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Published on: September 25, 2018
Integrating pleural PD-1+CD8+ T cell as a complement variable into LENT score to assess patients with lung
Weizhen Jiang1, Leilei Lv1, Yaoxin Zhang1
1Department of Respiratory and Critical Medicine, The First Affiliated Hospital of Soochow University, Suzhou, China.
Introduction:
Malignant pleural effusion (MPE) is a common complication of advanced non-small cell lung cancer (NSCLC), particularly in lung adenocarcinoma, and is associated with poor prognosis. A better understanding of the role of PD-1+CD8+ T cells in the pleural environment and their relevance to patient survival could facilitate better clinical decision-making.
Methods:
We performed a cohort study involving NSCLC patients with MPE. The abundance of pleural PD-1+CD8+ T cells was measured using flow cytometry. We also assessed the presence of epidermal growth factor receptor (EGFR) mutations and programmed death-ligand 1 (PD-L1) expression in the pleural fluid. The LENT score, a known prognostic tool, was combined with pleural PD-1+CD8+ T cell abundance to develop a novel scoring system, the Immuno-LENT score. The model's performance was validated using the bootstrap method and concordance index (C-index) calculation.
Results:
We found that PD-1+CD8+ T cells were present in the pleural fluid of all patients with MPE. Notably, the abundance of these cells was influenced by EGFR mutations, while PD-L1 expression had little effect. Patients with a higher abundance of pleural PD-1+CD8+ T cells also exhibited higher LENT scores, correlating with poorer survival. The Immuno-LENT score, incorporating both the LENT score and pleural PD-1+CD8+ T cell abundance, was found to be an independent prognostic factor. The model showed strong statistical robustness with a high C-index.
Discussion:
The combination of pleural PD-1+CD8+ T cells with the LENT score offers a more accurate prognostic tool for survival prediction in NSCLC patients with MPE. Our findings suggest that the Immuno-LENT score could guide clinical management and inform therapeutic decisions for these patients, improving patient outcomes by tailoring interventions based on a more comprehensive biomarker profile.
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