ESTRO 2023 - Abstract Book

S210

Saturday 13 May

ESTRO 2023

expressed as the standardized mean difference (SMD), calculated as the mean difference between groups divided by the pooled standard deviation. Confounders were eliminated using inverse probability of treatment weighting (IPTW). Outcomes were compared using log-binomial and Poisson regression. Results 664 patients were included in the SCRT-direct surgery and 238 in the SCRT-delay group. Before IPTW, patients in the SCRT- direct surgery group were slightly younger (67 (median, interquartile range (IQR): 58-74) versus (vs.) 68 (median, IQR: 60- 77), SMD = 0.18), had a lower American Society of Anaesthesiologists (ASA) classification (ASA 1-2: 81% vs. 72%, SMD = 0.26) and were more often treated with a (low) anterior resection without an ostomy (41% vs. 28, SMD = 0.29) and less often with a (low) anterior resection with an ostomy (42% vs. 51%) or an abdominoperineal resection (17% vs. 22%) than patients in the SCRT-delay group. After IPTW, confounders were well balanced. After IPTW, the 90-day postoperative complication rate was comparable between SCRT-direct surgery and SCRT-delay (40% vs. 42%, risk ratio (RR) = 1.1 [95%confidence interval (CI): 0.9; 1.3], p=0.6). All other postoperative outcomes were similar, except for pCR, which occurred more often following SCRT-delay than following SCRT-direct surgery (10% vs. 0.3%, RR = 39 [95%CI: 11, 139], p < 0.001). Conclusion Real-world evidence could not confirm the advantage in postoperative complications of SCRT-delay compared to SCRT- direct surgery, but did confirm the increased pCR rate following SCRT-delay. SCRT-delay followed by a response assessment should be offered to patients who are interested in non-operative management. SCRT-direct surgery still seems a valid option for patients who prefer surgery. OC-0276 Relationships between microbiome and response to neoadjuvant chemoradiotherapy in rectal cancer H.I. Lee 1 , B. Jang 1 , J.H. Chang 1 , T.H. Lee 1 , J.H. Park 1 , E.K. Chie 1 1 Seoul National University Hospital, Department of Radiation Oncology, Seoul, Korea Republic of Purpose or Objective Gut microbiome is known to be involved in antitumor immunotherapy and chemotherapy responses; however, few research has focused on the role of gut microbiome in the setting of concurrent chemoradiotherapy (CCRT). In this study, we investigated the tumor microbiome dynamics in patients undergoing neoadjuvant CCRT for locally advanced rectal cancer and sought to determine whether the diversity and composition of microbiome affect treatment response. Materials and Methods A total of 103 samples from 26 patients with locally advanced rectal cancer were collected and 16S ribosomal RNA amplicon sequencing was performed. All patients underwent neoadjuvant CCRT followed by surgical resection between 2008 and 2016. Samples were obtained from both tumor and normal rectal tissue at pre- and post-CCRT. According to the American Joint Committee on Cancer tumor regression grading (TRG) system, patients were divided into responders (TRG 0, 1) and non-responders (TRG 2, 3). We performed diversity, taxonomy, and network analyses to compare responders and non- responders. Then, we established the Bayesian network model to predict treatment response in patients with rectal cancer. Results Overall, we detected 1260 microbial genera from 287 families, 132 orders, 56 classes, and 32 phyla in the bacteria kingdom. Between tumor and normal rectal tissues, there was no difference in microbial diversity and composition. On the other hand, there was a significant decrease in diversity and compositional alterations when comparing pre- and post-CCRT samples (all p<0.001). Ten patients (38.5%) were classified as responders and 16 patients (61.5%) were classified as non- responders. In both groups, CCRT significantly reduced microbial diversity and altered their composition, but it was more pronounced in non-responders. In taxonomic analysis of pre-CCRT samples, butyrate-producing bacteria were differentially enriched in responders. Meanwhile, in post-CCRT samples, opportunistic pathogen were overrepresented in non-responders. The network analysis revealed that butyrate-producing bacteria had strong interactions in responders, whereas opportunistic pathogen demonstrated strong interactions in non-responders (Pearson’s coefficient>0.5). Finally, five microbes were selected as the optimal set for the response prediction model, which yielded an area under the curve value CCRT significantly changed the diversity and composition of microbiome, especially in non-responders. Several microbes might be related with treatment response. These findings highlight the potential of microbiome to play an important role as a biomarker in patients with rectal cancer. [Figure 1] of 82.33%. Conclusion