3019 - Differential Gene Expression in Patients with or without Downstaging Following Neoadjuvant CRT in Esophageal Cancer
Presenter(s)
L. A. Acevedo1, O. Franklin2, R. J. Afzal3, T. Matoska1, A. Memon4, C. Bulacan1, W. Lee5, A. Mathison6, S. Liu6, V. Jin6, A. Shreenivas7, H. A. Himburg1, M. Gasparri8, P. Linsky8, S. Madhavan9, M. Battle2, and L. Puckett1; 1Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 2Department of Molecular Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, 3University of Pikeville Kentucky College of Osteopathic Medicine, Pikeville, KY, 4Department of Otolaryngology and Communication Sciences, Medical College of Wisconsin, Milwaukee, WI, 5Department of Pathology, Medical College of Wisconsin, Milwaukee, WI, 6Mellowes Center for Genomic Science and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, 7Medical Oncology & Therapeutics Research, City of Hope Comprehensive Cancer Center, Duarte, CA, 8Department of Surgery, Division of Cardiothoracic Surgery, Medical College of Wisconsin, Milwaukee, WI, 9Department of Medicine, Division of Gastroenterology and Hepatology, Medical College of Wisconsin, Milwaukee, WI
Purpose/Objective(s):
Esophageal cancer is the sixth leading cause of cancer-related deaths worldwide, with a poor five-year survival rate of about 20%. The standard treatment often includes chemotherapy and radiation (CRT), followed by esophagectomy. Patients with pathologic complete response (pCR) at the time of surgery (following CRT) have better clinical outcomes; however, pCR rate remains low at <30%. The fundamental molecular mechanisms driving significant downstaging (e.g., T4N2 to ypT1N0) at surgery remain unknown. We hypothesized that applying a systematic molecular analytic approach to compare tumors from patients with and without significant downstaging would uncover novel pathways to explore as future candidates for diagnostics and therapies to improve patient outcomes. Therefore, we used RNA sequencing in an exploratory study to comprehensively delineate the molecular landscape of esophageal tumors with and without downstaging.Materials/Methods:
Patient samples were identified from a retrospective, IRB-approved esophageal database. H&E-stained slides of pre- and post-treatment samples were reviewed to determine the tumor extent and treatment response. Representative samples with significantly downstaged (n=15) vs. minimally/non-downstaged (n=8) were selected. Downstaged was defined as a decrease either in T or N stages without increases of any stage components (e.g., no N0 to ypN1); the majority (14/15) had multiple levels of downstaging (e.g., T4 to ypT2). Slides of the specimens were macro-dissected with RNA extracted and evaluated for quality. The genomic sciences facility at our academic institution prepared libraries, sequenced samples, and performed transcriptomic analysis. Raw RNA-seq reads were mapped to the human reference genome (build hg38), and differential gene expression analysis (=2-fold, p = 0.05) was completed using the DESeq2 package (Partek Flow). Ingenuity Pathway Analysis software was used to identify pathways of interest.Results:
Gene expression analysis between downstaged and non-downstaged esophageal cancer tissues identified 2,540 differentially expressed genes (DEGs), with 1145 downregulated and 1385 upregulated in downstaged tumors. Pathway analysis found alterations in transcription factors (FOXA2, FOXA3, HNF4A, RORA, LHX1), RAS signaling, and pathways regulating cell motility and metastasis.Conclusion:
Our approach identified candidate transcription factors and signaling pathways related to differential responses to esophageal cancer therapy. We uncovered specific candidates that differentiated downstaged tumors from non-downstaged tumors, including those involved in metastatic spread and poor prognosis. Future studies will evaluate the functional role of these candidates and their response to therapy. Limitations of the current pilot include a small sample size. Our long-term goals are to translate our findings into potential biomarkers to drive the development of novel therapeutic interventions.