Tumor-Resident Microbiota-Based Risk Model Predicts Neoadjuvant Therapy Response of Locally Advanced Esophageal Squamous Cell Carcinoma Patients.

Autor: Wu H; 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.; Jinfeng Laboratory, Chongqing, 400039, P. R. China.; Yu-Yue Pathology Scientific Research Center, Chongqing, 400039, P. R. China., Liu Q; 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.; Jinfeng Laboratory, Chongqing, 400039, P. R. China.; Yu-Yue Pathology Scientific Research Center, Chongqing, 400039, P. R. China., Li J; Thoracic Surgery Department, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510230, P. R. China., Leng X; Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610041, P. R. China., He Y; Department of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China., Liu Y; 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.; Jinfeng Laboratory, Chongqing, 400039, P. R. China., Zhang X; Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610041, P. R. China.; Institute of Pathology and Southwest Cancer Center, Ministry of Education of China, Southwest Hospital, Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology, Chongqing, 400038, P. R. China., Ouyang Y; Acupuncture and Massage College, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610072, P. R. China., Liu Y; Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610041, P. R. China., Liang W; Thoracic Surgery Department, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510230, P. R. China., Xu C; 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.; Jinfeng Laboratory, Chongqing, 400039, P. R. China.; Yu-Yue Pathology Scientific Research Center, Chongqing, 400039, P. R. China.
Jazyk: angličtina
Zdroj: Advanced science (Weinheim, Baden-Wurttemberg, Germany) [Adv Sci (Weinh)] 2024 Nov; Vol. 11 (41), pp. e2309742. Date of Electronic Publication: 2024 Sep 13.
DOI: 10.1002/advs.202309742
Abstrakt: 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.
(© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)
Databáze: MEDLINE
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