424.肠道微生物群对食管鳞状细胞癌患者新辅助免疫疗法联合化疗反应的影响

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-09-02 DOI:10.1093/dote/doae057.175
Jianfeng Zhou
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Microbiota analysis was conducted using the R language, and group differences were assessed using appropriate statistical methods. Results In this study, 43 patients (totally 122 stool samples) with neoadjuvant immunotherapy combined with chemotherapy for esophageal squamous cell carcinoma were enrolled from December 2020 to January 2022. 16S rDNA sequencing results suggested that patients in the sensitive group had higher total fecal microbiota diversity than those in the drug-resistant group, and a notable variation in the structure of the microbiota was detected between the two groups. During treatment, the results did not show any significant statistical difference in the number of species (alpha diversity) in the sensitive group compared to the resistant group, although there was a trend towards a higher number of species in the pre-treatment sensitive group. As the treatment progressed, starting from the second cycle of treatment, the sensitive group had a higher alpha diversity than the resistant group. And we found that Bray-Curtis distance assessment of beta diversity showed that the difference between the sensitive and resistant groups also became more pronounced as treatment progressed. At the phylum level, the gut flora composition of the sensitive group remained relatively stable as treatment progressed, while the microbial diversity of the resistant group appeared to decrease. At the genus level, Bifidobacterium, Faecalibacterium, Akkermansia and Ruminococcus were more abundant in the immunotherapy-sensitive group; whereas Ligilactobacillus and Escherichia-Shigella was more rich in the immunotherapy-resistant group. The prediction model based on the baseline differences of gut microbiota in the two groups using a random forest method can be applied to predict the efficacy of immunotherapy for esophageal squamous cell carcinoma. 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引用次数: 0

摘要

背景 探索肠道微生物群多样性对食管鳞状细胞癌(ESCC)患者免疫治疗联合化疗疗效的影响,旨在找出预测治疗结果的微生物标记物。方法 在这项研究中,我们前瞻性地招募了被诊断为局部晚期食管鳞状细胞癌并计划接受新辅助免疫疗法联合化疗的患者;在第一周期、第二周期免疫疗法前和术前疾病评估时采集粪便标本。为了检测肠道微生物群的组成和丰度,我们采用了 16S rDNA 测序技术。测序数据使用 FLASH 和 Uparse 软件进行预处理,并使用 SSUrRNA 数据库进行物种注释。使用 R 语言进行微生物群分析,并使用适当的统计方法评估组间差异。结果 2020年12月至2022年1月,43名食管鳞癌新辅助免疫疗法联合化疗患者(共122份粪便样本)被纳入本研究。16S rDNA测序结果显示,敏感组患者的粪便微生物群总多样性高于耐药组患者,两组患者的微生物群结构存在明显差异。在治疗过程中,结果显示敏感组与耐药组在物种数量(α多样性)上没有明显的统计学差异,但治疗前敏感组的物种数量有增加的趋势。随着治疗的进行,从第二个治疗周期开始,敏感组的α多样性高于抗性组。我们还发现,对贝塔多样性进行的布雷-柯蒂斯距离评估显示,随着处理的进行,敏感组和抗性组之间的差异也越来越明显。在门一级,随着治疗的进行,敏感组的肠道菌群组成保持相对稳定,而耐药组的微生物多样性似乎有所下降。在属的层面上,免疫治疗敏感组的双歧杆菌、粪便杆菌、阿克曼斯菌和反刍球菌含量更高,而免疫治疗耐药组的舌状乳杆菌和志贺氏杆菌含量更高。基于两组肠道微生物群基线差异的随机森林法预测模型可用于预测食管鳞癌免疫治疗的疗效。结论 接受免疫治疗联合化疗的食管鳞状细胞癌敏感组和耐受组在物种多样性和微生物群结构上存在差异,基于两组治疗前微生物群差异的预测模型可用于预测免疫治疗联合化疗对食管鳞状细胞癌的疗效。
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424. EFFECT OF GUT MICROBIOTA ON THE RESPONSE TO NEOADJUVANT IMMUNOTHERAPY COMBINED WITH CHEMOTHERAPY IN PATIENTS WITH ESOPHAGEAL SQUAMOUS CELL CARCINOMA
Background To explore the impact of gut microbiota diversity on the efficacy of immunotherapy combined with chemotherapy in esophageal squamous cell carcinoma (ESCC) patients, aiming to identify microbial markers for predicting treatment outcomes. Methods In this study, we prospectively enrolled patients diagnosed with locally advanced esophageal squamous cell carcinoma who were scheduled to undergo neoadjuvant immunotherapy combined with chemotherapy; stool specimens were collected before the first cycle, the second cycle of immunotherapy, and at the time of preoperative disease evaluation. To examine the gut microbiota's composition and abundance, we employed the 16S rDNA sequencing technique. The sequencing data were preprocessed with FLASH and Uparse software, and species annotation was performed using the SSUrRNA database. Microbiota analysis was conducted using the R language, and group differences were assessed using appropriate statistical methods. Results In this study, 43 patients (totally 122 stool samples) with neoadjuvant immunotherapy combined with chemotherapy for esophageal squamous cell carcinoma were enrolled from December 2020 to January 2022. 16S rDNA sequencing results suggested that patients in the sensitive group had higher total fecal microbiota diversity than those in the drug-resistant group, and a notable variation in the structure of the microbiota was detected between the two groups. During treatment, the results did not show any significant statistical difference in the number of species (alpha diversity) in the sensitive group compared to the resistant group, although there was a trend towards a higher number of species in the pre-treatment sensitive group. As the treatment progressed, starting from the second cycle of treatment, the sensitive group had a higher alpha diversity than the resistant group. And we found that Bray-Curtis distance assessment of beta diversity showed that the difference between the sensitive and resistant groups also became more pronounced as treatment progressed. At the phylum level, the gut flora composition of the sensitive group remained relatively stable as treatment progressed, while the microbial diversity of the resistant group appeared to decrease. At the genus level, Bifidobacterium, Faecalibacterium, Akkermansia and Ruminococcus were more abundant in the immunotherapy-sensitive group; whereas Ligilactobacillus and Escherichia-Shigella was more rich in the immunotherapy-resistant group. The prediction model based on the baseline differences of gut microbiota in the two groups using a random forest method can be applied to predict the efficacy of immunotherapy for esophageal squamous cell carcinoma. Conclusion There are differences in species diversity and microbiota structure between the sensitive and resistant groups of esophageal squamous cell carcinoma patients receiving immunotherapy combined with chemotherapy, and the predictive model, which is based on the microbiota differences between the two groups before treatment, could be clinically relevant in predicting the efficacy of immunotherapy combined with chemotherapy for esophageal squamous cell carcinoma.
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ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
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9.40
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2.10%
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464
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