Spontaneous canine cancers represent a unique opportunity for human cancer research. Pet dogs develop tumors naturally while sharing similar lifestyle factors with humans, including environmental and dietary exposures and medical surveillance1–3. Canine cancers have shown important similarities with human cancers including immune responses, histology findings, disease progression, and therapeutic responses4–6. Additionally, shared mutation profiles and affected molecular pathways have been found between canine and human tumors7.
Assessing the canine genome, 14,200 genes have been identified that are orthologous to genes in the human genome8. Hundreds of similar genetic variations and somatic driver mutations between human and canine cancer have been reported9–12. Genomic analysis of canine tumors has identified and provided insights into the role of oncogenes and tumor suppressor genes of specific tumors in both species, but there is still a lack of knowledge on the prognosis and effect of these gene alterations and responses to treatment in the dog13.
The shared mutations and pathways suggest that current and future targeted therapies may be effective in treating subsets of canine cancers as they are in human cancers. Including canine cancer patients in trials of new and repurposed cancer therapeutics is advantageous for both dogs and humans not only because of the clinical and molecular similarities between their cancers but also because of the comparatively higher rate at which dogs develop cancer compared to humans14.
Previous studies of real-world canine data have been limited in size and scope due to the scattered nature of data across veterinary clinics and the infrequent use of genomic tests in canine cancer treatment. In this article, we describe the first use of artificial intelligence (AI) to query a large, pan-cancer canine clinico-genomic dataset. We analyzed a unique real-world dataset of 2,119 dogs with cancer including 1,108 dogs with targeted genotyping via the FidoCure® Precision Medicine Platform. This dataset includes clinical outcomes from 700 veterinary clinics and hospitals across the United States.
AI methods have been applied to large-scale human clinico-genomic datasets to predict treatment outcomes using the molecular profile of cancer to prognosis15. In this study, we apply similar predictive modeling to real-world clinical-genomic data from the FidoCure® Precision Medicine Platform to identify biomarkers associated with prognosis and treatment prediction.
Figure 1: Summary of the dataset used for analysis
The size and scope of our dataset enable a systematic characterization of prognostic effects by (1) tumor type, (2) mutated gene, and then (3) mutated gene in combination with an individual targeted therapy. We find that somatic mutations in genes TP53 and PIK3CA are associated with worse survival across cancers in our dataset. Somatic mutations in these genes have also been associated with negative prognoses in human cancers.
Table 1. Hazard ratios associated with treatment and gene combination, confidence intervals, p-values, sample sizes, and MST
In addition, we identify specific tumor mutations that predict canine patients’ responses to targeted cancer therapies. Patients with somatic mutations in the BRAF gene had a significantly better response when treated with the targeted therapy Lapatinib. This gene/drug pair is known to be effective in humans and dogs, and somatic BRAF mutations are an indication for prescribing Lapatinib for dogs enrolled in the FidoCure platform. It is well known that BRAF mutations lead to the activation of EGFR downstream signaling pathway bypassing EGFR blockage. This can reduce EGFR inhibitor therapy action. For human patients with the common BRAF mutation V600E, the use of anti-EGFR has been an important treatment strategy.
Interestingly, we identified a better prognosis for dogs carrying BRCA1 mutation when treated with dasatinib. Dasatinib is a tyrosine kinase inhibitor used to treat Philadelphia chromosome-positive chronic myeloid leukemia. This small molecule can target BCR-ABL, SRC family, and various cancer kinases. In a previous study, dasatinib showed an inhibition effect on Chk1 in BRCA1 mutated breast cancer cells, inducing DNA damage when cells have a dysregulation of homologous recombination during the G2/S phase. Such associations, if validated in additional datasets, generate exciting potential hypotheses for drug repurposing research and trials.
This work is the most complete study of canine cancer genomics and treatment response to date and represents an important advance in precision medicine. Insights from our study can benefit canine patients by improving targeted treatment recommendations and providing an important new resource for models of human cancer.
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