433. UNRAVELLING THE MOLECULAR MECHANISMS BEHIND TUMOUR DIFFERENTIATION IN ESOPHAGEAL ADENOCARCINOMA

Abstract Background Esophageal adenocarcinoma tumors are divided into three grades based on the tumour’s histological differentiation: well, moderate and poor. Poorly differentiated tumours have a worse survival rate than moderate and well tumours. Understanding the molecular programs of this differentiation may lead to the identification of novel therapeutic interventions specific to tumour differentiation. We have utilized laser-capture microdissection to enrich tumour cells followed by gene expression profiling (RNA-seq) to identify gene expression programs and whole genome sequencing for differentiating specific mutations and copy number changes. Collectively, these results will enable us to unravel the molecular drivers of tumour differentiation. Methods Laser capture microdissection was applied to N=127 RNA-seq samples from N=74 patients and N=103 from N=81 patients from a mix of primary tumour biopsies, resections, and metastatic biopsies. Most samples have a matching RNA-seq and WGS sample. We used a standard pipeline to analyze the WGS data and produce somatic mutation, structural variant, and copy number calls. The gene expression data was segregated into two sets a test set consisting of N=74 samples and a test set of N=53 samples. Non-negative matrix factorization was used to identify eleven gene expression programs. Results Our testing RNA-seq cohort consisted of N=74 samples from N=74 patients with N=4 G1, N=26 G2, N=35 G3, and N=9 missing differentiation data. Our initial goal was to unravel the gene expression programs that correlate with tumour differentiation. Our non-negative matrix factorization analysis yielded 11 gene signatures, N=3 programs enriched in glandular gene expression, N=3 enriched in EMT pathways, N=2 with fibroblasts, and N=3 associated with immune / inflammation genes (not shown) (Figure 1). Moreover, the glandular signatures were associated with G1/G2 and the EMT and fibroblast signatures with G3. Moreover, the glandular 2 signature was associated with HER2 amplifications. Conclusion In this work we have begun to unravel the gene expression and genomic changes associated with tumour differentiation. We have found signatures enriched for both G1/G2 and G3 tumours and from these signatures we have observed gene expression heterogeneity within the different tumour differentiation categories. Moreover, the G3 tumours are enriched in fibroblasts despite our laser-capture microdissection. We are currently working on a classification model to predict tumor differentiation from these gene expression programs and are looking to further integrate our whole genome data to find additional genomic drivers.