Tumor Microenvironment Characterization Reveals Distinct Immune Subtypes in Lung Adenocarcinoma

Abstract

Background: Tumor microenvironment (TME) cells are vital components of tumor tissue. Increasing evidence suggest their significance in predicting outcomes and guiding therapies. However, no studies have reported a systematic analysis of clinicopathological significance of TME cells in lung adenocarcinoma (LUAD). Methods: The TME cells of 1,184 LUAD patients were inferenced using computational algorithms based on bulk tumor expression data. TME subtypes were determined using a consensus clustering method. Signature genes between TME subtypes were obtained using random forest classification algorithm. TME score was obtained using principal component analysis algorithms based on signature gene expression. The Kaplan-Meier method was applied to generate survival curves. Findings: LUAD patients showed heterogeneous abundance in TME cells. Infiltration of TME cells was influenced by clinicopathological features, such as age, gender, smoking and TNM stage. By clustering TME cells, we identified two clinically and molecularly distinct LUAD subtypes with immune active and immune repressed features. The immune active subtype showed active chemokine expression and repressed metabolism expression, while the immune repressed subtype showed repressed chemokine expression and active metabolism expression. The TME score facilitated the correct classification of LUAD patients and the prediction of their prognosis. What's more, TME score predicted TME phenotype in TCGA Pan-Cancer cohort. Interpretation: There is a heterogeneous infiltration of TME cells in LUAD patients, which was shaped by clinicopathological features. Based on TME cell pattern, we proposed two clinically and molecularly distinct LUAD subtypes that may be valuable in predicting clinical outcome and guiding immunotherapy. Funding Statement: This work was supported by the National Key Research and Development Program of China (2016YFC1303201, 2016YFC0901400), the National Natural Science Foundation of China (81802299, 81502514), the CAMS Innovation Fund for Medical Sciences (2016-I2M-1-001, 2017-I2M-1-005), the Fundamental Research Funds for the Central Universities (3332018070), and the National Key Basic Research Development Plan (2018YFC1312105). Declaration of Interests: The authors declare that they have no competing interests. Ethical Approval Statement: Not required.