Abstract

e15142 Background: Ulcerative colitis (UC), is a known risk-factor for colorectal cancer (CRC). 10% of UC-patients develop UC-specific CRC yet there are currently no biomarkers for early detection or understanding of drivers of disease. Unresolved inflammation is key in UC-CRC development, yet the aetiology is unclear. It is unknown if UC-CRC falls into a specific consensus molecular subgroup in the CMS classification (Guinney et al 2015). This bioinformatics approach aimed to identify patients at-risk of developing UC-CRC, whilst informing biological and clinical understanding of the disease. Methods: We used publicly available UC data and in-silico, cross platform, class label transfer (GECA) to determine mRNA similarity between UC/UC-CRC and sporadic CRC (sCRC) and to identify a UC-predominant CMS. Pathogenic mutations were identified for each CMS as well as gene set enrichment and survival analysis to determine UC-CRC behaviour comparative to sCRC. Transcriptional relationships were assessed via unsupervised and semi-supervised clustering by immune-signatures and scores to determine molecular patterns between phenotypes. We derived a gene list from UC data informed by clustering to stratify patients based on risk to UC-CRC progression. Results: UC-CRC featured predominantly in the NA group, with 3 attributed pathogenic mutations, an oxidative stress environment and neuroendocrine dysregulation. The subgroup had UC-like clinical features; younger age, distal tumour location and TP53 mutations along with the highest rate of cancer recurrence (~20%) and a unique cellular composition in CMS being highly immune and stromal. Importantly this immune signal was distinct from the interferon type signalling observed in CMS1 and was not associated with PD-L1 activation. Clustering revealed a loss of IFN-type gene expression in colon tissue as UC progressed to UC-CRC. Conclusions: The NA group was thought to be unclassifiable samples between CMS however, analysis shows UC-CRC enrichment with defined biological characteristics. We have identified novel biology that may inform the risk of developing UC-CRC and guide potential preventative and therapeutic approaches.

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