Neuro-oncology · 2025
DNA methylation profiling of pituitary neuroendocrine tumors identifies distinct clinical and pathological subtypes based on epigenetic differentiation.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Belakhoua Sarra, Vasudevaraja Varshini, Schroff Chanel, Galbraith Kristyn, Movahed-Ezazi Misha, Serrano Jonathan, Yang Yiying, Orringer Daniel, Golfinos John G, Sen Chandra, Pacione Donato, Agrawal Nidhi, Snuderl Matija
Аннотация
BACKGROUND: Pituitary neuroendocrine tumors (PitNETs) are the most common intracranial neuroendocrine tumors. PitNETs can be challenging to classify, and current recommendations include a large immunohistochemical panel to differentiate among 14 WHO-recognized categories.
METHODS: In this study, we analyzed clinical, immunohistochemical, and DNA methylation data of 118 PitNETs to develop a clinicomolecular approach to classifying PitNETs and identifying epigenetic classes.
RESULTS: CNS DNA methylation classifier has an excellent performance in recognizing PitNETs and distinguishing the 3 lineages when the calibrated score is ≥ 0.3. Unsupervised DNA methylation analysis separated PitNETs into 2 major clusters. The first was composed of silent gonadotrophs, which form a biologically distinct group of PitNETs characterized by clinical silencing, weak hormonal expression on immunohistochemistry, and simple copy number profile. The second major cluster was composed of corticotrophs and Pit1 lineage PitNETs, which could be further classified using DNA methylation into distinct subclusters that corresponded to clinically functioning and silent tumors and are consistent with transcription factor expression. Analysis of promoter methylation patterns correlated with lineage for corticotrophs and Pit1 lineage subtypes. However, the gonadotrophic genes did not show a distinct promoter methylation pattern in gonadotroph tumors compared to other lineages. Promoter of the NR5A1 gene, which encodes SF1, was hypermethylated across all PitNETs clinical and molecular subtypes including gonadotrophs with strong SF1 protein expression indicating alternative epigenetic regulation.
CONCLUSION: Our findings suggest that classification of PitNETs may benefit from DNA methylation for clinicopathological stratification.
METHODS: In this study, we analyzed clinical, immunohistochemical, and DNA methylation data of 118 PitNETs to develop a clinicomolecular approach to classifying PitNETs and identifying epigenetic classes.
RESULTS: CNS DNA methylation classifier has an excellent performance in recognizing PitNETs and distinguishing the 3 lineages when the calibrated score is ≥ 0.3. Unsupervised DNA methylation analysis separated PitNETs into 2 major clusters. The first was composed of silent gonadotrophs, which form a biologically distinct group of PitNETs characterized by clinical silencing, weak hormonal expression on immunohistochemistry, and simple copy number profile. The second major cluster was composed of corticotrophs and Pit1 lineage PitNETs, which could be further classified using DNA methylation into distinct subclusters that corresponded to clinically functioning and silent tumors and are consistent with transcription factor expression. Analysis of promoter methylation patterns correlated with lineage for corticotrophs and Pit1 lineage subtypes. However, the gonadotrophic genes did not show a distinct promoter methylation pattern in gonadotroph tumors compared to other lineages. Promoter of the NR5A1 gene, which encodes SF1, was hypermethylated across all PitNETs clinical and molecular subtypes including gonadotrophs with strong SF1 protein expression indicating alternative epigenetic regulation.
CONCLUSION: Our findings suggest that classification of PitNETs may benefit from DNA methylation for clinicopathological stratification.