Integrated Transcriptomic, Proteomic, and Pharmacologic Profiling of 2D and 3D Patient-Derived GCTB Cell Lines Reveals Culture-Dependent Drug Response Determinants.

Giant cell tumor of bone (GCTB) is an intermediate bone neoplasm defined by recurrent H3F3A mutations and limited systemic treatment options beyond denosumab. Patient-derived cancer cell lines (PDCs) offer a scalable platform for mechanistic studies and therapeutic discovery, yet the extent to which culture dimensionality alters baseline molecular states and drug response in GCTB remains unclear. Here, we performed integrated transcriptomic, proteomic, and pharmacologic profiling of thirteen patient-derived GCTB cell lines cultured under two-dimensional (2D) monolayer and three-dimensional (3D) spheroid conditions. RNA sequencing and data-independent acquisition (DIA)-based quantitative proteomics were conducted on paired cultures, and drug sensitivity was assessed using a panel of 221 anticancer agents. 3D culture reproducibly induced compact spheroid formation across all cell lines and was accompanied by broad remodeling of gene expression, protein abundance, and drug-response profiles. Unsupervised analyses consistently demonstrated that samples clustered primarily by culture condition rather than by cell-line identity at both the transcriptome and proteome levels. Although global trends were shared, a substantial fraction of molecules showed RNA-protein discordance, indicating that transcriptomic changes alone do not fully explain culture-dependent functional remodeling. Pathway analyses highlighted enrichment of extracellular matrix-related processes, stress-response programs, and metabolic regulation in 3D cultures, with several features more prominent at the protein level. Functionally, 3D culture generally reduced sensitivity to many agents while preserving compound-dependent vulnerabilities. These results establish culture dimensionality as a key determinant of therapeutic susceptibility in GCTB PDCs and support incorporating proteome-informed 3D models into translational pipelines to prioritize clinically relevant drug candidates and biomarkers.