Dataset Information

Leveraging Fecal Bacterial Survey Data to Predict Colorectal Tumors.

ABSTRACT: Colorectal cancer (CRC) ranks second in cancer-associated mortality and third in the incidence worldwide. Most of CRC follow adenoma-carcinoma sequence, and have more than 90% chance of survival if diagnosed at early stage. But the recommended screening by colonoscopy is invasive, expensive, and poorly adhered to. Recently, several studies reported that the fecal bacteria might provide non-invasive biomarkers for CRC and precancerous tumors. Therefore, we collected and uniformly re-analyzed these published fecal 16S rDNA sequencing datasets to verify the association and identify biomarkers to classify and predict colorectal tumors by random forest method. A total of 1674 samples (330 CRC, 357 advanced adenoma, 141 adenoma, and 846 control) from 7 studies were analyzed in this study. By random effects model and fixed effects model, we observed significant differences in alpha-diversity and beta-diversity between individuals with CRC and the normal colon, but not between adenoma and the normal. We identified various bacterial genera with significant odds ratios for colorectal tumors at different stages. Through building random forest model with 10-fold cross-validation as well as new test datasets, we classified individuals with CRC, advanced adenoma, adenoma and normal colon. All approaches obtained comparable performance at entire OTU level, entire genus level, and the common genus level as measured using AUC. When combined all samples, the AUC of random forest model based on 12 common genera reached 0.846 for CRC, although the predication performed poorly for advance adenoma and adenoma.

SUBMITTER: Zhang B

PROVIDER: S-EPMC6547015 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Leveraging Fecal Bacterial Survey Data to Predict Colorectal Tumors.

Zhang Bangzhou B Xu Shuangbin S Xu Wei W Chen Qiongyun Q Chen Zhangran Z Yan Changsheng C Fan Yanyun Y Zhang Huangkai H Liu Qi Q Yang Jie J Yang Jinfeng J Xiao Chuanxing C Xu Hongzhi H Ren Jianlin J

Frontiers in genetics 20190528

Colorectal cancer (CRC) ranks second in cancer-associated mortality and third in the incidence worldwide. Most of CRC follow adenoma-carcinoma sequence, and have more than 90% chance of survival if diagnosed at early stage. But the recommended screening by colonoscopy is invasive, expensive, and poorly adhered to. Recently, several studies reported that the fecal bacteria might provide non-invasive biomarkers for CRC and precancerous tumors. Therefore, we collected and uniformly re-analyzed thes ...[more]

PMID: 31191599

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Project description:An increasing body of literature suggests that both individual and collections of bacteria are associated with the progression of colorectal cancer. As the number of studies investigating these associations increases and the number of subjects in each study increases, a meta-analysis to identify the associations that are the most predictive of disease progression is warranted. We analyzed previously published 16S rRNA gene sequencing data collected from feces and colon tissue. We quantified the odds ratios (ORs) for individual bacterial taxa that were associated with an individual having tumors relative to a normal colon. Among the fecal samples, there were no taxa that had significant ORs associated with adenoma and there were 8 taxa with significant ORs associated with carcinoma. Similarly, among the tissue samples, there were no taxa that had a significant OR associated with adenoma and there were 3 taxa with significant ORs associated with carcinoma. Among the significant ORs, the association between individual taxa and tumor diagnosis was equal to or below 7.11. Because individual taxa had limited association with tumor diagnosis, we trained Random Forest classification models using only the taxa that had significant ORs, using the entire collection of taxa found in each study, and using operational taxonomic units defined based on a 97% similarity threshold. All training approaches yielded similar classification success as measured using the area under the curve. The ability to correctly classify individuals with adenomas was poor, and the ability to classify individuals with carcinomas was considerably better using sequences from feces or tissue.IMPORTANCE Colorectal cancer is a significant and growing health problem in which animal models and epidemiological data suggest that the colonic microbiota have a role in tumorigenesis. These observations indicate that the colonic microbiota is a reservoir of biomarkers that may improve our ability to detect colonic tumors using noninvasive approaches. This meta-analysis identifies and validates a set of 8 bacterial taxa that can be used within a Random Forest modeling framework to differentiate individuals as having normal colons or carcinomas. When models trained using one data set were tested on other data sets, the models performed well. These results lend support to the use of fecal biomarkers for the detection of tumors. Furthermore, these biomarkers are plausible candidates for further mechanistic studies into the role of the gut microbiota in tumorigenesis.

