PD-L1 and PD-L2 expression correlated genes in non-small-cell lung cancer

Trine Vilsbøll Larsen1, Dianna Hussmann1, Anders Lade Nielsen2

  • 1Department of Biomedicine, Aarhus University, 8000, Aarhus C, Denmark.

Abstract

Insights

This study identifies gene signatures correlated with PD-L1 and PD-L2 expression in non-small-cell lung cancer (NSCLC). These findings may aid in understanding immunotherapy response and developing predictive biomarkers for cancer treatment.

Area of Science:

  • Oncology
  • Immunology
  • Genetics

Background:

  • Programmed cell death ligand-1 (PD-L1) and ligand-2 (PD-L2) interactions with programmed cell death protein-1 (PD-1) are key immune checkpoints in cancer.
  • Understanding PD-1 blockade response and identifying predictive biomarkers in non-small-cell lung cancer (NSCLC) is crucial for effective immunotherapy.

Purpose of the Study:

  • To systematically describe genes correlated with PD-L1 and PD-L2 mRNA expression in NSCLC.
  • To identify potential gene signatures for predicting response to PD-1 checkpoint blockade immunotherapy.

Main Methods:

  • Comparative retrospective analyses of PD-L1 and PD-L2 mRNA expression correlated genes.
  • Utilized datasets from the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA) projects for NSCLC, lung adenocarcinoma (LUAD), and lung squamous cell carcinoma (LUSC).

Main Results:

  • Identified significant gene expression correlations with PD-L1 and PD-L2 across CCLE and TCGA datasets.
  • Discovered convergence of Interferon signaling-involved genes with PD-L1/PD-L2 expression, particularly in LUAD.
  • Observed correlation of PD-L1 expression with chromosome 9p24 genes in LUSC, suggesting its role in driving PD-L1 expression.

Conclusions:

  • Presented novel gene signatures associated with PD-L1 and PD-L2 mRNA expression in NSCLC.
  • These signatures hold potential for understanding PD-1 checkpoint blockade-responsive biology.
  • Findings may facilitate the development of gene signature-based biomarkers for predicting immunotherapy clinical response.

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