PROJECT 2: PROJECT ABSTRACT The World Health Organization (WHO) has historically graded meningiomas according to histological features, and many WHO grade 1 meningiomas can be effectively treated with surgery or radiotherapy, but many WHO grade 2 or grade 3 meningiomas are resistant to treatment and cause significant neurological morbidity and mortality. Approximately 30% of WHO grade 1 meningioma develop recurrences that cannot be predicted from histological features, and some WHO grade 2 or grade 3 meningiomas are unexpectedly well controlled with surgery and radiotherapy. These data indicate that improvements in risk stratification and new therapies for patients with meningiomas are needed, but limited understanding of meningioma biology and the misconception that all meningiomas are benign has encumbered medical and scientific advances for patients with meningiomas. Using multiplatform molecular profiling on 2092 meningiomas from 13 institutions, we discovered DNA methylation groups and a gene expression biomarker that improve risk stratification compared to all other classification systems across all WHO grades. Our data demonstrate that meningiomas from the DNA methylation group with the worst outcomes are vulnerable to cell cycle inhibitors (28%), and that gene expression profiling predicts which meningiomas will benefit from postoperative radiotherapy (32%). Cooperative group trials, clinical guidelines, and classification systems that incorporate our DNA methylation and gene expression biomarkers are under development. More broadly, our biomarkers provide a framework for understanding how meningiomas grow and respond to therapy, but biomarkers that are based on one sample from a single point in time may not be optimal. Here we propose to study (1) biological drivers, (2) imaging features, and (3) therapeutic vulnerabilities underlying DNA methylation and gene expression biomarkers using regionally distinct samples from individual meningiomas (Aim 1), patient-matched meningioma samples over time (Aim 2), and an organoid model of meningioma heterogeneity integrated with a novel functional genomic technique (Aim 3). Our central hypothesis is that understanding how meningiomas grow and respond to therapy will (1) identify drivers underlying meningioma biomarker performance to optimize risk stratification, (2) define magnetic resonance (MR) imaging features of meningioma biomarkers to establish a foundation for non-invasive risk stratification, and (3) inform future therapies to improve the care of patients with the most common primary intracranial tumor. Our studies will use bulk, single-cell, spatial, and functional genomics to investigate meningioma evolution and heterogeneity in the context of intratumor or whole tumor MR imaging features. To do so, we have assembled a biobank 367 patient-matched meningioma samples from 156 patients who were treated with serial surgery ± radiotherapy, 27 patient-derived meningioma cell lines that repre...