Despite being older, with higher American Society of Anesthesiologists classification, AIMS65, and Rockall scores, patients who have UGIB that requires endoscopic therapy and take anti-thrombotic drugs have lower mortality due to GI bleeding and shorter hospital stays, with similar rates of rebleeding, surgery, and transfusions, compared with those not taking anti-thrombotic drugs.

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Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the Western world. It is becoming increasingly clear that CRC is a diverse disease, as exemplified by the identification of subgroups of CRC tumours that are driven by distinct biology. Recently, a number of studies have begun to define panels of diagnostically relevant markers to align patients into individual subgroups in an attempt to give information on prognosis and treatment response. We examined the immunohistochemical expression profile of 18 markers, each representing a putative role in cancer development, in 493 primary colorectal carcinomas using tissue microarrays. Through unsupervised clustering in stage II cancers, we identified two cluster groups that are broadly defined by inflammatory or immune-related factors (CD3, CD8, COX-2 and FOXP3) and stem-like factors (CD44, LGR5, SOX2, OCT4). The expression of the stem-like group markers was associated with a significantly worse prognosis compared to cases with lower expression. In addition, patients classified in the stem-like subgroup displayed a trend towards a benefit from adjuvant treatment. The biologically relevant and poor prognostic stem-like group could also be identified in early stage I cancers, suggesting a potential opportunity for the identification of aggressive tumors at a very early stage of the disease.

Background: The tumor microenvironment is a key feature to understand cancer biology and may be used clinically. Quantification of tissue composition is usually based either on visual pathological review (VPR) or deconvolution of whole genome molecular data. Although the former is a direct measurement it has modest reproducibility while the latter is an indirect measurement of unclear accuracy, expensive and not always available. Here we test digital pathology coupled with machine learning as a new tool to assess tissue composition. Methods: As part of the Stratification in COloRecTal cancer (S:CORT) programme, a set of over 500 colorectal cancer (CRC) archival paraffin blocks from resections and biopsies were sequentially sectioned for Hematoxylin and Eosin staining (H&E), RNA extraction, a second H&E and DNA extraction. RNA expression microarrays, targeted DNA sequencing and DNA methylation arrays were applied. Tissue composition from the H&Es was obtained by VPR of expert pathologists and by a deep neural net (DNN) algorithm after supervised training on >1,500 tissue areas from S:CORT, TCGA, TEM and CORGI CRC cohorts. Tumor purity estimates were obtained from RNA and methylation arrays. Results: DNN estimates including area and cell counts were obtained for tumor, desmoplastic stroma, inflamed stroma, mucin/hypocellular stroma, muscle, necrosis and white space. An average of 6.8x105 total cells (range: 1.2x104-2.8x106) and 1.2x105 (range: 7.2x104-1.8x106) were classified for resections and biopsies respectively. Analyses performed twice on the same H&Es obtained matching results (r=1.0). Comparison of the paired first and second H&E showed very high correlations (r~0.9) and total cell counts correlated with DNA and RNA extraction yields (r~0.6). Tumor purity estimates by VPR mildly correlated with DNN (r~0.5) but they were underestimated and very variable. As a result, copy number adjusted by VPR purity tended to be overestimated compared to adjustment with DNN estimates. The improved performance of DNN is reflected in an accurate capture of non-linear association between area and cell counts in invasive cancer. In contrast, tumor purity estimates derived from RNA or DNA methylation arrays showed better correlations compared with DNN (r~0.6) but both overestimated purity in cases with low cell counts by up to a three-fold difference. Conclusions: Tissue composition analysis with DNN allows analytical robustness, automatization and standardization and provides very high reproducibility at single cell resolution. DNN-based estimation of tumor purity is more accurate than VPR or extrapolation from molecular data derived from genome-wide omic platforms which tend to under and overestimate tumor purity respectively. DNN could be used to better plan and asses downstream molecular analyses and to investigate tissue-based metrics as potential clinical biomarkers in clinical trials. Citation Format: Enric Domingo, Aikaterini Chatzipli, Susan Richman, Andrew Blake, Claire Hardy, Celina Whalley, Keara Redmon, Ian Tomlinson, Philip Dunne, Steven Walker, Andrew Beggs, Ultan McDermott, Graeme I. Murray, Leslie M. Samuel, Matthew Seymour, Philip Quirke, Tim Maughan, Viktor H. Koelzer. Assessment of tissue composition with digital pathology in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4446.

transplantation. Murine models of acute liver injury are characterised by changes in the hepatic macrophage compartment, with an initial accumulation of pro-inflammatory Ly6C hi monocytes and loss of Kupffer cells, followed by the dominance of recruited pro-reparative Ly6C lo macrophages. Current experimental immunomodulatory therapeutics like MC21 and Cenicriviroc inhibit Ly6C hi monocyte recruitment. In non-hepatic sterile injury models IL-4 is known to promote pro-reparative macrophage functions and IL-4 administered prior to injury with carbon tetrachloride (CCl 4 ) has been shown to be hepatoprotective by directly promoting hepatocyte proliferation. We therefore aimed to assess the potential pro-reparative and immunomodulatory effects of therapeutic IL-4 administered following acute liver injury with CCl 4 . Methods Male C57Bl/6 mice were given CCl 4 intraperitoneally to induce an acute liver injury. IL-4 was administered in the form of an immune complex subcutaneously. To investigate the role of IL-4Ra signalling in bone marrow derived cells, whole-body and tissue-protected chimeras were generated with wild type or IL-4Ra -/donor bone marrow. Results were analysed using immunohistochemistry of tissue sections, serum biochemistry and flow cytometric analysis of leukocytes. Results Therapeutic administration of IL-4 following CCl 4 reduced markers of hepatic injury (ALT and necrotic area) and enhanced hepatic regeneration as measured by hepatocyte proliferation. This was paralleled by profound alterations to the monocyte/macrophage pool, with increases in the number of pro-reparative Ly6C lo macrophages but also a significant reduction in the number of pro-inflammatory hepatic Ly6C hi monocytes. Using chimeras, we have shown that Ly6C lo macrophage accumulation required cell-intrinsic, IL-4Ra-dependent proliferation and the loss of hepatic Ly6C hi monocytes was dependent on cell-intrinsic, IL-4Ra signalling. Importantly, analysis of kidney, spleen and blood revealed that the loss of Ly6C hi monocytes secondary to IL-4 treatment was not limited to the liver and occurred systemically. In vitro and ex vivo assays showed that the reduction in Ly6C hi monocytes in response to administration of IL-4 was due to IL-4Ra-dependent apoptosis of circulating monocytes rather than decreased output from the bone marrow. Conclusion This novel role of IL-4 offers potential therapeutic benefits over monocyte inhibitors by not only reducing proinflammatory Ly6C hi monocyte numbers through apoptosis but also increasing the number of pro-reparative Ly6C lo macrophages.