Development and validation of a biopsy-based model using tumor-associated neutrophils to predict neoadjuvant chemotherapy response in osteosarcoma: a large single-center retrospective cohort study

利用肿瘤相关中性粒细胞建立和验证基于活检的模型以预测骨肉瘤新辅助化疗反应:一项大型单中心回顾性队列研究

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Abstract

BACKGROUND: Accurately predicting response to neoadjuvant chemotherapy (NACT) in osteosarcoma remains challenging, as the gold standard-tumor necrosis rate can only be assessed postoperatively. The tumor immune microenvironment, accessible via pretreatment biopsy, holds promise for early prediction, but the specific role of tumor-associated neutrophils (TANs) in this setting is unexplored. METHODS: TANs abundance in pretreatment biopsies was quantified as CD66b(+) cell density via immunohistochemistry and stratified into quartiles (Q1-Q4). The cohort was randomly split 6:4 into training and testing sets. A multivariable logistic regression model predicting poor NACT response was developed in the training set and tested in the testing set. Model performance was evaluated by area under the curve, calibration, and decision curve analysis. The association between TANs abundance and survival was explored using Kaplan-Meier analysis in the entire cohort. RESULTS: A total of 168 patients were analysed. Higher TANs density was independently associated with poor NACT response (adjusted odds ratios for Q2-Q4: 4.41-8.12; all P < 0.05). A predictive model incorporating TANs density demonstrated good discrimination (training AUC: 0.75; testing AUC: 0.74), calibration (Hosmer-Lemeshow P = 0.42 in training, P = 0.306 in testing), and clinical utility across risk thresholds of 30-80%. Survival analysis indicated that elevated TANs density correlated significantly with worse overall survival (P = 0.038) and recurrence-free survival (P = 0.026), but not with metastasis-free or disease-free survival. CONCLUSION: This study is the first to confirm that TANs density in osteosarcoma biopsy specimens is negatively correlated with NACT response. The predictive model constructed based on this finding enables pretreatment risk stratification and early intervention in clinical practice.

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