Evolutionary genetic algorithm identifies <i>IL2RB</i> as a potential predictive biomarker for immune-checkpoint therapy in colorectal cancer.
MetadataShow full item record
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 (<i>PDL1, PDCD1, CTLA4, LAG3, TIM3, TIGIT, ICOS, IDO1</i> and <i>BTLA</i>). <i>IL2RB</i> 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 <i>IL2RB</i> was expressed predominantly in a subset of T-cells associated with increased immune-checkpoint expression (<i>P</i> < 0.0001). Confirmatory IL2RB immunohistochemistry (IHC) analysis in a large MSI-H colon cancer tissue microarray (TMA; <i>n</i> = 115) revealed sensitive, specific staining of a subset of lymphocytes and a strong association with FOXP3+ lymphocytes (<i>P</i> < 0.0001). <i>IL2RB</i> mRNA positively correlated with three previously-published gene signatures of response to immune-checkpoint therapy (<i>P</i> < 0.0001). Our evolutionary algorithm has identified <i>IL2RB</i> 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.
Version of record
NAR genomics and bioinformatics, 2021, 3 (2), pp. lqab016 - ?