Bringing the Genomic Revolution to Comparative Oncology: Human and Dog Cancers.


Journal

Annual review of biomedical data science
ISSN: 2574-3414
Titre abrégé: Annu Rev Biomed Data Sci
Pays: United States
ID NLM: 101714020

Informations de publication

Date de publication:
22 Apr 2024
Historique:
medline: 23 4 2024
pubmed: 23 4 2024
entrez: 22 4 2024
Statut: aheadofprint

Résumé

Dogs are humanity's oldest friend, the first species we domesticated 20,000-40,000 years ago. In this unequaled collaboration, dogs have inadvertently but serendipitously been molded into a potent human cancer model. Unlike many common model species, dogs are raised in the same environment as humans and present with spontaneous tumors with human-like comorbidities, immunocompetency, and heterogeneity. In breast, bladder, blood, and several pediatric cancers, in-depth profiling of dog and human tumors has established the benefits of the dog model. In addition to this clinical and molecular similarity, veterinary studies indicate that domestic dogs have relatively high tumor incidence rates. As a result, there are a plethora of data for analysis, the statistical power of which is bolstered by substantial breed-specific variability. As such, dog tumors provide a unique opportunity to interrogate the molecular factors underpinning cancer and facilitate the modeling of new therapeutic targets. This review discusses the emerging field of comparative oncology, how it complements human and rodent cancer studies, and where challenges remain, given the rapid proliferation of genomic resources. Increasingly, it appears that human's best friend is becoming an irreplaceable component of oncology research.

Identifiants

pubmed: 38648188
doi: 10.1146/annurev-biodatasci-102423-111936
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Auteurs

James A Cahill (JA)

1Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida, USA.

Leslie A Smith (LA)

2Department of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, USA; email: kgraim@ufl.edu.

Soumya Gottipati (S)

3Department of Computer Science, Princeton University, Princeton, New Jersey, USA.

Tina Salehi Torabi (TS)

2Department of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, USA; email: kgraim@ufl.edu.

Kiley Graim (K)

2Department of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, USA; email: kgraim@ufl.edu.

Classifications MeSH