DNA Methylation Profiling Predicts Post-Surgical Regrowth in SF1-lineage Nonfunctioning Pituitary Neuroendocrine Tumors
Recommended Citation
Møller MW, Herrgott GA, Andersen MS, Halle B, Pedersen CB, Boldt HB, Petersen JK, Powell C, Castro AV, and Poulsen FR. DNA Methylation Profiling Predicts Post-Surgical Regrowth in SF1-lineage Nonfunctioning Pituitary Neuroendocrine Tumors. Neuro Oncol 2025.
Document Type
Article
Publication Date
12-10-2025
Publication Title
Neuro Oncol
Keywords
DNA methylation; Machine Learning; Non-functioning; PitNETs; Regrowth; SF1
Abstract
BACKGROUND: Nonfunctioning pituitary neuroendocrine tumors (NFPitNETs) account for ∼30-35% of PitNETs; ∼75% arise from the SF1 lineage. Recurrence remains common despite resection (∼30% in 10 years), and routine histopathology/IHC has limited value in predicting recurrence risk. This study evaluated whether DNA methylation profiling improves recurrence risk stratification.
MATERIAL AND METHODS: Genome-wide tissue methylation (Illumina EPIC v1, 850K) was analyzed in 117 retrospective NFPitNETs with clinical and imaging follow-up. Unsupervised consensus clustering defined methylation-based subgroups, followed by supervised differential methylation analysis to identify cluster-specific differentially methylated probes (DMPs). A classifier was trained using these signatures, with predicted subgroup memberships correlated with regrowth and progression-free survival (PFS). To ensure reliable estimations, longitudinal mixed-effects models were restricted to the interval of model stability (∼9 years), reflecting cohort follow-up. External validation was performed in three independent cohorts.
RESULTS: Five clusters (k1-k5) emerged: four SF1 positive-predominant (k1, k2, k3, k5) and one TPIT/PIT1-enriched NFPitNETs (k4). Among the 562 differentially methylated probes, many mapped to genes regulating cell-cycle and immune pathways. Compared with k1-k2, k3, k4, and k5 possessed significantly higher recurrence risk. Within SF1-lineage tumors, k3 exhibited postoperative tumor-volume expansion beginning at ∼6 years. The methylation-based classifier achieved ∼97% accuracy in assigning clusters and maintained prognostic separation across independent cohorts.
CONCLUSIONS: DNA methylation profiling identifies biologically and clinically distinct NFPitNET subgroups, particularly within the SF1 lineage, and may enhance prediction of recurrence risk. Prospective validation and demonstration of clinical utility are warranted to support integration into precision management workflows.
PubMed ID
41369112
ePublication
ePub ahead of print
