Frontiers in immunology · 2026
Peripheral endocrine-nutritional machine learning signature predicts short-term response to neoadjuvant immunochemotherapy in locally advanced gastric cancer.
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Li Fang, Mu Zefan, Guo Honghai, Zheng Tao, Yang Peigang, Wang Zhiqiang, Shi Yanlong, Xu Wen, Tian Yuan, Ma Wenqian, Zhao Qun
Аннотация
BACKGROUND: Major pathological response (MPR) to neoadjuvant PD-1 inhibitor plus chemotherapy in locally advanced gastric cancer (LAGC) varies widely and is incompletely explained by tumor-side biomarkers. Because checkpoint inhibitors act within a host environment shaped by endocrine, nutritional, and inflammatory state, we developed and validated the Endocrine-Nutritional Immunotherapy response score (ENI-Score) from routine pretreatment peripheral-blood markers to predict short-term response and early recurrence.
METHODS: We retrospectively analyzed 738 patients with LAGC or gastroesophageal junction adenocarcinoma who received neoadjuvant PD-1 inhibitor plus platinum-based chemotherapy and radical gastrectomy at four hospitals, partitioned into training (n=335), internal (n=114), and external (n=289) validation cohorts. Ten machine-learning algorithms were benchmarked, and a prespecified, domain-balanced five-marker panel comprising 8 AM cortisol, prognostic nutritional index (PNI), prealbumin, neutrophil-to-lymphocyte ratio (NLR), and C-reactive protein-to-albumin ratio (CAR) was carried forward in a regularized logistic model and rescaled to the ENI-Score (0 -100). Analyses included SHAP, tertile stratification, nested AUC comparison (DeLong), calibration, decision-curve analysis, and Kaplan-Meier survival analysis; the primary endpoint was MPR (residual viable tumor ≤10%).
RESULTS: The overall MPR rate was 40.9% (302/738). All five markers differed significantly between MPR and non-MPR patients in a biologically coherent direction consistent across all three cohorts (each P<0.001). Regularized logistic regression achieved the highest, most stable external-validation AUC among the ten algorithms (training 0.873, internal 0.816, external 0.830, and five-fold cross-validated 0.862), whereas tree-based ensembles overfit (training AUC up to 0.99, external ≤0.81). ENI-Score tertiles separated MPR rates steeply (Low 10.4%, Intermediate 38.2%, High 79.1%; P-trend<0.001), with an adjusted High-benefit versus Low-benefit odds ratio of 35.3 (95% CI 20.6-60.5) and a per 10-point adjusted OR of 1.72 (95% CI 1.59-1.86, P<0.001). The ENI-Score reached an overall AUC of 0.842 and significantly augmented clinical staging (0.656 to 0.864; DeLong P<0.001). Higher ENI-Score was associated with longer recurrence-free survival (RFS; log-rank P<0.001; adjusted HR per 10 points 0.78).
CONCLUSIONS: Built entirely from five low-cost routine peripheral-blood markers, the ENI-Score accurately and reproducibly stratifies MPR after neoadjuvant immunochemotherapy in LAGC, augments tumor-centered clinical staging, and is associated with RFS. It provides an inexpensive, interpretable host-state adjunct to tumor-side biomarkers that warrants prospective validation.
METHODS: We retrospectively analyzed 738 patients with LAGC or gastroesophageal junction adenocarcinoma who received neoadjuvant PD-1 inhibitor plus platinum-based chemotherapy and radical gastrectomy at four hospitals, partitioned into training (n=335), internal (n=114), and external (n=289) validation cohorts. Ten machine-learning algorithms were benchmarked, and a prespecified, domain-balanced five-marker panel comprising 8 AM cortisol, prognostic nutritional index (PNI), prealbumin, neutrophil-to-lymphocyte ratio (NLR), and C-reactive protein-to-albumin ratio (CAR) was carried forward in a regularized logistic model and rescaled to the ENI-Score (0 -100). Analyses included SHAP, tertile stratification, nested AUC comparison (DeLong), calibration, decision-curve analysis, and Kaplan-Meier survival analysis; the primary endpoint was MPR (residual viable tumor ≤10%).
RESULTS: The overall MPR rate was 40.9% (302/738). All five markers differed significantly between MPR and non-MPR patients in a biologically coherent direction consistent across all three cohorts (each P<0.001). Regularized logistic regression achieved the highest, most stable external-validation AUC among the ten algorithms (training 0.873, internal 0.816, external 0.830, and five-fold cross-validated 0.862), whereas tree-based ensembles overfit (training AUC up to 0.99, external ≤0.81). ENI-Score tertiles separated MPR rates steeply (Low 10.4%, Intermediate 38.2%, High 79.1%; P-trend<0.001), with an adjusted High-benefit versus Low-benefit odds ratio of 35.3 (95% CI 20.6-60.5) and a per 10-point adjusted OR of 1.72 (95% CI 1.59-1.86, P<0.001). The ENI-Score reached an overall AUC of 0.842 and significantly augmented clinical staging (0.656 to 0.864; DeLong P<0.001). Higher ENI-Score was associated with longer recurrence-free survival (RFS; log-rank P<0.001; adjusted HR per 10 points 0.78).
CONCLUSIONS: Built entirely from five low-cost routine peripheral-blood markers, the ENI-Score accurately and reproducibly stratifies MPR after neoadjuvant immunochemotherapy in LAGC, augments tumor-centered clinical staging, and is associated with RFS. It provides an inexpensive, interpretable host-state adjunct to tumor-side biomarkers that warrants prospective validation.