Pathobionts in the tumour microbiota predict survival following resection for colorectal cancer.
Pathobionts in the tumour microbiota predict survival following resection for colorectal cancer.
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DOI：
10.1186/s40168-023-01518-w
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发表时间：
2023-05-08
期刊：
影响因子：
15.5
通讯作者：
Kinross, James
中科院分区：
文献类型：
--
作者：
Alexander, James L.;Posma, Joram M.;Scott, Alasdair;Poynter, Liam;Mason, Sam E.;Doria, M. Luisa;Herendi, Lili;Roberts, Lauren;McDonald, Julie A. K.;Cameron, Simon;Hughes, David J.;Liska, Vaclav;Susova, Simona;Soucek, Pavel;Der Sluis, Verena Horneffer-van;Gomez-Romero, Maria;Lewis, Matthew R.;Hoyles, Lesley;Woolston, Andrew;Cunningham, David;Darzi, Ara;Gerlinger, Marco;Goldin, Robert;Takats, Zoltan;Marchesi, Julian R.;Teare, Julian;Kinross, James
肠道微生物群与结直肠癌（CRC）的发病机制有关。我们的目的是绘制CRC粘膜微生物群和代谢组，并确定肿瘤微生物群对肿瘤学结果的影响。在英国（n = 74）和捷克共和国（n = 61）接受初次手术切除的CRC患者中进行了一项多中心、前瞻性观察性研究。使用metataxonomics、超高效液相色谱-质谱（UPLC-MS）、靶向细菌qPCR和肿瘤外显子组测序进行分析。对临床和肿瘤学协变量进行分层聚类，以确定与CRC相关的细菌和代谢物簇。考克斯比例风险回归用于确定与中位随访50个月的无病生存率相关的聚类。鉴定了13个粘膜微生物群簇，其中5个在肿瘤和配对的正常粘膜之间显著不同。簇7，含有致病菌具核梭杆菌和颗粒状芽孢杆菌，与CRC密切相关（PFDR = 0.0002）。此外，第7组的肿瘤优势独立预测了有利的无病生存期（校正p = 0.031）。簇1，包含普氏粪杆菌和活泼瘤胃球菌，与癌症呈负相关（PFDR = 0.0009），丰度独立预测无病生存率较差（校正p = 0.0009）。UPLC-MS分析揭示了两个主要的代谢（Met）簇。Met 1由中链（MCFA）、长链（LCFA）和极长链（VLCFA）脂肪酸种类、神经酰胺和溶血磷脂组成，与CRC呈负相关（PFDR = 2.61 × 10−11）; Met 2由磷脂酰胆碱、核苷和氨基酸组成，与结直肠癌密切相关（PFDR = 1.30 × 10−12），但代谢物簇与无病生存率无关（p = 0.358）。Met 1与DNA错配修复缺陷之间存在相关性（p = 0.005）。FBXW 7突变仅在微生物群簇7中占主导地位的癌症中发现。肿瘤粘膜生态位中的致病菌网络与肿瘤突变和代谢亚型相关，并预测结直肠癌切除术后的良好结局。视频摘要在线版本包含补充材料，可通过10. 1186/s40168-023-01518-w获取。
The gut microbiota is implicated in the pathogenesis of colorectal cancer (CRC). We aimed to map the CRC mucosal microbiota and metabolome and define the influence of the tumoral microbiota on oncological outcomes. A multicentre, prospective observational study was conducted of CRC patients undergoing primary surgical resection in the UK (n = 74) and Czech Republic (n = 61). Analysis was performed using metataxonomics, ultra-performance liquid chromatography-mass spectrometry (UPLC-MS), targeted bacterial qPCR and tumour exome sequencing. Hierarchical clustering accounting for clinical and oncological covariates was performed to identify clusters of bacteria and metabolites linked to CRC. Cox proportional hazards regression was used to ascertain clusters associated with disease-free survival over median follow-up of 50 months. Thirteen mucosal microbiota clusters were identified, of which five were significantly different between tumour and paired normal mucosa. Cluster 7, containing the pathobionts Fusobacterium nucleatum and Granulicatella adiacens, was strongly associated with CRC (PFDR = 0.0002). Additionally, tumoral dominance of cluster 7 independently predicted favourable disease-free survival (adjusted p = 0.031). Cluster 1, containing Faecalibacterium prausnitzii and Ruminococcus gnavus, was negatively associated with cancer (PFDR = 0.0009), and abundance was independently predictive of worse disease-free survival (adjusted p = 0.0009). UPLC-MS analysis revealed two major metabolic (Met) clusters. Met 1, composed of medium chain (MCFA), long-chain (LCFA) and very long-chain (VLCFA) fatty acid species, ceramides and lysophospholipids, was negatively associated with CRC (PFDR = 2.61 × 10−11); Met 2, composed of phosphatidylcholine species, nucleosides and amino acids, was strongly associated with CRC (PFDR = 1.30 × 10−12), but metabolite clusters were not associated with disease-free survival (p = 0.358). An association was identified between Met 1 and DNA mismatch-repair deficiency (p = 0.005). FBXW7 mutations were only found in cancers predominant in microbiota cluster 7. Networks of pathobionts in the tumour mucosal niche are associated with tumour mutation and metabolic subtypes and predict favourable outcome following CRC resection. Video Abstract The online version contains supplementary material available at 10.1186/s40168-023-01518-w.
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影响因子：
24.5
作者：
Mima K;Nishihara R;Qian ZR;Cao Y;Sukawa Y;Nowak JA;Yang J;Dou R;Masugi Y;Song M;Kostic AD;Giannakis M;Bullman S;Milner DA;Baba H;Giovannucci EL;Garraway LA;Freeman GJ;Dranoff G;Garrett WS;Huttenhower C;Meyerson M;Meyerhardt JA;Chan AT;Fuchs CS;Ogino S
通讯作者：
Ogino S
影响因子：
5.9
作者：
Brown DG;Rao S;Weir TL;O'Malia J;Bazan M;Brown RJ;Ryan EP
通讯作者：
Ryan EP
影响因子：
14.9
作者：
NCBI Resource Coordinators
通讯作者：
NCBI Resource Coordinators
影响因子：
30.3
作者：
Chung L;Thiele Orberg E;Geis AL;Chan JL;Fu K;DeStefano Shields CE;Dejea CM;Fathi P;Chen J;Finard BB;Tam AJ;McAllister F;Fan H;Wu X;Ganguly S;Lebid A;Metz P;Van Meerbeke SW;Huso DL;Wick EC;Pardoll DM;Wan F;Wu S;Sears CL;Housseau F
通讯作者：
Housseau F
影响因子：
3.9
作者：
NOBILE, S;DESHUSSES, J
通讯作者：
DESHUSSES, J