Meningiomas are the most common primary intracranial tumors. Current treatment relies on surgical resection and radiotherapy, but molecular predictors for recurrence are lacking. This study aims to investigate epigenetic features, specifically histone post-translational modifications (PTMs) and DNA methylation, to stratify patients. The study involves a retrospective cohort to define an epigenetic signature and a prospective cohort to validate it in tissues and liquid biopsies (plasma/EVs).
The study is shaped by two phases. The first is a histone PTMs analysis in solid tissues from a retrospective cohort of 150 meningioma FFPE samples. The second step consists of validating the epigenetic signature in a prospective cohort of patients (n=60). The study addresses four main objectives: 1) Dissecting the informative power of epigenetic signatures (histone PTMs by MS) in tissues; 2) Validating signatures in prospective tissues and matched sera (circulating nucleosomes); 3) Assessing DNA methylation profiles from plasma-derived Extracellular Vesicles (EVs); 4) Developing a Machine Learning model integrating epi-proteomics, DNA-methylation, and clinical data for prognostic subtyping.
Study Type
OBSERVATIONAL
Enrollment
210
Fondazione IRCCS Istituto Neurologico Carlo Besta
Milan, Italy
RECRUITINGDefinition of a novel epigenetic signature based on MS-profiling
Identification of histone post-translational modifications (PTMs) patterns in FFPE tissue samples capable of classifying tumor recurrence.
Time frame: Months 1-12
Validation of epigenetic signature in prospective tissues
Validation of the histone PTMs signature using Mass Spectrometry on FFPE samples from the prospective cohort.
Time frame: Months 13-18
Validation of epigenetic biomarkers in circulating nucleosomes
Profiling histone PTMs in circulating nucleosomes from patient sera matching the tissues profiled.
Time frame: Months 13-20
DNA methylation profiling in plasma-EVs
Assessment of genome-wide DNA methylation profile from DNA extracted from plasma-derived Extracellular Vesicles.
Time frame: Months 10-20
Development of a Machine Learning prognostic classifier
Integration of epi-proteomics data, DNA-methylation profiles, and clinico-pathological information to predict recurrence.
Time frame: Months 18-24
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