The prognostic effects of circulating myeloid-derived suppressor cells in non-small cell lung cancer: systematic review and meta-analysis

Clin Exp Med. 2023 Sep;23(5):1551-1561. doi: 10.1007/s10238-022-00946-6. Epub 2022 Nov 19.

Abstract

Immunotherapy is the main standard treatment for non-small cell lung cancer (NSCLC) patients. Immune suppressive cells in tumor microenvironment can counteract its efficacy. Myeloid-derived suppressor cells (MDSCs) include two major subsets: polymorphonuclear (PMN-MDSCs) and monocytic (M-MDSCs). Many studies explored the prognostic impact of these cell populations in NSCLC patients. The aim of this systematic review is to select studies for a meta-analysis, which compares prognosis between patients with high vs low circulating MDSC levels. We collected hazard ratios (HRs) and relative 95% confidence intervals (CIs) in terms of progression-free survival (PFS) or recurrence-free survival (RFS), and overall survival (OS). Among 139 studies retrieved from literature search, 14 eligible studies (905 NSCLC patients) met inclusion criteria. Low circulating MDSC levels favor a better PFS/RFS (HR = 1.84; 95% CI = 1.28-2.65) and OS (HR = 1.78; 95% CI = 1.29-2.46). The subgroup analysis based on MDSC subtypes (total-, PMN-, and M-MDSCs) obtained a statistical significance only for M-MDSCs, both in terms of PFS/RFS (HR = 2.67; 95% CI = 2.04-3.50) and OS (HR = 2.10; 95% CI = 1.61-2.75). NSCLC patients bearing high M-MDSC levels in peripheral blood experience a worse prognosis than those with low levels, both in terms of PFS/RFS and OS. This finding suggests that detecting and targeting this MDSC subset could help to improve NSCLC treatment efficacy.

Keywords: Myeloid-derived suppressor cells; Non-small cell lung cancer; Prognosis.

Publication types

  • Meta-Analysis
  • Systematic Review
  • Review

MeSH terms

  • Carcinoma, Non-Small-Cell Lung* / pathology
  • Humans
  • Lung Neoplasms* / pathology
  • Myeloid-Derived Suppressor Cells* / pathology
  • Prognosis
  • Proportional Hazards Models
  • Tumor Microenvironment