Department of Oncology & Cancer Institute, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610072, P. R. China.
Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610041, P. R. China.
Adv Sci (Weinh). 2024 Nov;11(41):e2309742. doi: 10.1002/advs.202309742. Epub 2024 Sep 13.
Few predictive biomarkers exist for identifying patients who may benefit from neoadjuvant therapy (NAT). The intratumoral microbial composition is comprehensively profiled to predict the efficacy and prognosis of patients with esophageal squamous cell carcinoma (ESCC) who underwent NAT and curative esophagectomy. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis is conducted to screen for the most closely related microbiota and develop a microbiota-based risk prediction (MRP) model on the genera of TM7x, Sphingobacterium, and Prevotella. The predictive accuracy and prognostic value of the MRP model across multiple centers are validated. The MRP model demonstrates good predictive accuracy for therapeutic responses in the training, validation, and independent validation sets. The MRP model also predicts disease-free survival (p = 0.00074 in the internal validation set and p = 0.0017 in the independent validation set) and overall survival (p = 0.00023 in the internal validation set and p = 0.11 in the independent validation set) of patients. The MRP-plus model basing on MRP, tumor stage, and tumor size can also predict the patients who can benefit from NAT. In conclusion, the developed MRP and MRP-plus models may function as promising biomarkers and prognostic indicators accessible at the time of diagnosis.
目前,用于识别可能从新辅助治疗(NAT)中获益的患者的预测性生物标志物很少。本研究全面分析肿瘤内微生物组成,以预测接受 NAT 和根治性食管切除术的食管鳞癌(ESCC)患者的疗效和预后。采用最小绝对收缩和选择算子(LASSO)回归分析筛选与 ESCC 患者 NAT 疗效和预后最相关的微生物,并基于 TM7x、Sphingobacterium 和 Prevotella 属建立基于微生物的风险预测(MRP)模型。验证该模型在多个中心的预测准确性和预后价值。该模型在训练集、验证集和独立验证集中对治疗反应具有良好的预测准确性。MRP 模型还预测了患者的无病生存(内部验证集 p=0.00074,独立验证集 p=0.0017)和总生存(内部验证集 p=0.00023,独立验证集 p=0.11)。基于 MRP、肿瘤分期和肿瘤大小的 MRP-plus 模型也可以预测从 NAT 中获益的患者。总之,开发的 MRP 和 MRP-plus 模型可以作为有前途的生物标志物和诊断时可用的预后指标。