Gut Microbiome Components Predict Response to Neoadjuvant Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer: A Prospective, Longitudinal Study
Gut Microbiome Components Predict Response to Neoadjuvant Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer: A Prospective, Longitudinal Study
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肠道微生物组成分可预测局部晚期直肠癌患者对新辅助放化疗的反应：一项前瞻性纵向研究
DOI：
10.1158/1078-0432.ccr-20-3445
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发表时间：
2021
影响因子：
11.5
通讯作者：
Zhang Zhen
中科院分区：
文献类型：
--
作者：
Yi Yuxi;Shen Lijun;Shi Wei;Xia Fan;Zhang Hui;Wang Yan;Zhang Jing;Wang Yaqi;Sun Xiaoyang;Zhang Zhiyuan;Zou Wei;Yang Wang;Zhang Lingyi;Zhu Ji;Goel Ajay;Ma Yanlei;Zhang Zhen
目的肠道菌群参与抗肿瘤免疫治疗和化疗反应;然而，关于肠道菌群在预测局部晚期直肠癌（LARC）患者新辅助放化疗（nCRT）反应中的作用的循证研究仍然很少。这项前瞻性的纵向研究旨在评估肠道微生物组预测nCRT responses.Experimental DesignWe的可行性收集了167个粪便样本从84例LARC患者nCRT前后和31个标本从健康人16 S rRNA测序。根据nCRT的病理反应将患者分为反应者和非反应者。在确定了与nCRT反应相关的微生物生物标志物后，我们构建了一个随机森林分类器，用于对来自37例患者的基线样本的训练队列进行nCRT反应预测，并在另一个47 patients.ResultsWe的队列中验证了该分类器。此外，在基线样本中注意到应答者和无应答者之间的显著微生物群差异。与丁酸盐产生相关的微生物，包括罗斯拜瑞氏菌属、多雷氏菌属和厌氧柄菌属，在应答者中的比例过高，而Coriobacteriaceae和Fusobacterium在无应答者中的比例过高。选择了10种生物标志物用于响应预测分类器，包括Dorea、Anaerostipes和Streptococcus，其在训练队列中产生的曲线下面积值为93.57% [95%置信区间（CI），85.76%-100%]，在训练队列中产生的曲线下面积值为73.53%[95%置信区间（CI），85.76%-100%]。（95%CI，58.96%-88.11%.ConclusionsThe gut microbiome provides new potential biomarkers for predicting nCRT responses，which has important manifestations in the clinical management of these patients.
PurposeThe gut microbiome is involved in antitumor immunotherapy and chemotherapy responses; however, evidence-based research on the role of gut microbiome in predicting response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) remains scarce. This prospective, longitudinal study aimed to evaluate the feasibility of the gut microbiome in predicting nCRT responses.Experimental DesignWe collected 167 fecal samples from 84 patients with LARC before and after nCRT and 31 specimens from healthy individuals for 16S rRNA sequencing. Patients were divided into responders and nonresponders according to pathologic response to nCRT. After identifying microbial biomarkers related to nCRT responses, we constructed a random forest classifier for nCRT response prediction of a training cohort of baseline samples from 37 patients and validated the classifier in another cohort of 47 patients.ResultsWe observed significant microbiome alterations represented by a decrease in LARC-related pathogens and an increase inLactobacillusandStreptococcusduring nCRT. Furthermore, a prominent microbiota difference between responders and nonresponders was noticed in the baseline samples. Microbes related with butyrate production, includingRoseburia, Dorea, andAnaerostipes, were overrepresented in responders, whereasCoriobacteriaceaeandFusobacteriumwere overrepresented in nonresponders. Ten biomarkers were selected for the response-prediction classifier, includingDorea, Anaerostipes, andStreptococcus, which yielded an area under the curve value of 93.57% [95% confidence interval (CI), 85.76%–100%] in the training cohort and 73.53% (95% CI, 58.96%–88.11%) in the validation cohort.ConclusionsThe gut microbiome offers novel potential biomarkers for predicting nCRT responses, which has important manifestations in the clinical management of these patients.