Pan-immune-inflammation value as a novel predictor of pathological response to neoadjuvant chemotherapy combined with anti-PD-1 therapy in esophageal squamous cell carcinoma: a multicenter real-world retrospective clinical study

Abstract

Background: Current biomarkers for predicting pathological response to neoadjuvant chemoimmunotherapy in esophageal squamous cell carcinoma (ESCC) remain limited.

Objective: This study investigates the potential of the pan-immune-inflammation value (PIV) as a biomarker for predicting pathological response after neoadjuvant chemoradiotherapy combined with anti-programmed death protein-1 (PD-1) therapy in ESCC.

Design: A multicenter, real-world, retrospective clinical study conducted across five centers in Southern China (January 2021-March 2024).

Methods: A multicenter retrospective study included 334 patients with ESCC, divided into pathological complete response (pCR) and non-pathological complete response (non-pCR) groups. Clinical and laboratory data were analyzed using univariate and multivariate logistic regression to evaluate the relationship between post-treatment PIV and pathological response. The threshold effect of PIV was explored using restricted cubic spline analysis.

Results: Subgroup analysis showed no significant interactions across clinical subgroups. Post-treatment PIV was positively associated with non-pCR risk (odds ratio = 1.002; 95% confidence interval: 1.001-1.003, p < 0.005). A positive association was observed in the high-PIV stratum (⩾280), where elevated PIV levels significantly correlated with increased non-pCR risk. Receiver operating characteristic analysis showed an area under the curve of 0.86 for predicting non-pCR, with a sensitivity of 86.6% and specificity of 72% at an optimal cutoff of 438.04. The high-PIV group exhibited inferior survival outcomes with significantly increased mortality risk.

Conclusion: Post-treatment PIV shows a nonlinear relationship with pathological response in patients receiving neoadjuvant chemoradiotherapy combined with anti-PD-1 therapy and may serve as a predictive biomarker.

Keywords: esophageal squamous cell carcinoma; inflammatory markers; neoadjuvant chemotherapy; pathological response; robotic-assisted.

Figure 1.

The flow chart of the study. ESCC, esophageal squamous cell carcinoma; GI, gastrointestinal; GNRI, Geriatric Nutritional Risk Index; ICI, immune checkpoint inhibitors; LND, lymph node dissection; MIE, minimally invasive esophagectomy; OS, overall survival; PIV, Pan-Immune-Inflammation Value; TP, taxol + platinum; TRG, tumor regression grade.

Figure 2.

Subgroup analysis between PIV and the pathological response. PIV, Pan-Immune-Inflammation Value.

Figure 3.

Nonlinear relationship between PIV and pathological complete response. Adjustment factors included age, sex, smoking, alcohol, diabetes, weight, BMI, hypertension, CHD, COPD, CVD, location of tumor, ASA, clinical stage, and GRNI. The red curve represents the principal curve, depicting the fitted nonlinear relationship between pathological response and PIV. The principal curve illustrates the nonlinear trend of adverse pathological response with increasing PIV. The red shaded area indicates the 95% CI of the principal curve. The black dashed line (Y = 1) serves as the risk reference baseline (OR = 1). The position of the principal curve and its CI relative to this line determines the statistical significance of risk changes. ASA, American Society of Anesthesiologists; BMI, body mass index; CHD, coronary heart disease; CI, confidence interval; COPD, chronic obstructive pulmonary disease; CVD, cerebrovascular disease; GRNI, Geriatric Nutritional Risk Index; OR, odds ratio; PIV, Pan-Immune-Inflammation Value.

Figure 4.

Receiver operating characteristic analysis of PIV in predicting the risk of postoperative non-pCR. pCR, pathological complete response; PIV, Pan-Immune-Inflammation Value.

Figure 5.

Kaplan–Meier analysis of PIV groups classified using the ROC-optimized cutoff value. PIV, Pan-Immune-Inflammation Value; ROC, receiver operating characteristic.