Predictive model based on multiple immunofluorescence quantitative analysis for pathological complete response to...

Area of Science:

• Oncology
• Immunology
• Pathology

Background:

• Lung squamous cell carcinoma (LSCC) necessitates effective neoadjuvant immunochemotherapy (NICT).
• Predicting treatment response in NICT for LSCC remains a clinical challenge.
• Understanding the tumor immune microenvironment is crucial for optimizing LSCC treatment strategies.

Purpose of the Study:

• To establish a predictive model for neoadjuvant immunochemotherapy (NICT) response in lung squamous cell carcinoma (LSCC).
• To identify key immune cell markers associated with pathological complete response (PCR) after NICT.
• To guide clinical treatment decisions for LSCC patients undergoing NICT.

Main Methods:

• Retrospective analysis of 50 LSCC patients treated with NICT.
• Histopathological analysis using HE staining and multiple immunofluorescence (mIF) to assess immune cell densities.
• Development of a prediction model using LASSO and optimal subset regression based on significant immune markers.

Main Results:

• Higher densities of CD8+, PD-L1+, and CD8+PD-L1+ immune cells in the tumor region were associated with pathological complete response (PCR).
• The developed prediction model demonstrated high performance (AUC=0.965 training, AUC=0.786 validation).
• The model outperformed conventional TPS scoring criteria in accuracy, specificity, and sensitivity for predicting NICT outcomes.

Conclusions:

• CD8+, PD-L1+, and CD8+PD-L1+ cell abundances are key predictors of NICT efficacy in LSCC.
• The established prediction model offers a reliable tool for forecasting treatment outcomes.
• This model can aid in selecting optimal NICT treatment strategies for LSCC patients.