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Predicting cancer prognosis and drug response from the tumor microbiome

Authors: Hermida, Leandro Cruz; Gertz, E. Michael; Ruppin, Eytan;

Predicting cancer prognosis and drug response from the tumor microbiome

Abstract

Tumor gene expression is predictive of patient prognosis in some cancers. However, RNA-seq and whole genome sequencing data contain not only reads from host tumor and normal tissue, but also reads from the tumor microbiome, which can be used to infer the microbial abundances in each tumor. Here, we show that tumor microbial abundances, alone or in combination with tumor gene expression data, can predict cancer prognosis and drug response to some extent – microbial abundances are significantly less predictive of prognosis than gene expression, although remarkably, similarly as predictive of drug response, but in mostly different cancer-drug combinations. Thus, it appears possible to leverage existing sequencing technology, or develop new protocols, to obtain more non-redundant information about prognosis and drug response from RNA-seq and whole genome sequencing experiments than could be obtained from tumor gene expression or genomic data alone.

{"references": ["Hermida, L.C., Gertz, E.M. & Ruppin, E. Predicting cancer prognosis and drug response from the tumor microbiome. Nat Commun 13, 2896 (2022). https://doi.org/10.1038/s41467-022-30512-3"]}

Keywords

Cancer microenvironment, Tumour microbiome, Microbial abundances, Drug response, Tumor biomarkers, Tumor microbiome, Prognosis, Computational biology and bioinformatics, Tumour biomarkers, Predictive models, Cancer genomics, Computational models, Chemotherapy, Gene expression, Cancer

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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2
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158
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Cancer Research