RETRACTED ARTICLE: Microbiome analyses of blood and tissues suggest cancer
diagnostic approach

Poore, Gregory; Kopylova, Evguenia; Zhu, Quanyin; Carpenter, Carolina S.; Fraraccio, Serena; Wandro, Stephen; Kościółek, Tomasz; Janssen, Stefan; Metcalf, Jessica L.; Song, Se Jin; Kanbar, Jad; Miller‐Montgomery, Sandrine; Heaton, Robert K.; McKay, Rana R.; Patel, Sandip Pravin; Swafford, Austin D.; Knight, Rob

doi:10.1038/s41586-020-2095-1

Cited by 797 publications

(1,048 citation statements)

References 81 publications

Supporting

Mentioning

925

Contrasting

Unclassified

Order By: Relevance

“…However, there is no correlation between Streptomyces abundance and RNA spike-in transcript levels across cells from patient BC06 FDR=.22). A recent survey of the tumor microbiome across TCGA found reads belonging to the Streptomyces genus in all but one of the breast cancer samples and high abundance of Streptomyces in ~10% (116/1086) of all breast cancer samples (Poore et al, 2020). Together with its cell type specificity in that patient, these findings testify that the presence of Streptomyces is not a result of sample contamination and that it is located intracellularly within the tumor cells.…”

Section: Resultsmentioning

confidence: 81%

“…In pancreatic cancer, a subset of Gammaproteobacteria were shown to mediate tumor resistance to chemotherapy (Geller et al, 2017). Recently, an analysis of the Cancer Genome Atlas (TCGA) cohort identified a variety of bacterial genera that reside in different tumor types, demonstrating that after filtering out potentially contaminant species, one can successfully build a predictor of cancer type based on tumors' microbial composition (Poore et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

scRNA-seq analysis of colon and esophageal tumors uncovers abundant microbial reads in myeloid cells undergoing proinflammatory transcriptional alterations

Robinson

Stone²,

Ruppin

et al. 2020

Preprint

View full text Add to dashboard Cite

Microbial taxa that are differentially abundant between cell types are likely to be intracellular. Here we describe a new computational pipeline called CSI-Microbes (computational identification of Cell type Specific Intracellular Microbes) that aims to identify such putative intracellular species from single cell RNA-seq data in a given tumor sample. CSI-microbes also includes additional steps that can be applied to filter out microbial contaminants from the bona fide microbial residents of cells in the patients. We first test and validate CSI-microbes on a dataset of immune cells deliberately infected with Salmonella. We then apply CSI-microbes to identify intracellular microbes in breast cancer and melanoma. We identify Streptomyces as differentially abundant in the tumor cells of one breast cancer sample. We further identify three bacterial genera and four fungal genera that are differentially abundant and hence likely to be intracellular in the tumor cells in melanoma samples. No cell type specific bacteria were identified in our analysis of brain tumor samples. In sum, CSI-Microbes offers a new way to identify likely intracellular microbes living within specific cell populations in malignant tumors, markedly extending upon previous studies aimed at inferring microbial abundance from bulk tumor expression data.

show abstract

Section: Resultsmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

scRNA-seq analysis of colon and esophageal tumors uncovers abundant microbial reads in myeloid cells undergoing proinflammatory transcriptional alterations

Robinson

Stone²,

Ruppin

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The abundance of 1,093 genus-level microbial taxa was quantified from tissue RNAseq data after rigorous QC, batch correction, and contamination filtering, and normalized to 1 million reads to make data comparable across samples [30]. Seven hundred six normal tissues and 9,801 tumor samples were included in the analysis where data were available.…”

Section: Analysis Of Microbial Correlatesmentioning

confidence: 99%

“…We also acknowledge that the microbiota we analyzed were identified from tissue RNAseq data, and the sample collection and preparation of tissue RNAseq was not designed originally to completely rule out potential contamination or confirm the vitality of identified microbes. However, these source data constitute the largest collection of microbiota communities identified from patients with cancer, have previously been used in this manner to build prediction algorithms, and the data were optimized via rigorous methodology to control for noise across the data set [30]. We also note that we are unable in this analysis to comment on respiratory or fecal samples from patients infected with COVID-19 and very much look forward to better understanding the functional mechanisms associated with those commensal and pathogenic microbiota related to COVID-19.…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

ACE2 and TMPRSS2 expression by clinical, HLA, immune, and microbial correlates across 34 human cancers and matched normal tissues: implications for SARS-COV-2 COVID-19

Bao

Hernandez

Huang

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Pandemic COVID-19 by SARS-COV-2 infection is facilitated by the ACE2 receptor and protease TMPRSS2. Modestly sized case series have described clinical factors associated with COVID-19, while ACE2 and TMPRSS2 expression analyses have been described in some cell types. Cancer patients may have worse outcomes to COVID-19. Methods:We performed an integrated study of ACE2 and TMPRSS2 gene expression across and within organ systems, by normal versus tumor, across several existing databases (The Cancer Genome Atlas, Census of Immune Single Cell Expression Atlas, The Human Cell Landscape, and more). We correlated gene expression with clinical factors (including but not limited to age, gender, race, BMI and smoking history), HLA genotype, immune gene expression patterns, cell subsets, and single-cell sequencing as well as commensal microbiome.Results: Matched normal tissues generally display higher ACE2 and TMPRSS2 expression compared with cancer, with normal and tumor from digestive organs expressing the highest levels. No clinical factors were consistently identified to be significantly associated with gene expression levels though outlier organ systems were observed for some factors. Similarly, no HLA genotypes were consistently associated with gene expression levels. Strong correlations were observed between ACE2 expression levels and multiple immune gene signatures including interferon-stimulated genes and the T cell-inflamed phenotype as well as inverse associations with angiogenesis and transforming growth factor-β signatures. ACE2 positively correlated with macrophage subsets across tumor types. TMPRSS2 was less associated with immune gene expression but was strongly associated with epithelial cell abundance. Single-cell sequencing analysis across nine independent studies demonstrated little to no ACE2 or

show abstract

“…We distinguish two major types of study. First, work on single cases, where a unique dataset or problem is addressed, for example, microbial composition being used to predict productivity in soil (Chang et al, 2017), contaminants and geochemical features in wells (Smith et al, 2015), presence/absence of disease due to changes in abundances of microbes over time (Bogart et al, 2019), or biomarkers of cancer (and the type of cancer) from the human blood microbiome (Poore et al, 2020). Second, more general studies are emerging, where multiple datasets of different origin are addressed together, applying the same prediction procedure or tool.…”

Section: Introductionmentioning

confidence: 99%

Predicting microbiomes through a deep latent space

García-Jiménez

Muñoz²,

Cabello

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

RETRACTED ARTICLE: Microbiome analyses of blood and tissues suggest cancer diagnostic approach

Cited by 797 publications

References 81 publications

scRNA-seq analysis of colon and esophageal tumors uncovers abundant microbial reads in myeloid cells undergoing proinflammatory transcriptional alterations

scRNA-seq analysis of colon and esophageal tumors uncovers abundant microbial reads in myeloid cells undergoing proinflammatory transcriptional alterations

ACE2 and TMPRSS2 expression by clinical, HLA, immune, and microbial correlates across 34 human cancers and matched normal tissues: implications for SARS-COV-2 COVID-19

Predicting microbiomes through a deep latent space

Contact Info

Product

Resources

About