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

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, Carlo C. Maley, Luca Aresu, Chiara Palmieri and Yinyin Yuan ()
Additional contact information
Khalid AbdulJabbar: The Institute of Cancer Research
Simon P. Castillo: The Institute of Cancer Research
Katherine Hughes: University of Cambridge, Madingley Road
Hannah Davidson: Zoological Society of London
Amy M. Boddy: University of California Santa Barbara
Lisa M. Abegglen: University of Utah
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
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

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-37879-x

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