Construction and Validation of a Tumor Microenvironment-Based Scoring System to Evaluate Prognosis and Response to Immune Checkpoint Inhibitor Therapy in Lung Adenocarcinoma Patients

Abstract

Background: Lung cancer is among the most dangerous malignant tumors to human health. Lung adenocarcinoma (LUAD) accounts for about 40% of all lung cancers. Accumulating evidence suggests that the tumor microenvironment (TME) is a crucial regulator of carcinogenesis and therapeutic efficacy in LUAD. However, the impact of tumor microenvironment-related signatures (TMERSs) representing the TME characteristics on the prognosis and therapeutic outcome of LUAD patients remains to be further explored. Materials and methods: Gene expression files and clinical information of 1630 LUAD samples and 275 samples with immunotherapy information from different databases such as The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Cancer Research Institute (CRI) iAtlas were downloaded and analyzed. Three hundred tumor microenvironment-related signatures (TMERS) based on a comprehensive collection of marker genes were quantified by single sample gene set enrichment analysis (ssGSEA), and then eight significant signatures were selected to construct the tumor microenvironment-related signature score (TMERSscore) by performing Least Absolute Shrinkage and Selection Operator (LASSO)-Cox analysis. Results: In this study, we constructed a TME-based prognostic stratification model for patients with LUAD and validated it in several external datasets. Furthermore, the TMERSscore was found to be positively correlated with tumor malignancy and a high TMERSscore predicted a poor prognosis. Moreover, the TMERSscore of responders treated with Immune Checkpoint Inhibitor (ICI) therapies was significantly lower than that of non-responders, and the TMERSscore was positively correlated with the tumor immune dysfunction and exclusion (TIDE) score, implying that a low TMERSscore predicts a better response to ICI treatment and may provide independent and incremental predictive value over current biomarkers. Conclusions: Overall, we constructed a TMERSscore that can be used for LUAD patient prognosis stratification as well as ICI therapeutic efficacy evaluation, supportive results from independent external validation sets showed its robustness and effectiveness.

Checkpoint Markers and Tumor Microenvironment: What Do We Know?

“…Tumor-associated macrophages are highly immunosuppressive protecting cancer cells through eliminating the anti-tumor T cells by overexpressing PD-L1, PD-L2, CD80, and Siglec-15 [ 44 ]. Immune checkpoint blockade therapies based on the TME have achieved many successes, such as PD-1/PD-L1 and CTLA4 checkpoint inhibitors, which have revolutionized the treatments [ 45 ]. Immune checkpoint molecules such as PD-1, IDO-1, LAG-3, and TIM-3 restrict immune function, and tumor cells use them to avoid host immune surveillance [ 46 ].…”

Application of oncolytic virus in tumor therapy

“…Immunotherapy mainly includes immune checkpoint inhibitors (ICIs), T‐cell transfer therapy, monoclonal antibodies, treatment vaccines, immune system modulators, and so on. ICIs can enhance the antitumor effect of immune cells 14–16 . T‐cell transfer therapy (also called adoptive cell therapy) boosts the natural ability of T cells in killing cancer cells, which mainly includes tumor‐infiltrating lymphocyte (TIL) therapy and Coxsackievirus and adenovirus receptor (CAR) T‐cell therapy 17–19 .…”

“…ICIs can enhance the antitumor effect of immune cells. [14][15][16] T-cell transfer therapy (also called adoptive cell therapy) boosts the natural ability of T cells in killing cancer cells, which mainly includes tumor-infiltrating lymphocyte (TIL) therapy and Coxsackievirus and adenovirus receptor (CAR) T-cell therapy. [17][18][19] However, immunotherapies can only be beneficial to a few patients due to the existence of various immunosuppressive factors, such as immune system suppression, lack of cytokine types, poor antigenpresenting cell (APC) function, less TIL, and weak effector T-cell activity.…”