Splicing diversity enhances the molecular classification of pituitary neuroendocrine tumors

Pituitary neuroendocrine tumors (PitNETs) are one of the most common intracranial tumors with diverse clinical manifestations. Current pathological classification systems rely primarily on histological hormone staining and transcription factors (TFs) expression. While effective in identifying three major lineages, molecular characteristics based on hormones and TFs lack sufficient resolution to fully capture the complex tumor heterogeneity. Transcriptional diversity by alternative splicing (AS) offered additional insight to address this challenge. Here, we perform bulk and full-length single-cell RNA sequencing to comprehensively investigate AS dysregulation across all PitNET lineages. We reveal pervasive splicing dysregulations that better depict tumor heterogeneity. Additionally, we delineate fundamental splicing heterogeneity at single-cell resolution, confirming bulk findings and refining splicing dysregulation varying among tumor cell types. Notably, we effectively distinguish the silent corticotroph subtype and define a distinct TPIT lineage subtype, which is associated with worse clinical outcomes and increased splicing abnormalities driven by altered ESRP1 expression. In conclusion, our results characterize the subtype specific AS landscape in PitNETs, enhancing the understanding of the PitNETs subtyping. Current classification systems of pituitary neuroendocrine tumors (PitNETs) rely on histological hormone staining and transcription factors. Here, the authors analyze alternative splicing at bulk and single-cell resolution in PitNETs, revealing additional subtypes associated with worse clinical outcomes and splicing abnormalities.