The tumor immune microenvironment transcriptomic subtypes of colorectal cancer for prognosis and development of precise immunotherapy

Abstract

Background: Biomarkers based on immune context may guide prognosis prediction. T-cell inactivation, exclusion, or dysfunction could cause unfavorable tumor microenvironments, which affect immunotherapy and prognosis. However, none of the immuno-biomarkers reported to date can differentiate colorectal-cancer (CRC) patients. Thus, we aimed to classify CRC patients according to the levels of T-cell activation, exclusion, and dysfunction in the tumor microenvironment. Methods: RNAseq data of 618 CRC patients from The Cancer Genome Atlas and microarray data of 316 CRC patients from Gene Expression Omnibus were analysed using the Tumor Immune Dysfunction and Exclusion algorithm. Unsupervised clustering was used to classify patients. Results: Based on the expression signatures of myeloid-derived suppressor cells, cancer-associated fibroblasts, M2-like tumor-associated macrophages, cytotoxic T-lymphocytes, and PD-L1, all patients were clustered into four subtypes: cluster 1 had a high level of immune dysfunction, cluster 2 had a low level of immune activation, cluster 3 had intense immune exclusion, and cluster 4 had a high level of immune activation and a moderate level of both dysfunction and exclusion signatures. Compared with cluster 1, the hazard ratios and 95% confidential intervals for overall survival were 0.63 (0.35–1.13) for cluster 2, 0.55 (0.29–1.03) for cluster 3, and 0.30 (0.14–0.64) for cluster 4 in multivariate Cox regression. Similar immune clustering and prognosis patterns were obtained upon validation in the GSE39582 cohort. In subgroup analysis, immune clustering was significantly associated with overall survival among stage I/II patients, microsatellite stable/instability-low patients, and patients not treated with adjuvant therapy. Conclusions: Our findings demonstrated that classifying CRC patients into different immune subtypes serves as a reliable prognosis predictor and may help to refine patient selection for personalized cancer immunotherapy.