Related Experiment Video
Updated: Sep 13, 2025
Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells
Published on: February 8, 2018
Predicting pathological response of resectable esophageal squamous cell carcinoma to neoadjuvant anti-PD-1 with
Peng Song1, Zhiyuan Yao2, Shuai Song1
1Department of Thoracic Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Background:
Inflammatory indexes are increasingly being considered to predict treatment response in tumors. This study aimed to investigate the efficacy of serum inflammatory indexes in predicting pathological response in patients with esophageal squamous cell carcinoma (ESCC) receiving anti-PD-1 neoadjuvant immunochemotherapy (NICT).
Methods:
We retrospectively collected clinical and laboratory data from 116 ESCC patients who received NICT. We set three outcome variables: pathologic complete response (PCR), good response (GR), and response (R). We assessed between-group differences in inflammation indexes and their diagnostic efficacy. Independent diagnostic markers were filtered using least absolute shrinkage and selection operator (LASSO) logistic regression and multivariable analysis, and the corresponding nomograms for PCR and GR were constructed, respectively. Receiver operating characteristic curves (ROC) and calibration curves assessed the efficiency and accuracy of the models. Decision curve analysis (DCA) and clinical impact curves (CIC) evaluated the clinical value. Moreover, we internally validated the predictive model with a random sample of 30% of patients.
Results:
The prognostic nutritional index (PNI) predicted a cutoff value of 53.585 for PCR with an area under curve (AUC) value of 0.720, a cutoff value of 47.85 for GR with an AUC of 0.723, a cutoff value of 47.85 for R with an AUC of 0.629. Smoking and PNI were independent predictors of PCR, platelet-to-lymphocyte ratio (PLR) and PNI were independent predictors of GR, and PNI was an independent predictor of R. We built PNI-based nomograms to predict PCR and GR with AUC values of 0.795 and 0.763 for the training cohort and 0.907 and 0.757 for the validation cohort, respectively. The predicted and actual results of the calibration curves for both the training and validation groups showed good agreement, with Brier scores below 0.25.
Conclusion:
High PNI value is a shared independent predictor of achieving PCR, GR, and R in ESCC patients receiving anti-PD1 NICT. PNI-based diagnostic models can be used as a practical tool to identify ideal patients for personalized clinical decisions.
