Abstract 2519: Analysis of gene expression patterns and metabolomics correlated to obesity, diabetes, and outcomes in patients with epithelial ovarian cancer
Authors: Allison M. Montgomery, Angelina I. Londono, Eric R. Craig, Cindy Tawfik, Haller J. Smith, Charles A. Leath, Ashwini A. Katre, Sara J. Cooper, Rebecca C. Arend
Journal: Cancer Research (2017)

Abstract Objective: Diabetes and obesity have been associated with a poor prognosis in ovarian cancer (OVCA); the exact mechanism has yet to be determined. Data suggests that obesity diminishes normal immunological response and better prognoses in non-obese patients may be attributable to an intact immune response. The objective of this study was to analyze the correlation between gene expression pattern/metabolomics and obesity/diabetes in OVCA patients.. Methods: Following IRB approval, UAB patients with suspected OVCA undergoing surgery were consented. Tissue was collected during cytoreduction and 35 samples were analyzed using RNAseq technology and mass spectrometry-based metabolomics. DESeq2 was used for RNAseq analysis to identify gene expression differences between diabetics and non-diabetics stratified by BMI: obese (BMI ≥30) and non-obese (BMI &amp;lt;30). Gene set enrichment analysis was conducted to determine whether there was an over-representation of immune pathways among altered genes. Profiles were normalized in the analysis of the metabolite profiles using ChromaTOF. Results: 76 genes (p-value &amp;lt;0.05) were differentially expressed in the tumor samples from patients with BMI ≥30 to those &amp;lt;30. These genes were highly enriched for immune-related genes, including 34 immunoglobulin genes, and complement activation. The list of identified genes also included 3 HLA genes (HLA-G(down), HLA-H(up) and HLA-DMA(down). Furthermore, when analyzing tumor from diabetics (n=7), there were 18 genes differentially expressed compared to controls. These genes are not statistically enriched for any functional class. Additionally, genes associated with response to platinum-based therapy, differentiating patients with BMI≥30 v. &amp;lt;30 were analyzed. No genes met genome-wide significance; however, there were 14 genes with a genome-wide p-value&amp;lt;0.1. When diabetes status as a covariate was controlled for, this number was reduced to 9. Conclusion: By evaluating the transcriptomic profiles generated through RNAseq analysis, a significant number of differences in RNA expression were identified in comparing obese to non-obese OVCA patients. Due to the small sample size, no genes were identied as being associated with the presence or absence of diabetes. Citation Format: Allison M. Montgomery, Angelina I. Londono, Eric R. Craig, Cindy Tawfik, Haller J. Smith, Charles A. Leath, Ashwini A. Katre, Sara J. Cooper, Rebecca C. Arend. Analysis of gene expression patterns and metabolomics correlated to obesity, diabetes, and outcomes in patients with epithelial ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2519. doi:10.1158/1538-7445.AM2017-2519