OTME-5. Meningioma liquid biopsy specimens exhibit contrasting immune-cell landscapes across methylation-subtypes and estimated recurrence risk subgroups. (5th July 2021)

• Record Type: Journal Article
• Authors: Herrgott, Grayson; She, Ruicong; Sabedot, Thais; Wells, Michael; Asmaro, Karam; Malta, Tathiane; Mosella, Maritza; Nelson, Kevin; deCarvalho, Ana; Poisson, Laila; Mukherjee, Abir; Cazacu, Simona; Robin, Adam; Lee, Ian; Snyder, James; Walbert, Tobias; Rosenblum, Mark; Mikkelsen, Tom; Kalkanis, Steven; Rock, Jack; Noushmehr, Houtan; Castro, Ana Valeria
• Is Part Of: Neuro-oncology advances. Volume 3(2021)Supplement 2
• Page Start: ii14
• Page End: ii14
• Publication Date: 2021-07-05
• Subjects: 616.99481
• DOI: 10.1093/noajnl/vdab070.056
• Languages: English
• ISSNs: 2632-2498

Abstract

Abstract: Background: Tumor-infiltrating immune cell compositions have been previously correlated to encouragement or inhibition of tumor growth. This association highlights immune-landscape profiling through non-invasive methods as a crucial step in approaches to treatment of patients with meningioma (MNG), a prevalent primary intracranial tumor. Genome-wide DNA methylation patterns can aid in definition and assessment of cell compositions in liquid biopsy serum specimens, and allow for development of machine-learning models with predictive capabilities. Methods: We profiled the cfDNA methylome (EPIC array) in liquid biopsy specimens from patients with MNG (n = 63) and nontumor controls (n = 6). We conducted both unsupervised epigenome-wide and supervised analyses of the meningioma methylome. Estimation of immune cell composition was conducted using Python-based methodology, where a reference methylome atlas of chosen cell types (B-cells, CD4- and CD8T-cells, neutrophils, natural killer cells, monocytes, cortical neuron, vascular endothelial cells, and healthy meninge) was used to deconvolute the MNG samples. Recurrence risk was estimated using an existing methylation-based Random-Forest classifier previously reported and validated, adapted to our serum-based cohort through employment of translatable meningioma subgroup-specific methylation markers (differentially methylated probes). Results: We identified four distinct genome-wide methylation subgroups (k-clusters) of MNG which presented differential tumor micro-environments across all cell types investigated. Application of the DNA methylation-based Random-Forest classifier allowed for categorization of primary MNG serum samples into estimated recurrence-risk subgroups. Significantly contrasting micro-environments for the subgroups were observed across several cell-types, with those MNG more likely to recur displaying depletion in cell types reported to improve anti-tumoral response in many tumors (e.g. T-Cells). Conclusions: DNA methylation based deconvolution allowed for detection of contrasting tumor microenvironment compositions across MNG methylation subtypes and recurrence-risk estimation subgroups. These results suggest that microenvironment profiling can be informative of probable tumor behavior and prognostic outcomes, helping guide therapeutic approaches towards treatment of patients with MNG.