.