Purpose To determine if sex differences in abdominal visceral fat composition, measured by using computed tomography (CT), and tumor glucose metabolism, measured by gene expression, can help predict outcomes in patients with clear cell renal cell carcinoma (RCC). Materials and Methods This retrospective cohort study included 222 patients with clear cell RCC from The Cancer Imaging Atlas. By using CT, body fat was segmented into subcutaneous fat and visceral fat areas (VFAs) and normalized to total fat to obtain the relative VFA (rVFA) and relative subcutaneous fat area. Multivariate Cox proportional hazard regression models were performed to identify effects of rVFA on sex-specific survival. Expression profiles for 39 glycolytic genes in tumors from these patients were obtained from The Cancer Genome Atlas to determine sex differences in metabolism and compared with rVFA. Key mutations in clear cell RCC were analyzed for association with rVFA and tumor glycolytic profiles. Results Women with rVFA greater than 30.9% had an increased risk of death (hazard ratio, 3.66 [95% confidence interval: 1.64, 8.19]) for women vs 1.13 ([95% confidence interval: 0.58, 2.18] for men, P = .028). Glycolytic gene expression stratified both men and women, and the combination of low rVFA and low glycolysis identified 19 women with excellent overall survival (P < .001). SETD2 and BAP1 mutations were uniquely enriched in female tumors with high glycolysis (P = .036 and .001, respectively). No significant differences were identified in tumor mutations between patients with high and low rVFA. Conclusion Sex differences in visceral fat and tumor glucose metabolism may provide a new risk-stratification system for patients with clear cell RCC.©RSNA, 2018 Online supplemental material is available for this article.
Radiology. 2018 Mar 20 [Epub ahead of print]
Gerard K Nguyen, Vincent M Mellnick, Aldrin Kay-Yuen Yim, Amber Salter, Joseph E Ippolito
From the Mallinckrodt Institute of Radiology (G.K.N., V.M.M., J.E.I.), Department of Genetics (A.K.Y.Y., J.E.I.), Computational and Systems Biology Program (A.K.Y.Y.), and Division of Biostatistics (A.S.), Washington University School of Medicine, 660 S Euclid Ave, Campus Box 8131, St Louis, MO 63110.