Spatiotemporal Profiling Defines Persistence and Resistance Dynamics During Targeted Treatment of Melanoma

Abstract

Resistance of BRAF-mutant melanomas to targeted therapy arises from the ability of cells to enter a persister state, evade treatment with relative dormancy, and repopulate the tumor when reactivated. A better understanding of the temporal dynamics and specific pathways leading into and out of the persister state is needed to identify strategies to prevent treatment failure. Using spatial transcriptomics in patient-derived xenograft models, we captured clonal lineage evolution during treatment. The persister state showed increased oxidative phosphorylation, decreased proliferation, and increased invasive capacity, with central-to-peripheral gradients. Phylogenetic tracing identified intrinsic and acquired resistance mechanisms (e.g., dual specific phosphatases, reticulon-4, and CDK2) and suggested specific temporal windows of potential therapeutic susceptibility. Deep learning-enabled analysis of histopathological slides revealed morphological features correlating with specific cell states, demonstrating that juxtaposition of transcriptomics and histological data enabled identification of phenotypically distinct populations from using imaging data alone. In summary, this study defined state change and lineage selection during melanoma treatment with spatiotemporal resolution, elucidating how choice and timing of therapeutic agents will impact the ability to eradicate resistant clones.