Evolutionary genetic algorithm identifies IL2RB as a potential predictive biomarker for immune-checkpoint therapy in colorectal cancer.
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Date
2021-04-09ICR Author
Author
Alderdice, M
Craig, SG
Humphries, MP
Gilmore, A
Johnston, N
Bingham, V
Coyle, V
Senevirathne, S
Longley, DB
Loughrey, MB
McQuaid, S
James, JA
Salto-Tellez, M
Lawler, M
McArt, DG
Type
Journal Article
Metadata
Show full item recordAbstract
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.
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Research team
Integrated Pathology
Integrated Pathology
Language
eng
Date accepted
2021-03-26
Citation
NAR genomics and bioinformatics, 2021, 3 (2), pp. lqab016 - ?
Publisher
OXFORD UNIV PRESS