A novel gene signature for predicting response to chemoradiotherapy in esophageal adenocarcinoma.

Abstract

445 Background: While neoadjuvant chemoradiotherapy (CRT) has emerged as an important treatment modality in patients with locally advanced esophageal adenocarcinoma (EAC), ~60%-70% of patients do not respond to such treatments; but are exposed to their toxicity nonetheless. This highlights the clinical need for the development of biomarkers that can robustly predict response to CRT and spare others from the toxicity and expense associated with these treatments. Herein, we systematically identified a biomarker signature that predicts response to CRT in EAC patients. Methods: Using a clinical-trial driven cohort of 25 EAC patients treated with 5- fluorouracil plus carboplatin and concurrent radiation therapy, we performed whole-exome sequencing (WES) in paired biopsy specimens obtained at baseline and 3-6 weeks post-treatment. In addition, we also analyzed the predictive potential of a panel of immune-related genes (TIM3, LAG3, IDO1 and CXCL9) in these matched tissues. Results: In our cohort, based upon RECIST criteria, 14 EAC patients were categorized as non-responders, while 11 were deemed as responders to CRT. Among responders, the most frequently mutated genes were NOTCH1, NOTCH2, NOTCH3, and MLL2; and the overall tumor mutation burden (TMB) was significantly reduced for these genes in post-treatment specimens (P<0.001). In contrast, in non-responders, NFE2L2, KEAP1, FAT1, FAT2, FAT3 and PIK3CA, harbored frequent mutations. Similarly, all four immune-related genes were significantly up-regulated in post-treatment specimens (p<0.05-0.001). Interestingly, the progression-free survival was significantly greater in patients with lower TMB (64.1%, p=0.04) and increased immunogenic scoring (62.7%, p=0.01). We finally constructed a risk-stratification model that comprised of mutational scores from 10 most frequently mutated genes, together with 4 immune-related genes, which achieved an AUC of 0.83 in predicting response to CRT in EAC patients. Conclusions: Using a systematic biomarker discovery approach, we have developed a novel biomarker signature that robustly predicts response to CRT in EAC patients and has a significant potential for personalized management of EAC patients.