TITLE: Epigenetic profiling and liquid biopsy: perspectives for personalized medicine in meningioma patients ACRONYM: MIND: Meningioma epIgeNetic liquiD biopsy

Meningiomas are the most frequent primary intracranial tumours, accounting for 38% of all cases and determining 32% of all deaths due to central nervous system malignancies. They are classified according to the World Health Organization (WHO) grading system into three grades: grade 1 meningiomas are usually benign (90% of cases), grade 2 comprise atypical tumors (5–15%) and grade 3 include anaplastic tumors (1–3%). While being the most diffused stratification algorithm to guide the therapeutic approach and bearing prognostic value, the WHO classification has limitations. For instance, grade 1 meningiomas can recur or invade locally, while higher grade meningiomas can have an indolent clinical course. The other existing predictor associated with time of recurrence in meningiomas is the extent of tumor resection at surgery (Simpson scale). Although both are basically associated with recurrence rates on a population, they are limited by the interrater variability of grading and considerable within-grade variation of recurrence risk for individual patients. Consequently, the treatment of meningiomas is still discussed case by case, commonly on the basis of previous experiences and low-level evidence. An evidenced based and personalized approach still represents a major unmet clinical need. Hence, it is crucial to achieve a better insight in the molecular biology of these tumors, allowing to stratify patients and to select the therapeutic approach according to molecular signatures. Elaborating on recent publications linking epigenetic features to meningioma classification, we propose to simultaneously investigate histone post-translational modifications and DNA methylation as tools for meningioma patient stratification. To this aim, we will take advantage on our recognized expertise in systematic epigenetic profiling of cancer patients by mass spectrometry (MS) and in extracellular vesicles characterisation from brain cancer patient plasma. The specific goals of this project are: 1) defining a novel epigenetic signature based on MS-profiling of a retrospective cohort of formalin-fixed paraffin embedded tissue samples, for classification and recurrence prediction; 2) validating such signature on circulating nucleosomes, from liquid biopsies collected prospectively, 3) assessing the DNA-methylation predictive signature from plasma-derived extracellular vesicles; 4) developing a machine learning algorithm for the integration of epi-proteomics data, DNA-methylation profiles and clinico-pathological information, to generate a potent molecular classifier and predictor. The successful completion of this project will generate crucial knowledge that will enable tailoring the treatment of meningioma patients in an unprecedented, personalized, shared and non-invasive way.