Disseminating cells in human oral tumours possess an EMT cancer stem cell marker profile that is predictive of metastasis in image-based machine learning

  1. Gehad Youssef
  2. Luke Gammon
  3. Leah Ambler
  4. Sophia Lunetto
  5. Alice Scemama
  6. Hannah Cottom
  7. Kim Piper
  8. Ian C Mackenzie
  9. Michael P Philpott
  10. Adrian Biddle  Is a corresponding author
  1. Queen Mary University of London, United Kingdom
  2. Barts Health NHS Trust, United Kingdom

Abstract

Cancer stem cells (CSCs) undergo epithelial-mesenchymal transition (EMT) to drive metastatic dissemination in experimental cancer models. However, tumour cells undergoing EMT have not been observed disseminating into the tissue surrounding human tumour specimens, leaving the relevance to human cancer uncertain. We have previously identified both EpCAM and CD24 as CSC markers that, alongside the mesenchymal marker Vimentin, identify EMT CSCs in human oral cancer cell lines. This afforded the opportunity to investigate whether the combination of these three markers can identify disseminating EMT CSCs in actual human tumours. Examining disseminating tumour cells in over 12,000 imaging fields from 74 human oral tumours, we see a significant enrichment of EpCAM, CD24 and Vimentin co-stained cells disseminating beyond the tumour body in metastatic specimens. Through training an artificial neural network, these predict metastasis with high accuracy (cross-validated accuracy of 87-89%). In this study, we have observed single disseminating EMT CSCs in human oral cancer specimens, and these are highly predictive of metastatic disease.

Data availability

There are no sequencing datasets associated with this study. Publicly available packages used to analyse immunofluorescent images are listed in the methods section.

The following previously published data sets were used

Article and author information

Author details

  1. Gehad Youssef

    Blizard Institute, Queen Mary University of London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  2. Luke Gammon

    Blizard Institute, Queen Mary University of London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1233-2665
  3. Leah Ambler

    Blizard Institute, Queen Mary University of London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Sophia Lunetto

    Blizard Institute, Queen Mary University of London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Alice Scemama

    Blizard Institute, Queen Mary University of London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Hannah Cottom

    Department of Cellular Pathology, Barts Health NHS Trust, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  7. Kim Piper

    Department of Cellular Pathology, Barts Health NHS Trust, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  8. Ian C Mackenzie

    Blizard Institute, Queen Mary University of London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  9. Michael P Philpott

    Blizard Institute, Queen Mary University of London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1255-4612
  10. Adrian Biddle

    Blizard Institute, Queen Mary University of London, London, United Kingdom
    For correspondence
    a.biddle@qmul.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4371-9720

Funding

Animal Free Research UK

  • Gehad Youssef
  • Michael P Philpott
  • Adrian Biddle

Oracle Cancer Trust

  • Leah Ambler
  • Adrian Biddle

National Centre for the Replacement Refinement and Reduction of Animals in Research (NC/S001573/1)

  • Alice Scemama
  • Adrian Biddle

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Lynne-Marie Postovit, University of Alberta, Canada

Ethics

Human subjects: Archival human specimens and associated de-identified clinical data was accessed under UK HRA approval with REC ref 18/WM/0326.

Version history

  1. Preprint posted: April 8, 2020 (view preprint)
  2. Received: June 19, 2023
  3. Accepted: November 15, 2023
  4. Accepted Manuscript published: November 17, 2023 (version 1)
  5. Version of Record published: January 10, 2024 (version 2)

Copyright

© 2023, Youssef et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Gehad Youssef
  2. Luke Gammon
  3. Leah Ambler
  4. Sophia Lunetto
  5. Alice Scemama
  6. Hannah Cottom
  7. Kim Piper
  8. Ian C Mackenzie
  9. Michael P Philpott
  10. Adrian Biddle
(2023)
Disseminating cells in human oral tumours possess an EMT cancer stem cell marker profile that is predictive of metastasis in image-based machine learning
eLife 12:e90298.
https://doi.org/10.7554/eLife.90298

Share this article

https://doi.org/10.7554/eLife.90298

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