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Predicting cancer prognosis and drug response from the tumor microbiome

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  • Leandro C. Hermida

    (National Institutes of Health (NIH)
    University of Maryland)

  • E. Michael Gertz

    (National Institutes of Health (NIH))

  • Eytan Ruppin

    (National Institutes of Health (NIH))

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, can predict cancer prognosis and drug response to some extent—microbial abundances are significantly less predictive of prognosis than gene expression, although 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.

Suggested Citation

  • Leandro C. Hermida & E. Michael Gertz & Eytan Ruppin, 2022. "Predicting cancer prognosis and drug response from the tumor microbiome," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30512-3
    DOI: 10.1038/s41467-022-30512-3
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    1. Gregory D. Poore & Evguenia Kopylova & Qiyun Zhu & Carolina Carpenter & Serena Fraraccio & Stephen Wandro & Tomasz Kosciolek & Stefan Janssen & Jessica Metcalf & Se Jin Song & Jad Kanbar & Sandrine Mi, 2020. "Microbiome analyses of blood and tissues suggest cancer diagnostic approach," Nature, Nature, vol. 579(7800), pages 567-574, March.
    2. Andrius Vabalas & Emma Gowen & Ellen Poliakoff & Alexander J Casson, 2019. "Machine learning algorithm validation with a limited sample size," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-20, November.
    3. Yishai Shimoni, 2018. "Association between expression of random gene sets and survival is evident in multiple cancer types and may be explained by sub-classification," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-15, February.
    4. Simon, Noah & Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2011. "Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i05).
    5. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    6. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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