The Prognostic Significance of PD1 and PDL1 Gene Expression in Lung Cancer: A Meta-Analysis
Chih‐Hao Chang, Arthur Chun-Chieh Shih, Ya‐Hsuan Chang, Hsuan‐Yu Chen, Ying‐Ting Chao, Yi‐Chiung Hsu
Abstract
BACKGROUND: Immune checkpoint blockade therapy represents an extraordinary advance in lung cancer treatment. It is important to determine the expression of immune checkpoint genes, such as programmed cell death 1 (PD1) and programmed cell death-ligand 1 (PDL1), to develop immunotherapeutic strategies. The aim of this study was to explore the association between PD1 and PDL1 gene expression and prognoses and outcomes in lung cancer. METHODS: This meta-analysis analyzed 1,251 patients from eight different microarray gene expression datasets and were evaluated for their prognostic implications and verified using another independent research. RESULTS: The mean expression levels of PDL1 in adenocarcinoma (AD) and squamous cell carcinoma (SC) were significantly higher in patients who died than in patients who did not. There was a trend toward incremental increases in PD1 and PDL1 expression significantly decreasing the risk of relapse and death among AD patients (HR = 0.69; 95% CI = 0.53 ~ 0.91; HR = 0.68; 95% CI = 0.54 ~ 0.84, respectively) and SC patients (HR = 0.53; 95% CI = 0.32 ~ 0.89; HR = 0.78; 95% CI = 0.57 ~ 1.00 respectively), as early-stage patients in this study were more likely to have high expression of both PD1 and PDL1 than late-stage patients (P-trend < 0.05). In contrast, late-stage SC patients expressing one or more of the genes at a high level had a significantly elevated risk of relapse (HR = 1.51; 95% CI = 1.07 ~ 2.11) and death (HR = 1.41; 95% CI = 1.08 ~ 1.84). This result was consistent with the validation data set. CONCLUSION: These findings indicate that high expression of PD1 and PDL1 is associated with superior outcome in early-stage lung cancer but an adverse outcome in late-stage lung cancer. The expression levels of PD1 and PDL1 individually or jointly are potential prognostic factors for predicting patient outcomes in lung cancer.