Meningioma, Meningioma of Brain
Conditions
Keywords
Meningioma, Machine Learning
Brief summary
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).
Detailed description
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.
Interventions
None listed
Sponsors
Fondazione I.R.C.C.S. Istituto Neurologico Carlo Besta
University of Milan
Università degli Studi del Piemonte Orientale Amedeo Avogadro
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
Inclusion Criteria (Retrospective \& Prospective): * Patient aged 18 years or older. * First diagnosis of uni-focal meningioma of the convexity. * Macroscopical total resection (Simpson 1-3). (Retrospective only): * Surgery performed between 2007 and 2016; * availability of FFPE sample and medical records.
Exclusion criteria
* Genetic syndromes. * Diffuse Meningeal Meningiomatosis. * Patients who underwent experimental treatment in neo-adjuvant setting. (Prospective only): * Previous surgical/medical treatment for another meningioma; * positive oncological history (e.g., breast cancer).
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Definition of a novel epigenetic signature based on MS-profiling | Months 1-12 | Identification of histone post-translational modifications (PTMs) patterns in FFPE tissue samples capable of classifying tumor recurrence. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Validation of epigenetic signature in prospective tissues | Months 13-18 | Validation of the histone PTMs signature using Mass Spectrometry on FFPE samples from the prospective cohort. |
| Validation of epigenetic biomarkers in circulating nucleosomes | Months 13-20 | Profiling histone PTMs in circulating nucleosomes from patient sera matching the tissues profiled. |
| DNA methylation profiling in plasma-EVs | Months 10-20 | Assessment of genome-wide DNA methylation profile from DNA extracted from plasma-derived Extracellular Vesicles. |
| Development of a Machine Learning prognostic classifier | Months 18-24 | Integration of epi-proteomics data, DNA-methylation profiles, and clinico-pathological information to predict recurrence. |
Countries
Italy
Outcome results
None listed