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Exogenous Sequences in Tumors and Immune Cells (Exotic): A Tool for Estimating the Microbe Abundances in Tumor RNA-seq Data

The microbiome affects cancer, from carcinogenesis to response to treatments. New evidence suggests that microbes are also present in many tumors, though the scope of how they affect tumor biology and clinical outcomes is in its early stages. A broad survey of tumor microbiome samples across several...

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Autores principales: Hoyd, Rebecca, Wheeler, Caroline E., Liu, YunZhou, Jagjit Singh, Malvenderjit S., Muniak, Mitchell, Jin, Ning, Denko, Nicholas C., Carbone, David P., Mo, Xiaokui, Spakowicz, Daniel J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for Cancer Research 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10662017/
https://www.ncbi.nlm.nih.gov/pubmed/37850841
http://dx.doi.org/10.1158/2767-9764.CRC-22-0435
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author Hoyd, Rebecca
Wheeler, Caroline E.
Liu, YunZhou
Jagjit Singh, Malvenderjit S.
Muniak, Mitchell
Jin, Ning
Denko, Nicholas C.
Carbone, David P.
Mo, Xiaokui
Spakowicz, Daniel J.
author_facet Hoyd, Rebecca
Wheeler, Caroline E.
Liu, YunZhou
Jagjit Singh, Malvenderjit S.
Muniak, Mitchell
Jin, Ning
Denko, Nicholas C.
Carbone, David P.
Mo, Xiaokui
Spakowicz, Daniel J.
author_sort Hoyd, Rebecca
collection PubMed
description The microbiome affects cancer, from carcinogenesis to response to treatments. New evidence suggests that microbes are also present in many tumors, though the scope of how they affect tumor biology and clinical outcomes is in its early stages. A broad survey of tumor microbiome samples across several independent datasets is needed to identify robust correlations for follow-up testing. We created a tool called {exotic} for “exogenous sequences in tumors and immune cells” to carefully identify the tumor microbiome within RNA sequencing (RNA-seq) datasets. We applied it to samples collected through the Oncology Research Information Exchange Network (ORIEN) and The Cancer Genome Atlas. We showed how the processing removes contaminants and batch effects to yield microbe abundances consistent with non–high-throughput sequencing–based approaches and DNA-amplicon–based measurements of a subset of the same tumors. We sought to establish clinical relevance by correlating the microbe abundances with various clinical and tumor measurements, such as age and tumor hypoxia. This process leveraged the two datasets and raised up only the concordant (significant and in the same direction) associations. We observed associations with survival and clinical variables that are cancer specific and relatively few associations with immune composition. Finally, we explored potential mechanisms by which microbes and tumors may interact using a network-based approach. Alistipes, a common gut commensal, showed the highest network degree centrality and was associated with genes related to metabolism and inflammation. The {exotic} tool can support the discovery of microbes in tumors in a way that leverages the many existing and growing RNA-seq datasets. SIGNIFICANCE: The intrinsic tumor microbiome holds great potential for its ability to predict various aspects of cancer biology and as a target for rational manipulation. Here, we describe a tool to quantify microbes from within tumor RNA-seq and apply it to two independent datasets. We show new associations with clinical variables that justify biomarker uses and more experimentation into the mechanisms by which tumor microbiomes affect cancer outcomes.
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spelling pubmed-106620172023-11-21 Exogenous Sequences in Tumors and Immune Cells (Exotic): A Tool for Estimating the Microbe Abundances in Tumor RNA-seq Data Hoyd, Rebecca Wheeler, Caroline E. Liu, YunZhou Jagjit Singh, Malvenderjit S. Muniak, Mitchell Jin, Ning Denko, Nicholas C. Carbone, David P. Mo, Xiaokui Spakowicz, Daniel J. Cancer Res Commun Research Article The microbiome affects cancer, from carcinogenesis to response to treatments. New evidence suggests that microbes are also present in many tumors, though the scope of how they affect tumor biology and clinical outcomes is in its early stages. A broad survey of tumor microbiome samples across several independent datasets is needed to identify robust correlations for follow-up testing. We created a tool called {exotic} for “exogenous sequences in tumors and immune cells” to carefully identify the tumor microbiome within RNA sequencing (RNA-seq) datasets. We applied it to samples collected through the Oncology Research Information Exchange Network (ORIEN) and The Cancer Genome Atlas. We showed how the processing removes contaminants and batch effects to yield microbe abundances consistent with non–high-throughput sequencing–based approaches and DNA-amplicon–based measurements of a subset of the same tumors. We sought to establish clinical relevance by correlating the microbe abundances with various clinical and tumor measurements, such as age and tumor hypoxia. This process leveraged the two datasets and raised up only the concordant (significant and in the same direction) associations. We observed associations with survival and clinical variables that are cancer specific and relatively few associations with immune composition. Finally, we explored potential mechanisms by which microbes and tumors may interact using a network-based approach. Alistipes, a common gut commensal, showed the highest network degree centrality and was associated with genes related to metabolism and inflammation. The {exotic} tool can support the discovery of microbes in tumors in a way that leverages the many existing and growing RNA-seq datasets. SIGNIFICANCE: The intrinsic tumor microbiome holds great potential for its ability to predict various aspects of cancer biology and as a target for rational manipulation. Here, we describe a tool to quantify microbes from within tumor RNA-seq and apply it to two independent datasets. We show new associations with clinical variables that justify biomarker uses and more experimentation into the mechanisms by which tumor microbiomes affect cancer outcomes. American Association for Cancer Research 2023-11-21 /pmc/articles/PMC10662017/ /pubmed/37850841 http://dx.doi.org/10.1158/2767-9764.CRC-22-0435 Text en © 2023 The Authors; Published by the American Association for Cancer Research https://creativecommons.org/licenses/by/4.0/This open access article is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
spellingShingle Research Article
Hoyd, Rebecca
Wheeler, Caroline E.
Liu, YunZhou
Jagjit Singh, Malvenderjit S.
Muniak, Mitchell
Jin, Ning
Denko, Nicholas C.
Carbone, David P.
Mo, Xiaokui
Spakowicz, Daniel J.
Exogenous Sequences in Tumors and Immune Cells (Exotic): A Tool for Estimating the Microbe Abundances in Tumor RNA-seq Data
title Exogenous Sequences in Tumors and Immune Cells (Exotic): A Tool for Estimating the Microbe Abundances in Tumor RNA-seq Data
title_full Exogenous Sequences in Tumors and Immune Cells (Exotic): A Tool for Estimating the Microbe Abundances in Tumor RNA-seq Data
title_fullStr Exogenous Sequences in Tumors and Immune Cells (Exotic): A Tool for Estimating the Microbe Abundances in Tumor RNA-seq Data
title_full_unstemmed Exogenous Sequences in Tumors and Immune Cells (Exotic): A Tool for Estimating the Microbe Abundances in Tumor RNA-seq Data
title_short Exogenous Sequences in Tumors and Immune Cells (Exotic): A Tool for Estimating the Microbe Abundances in Tumor RNA-seq Data
title_sort exogenous sequences in tumors and immune cells (exotic): a tool for estimating the microbe abundances in tumor rna-seq data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10662017/
https://www.ncbi.nlm.nih.gov/pubmed/37850841
http://dx.doi.org/10.1158/2767-9764.CRC-22-0435
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