Bum-Sup Jang1, Ji Hyun Chang2, Eui Kyu Chie3, Kyubo Kim4, Ji Won Park5, Min Jung Kim5, Eun-Ji Song6, Young-Do Nam6, Seung Wan Kang7, Seung-Yong Jeong5, Hak Jae Kim8. 1. Department of Radiation Oncology, Seoul National University Bundang Hospital, Seongnam, Korea. 2. Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea. 3. Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea; Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Korea. 4. Department of Radiation Oncology, Ewha Womans University College of Medicine, Seoul, Korea. 5. Department of Surgery, Seoul National University Hospital, Seoul, Korea; Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea. 6. Department of Food Biotechnology, Korea University of Science and Technology, Daejeon, Korea; Research Group of Healthcare, Korea Food Research Institute, Wanju, Korea. 7. Department of Nursing, Seoul National University College of Nursing, Seoul, Korea. 8. Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea; Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Korea; Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea. Electronic address: khjae@snu.ac.kr.
Abstract
PURPOSE: There are ongoing investigations to find promising biomarkers for predicting a complete response (CR) after concurrent chemoradiation (CCRT) in rectal cancer. We aimed to find the predictive value in the gut microbiome in terms of response after preoperative CCRT. METHODS AND MATERIALS: We collected a total of 45 fecal samples from patients with rectal cancer before CCRT. Tumor response after CCRT was assessed according to the American Joint Committee on Cancer tumor regression grading system. Analysis of linear discriminant analysis effect size and MetaCyc pathway abundance predictions were performed to compare composition and metabolic function of microbiome between patients with and without CR. We also established a Bayesian network model to identify microbial networks and species to be related with CCRT response. RESULTS: Seven patients (15.6%) demonstrated pathologically CR, and 38 patients (84.4%) showed non-CR after preoperative CCRT. Between CR and non-CR patients, there was a significant difference in terms of β-diversity (P = .028), but no difference in α-diversity was found. Bacteroidales (Bacteroidaceae, Rikenellaceae, Bacteroides) were relatively more abundant in patients with non-CR than those with CR. Pathways related to anabolic function predominated in CR patients. According to Bayesian network analysis, Duodenibacillus massiliensis was linked with the improved CR rate. CONCLUSIONS: From the fecal microbiome using samples obtained before preoperative CCRT, differences in microbial community composition and functions were observed between patients with and without CR in rectal cancer. However, the finding that a specific taxon may be linked with the improved therapeutic response should be verified in a prospective setting.
PURPOSE: There are ongoing investigations to find promising biomarkers for predicting a complete response (CR) after concurrent chemoradiation (CCRT) in rectal cancer. We aimed to find the predictive value in the gut microbiome in terms of response after preoperative CCRT. METHODS AND MATERIALS: We collected a total of 45 fecal samples from patients with rectal cancer before CCRT. Tumor response after CCRT was assessed according to the American Joint Committee on Cancer tumor regression grading system. Analysis of linear discriminant analysis effect size and MetaCyc pathway abundance predictions were performed to compare composition and metabolic function of microbiome between patients with and without CR. We also established a Bayesian network model to identify microbial networks and species to be related with CCRT response. RESULTS: Seven patients (15.6%) demonstrated pathologically CR, and 38 patients (84.4%) showed non-CR after preoperative CCRT. Between CR and non-CR patients, there was a significant difference in terms of β-diversity (P = .028), but no difference in α-diversity was found. Bacteroidales (Bacteroidaceae, Rikenellaceae, Bacteroides) were relatively more abundant in patients with non-CR than those with CR. Pathways related to anabolic function predominated in CR patients. According to Bayesian network analysis, Duodenibacillus massiliensis was linked with the improved CR rate. CONCLUSIONS: From the fecal microbiome using samples obtained before preoperative CCRT, differences in microbial community composition and functions were observed between patients with and without CR in rectal cancer. However, the finding that a specific taxon may be linked with the improved therapeutic response should be verified in a prospective setting.
Authors: Kasun Wanigasooriya; Joao D Barros-Silva; Louise Tee; Mohammed E El-Asrag; Agata Stodolna; Oliver J Pickles; Joanne Stockton; Claire Bryer; Rachel Hoare; Celina M Whalley; Robert Tyler; Toritseju Sillo; Christopher Yau; Tariq Ismail; Andrew D Beggs Journal: Front Oncol Date: 2022-07-04 Impact factor: 5.738
Authors: Byeongsang Oh; Thomas Eade; Gillian Lamoury; Susan Carroll; Marita Morgia; Andrew Kneebone; George Hruby; Mark Stevens; Frances Boyle; Stephen Clarke; Brian Corless; Mark Molloy; David Rosenthal; Michael Back Journal: Cancers (Basel) Date: 2021-05-13 Impact factor: 6.639