PPARγ targeted oral cancer treatment and additional utility of genomics analytic techniques. (29th November 2016) - Handley, Nathan; Eide, Jacob; Taylor, Randall; Wuertz, Beverly; Gaffney, Patrick; Ondrey, Frank
Abstract : Objective: Peroxisome proliferator‐activated receptor γ (PPARγ) agonists have been shown to have anti‐proliferative, anti‐angiogenic, and proapoptotic effects, leading to interest in their use as cancer therapeutics. Pioglitazone, a U.S. Food and Drug Administration‐approved type II diabetes medication and PPARγ agonist, may have a role in adjuvant head‐and‐neck squamous cell carcinoma treatment or prevention. Therefore, the purpose of this study was: 1) to treat oral cavity cancer cells with the PPARγ activator, pioglitazone, to analyze gene expression changes; and 2) to compare those changes with our preexisting genomic data for development of hypothesis‐driven additional basic and clinical studies. Study Design: Prospective in vitro. Methods: We utilized microarray technology, as well as OCPlus (Bioconductor open source software) and Ingenuity Pathway Analysis (Qiagen, Redwood City, CA), to analyze differential gene expression in tumor and pioglitazone‐treated tumor cells on a genome‐wide level to demonstrate the feasibility of such an approach and determine appropriate sample size for future investigations. Results: We found that approximately 35 samples are required to adequately power future studies. We next discovered that pioglitazone significantly affects Inducible T‐Cell Costimulator (iCOS)‐Ligand for the T‐cell‐specific cell surface receptor ICOS (iCOSL) and type II diabetes mellitus pathways as a putative anti‐cancer mechanism. Conclusion: Genome‐wide analysis is possible for the exploration of differential pathway modulation and rapid hypothesis generation. Both inflammation and type II diabetes pathways were significantly altered and therefore might provide unique hypothesis‐driven pharmacodynamic parameters for future in vitro or in vivo studies utilizing thiazolidinediones. These techniques could be applied to microarray or other high throughput data from a variety of hypothesis‐generating research scenarios in otolaryngology (e.g., middle ear proteomics, sinus microbiome studies). Level of Evidence: NA. Laryngoscope, 127:E124–E131, 2017