Data Sheet 3_Integrated liquid biopsy model for predicting metastasis and guiding PD-1 therapy in esophageal squamous cell carcinoma.pdf

ObjectiveThis study endeavors to develop and validate an integrated biomarker signature (IBS) grounded in serum carbohydrate antigen 72-4 (CA72-4), vascular endothelial growth factor-C (VEGF-C), and the pepsinogen I/II ratio (PGI/PGII). A practical IBS model will be constructed to substantially enhance the accuracy of lymph node metastasis (LNM) risk stratification following surgery for esophageal squamous cell carcinoma (ESCC). This model is anticipated to refine prognostic assessments for patients and to identify novel research avenues within the field, thereby providing guidance for both prognostic determination and future investigations.
MethodsA prospective three-cohort design was adopted, encompassing a training cohort of 220 patients, a temporal validation cohort of 138 patients, and a regional external validation cohort of 94 patients. This design was selected for its robustness in ensuring the validity and reliability of the findings. A total of 452 patients with esophageal squamous cell carcinoma (ESCC) who underwent R0 resection were enrolled between March 2022 and June 2024. The predictive model was constructed using the XGBoost algorithm combined with Shapley Additive exPlanations (SHAP) for interpretability. Model performance was evaluated via receiver operating characteristic (ROC) curves, decision curve analysis, and net reclassification index.
ResultsThe IBS model demonstrated superior discriminative ability in the training cohort (n = 220; area under the receiver operating characteristic curve (AUC) = 0.936; 95% confidence interval (CI): 0.908–0.964) compared to the AJCC ninth edition staging system (ΔAUC = 0.154; p < 0.001). Performance was maintained in validation cohorts. High-risk patients (IBS > 0.5) receiving PD-1 inhibitor plus chemotherapy achieved a pathological complete response (pCR) rate of 28.6%, representing a 115% increase over conventional therapy (p = 0.009).
ConclusionThe validated IBS model provides high-precision prediction of postoperative LNM risk in ESCC. It offers a novel framework (“tumor antigen burden–lymphangiogenesis–immune microenvironment”) for improving patient risk stratification, guiding adjuvant therapy decisions (including immunotherapy response prediction), and optimizing resource allocation, thereby potentially impacting ESCC management paradigms.