Evolutionary genetic algorithm identifies IL2RB as a potential predictive biomarker for immune-checkpoint therapy in colorectal cancer

Matthew Alderdice, Stephanie G Craig, Matthew P Humphries, Alan Gilmore, Nicole Johnston, Victoria Bingham, Vicky Coyle, Seedevi Senevirathne, Daniel B Longley, Maurice B Loughrey, Stephen McQuaid, Jacqueline A James, Manuel Salto-Tellez, Mark Lawler, Darragh G McArt

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Identifying robust predictive biomarkers to stratify colorectal cancer (CRC) patients based on their response to immune-checkpoint therapy is an area of unmet clinical need. Our evolutionary algorithm Atlas Correlation Explorer (ACE) represents a novel approach for mining The Cancer Genome Atlas (TCGA) data for clinically relevant associations. We deployed ACE to identify candidate predictive biomarkers of response to immune-checkpoint therapy in CRC. We interrogated the colon adenocarcinoma (COAD) gene expression data across nine immune-checkpoints (PDL1, PDCD1, CTLA4, LAG3, TIM3, TIGIT, ICOS, IDO1 and BTLA). IL2RB was identified as the most common gene associated with immune-checkpoint genes in CRC. Using human/murine single-cell RNA-seq data, we demonstrated that IL2RB was expressed predominantly in a subset of T-cells associated with increased immune-checkpoint expression (P < 0.0001). Confirmatory IL2RB immunohistochemistry (IHC) analysis in a large MSI-H colon cancer tissue microarray (TMA; n = 115) revealed sensitive, specific staining of a subset of lymphocytes and a strong association with FOXP3+ lymphocytes (P < 0.0001). IL2RB mRNA positively correlated with three previously-published gene signatures of response to immune-checkpoint therapy (P < 0.0001). Our evolutionary algorithm has identified IL2RB to be extensively linked to immune-checkpoints in CRC; its expression should be investigated for clinical utility as a potential predictive biomarker for CRC patients receiving immune-checkpoint blockade.

Original languageEnglish
Pages (from-to)lqab016
JournalNAR Genomics and Bioinformatics
Issue number2
Early online date20 Apr 2021
Publication statusPublished - 01 Jun 2021

Bibliographical note

© The Author(s) 2021. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.


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