| الوصف: |
Over half of BRAF mutant melanoma patients with response to targeted therapy recur within 15 months. Resistance is postulated to arise from certain clonal populations that enter a slow cycling persister state, evade treatment with relative dormancy, and repopulate the tumor when reactivated. Via longitudinal profiling we define expression states of clonal populations and track their evolutionary progression. BRAF mutant patient derived melanoma xenografts were treated with BRAF/MEK inhibitors through maximum tumor size reduction and re-growth. Spatial transcriptomics (ST) was performed at 5 timepoints across 94 days of treatment, tracking clonal populations with intact tissue structure. Deep learning on H&E-stained images automatically detected histological features that co-localize with expression levels. A novel computational pipeline performed clustering, differential expression and pathway enrichment analysis, copy number variation detection, and pseudotemporal ordering, allowing spatial recreation of clonal phylogenies and cell fate trajectory inference during the treatment course. Three clonal categories were defined; Sensitive: predominant in treatment-naïve samples, Persister: remaining clusters in maximally shrunken specimens, Resistant: re-emergent, fast-proliferating. Analysis of clonal composition identified GAPDH and CCND1 as highly expressed in sensitive clones, suppressed in persisters, and reactivated in resistant clones; TYRP1, DCT, MITF, and NGFR showed an opposite pattern. Persister clones evolved from oxidative phosphorylation toward glycolysis and MAPK regulation. Spatial analysis showed increased glycolysis:oxidative phosphorylation ratio in centrally located sensitive clusters, with more balanced peripheral expression. Resistant clones showed upregulated DUSP6 expression and enrichment in Orexin signaling and MAPK regulation. Imaging- and expression-based clones were highly concordant. Spatial profiling during melanoma treatment identified transient persister and emergent resistant clones, defining expression profiles and phenotypic features. Altered ratio of oxidative to glycolytic metabolic pathways was a hallmark of clonal evolution in both space and time. Specific MAPK pathway re-entry points were identified as candidate resistance mechanisms, suggesting potential therapeutic targets. Deep learning features derived from histological images showed good correlation with ST profiles, providing a promising method for clonal tracking via images alone. This longitudinal experiment mimics a tumor’s clinical course, inducing persistence and resistance via treatment that parallels that seen by patients. Combining ST and imaging techniques, we provide insight into clonal dynamics with novel spatiotemporal resolution, defining an evolutionary roadmap to acquired treatment resistance. Citation Format: Jill Carol Rubinstein, Sergii Domanskyi, Todd B. Sheridan, Brian J. Sanderson, SungHee Park, Jessica Kaster, Haiyin Li, Olga Anczukow, Meenhard Herlyn, Jeffrey H. Chuang. Spatiotemporal profiling defines persister and resistance signatures in targeted treatment of melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1164. |
