Immune gene patterns and characterization of the tumor immune microenvironment associated with cancer immunotherapy efficacy

Lili Lin; Wenda Zhang; Yongjian Chen; Wei Ren; Jianli Zhao; Wenhao Ouyang; Zifan He; Weifeng Su; Herui Yao; Yunfang Yu

doi:10.1016/j.heliyon.2023.e14450

Immune gene patterns and characterization of the tumor immune microenvironment associated with cancer immunotherapy efficacy

Heliyon. 2023 Mar 12;9(3):e14450. doi: 10.1016/j.heliyon.2023.e14450. eCollection 2023 Mar.

Authors

Lili Lin¹, Wenda Zhang², Yongjian Chen³, Wei Ren¹, Jianli Zhao¹, Wenhao Ouyang¹, Zifan He¹, Weifeng Su⁴, Herui Yao¹, Yunfang Yu^{1

4

5}

Affiliations

¹ Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Medical Research Center, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China.
² Department of Oncology, Zhujiang Hospital of Southern Medical University, Guangzhou, China.
³ Department of Medical Oncology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
⁴ Division of Science and Technology, Beijing Normal University-Hong Kong Baptist University United International College, Hong Kong Baptist University, Zhuhai, China.
⁵ Faculty of Medicine, Macau University of Science and Technology, Taipa, Macao, PR China.

Abstract

Although immunotherapy has revolutionized cancer management, most patients do not derive benefits from it. Aiming to explore an appropriate strategy for immunotherapy efficacy prediction, we collected 6251 patients' transcriptome data from multicohort population and analyzed the data using a machine learning algorithm. In this study, we found that patients from three immune gene clusters had different overall survival when treated with immunotherapy (P < 0.001), and that these clusters had differential states of hypoxia scores and metabolism functions. The immune gene score showed good immunotherapy efficacy prediction (AUC was 0.737 at 20 months), which was well validated. The immune gene score, tumor mutation burden, and long non-coding RNA score were further combined to build a tumor immune microenvironment signature, which correlated more strongly with overall survival (AUC, 0.814 at 20 months) than when using a single variable. Thus, we recommend using the characterization of the tumor immune microenvironment associated with immunotherapy efficacy via a multi-omics analysis of cancer.

Keywords: AUC, Area under the curve; CIs, Confidence intervals; CTL, Cytotoxic T-lymphocyte infiltration; Cancer; GEO, Gene Expression Omnibus; GO, Gene Ontology; GSEA, Gene set enrichment analysis; GSVA, Gene set variation analysis; HLAs, Human leukocyte antigens; HRs, Hazard ratios; Immunotherapy; KEGG, Kyoto Encyclopedia of Genes and Genomes; LASSO, Penalized logistic least absolute shrinkage and selector operation; Machine learning; NSCLC, Non-small cell lung cancer; OS, Overall survival; PCA, Principal componentanalysis; PD-L1, Programmed death ligand-1; PFS, Profession-free survival; RNA-seq, Transcriptome RNA sequencing; ROC, receiver operating characteristic curves; TCGA, The Cancer Genome Atlas; TMB, Tumor mutation burden; TME, Tumor immunemicroenvironment; Tumor immune microenvironment; WGCNA, Weighted gene co-expression network analysis; lncRNA, Long non-coding RNA.