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Bridging clinic and wildlife care with AI-powered pan-species computational pathology

Author

Listed:
  • Khalid AbdulJabbar

    (The Institute of Cancer Research
    The Institute of Cancer Research)

  • Simon P. Castillo

    (The Institute of Cancer Research
    The Institute of Cancer Research)

  • Katherine Hughes

    (University of Cambridge, Madingley Road)

  • Hannah Davidson

    (Zoological Society of London
    Queen Mary University of London, Charterhouse Sq)

  • Amy M. Boddy

    (University of California Santa Barbara)

  • Lisa M. Abegglen

    (University of Utah
    PEEL Therapeutics, Inc.)

  • Lucia Minoli

    (University of Turin)

  • Selina Iussich

    (University of Turin)

  • Elizabeth P. Murchison

    (University of Cambridge, Madingley Road)

  • Trevor A. Graham

    (The Institute of Cancer Research
    Queen Mary University of London, Charterhouse Sq)

  • Simon Spiro

    (Zoological Society of London)

  • Carlo C. Maley

    (Biodesign Institute and School of Life Sciences, Arizona State University)

  • Luca Aresu

    (University of Turin)

  • Chiara Palmieri

    (The University of Queensland)

  • Yinyin Yuan

    (The Institute of Cancer Research
    The Institute of Cancer Research
    The University of Texas MD Anderson Cancer Center)

Abstract

Cancers occur across species. Understanding what is consistent and varies across species can provide new insights into cancer initiation and evolution, with significant implications for animal welfare and wildlife conservation. We build a pan-species cancer digital pathology atlas (panspecies.ai) and conduct a pan-species study of computational comparative pathology using a supervised convolutional neural network algorithm trained on human samples. The artificial intelligence algorithm achieves high accuracy in measuring immune response through single-cell classification for two transmissible cancers (canine transmissible venereal tumour, 0.94; Tasmanian devil facial tumour disease, 0.88). In 18 other vertebrate species (mammalia = 11, reptilia = 4, aves = 2, and amphibia = 1), accuracy (range 0.57–0.94) is influenced by cell morphological similarity preserved across different taxonomic groups, tumour sites, and variations in the immune compartment. Furthermore, a spatial immune score based on artificial intelligence and spatial statistics is associated with prognosis in canine melanoma and prostate tumours. A metric, named morphospace overlap, is developed to guide veterinary pathologists towards rational deployment of this technology on new samples. This study provides the foundation and guidelines for transferring artificial intelligence technologies to veterinary pathology based on understanding of morphological conservation, which could vastly accelerate developments in veterinary medicine and comparative oncology.

Suggested Citation

  • Khalid AbdulJabbar & Simon P. Castillo & Katherine Hughes & Hannah Davidson & Amy M. Boddy & Lisa M. Abegglen & Lucia Minoli & Selina Iussich & Elizabeth P. Murchison & Trevor A. Graham & Simon Spiro , 2023. "Bridging clinic and wildlife care with AI-powered pan-species computational pathology," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37879-x
    DOI: 10.1038/s41467-023-37879-x
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    References listed on IDEAS

    as
    1. Andrea Strakova & Thomas J. Nicholls & Adrian Baez-Ortega & Máire Ní Leathlobhair & Alexander T. Sampson & Katherine Hughes & Isobelle A. G. Bolton & Kevin Gori & Jinhong Wang & Ilona Airikkala-Otter , 2020. "Recurrent horizontal transfer identifies mitochondrial positive selection in a transmissible cancer," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    2. Devis Tuia & Benjamin Kellenberger & Sara Beery & Blair R. Costelloe & Silvia Zuffi & Benjamin Risse & Alexander Mathis & Mackenzie W. Mathis & Frank Langevelde & Tilo Burghardt & Roland Kays & Holger, 2022. "Perspectives in machine learning for wildlife conservation," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    3. Kim Wong & Louise van der Weyden & Courtney R. Schott & Alastair Foote & Fernando Constantino-Casas & Sionagh Smith & Jane M. Dobson & Elizabeth P. Murchison & Hong Wu & Iwei Yeh & Douglas R. Fullen &, 2019. "Cross-species genomic landscape comparison of human mucosal melanoma with canine oral and equine melanoma," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
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