Typical tumor immune microenvironment status determine prognosis in lung adenocarcinoma

Caixia Ren; Jinyu Li; Yang Zhou; Shuyu Zhang; Qi Wang

doi:10.1016/j.tranon.2022.101367

Typical tumor immune microenvironment status determine prognosis in lung adenocarcinoma

Transl Oncol. 2022 Apr:18:101367. doi: 10.1016/j.tranon.2022.101367. Epub 2022 Feb 14.

Authors

Caixia Ren¹, Jinyu Li², Yang Zhou³, Shuyu Zhang⁴, Qi Wang⁵

Affiliations

¹ Department of Respiratory Medicine, The Second Hospital of Dalian Medical University, Dalian, 116023, China. Electronic address: cxiar0306@163.com.
² Department of Breast Oncology, The Second Hospital of Dalian Medical University, Dalian, Liaoning, 116023, China. Electronic address: lijinyu1012@qq.com.
³ Liaoning Clinical Research Center for Lung Cancer,The Second Hospital of Dalian Medical University, Dalian, 116023, China. Electronic address: zhouyang610@163.com.
⁴ China National Nuclear Corporation 416 Hospital, The Second Affiliated Hospital of Chengdu Medical College, Chengdu 610051, China. Electronic address: zhang.shuyu@hotmail.com.
⁵ Department of Respiratory Medicine, The Second Hospital of Dalian Medical University, Dalian, 116023, China. Electronic address: wqdlmu@163.com.

Abstract

Background: Immune cells, vital components of tumor microenvironment, regulate tumor survival and progression. Lung adenocarcinoma (LUAD), the tumor with the highest mortality rate worldwide, reconstitutes tumor immune microenvironment (TIME) to avoid immune destruction. Data have shown that TIME influences LUAD prognosis and predicts immunotherapeutic efficacy. The related information about the role of TIME's characteristics in LUAD is limited.

Methods: We performed unsupervised consensus clustering via machine-learning techniques to identify TIME clusters among 1906 patients and gathered survival data. The characteristics of TIME clusters of LUAD were visualized by multi-omics analysis, pseudo-time dynamic analysis, and enrichment analysis. TIME score model was constructed by principal component analysis. Comprehensive analysis and validation were conducted to test the prognostic efficacy and immunotherapeutic response of TIME score.

Results: TIME clusters (A, B and C) were constructed and exhibited different immune infiltration states. Multi-omics analyses included significant mutated genes (SMG), copy number variation (CNV) and cancer stemness that were significantly different among the three clusters. TIME cluster A had a lower SMG, lower CNV, and lower stemness but a higher immune infiltration level compared to TIME clusters B and C. TIME score showed that patients in low TIME score group had higher overall survival rates, higher immune infiltration level and high expression of immune checkpoints. In validation cohorts, low TIME score subgroup had better drug sensitivity and favorable immunotherapeutic response.

Conclusion: We constructed a stable model of LUAD immune microenvironment characteristics that may improve the prognostic accuracy of patients, provide improved explanations of LUAD responses to immunotherapy, and provide new strategies for LUAD treatment.

Keywords: Immunotherapy; Lung adenocarcinoma; Machine-learning; Multi-omics; Tumor immune microenvironment.