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Volume 9, Issue 8

Caution regarding the specificities of pan-cancer microbial structure Open Access

Abstract

Results published in an article by Poore . (. 2020;579:567–574) suggested that machine learning models can almost perfectly distinguish between tumour types based on their microbial composition using machine learning models. Whilst we believe that there is the potential for microbial composition to be used in this manner, we have concerns with the paper that make us question the certainty of the conclusions drawn. We believe there are issues in the areas of the contribution of contamination, handling of batch effects, false positive classifications and limitations in the machine learning approaches used. This makes it difficult to identify whether the authors have identified true biological signal and how robust these models would be in use as clinical biomarkers. We commend Poore . on their approach to open data and reproducibility that has enabled this analysis. We hope that this discourse assists the future development of machine learning models and hypothesis generation in microbiome research.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): bacteria , cancer , contamination , machine learning , microbiome and viruses
Funding
This study was supported by the:
  • Bob Champion Cancer Trust (Award NA)
    • Principle Award Recipient: ColinS. Cooper
  • Prostate Cancer UK (Award MA-ETNA19-003)
    • Principle Award Recipient: DanielS. Brewer
  • Big C Cancer Charity (Award 16-09R)
    • Principle Award Recipient: DanielS. Brewer
© 2023 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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2023-08-09
2024-05-10
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Caution regarding the specificities of pan-cancer microbial structure
M Gen 9, 001088 (2023); https://doi.org/10.1099/mgen.0.001088
/content/journal/mgen/10.1099/mgen.0.001088
/content/journal/mgen/10.1099/mgen.0.001088
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