Translational oncotargets for immunotherapy: From pet dogs to humans

Mestrinho, Lisa A; Santos, Ricardo

doi:10.1016/j.addr.2021.02.020

Cited by 11 publications

(13 citation statements)

References 222 publications

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“…Medical treatment for animals has dramatically improved in veterinary clinics, zoological institutions and even wildlife veterinarians 61 , leading to better options for diagnosing and treating cancer in animal patients 24 . Despite these significant advances in veterinary oncology 62 , there are crucial constraints and limited availability of veterinary specialists 63 , and consequently, digital tools are not widely used 25 , 26 .…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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“…Based on our data, the transferability of existing AI technologies developed for humans to the veterinary domain may be significantly higher than previously thought. Medical treatment for animals has dramatically improved in veterinary clinics, zoological institutions and even wildlife veterinarians 37 , leading to better options for diagnosing and treating cancer in animal patients 23 . Despite these significant advances in veterinary oncology 38 , there are significant constraints and limited availability of veterinary specialists 39 , and consequently, digital tools are not widely used 24,25 .…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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AI-powered pan-species computational pathology: bridging clinic and wildlife care

AbdulJabbar

Castillo

Hughes

et al. 2022

Preprint

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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 built the pan-species cancer digital pathology atlas (PANCAD) and conducted the first 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). Furthermore, in 18 other vertebrate species (mammalia=11, reptilia=4, aves=2, and amphibia=1), accuracy (0.57-0.94) was influenced by cell morphological similarity preserved across different taxonomic groups, tumour sites, and variations in the immune compartment. A new metric, named morphospace overlap, was 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 a new understanding of morphological conservation, which could vastly accelerate new developments in veterinary medicine and comparative oncology.

show abstract

“…Based on our data, the transferability of existing AI technologies developed for humans to the veterinary domain may be signi cantly higher than previously thought. Medical treatment for animals has dramatically improved in veterinary clinics, zoological institutions and even wildlife veterinarians 37 , leading to better options for diagnosing and treating cancer in animal patients 23 . Despite these signi cant advances in veterinary oncology 38 , there are signi cant constraints and limited availability of veterinary specialists 39 , and consequently, digital tools are not widely used 24,25 .…”

Section: Discussionmentioning

confidence: 99%

AI-powered pan-species computational pathology: bridging clinic and wildlife care

AbdulJabbar

Castillo

Hughes

et al. 2022

Preprint

View full text Add to dashboard Cite

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 advancing human healthcare, animal welfare, and wildlife conservation. We built the pan-species cancer digital pathology atlas (PANCAD) and conducted the first 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). Furthermore, in 18 other vertebrate species (mammalia=11, reptilia=4, aves=2, and amphibia=1), accuracy (0.57-0.94) was influenced by cell morphological similarity preserved across different taxonomic groups, tumour sites, and variations in the immune compartment. A new metric, named morphospace overlap, was 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 a new understanding of morphological conservation, which could vastly accelerate new developments in veterinary medicine and comparative oncology.

show abstract

Translational oncotargets for immunotherapy: From pet dogs to humans

Cited by 11 publications

References 222 publications

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

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

AI-powered pan-species computational pathology: bridging clinic and wildlife care

AI-powered pan-species computational pathology: bridging clinic and wildlife care

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