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OBJECTIVE: To validate the performance of a novel, integrated test for canine cancer screening that combines cell-free DNA quantification with next-generation sequencing (NGS) analysis. SAMPLE: Retrospective data from a total of 1,947 cancer-diagnosed and presumably cancer-free dogs were used to validate test performance for the detection of 7 predefined cancer types (lymphoma, hemangiosarcoma, osteosarcoma, leukemia, histiocytic sarcoma, primary lung tumors, and urothelial carcinoma), using independent training and testing sets. METHODS: Cell-free DNA quantification data from all samples were analyzed using a proprietary machine learning algorithm to determine a Cancer Probability Index (High, Moderate, or Low). High and Low Probability of Cancer were final result classifications. Moderate cases were additionally analyzed by NGS to arrive at a final classification of High Probability of Cancer (Cancer Signal Detected) or Low Probability of Cancer (Cancer Signal Not Detected). RESULTS: Of the 595 dogs in the testing set, 89% (n = 530) received a High or Low Probability result based on the machine learning algorithm; 11% (65) were Moderate Probability, and NGS results were used to assign a final classification. Overall, 87 of 122 dogs with the 7 predefined cancer types were classified as High Probability and 467 of 473 presumably cancer-free dogs were classified as Low Probability, corresponding to a sensitivity of 71.3% for the predefined cancer types at a specificity of 98.7%. CLINICAL RELEVANCE: This integrated test offers a novel option to screen for cancer types that may be difficult to detect by physical examination at a dog's wellness visit.
RESUMO
BACKGROUND: Animal-associated microbiomes can be influenced by both host and environmental factors. Comparing wild animals to those in zoos or aquariums can help disentangle the effects of host versus environmental factors, while also testing whether managed conditions foster a 'natural' host microbiome. Focusing on an endangered elasmobranch species-the whitespotted eagle ray Aetobatus narinari-we compared the skin, gill, and cloaca microbiomes of wild individuals to those at Georgia Aquarium. Whitespotted eagle ray microbiomes from Georgia Aquarium were also compared to those of cownose rays (Rhinoptera bonasus) in the same exhibit, allowing us to explore the effect of host identity on the ray microbiome. RESULTS: Long-term veterinary monitoring indicated that the rays in managed care did not have a history of disease and maintained health parameters consistent with those of wild individuals, with one exception. Aquarium whitespotted eagle rays were regularly treated to control parasite loads, but the effects on animal health were subclinical. Microbiome α- and ß-diversity differed between wild versus aquarium whitespotted eagle rays at all body sites, with α-diversity significantly higher in wild individuals. ß-diversity differences in wild versus aquarium whitespotted eagle rays were greater for skin and gill microbiomes compared to those of the cloaca. At each body site, we also detected microbial taxa shared between wild and aquarium eagle rays. Additionally, the cloaca, skin, and gill microbiomes of aquarium eagle rays differed from those of cownose rays in the same exhibit. Potentially pathogenic bacteria were at low abundance in all wild and aquarium rays. CONCLUSION: For whitespotted eagle rays, managed care was associated with a microbiome differing significantly from that of wild individuals. These differences were not absolute, as the microbiome of aquarium rays shared members with that of wild counterparts and was distinct from that of a cohabitating ray species. Eagle rays under managed care appear healthy, suggesting that their microbiomes are not associated with compromised host health. However, the ray microbiome is dynamic, differing with both environmental factors and host identity. Monitoring of aquarium ray microbiomes over time may identify taxonomic patterns that co-vary with host health.