Abstract Identifying robust predictive biomarkers to enable stratification of colorectal cancer (CRC) patients, based on their response to immune checkpoint therapy, is an area of unmet clinical need. Genetic algorithms represent an exciting branch of artificial intelligence which can be used to extract meaningful associations from ‘big data’ now emerging more frequently in oncological research. We have employed Atlas Correlation Explorer (ACE), a user-friendly workbench that utilises a genetic algorithm to mine data deposited in The Cancer Genome Atlas (TCGA). Our aim was to establish common intersections between gene expression analyses in ACE using nine well established immune checkpoint markers (CD274, PDCD1, CTLA4, LAG3, TIM3, TIGIT, ICOS, IDO1 and BTLA). We observed IL2RB to be the common gene associated with immune checkpoints in both microarray and RNA sequencing data from the TCGA (7/9 gene lists). Assessment of IL2RB indicates that it is highly expressed on CD56+ natural killer cells and is associated with an increased infiltration of cytotoxic lymphocytes and a decreased infiltration of fibroblasts. It is also significantly enriched in the immune consensus molecular subtype group CMS1. We next demonstrated that patients with high IL2RB gene expression have better relapse free survival in the TCGA CRC cohort (n = 322, log-rank p = 0.011) and an all stage CRC validation cohort GSE39582 (n = 519, log-rank p = 0.006). It is also an independent prognostic factor by multivariate analysis (p = 0.01). We next observed strong correlations between IL2RB gene expression in CRC and previously published predictive gene signatures for anti-PD1 therapies in other solid tumours (Pearson correlation, R = 0.88). Finally, we optimised assessment of IL2RB immunohistochemistry in a large CRC cohort (n=661) using a digital pathology approach with the open-source QuPath software. To conclude, we have validated IL2RB as prognostic biomarker and have provided evidence to demonstrate that IL2RB expression could be used for CRC patient stratification in future immunotherapy based clinical trials. Citation Format: Matthew Alderdice, Stephanie Craig, Matt Humphries, Alan Gilmore, Victoria Bingham, Nicole Johnston, Stephen McQuaid, Manuel Salto-Tellez, Mark Lawler, Darragh G. McArt. Artificial intelligence approach identifies IL2RB as a common prognostic and potential predictive biomarker associated with immune checkpoints 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 2787.