bioRxivCancer BiologyPreprint
Developmental Plasticity and Stromal Co-option Shape a Pituitary Neuroendocrine Tumor Transcriptional Continuum
Robert C. OsorioiD, Jun Yeop OhiD, Ankita Sati, Jangham Jung, Alexander J. EhrenbergiD, Atul Saha, Meeki Lad, Harmon Khela, Nicole Brennick, Petros GiannikopoulosiD, William W. SeeleyiD, Lea T. GrinbergiD, Aaron DiaziD, Manish Kumar AghiiD
Abstract
Pituitary neuroendocrine tumors (PitNETs) are common intracranial neoplasms with complex biology underpinned by unresolved cellular origins, molecular heterogeneity, and microenvironment interactions. Here, we employ single-nuclei RNA-sequencing (snRNA-seq) of 419,874 cells from human normal pituitaries and PitNETs with spatial transcriptomics to resolve these challenges. We identify multi-hormonal neuroendocrine cells in both normal and tumor tissues, originating as early pseudotime intermediates from pituitary stem cells, revealing an inherent plasticity that blurs traditional lineage boundaries. PitNETs exhibit a transcriptional continuum across subtypes, challenging their classification into discrete categories. Trajectory analysis uncovers divergent cellular origins: silent gonadotroph adenomas (SGAs), prolactinomas, silent corticotroph adenomas (SCAs), and Cushings adenomas are closely linked to differentiated neuroendocrine cells, while somatotroph and null cell adenomas (NCAs) appear to derive more directly from adult stem cells. Tumor cells co-opt robust cell-cell communication networks found in normal adult neuroendocrine cells. Spatial profiling further demonstrates that perivascular niches enhance tumorigenicity through angiogenic and epithelial-mesenchymal transition programs. Our work redefines PitNETs as ecosystems shaped by developmental plasticity and microenvironmental crosstalk, offering a roadmap for future therapies targeting lineage fluidity and stromal dependencies.
- organism: human
- assay: snRNA-seq
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: trajectory inference
- research focus: cell-cell communication
- approach: deep learning
- approach: statistical model
- Citations
- 0
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