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Updated: Jan 9, 2026
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Methyl-binding DNA capture Sequencing for Patient Tissues
Published on: October 31, 2016
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DNA methylation profiling predicts postsurgical regrowth in SF1-lineage nonfunctioning pituitary neuroendocrine
Morten Winkler Møller1,2, Grayson A Herrgott3, Marianne Skovsager Andersen4
1Department of Neurosurgery, Odense University Hospital (M.W.M., B.H., C.B.P., F.R.P.).
Neuro-Oncology
|December 10, 2025
Summary
DNA methylation profiling identifies distinct nonfunctioning pituitary neuroendocrine tumor (NFPitNET) subgroups, improving recurrence risk prediction, especially within the SF1 lineage. This advances precision management for these common pituitary tumors.
Area of Science:
- Endocrinology
- Oncology
- Genomics
Background:
- Nonfunctioning pituitary neuroendocrine tumors (NFPitNETs) represent 30-35% of pituitary tumors, with ~75% originating from the SF1 lineage.
- Recurrence rates for NFPitNETs remain high (~30% at 10 years), and current histopathology/IHC methods offer limited predictive value for recurrence risk.
- This study investigates the utility of DNA methylation profiling for enhanced recurrence risk stratification in NFPitNETs.
Purpose of the Study:
- To evaluate DNA methylation profiling for improved recurrence risk stratification in nonfunctioning pituitary neuroendocrine tumors (NFPitNETs).
- To identify distinct molecular subgroups within NFPitNETs based on DNA methylation patterns.
- To assess the prognostic value of methylation-based classifiers for predicting tumor regrowth and progression-free survival.
Main Methods:
- Genome-wide DNA methylation analysis (Illumina EPIC v1, 850K) was performed on 117 retrospective NFPitNET samples.
- Unsupervised consensus clustering identified methylation-based subgroups, followed by differential methylation analysis to pinpoint key probes.
- A predictive classifier was trained and validated in independent cohorts, correlating subgroup membership with clinical outcomes like progression-free survival.
Main Results:
- Five distinct methylation-based clusters (k1-k5) were identified, with four SF1-lineage predominant and one TPIT/PIT1-enriched.
- Clusters k3, k4, and k5 showed significantly higher recurrence risk compared to k1-k2, with k3 exhibiting postoperative expansion around 6 years.
- The developed methylation classifier achieved ~97% accuracy, demonstrating robust prognostic separation across independent cohorts.
Conclusions:
- DNA methylation profiling effectively delineates biologically and clinically distinct NFPitNET subgroups, particularly within the SF1 lineage.
- This approach shows promise for enhancing the prediction of recurrence risk in NFPitNETs.
- Prospective validation is recommended to integrate this method into precision management strategies for NFPitNETs.
