Proteomic analysis of meningiomas reveals clinically distinct molecular patterns

Abstract

Abstract Background Meningiomas represent one of the most common brain tumors and exhibit a clinically heterogeneous behavior, sometimes difficult to predict with classic histopathologic features. While emerging molecular profiling efforts have linked specific genomic drivers to distinct clinical patterns, the proteomic landscape of meningiomas remains largely unexplored. Methods We utilize liquid chromatography tandem mass spectrometry with an Orbitrap mass analyzer to quantify global protein abundances of a clinically well-annotated formalin-fixed paraffin embedded (FFPE) cohort (n = 61) of meningiomas spanning all World Health Organization (WHO) grades and various degrees of clinical aggressiveness. Results In total, we quantify 3042 unique proteins comparing patterns across different clinical parameters. Unsupervised clustering analysis highlighted distinct proteomic (n = 106 proteins, Welch’s t-test, P < 0.01) and pathway-level (eg, Notch and PI3K/AKT/mTOR) differences between convexity and skull base meningiomas. Supervised comparative analyses of different pathological grades revealed distinct patterns between benign (grade I) and atypical/malignant (grades II‒III) meningiomas with specific oncogenes enriched in higher grade lesions. Independent of WHO grade, clinically aggressive meningiomas that rapidly recurred (<3 y) had distinctive protein patterns converging on mRNA processing and impaired activation of the matrisome complex. Larger sized meningiomas (>3 cm maximum tumor diameter) and those with previous radiation exposure revealed perturbed pro-proliferative (eg, epidermal growth factor receptor) and metabolic as well as inflammatory response pathways (mitochondrial activity, interferon), respectively. Conclusions Our proteomic study demonstrates that meningiomas of different grades and clinical parameters present distinct proteomic profiles. These proteomic variations offer potential future utility in helping better predict patient outcome and in nominating novel therapeutic targets for personalized care.