P-OGC08 Translational Insights from the Dual ErbB Inhibition in Oesophago-gastric Cancer (DEBIOC) Clinical Trial - A Bioinformatic Analysis

Abstract

Abstract Background Oesophageal Adenocarcinoma (OAC) incidence in the Western-world has increased markedly over 30 years. 5-year survival rates for patients remains below 20% with dismal response to neo-adjuvant or perioperative chemotherapy for operable tumours. The Dual ErbB Inhibition in Oesophago-gastric Cancer (DEBIOC) clinical trial assessed efficacy of combined oxaliplatin and capecitabine (Xelox) with dual ErbB inhibitor AZD8931 in providing additional benefit to operable patients compared to Xelox alone. We utilised a bioinformatic approach combing Almac Clara-T Transcriptional Discovery software with unsupervised machine learning methods to unveil translational clinical potential and biological insights from DEBIOC patient biopsy and resection specimens. Methods Using microarrays of DEBIOC patient specimens with documented clinical observations, we combined unsupervised machine learning techniques with state-of-the-art Almac Clara-T software to assess transcriptional changes between treatment types regarding the 10 hallmarks of cancer, characterised by representative gene-expression signatures and scores. These methods were employed to identify possible mechanisms of treatment resistance, evaluate changes in the tumour-microenvironment and determine clinically significant molecular subgroups in OAC. Differential expression and pathway analytics were used to describe signalling dissimilarities between clusters from unsupervised analysis and phenotypes respective to hallmarks of cancer, with alignment of sensitivities to single-gene drug targets for subgroups of interest. Results Unsupervised clustering analysis of biopsy specimens, resulted in the identification of two robust subgroups pre-treatment in OAC, determined to be significantly associated with the prediction of Mandard Score (Tumour Regression Grade 1-5) post-treatment (fishers exact p < 0.05). Differential expression analysis revealed distinguishing biology between subtypes and noted increased ErbB signalling in non-responding patients in addition to increased PI3K signalling, highlighting a potential mechanism of resistance to dual ErbB inhibition (nominal p-value <0.05, FDR p-value <0.2). Semi-supervised clustering revealed hallmark-specific-phenotypes associated with clinical observations including lymph node involvement, EGFR FISH classification, vascular invasion and progression events at BH adjusted p-values <0.05. Conclusions Our analysis has revealed translational insights into possible mechanisms of drug resistance as well as cancer hallmark-specific phenotypes significantly associated with clinico-pathological factors during the DEBIOC clinical trial. Continued analysis into resulting phenotypes and clusters combined with the alignment of single gene drug target sensitivities is anticipated to reveal novel molecular pathways driving phenotypic differences in an effort to further inform biological understanding and improve treatment response and survival outcomes in OAC patients.