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Original Article
Relationships between the Microbiome and Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer
Hye In Lee1,2orcid , Bum-Sup Jang1orcid , Ji Hyun Chang1, Eunji Kim3, Tae Hoon Lee1,4, Jeong Hwan Park5, Eui Kyu Chie1,6orcid

DOI: https://doi.org/10.4143/crt.2024.521 [Accepted]
Published online: December 16, 2024
1Department of Radiation Oncology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
2Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
3Department of Radiation Oncology, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Korea
4Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
5Department of Pathology, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Korea
6Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Korea
Corresponding author:  Eui Kyu Chie
Tel: 82-2-2072-3705 Email: ekchie93@snu.ac.kr
Hye In Lee and Bum-Sup Jang contributed equally to this work.
Received: 2 June 2024   • Accepted: 13 December 2024
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Purpose
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.
Materials and Methods
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.
Results
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.
Conclusion
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.

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