A Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine. / Approximately 25% of patients with locoregional esophageal adenocarcinoma (EC) are resistant (marked by minimal tumor regression; TRG 3) to preoperative chemoradiation, including 5FU‐based and CROSS regimens. Previously, an immunohistochemistry (IHC) test that accurately identifies patients as responders (TRG 0‐2) or non‐responders (TRG 3) to neoadjuvant CTRT was developed and validated. The current study was designed to identify gene expression profile (GEP) signatures able to predict response to preoperative treatment. Methods: Formalin‐fixed, paraffin‐embedded (FFPE) tumor tissue from 24 diagnostic biopsies (14 responders, 10 non‐responders) was collected. RNA was isolated, and RT‐PCR performed to assess the expression of 96 candidate genes chosen from in silicoanalysis. Genetic signatures incorporating genes with significant expression differences in pathologically determined responders versus non‐responders were identified, and linear and non‐linear predictive modeling methods were used to assess the accuracy of the signatures for predicting treatment response. Cross validation was performed to attain corrected accuracy values. Ten‐, 18‐, and 24‐gene signatures were identified with significantly different gene expression levels in responders compared to non‐responders (p < 0.05). Functional groups represented by the signatures included DNA damage repair, extracellular matrix remodeling, and 5FU metabolism. Partial Least Squares (PLS) prediction of treatment response was compared to pathologic TRG determined by blinded pathologic reading, and resulted in an area under the curve (AUC) of 0.99 and overall accuracy of 100% for the 24‐gene signature. Corrected AUC of 0.99 and accuracy of 95% resulted from five‐fold cross validation with 20 iterations. Heatmap analysis of the 24‐gene signature separated the EC cases into two distinct clusters, the first with 93% responders and the second with 90% non‐responders. The current study identifies novel gene signatures able to accurately predict EC patient response to preoperative treatment. The GEP may allow non‐responders to avoid unnecessary toxicities associated with chemoradiation therapy.