Abstract B029: Quantifying the translational relevance of naturally occurring dog cancers as models of adult and pediatric tumors

Abstract Comparative genomics enables scientific exploration of genetic variations across species, enabling insights into human diseases that are indiscernible from analysis of human data alone. Dogs, in particular, are an excellent model organism in comparative oncology because they develop spontaneous tumors that closely resemble human cancers. Many recent studies have investigated parallels between dog and human cancers in a single tumor type. However, to date, no study has systematically quantified the effectiveness of the dog model as a pan-cancer representation of adult and pediatric cancers. To address this gap, we performed a pan-cancer RNA-seq analysis of human and dog cancers spanning 6,048 samples and 11 tumor types, including 987 dog tumor samples from Cahill et al., 4,272 adult samples from TCGA, and 789 pediatric samples from the Treehouse compendium. We analyzed 10 adult and 5 pediatric cancers with at least 10 tumor samples for each species. Using a combination of exploratory data analysis, differential expression and enrichment analysis, unsupervised clustering, and supervised modeling, we identified shared expression patterns across species. We developed a weighted formula incorporating results from these analyses to quantitatively assess the translational relevance of the dog model for each cancer and applied it across cancer types. Our formula ranked dog cancers by transcriptional similarity to human cancers and revealed which models best capture tumor heterogeneity. The cancer-type specific results from this study match those from individual cancer studies, suggesting that our formula effectively identifies the tumor types where dog models best reflect human cancers. Our work introduces a unified, quantitative framework for evaluating cross-species cancer models through combining multiple analysis methods to support preclinical studies and accelerate therapy development. This scalable approach enables fast and accurate identification of model systems to study rare human cancers. As genome sequencing increases, this automated approach minimizes the manual effort required to identify suitable model organisms across the tree of life and opens new opportunities for comparative oncology studies. Citation Format: Geesa Daluwatumulle, Leslie Smith, Nathan Glen, James Cahill, Kiley Graim. Quantifying the translational relevance of naturally occurring dog cancers as models of adult and pediatric tumors [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Cancer Evolution: The Dynamics of Progression and Persistence; 2025 Dec 4-6; Albuquerque, NM. Philadelphia (PA): AACR; Cancer Res 2025;85(23_Suppl):Abstract nr B029.