Oesophageal adenocarcinoma (OAC) is a poor prognosis cancer and the molecular features underpinning response to treatment remain unclear. We investigated whole genome, transcriptomic and methylation data from over 100 OAC patients mostly from a phase II clinical trial (ANZCTR-ACTRN12609000665235). We identified genomic features associated with poorer overall survival, such as the APOBEC mutational and RS3-like rearrangement signatures. Transcriptomic analysis categorised patients into four immune clusters which correlated with survival and were verified by immunohistochemistry. The immune hot cluster was associated with better survival, enriched with lymphocytes, myeloid-derived cells, and an immune signature including CCL5, CD8A, and NKG7. The immune clusters potentially highlight patients who may respond to immunotherapy and thus may guide future clinical trials. Using this data we have designed a multi-omic machine learning workflow to integrate genomic, transcriptomics and histology features of the TME to predict progression-free survival (PFS) and overall survival (OS) of oesophageal cancer patients. This presentation will provide an update of our latest findings.