Beyond the Syntax of the Genome: Toward Predictive World Models in Triple-Negative Breast Cancer

Abstract Current artificial intelligence applications in triple-negative breast cancer (TNBC) rely predominantly on generative architectures that synthesize existing knowledge but lack the capacity to model tumor dynamics prospectively. This editorial argues that TNBC, with its characteristic clonal plasticity and therapeutic evasion, may require a fundamentally different computational approach. Drawing on recent advances in predictive architectures—particularly joint-embedding models and world models—I propose that the future of AI in TNBC lies in systems capable of anticipating tumor trajectories through biological state space, rather than merely pattern-matching against historical data. While this remains a hypothesis requiring empirical validation, the conceptual shift from reactive to predictive modeling may prove essential for managing cancers that behave as adaptive, navigating agents. Keywords: Triple-negative breast cancer; Joint-embedding predictive architecture; World models; Clonal evolution; Tumor heterogeneity; Artificial intelligence.