Caution regarding the specificities of pan-cancer microbial structure

Gihawi, Abraham ORCID:, Cooper, Colin S. ORCID: and Brewer, Daniel S. ORCID: (2023) Caution regarding the specificities of pan-cancer microbial structure. Microbial Genomics, 9 (8). ISSN 2057-5858

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Results published in an article by Poore et al. (Nature. 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 et al. 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.

Item Type: Article
Additional Information: Funding Information: This work was funded by Big C Cancer Charity (ref 16-09R), Prostate Cancer UK (MA-ETNA19-003) and The Bob Champion Cancer Trust.
Uncontrolled Keywords: bacteria,cancer,contamination,machine learning,microbiome,viruses,genetics,molecular biology,epidemiology,microbiology,sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/1300/1311
Faculty \ School: Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups: Faculty of Medicine and Health Sciences > Research Groups > Cancer Studies
Faculty of Medicine and Health Sciences > Research Centres > Metabolic Health
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Depositing User: LivePure Connector
Date Deposited: 22 Aug 2023 09:30
Last Modified: 19 Oct 2023 03:38
DOI: 10.1099/mgen.0.001088


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