Lee Hye In, Jang Bum-Sup, Chang Ji Hyun, Kim Eunji, Lee Tae Hoon, Park Jeong Hwan, Chie Eui Kyu
Department of Radiation Oncology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Cancer Res Treat. 2025 Jul;57(3):840-851. doi: 10.4143/crt.2024.521. Epub 2024 Dec 16.
This study aimed to investigate the dynamic changes in the microbiome of patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy (nCRT), focusing on the relationship between the microbiome and response to nCRT.
We conducted a longitudinal study involving 103 samples from 26 patients with LARC. Samples were collected from both the tumor and normal rectal tissues before and after nCRT. Diversity, taxonomic, and network analyses were performed to compare the microbiome profiles across different tissue types, pre- and post-nCRT time-points, and nCRT responses.
Between the tumor and normal tissue samples, no differences in microbial diversity and composition were observed. However, when pre- and post-nCRT samples were compared, there was a significant decrease in diversity, along with notable changes in composition. Non-responders exhibited more extensive changes in their microbiome composition during nCRT, characterized by an increase in pathogenic microbes. Meanwhile, responders had relatively stable microbiome communities with more enriched butyrate-producing bacteria. Network analysis revealed distinct patterns of microbial interactions between responders and non-responders, where butyrate-producing bacteria formed strong networks in responders, while opportunistic pathogens formed strong networks in non-responders. A Bayesian network model for predicting the nCRT response was established, with butyrate-producing bacteria playing a major predictive role.
Our study demonstrated a significant association between the microbiome and nCRT response in LARC patients, leading to the development of a microbiome-based response-prediction model. These findings suggest potential applications of microbiome signatures for predicting and optimizing nCRT treatment in LARC patients.
本研究旨在调查接受新辅助放化疗（nCRT）的局部晚期直肠癌（LARC）患者微生物组的动态变化，重点关注微生物组与nCRT反应之间的关系。
我们进行了一项纵向研究，纳入了26例LARC患者的103份样本。在nCRT前后，从肿瘤组织和正常直肠组织中采集样本。进行多样性、分类学和网络分析，以比较不同组织类型、nCRT前后时间点以及nCRT反应的微生物组谱。
在肿瘤组织和正常组织样本之间，未观察到微生物多样性和组成的差异。然而，比较nCRT前后的样本时，多样性显著降低，同时组成也有明显变化。无反应者在nCRT期间微生物组组成变化更为广泛，其特征是致病微生物增加。与此同时，有反应者的微生物组群落相对稳定，产丁酸菌更为丰富。网络分析揭示了有反应者和无反应者之间微生物相互作用的不同模式，其中产丁酸菌在有反应者中形成强大网络，而机会致病菌在无反应者中形成强大网络。建立了一个用于预测nCRT反应的贝叶斯网络模型，产丁酸菌起主要预测作用。
我们的研究表明LARC患者的微生物组与nCRT反应之间存在显著关联，从而开发出基于微生物组的反应预测模型。这些发现提示微生物组特征在预测和优化LARC患者nCRT治疗方面的潜在应用。
