Lineage-specific steroidomic signatures in pituitary neuroendocrine tumors: pregnenolone enrichment in SF-1 lineage and associations with tumor behavior.

Abstract

Pituitary neuroendocrine tumors (PitNETs) exhibit diverse biological behaviors, yet the metabolic determinants underlying lineage-specific characteristics remain poorly understood. This study characterized intratumoral steroid metabolite profiles in relation to transcription factor (TF)-defined lineages and radiological tumor features in 69 PitNET specimens obtained during transsphenoidal surgery between 2021 and 2023. Tumors were classified according to the WHO 2022 TF-based criteria (SF-1, PIT-1, and TPIT). Tissue concentrations of 14 steroid metabolites were quantified via liquid chromatography-tandem mass spectrometry, with associations evaluated using multivariable regression and partial least squares discriminant analysis (PLS-DA). Gonadotroph tumors (n = 34) exhibited marked pregnenolone (PREG) elevation compared with other lineages (median: 104.6 vs 1.2 ng/g, P < 0.001, ε2 = 0.38). Corticotroph tumors (n = 9) showed enrichment of glucocorticoid metabolites, particularly 11-deoxycortisol (adjusted P = 0.002, ε2 = 0.30), while PIT-1 tumors (n = 23) displayed intermediate levels. PLS-DA corroborated these patterns, with 11-deoxycortisol and pregnenolone emerging as the primary discriminators (68% total variance). Multivariable analysis confirmed SF-1 lineage to be independently associated with PREG levels after adjusting for clinical and radiological covariates (adjusted R2 = 0.56, P < 0.001). Inverse associations were observed between deoxycorticosterone/11-deoxycortisol and tumor expansion (r = -0.7 and r = -0.4, both P < 0.001), and PREG correlated with cavernous sinus invasion within gonadotroph tumors (P = 0.03). During follow-up (median: 32-38 months), steroid profiles showed no association with clinical outcomes. TF-defined PitNET lineages exhibit distinct intratumoral steroid profiles reflecting intrinsic metabolic properties of lineage-specific cell populations, revealing a novel metabolic dimension to PitNET biology.