Login / Signup

Contrasting Roles of Programmed Death-Ligand 1 Expression in Tumor and Stroma in Prognosis of Esophageal Squamous Cell Carcinoma.

Tomohiro MurakamiEisuke BookaSatoru FuruhashiYuki SakaiKenichi SekimoriRyoma HanedaMayu FujihiroTomohiro MatsumotoYoshifumi MoritaHirotoshi KikuchiYoshihiro HiramatsuSatoshi BabaHiroya Takeuchi
Published in: Cancers (2024)
The assessment of programmed death-ligand 1 (PD-L1) expression in esophageal squamous cell carcinoma (ESCC) has become increasingly important with the rise of immune checkpoint inhibitors (ICIs). However, challenges persist, including subjective interpretation and the unclear significance of staining intensity, as well as contrasting roles in tumoral and stromal regions. Our study enhances the understanding of PD-L1 in ESCCs by analyzing its expression in tumors and stroma with H-scores, highlighting its distinct clinicopathological impacts. In a retrospective cohort of 194 ESCC specimens from surgical resection, we quantified PD-L1 expression in tumoral and stromal compartments using H-scores, analyzing whole slide images with digital pathology analysis software. Kaplan-Meier analysis demonstrated that higher PD-L1 expression is significantly associated with improved postoperative overall survival (OS) and recurrence-free survival (RFS) in both tumoral and stromal areas. Multivariable analysis identified high tumoral PD-L1 expression as an independent prognostic factor for prolonged OS and RFS (HR = 0.47, p = 0.007; HR = 0.54, p = 0.022, respectively). In a separate analysis, high stromal PD-L1 expression was found to correlate with less advanced pathological stages and a prolonged response to cytotoxic chemotherapy, with no similar correlation found for ICI treatment response. This study reveals PD-L1's contrasting role in the ESCC tumor immune microenvironment, impacting prognosis, tumor stage, and treatment response.
Keyphrases
  • free survival
  • bone marrow
  • poor prognosis
  • machine learning
  • patients undergoing
  • deep learning
  • high intensity
  • depressive symptoms
  • data analysis
  • sleep quality