Project description:BackgroundCancer patients experience gastrointestinal and behavioral symptoms, and are at increased risk of systemic infection and inflammation. These conditions are a major source of morbidity and decreased quality of life prior to cancer treatment, but poorly defined etiologies impede successful treatment. The gastrointestinal microbiota shape inflammation, influence cancer progression and treatment, and colonize tumors. However, research has not directly determined if peripheral tumors influence the microbiome and intestinal physiology, thus influencing gastrointestinal and behavioral symptoms. Therefore, the purpose of this study was to examine consequences of orthotopic, syngeneic mammary tumor implantation, growth, and resection on fecal bacteriome composition and intestinal barrier function in relation to systemic inflammation and enteric bacterial translocation in mice.MethodsFemale mice were randomized to 3 experimental groups: sham surgical control, tumor recipients, and tumor recipients later receiving tumor-resection. Mice were sacrificed three weeks after tumor implantation or resection for collection of stool, colon, spleen, and brain tissue and analysis.ResultsTumor-bearing mice exhibited several markers of colonic barrier disruption, including dampened expression of tight junction proteins (Cldn1 and Ocln) and elevated circulating lipopolysaccharide binding protein (LBP). Compromised colonic barrier integrity was associated with altered fecal bacterial profiles in tumor-mice, including lower relative abundance of Lactobacillus, but higher Bacteroides. Consistent with colonic barrier disruption and altered microbiomes, tumor-mice displayed markers of systemic inflammation including splenomegaly, higher splenic bacterial load, and elevated splenic and brain pro-inflammatory cytokines. Several bacteria cultured from spleens had 16S rRNA gene amplicons matching those in fecal samples, suggesting they were of intestinal origin. Fecal Lactobacillus was highly-interrelated to physiological parameters disrupted by tumors via correlation network analysis. Tumor resection ameliorated circulating LBP, splenomegaly, and splenic cytokines, but not other parameters associated with loss of colonic barrier integrity and bacterial translocation.ConclusionsOrthotopic mammary tumors alter the microbiome, reduce intestinal barrier function, increase translocation of enteric bacteria, and alter systemic inflammation. This provides insight into how tumors commence gastrointestinal and behavioral symptoms prior to treatment, and identify targets for future therapeutics, such as probiotic Lactobacillus supplementation.

Project description:Background:Colorectal cancer (CRC) is a complex multifactorial disease. Increasing evidence suggests that the microbiome is involved in different stages of CRC initiation and progression. Beyond specific pro-oncogenic mechanisms found in pathogens, metagenomic studies indicate the existence of a microbiome signature, where particular bacterial taxa are enriched in the metagenomes of CRC patients. Here, we investigate to what extent the abundance of bacterial taxa in CRC metagenomes can be explained by the growth advantage resulting from the presence of specific CRC metabolites in the tumor microenvironment. Methods:We composed lists of metabolites and bacteria that are enriched on CRC samples by reviewing metabolomics experimental literature and integrating data from metagenomic case-control studies. We computationally evaluated the growth effect of CRC enriched metabolites on over 1500 genome-based metabolic models of human microbiome bacteria. We integrated the metabolomics data and the mechanistic models by using scores that quantify the response of bacterial biomass production to CRC-enriched metabolites and used these scores to rank bacteria as potential CRC passengers. Results:We found that metabolic networks of bacteria that are significantly enriched in CRC metagenomic samples either depend on metabolites that are more abundant in CRC samples or specifically benefit from these metabolites for biomass production. This suggests that metabolic alterations in the cancer environment are a major component shaping the CRC microbiome. Conclusion:Here, we show with in sillico models that supplementing the intestinal environment with CRC metabolites specifically predicts the outgrowth of CRC-associated bacteria. We thus mechanistically explain why a range of CRC passenger bacteria are associated with CRC, enhancing our understanding of this disease. Our methods are applicable to other microbial communities, since it allows the systematic investigation of how shifts in the microbiome can be explained from changes in the metabolome.

Dataset Information

Leveraging Fecal Bacterial Survey Data to Predict Colorectal Tumors.

Publications

Leveraging Fecal Bacterial Survey Data to Predict Colorectal Tumors.

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