MON-331 - Robust Transcriptomic Subtyping of Pheochromocytomas and Paragangliomas Reveals Genetically and Clinically Informative Programs with Cross-Platform Validation

Background: Pheochromocytomas and paragangliomas (PPGL) have well-characterized genetic drivers, yet their transcriptional heterogeneity remains incompletely defined. Existing studies proposed limited subtype frameworks without cross-platform validation or clear integration of transcriptional programs with genetics and clinical behavior.

Methods: We constructed and analyzed the largest uniformly processed PPGL transcriptomic atlas to date (n=240), generated on a single microarray platform. Subtype-specific gene signatures were defined using unsupervised clustering and differential expression and independently validated in TCGA-PCPG RNA-seq data (n=187). We integrated mutation data, clinical variables, gene ontology enrichment, and weighted gene co-expression network analysis to identify subtype-specific biological programs and hub transcription factors.

Results: Six robust transcriptional subtypes emerged and were reproducibly validated across platforms: Neuronal, Vascular, Metabolic, Steroidal, Developmental, and Indeterminate. Each subtype exhibited distinct gene expression programs, genetic drivers, and clinical associations. Neuronal tumors showed synaptic and neuroendocrine signaling and were enriched for RAS/MAPK pathway alterations. Vascular tumors demonstrated angiogenic and hypoxia-adaptive programs with strong enrichment of VHL and EPAS1 alterations. Metabolic tumors were characterized by nutrient-handling and mitochondrial pathways and were strongly associated with SDHx mutations. Steroidal tumors displayed high expression of steroid biosynthesis and hormone transport genes and were uniformly clinically indolent. In contrast, the Developmental subtype—defined by neural patterning and endocrine maturation programs and enriched for MAML3 fusions—was the most clinically aggressive subtype compared to others (P < 0.001). Co-expression network analysis identified conserved hub transcription factors defining subtype identity, including TFAP2C and SOX11 (Vascular), RXRG, AR, and GLI2 (Developmental), and TWIST1 and ZEB1 among Neuronal regulators. Clinically, subtypes differed by tumor location, age at diagnosis, and metastatic potential.

Conclusions: Using the largest harmonized PPGL transcriptomic resource with cross-platform microarray and RNA-seq validation, we redefine PPGL into six biologically and clinically meaningful transcriptional subtypes. This framework clarifies the relationships among genetic alterations, transcriptional programs, and clinical behavior, identifies novel regulatory hubs, and highlights actionable targets—particularly within the most aggressive Developmental subtype—providing a refined foundation for precision diagnostics and therapeutic stratification in PPGL. For details, see PMID: 40423244 or doi: 10.1093/neuonc/noaf130

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