PitNET tissue deconvolution: tracing normal tissue residues and immune dynamics - Dalle Nogare, Mattia; Avallone, Serena; Picello, Luna; Puggina, Daniele; Denaro, Luca; Sales, Gabriele; Vazza, Giovanni; Occhi, Gianluca (Frontiers in Endocrinology, 2025)
BackgroundBulk RNA sequencing (RNA-seq) has substantially advanced the understanding of pituitary neuroendocrine tumors (PitNETs). However, its limited ability to resolve cellular heterogeneity – particularly in samples containing residual non-tumor pituitary cells – remains a significant challenge.ObjectiveWe developed and validated a tissue deconvolution framework using a reference dataset derived from single-nucleus RNA sequencing (snRNA-seq) of normal pituitary tissue, aimed at estimating cellular composition in PitNETs from bulk RNA-seq data and characterizing the tumor microenvironment (TME).MethodsMarker-based (CIBERSORT, MuSiC) and single-cell–based (CIBERSORTx, MuSiC) deconvolution approaches were benchmarked across simulated, pseudobulk, and bulk RNA-seq datasets to identify the most reliable tools.ResultsCIBERSORTx demonstrated the highest sensitivity (r > 0.85) for detecting pituitary cell types, although accuracy decreased for TME components. Application to ten GH-secreting PitNETs with known histological contamination and to public datasets consistently revealed residual normal tissue across hormone-secreting subtypes, excluding silent tumors. Contaminated samples – averaging 43% ± 19% with CIBERSORTx and 37% ± 22% with CIBERSORT – displayed distinct transcriptomic profiles compared to uncontaminated, lineage-matched tumors, based on clustering analyses.ConclusionThis study establishes snRNA-seq–based deconvolution as a robust strategy for reconstructing cellular composition in PitNETs, mitigating the impact of histological contamination and improving the reliability of downstream transcriptomic analyses.