From comparative oncology to AI-enabled precision medicine: translational biomodels for triple-negative breast cancer.

Triple-negative breast cancer (TNBC), defined by the absence of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 expression or amplification, comprises a biologically heterogeneous group of tumors with aggressive clinical behavior and limited biomarker-guided treatment options. Although chemotherapy, immune-checkpoint inhibition, poly(ADP-ribose) polymerase inhibitors, and antibody-drug conjugates have expanded the therapeutic landscape, durable benefit remains constrained by genomic instability, homologous recombination deficiency, phenotypic plasticity, immune-stromal interactions, and treatment-driven evolution. This Perspective critically examines how complementary translational biomodels can be organized into a fit-for-purpose framework for TNBC precision oncology. Spontaneous canine mammary tumors provide naturally evolving disease in immunocompetent hosts, whereas patient- and species-derived organoids enable scalable functional perturbation and drug-response profiling. Patient-derived xenografts preserve clinically relevant tumor heterogeneity and treatment-selected states, while genetically engineered mouse models support mechanistic interrogation of defined oncogenic events