DNA methylation profiling of meningiomas highlights clinically distinct molecular subgroups

Enhancing mitosis quantification and detection in meningiomas with computational digital pathology

Mitosis is a critical criterion for meningioma grading. However, pathologists' assessment of mitoses is subject to significant inter-observer variation due to challenges in locating mitosis hotspots and accurately detecting mitotic figures. To address this issue, we leverage digital pathology and propose a computational strategy to enhance pathologists' mitosis assessment. The strategy has two components: (1) A depth-first search algorithm that quantifies the mathematically maximum mitotic count in 10 consecutive high-power fields, which can enhance the preciseness, especially in cases with borderline mitotic count. (2) Implementing a collaborative sphere to group a set of pathologists to detect mitoses under each high-power field, which can mitigate subjective random errors in mitosis detection originating from individual detection errors. By depth-first search algorithm (1) , we analyzed 19 meningioma slides and discovered that the proposed algorithm upgraded two borderline cases verified at consensus conferences. This improvement is attributed to the algorithm's ability to quantify the mitotic count more comprehensively compared to other conventional methods of counting mitoses. In implementing a collaborative sphere (2) , we evaluated the correctness of mitosis detection from grouped pathologists and/or pathology residents, where each member of the group annotated a set of 48 high-power field images for mitotic figures independently. We report that groups with sizes of three can achieve an average precision of 0.897 and sensitivity of 0.699 in mitosis detection, which is higher than an average pathologist in this study (precision: 0.750, sensitivity: 0.667). The proposed computational strategy can be integrated with artificial intelligence workflow, which envisions the future of achieving a rapid and robust mitosis assessment by interactive assisting algorithms that can ultimately benefit patient management.

Current status of DNA methylation profiling in neuro-oncology as a diagnostic support tool: A review

Over the last 2 decades, high throughput genome-wide molecular profiling has revealed characteristic genetic and epigenetic alterations associated with different types of central nervous system (CNS) tumors. DNA methylation profiling has emerged as an important molecular platform for CNS tumor classification with improved diagnostic accuracy and patient risk stratification in comparison to the standard of care histopathological analysis and any single molecular tests. The emergence of DNA methylation arrays have also played a crucial role in refining existing types and the discovery of new tumor types or subtypes. The adoption of methylation data into neuro-oncology has been greatly aided by the development of a freely accessible machine learning-based classifier. In this review, we discuss methylation workflow, address the utility of DNA methylation profiling in CNS tumors in a routine diagnostic setting, and provide an overview of the methylation-based tumor types and new types or subtypes identified with this platform.

Unveiling a Biomarker Signature of Meningioma: The Need for a Panel of Genomic, Epigenetic, Proteomic, and RNA Biomarkers to Advance Diagnosis and Prognosis

Meningiomas are the most prevalent primary intracranial tumors. The majority are benign but can undergo dedifferentiation into advanced grades classified by World Health Organization (WHO) into Grades 1 to 3. Meningiomas' tremendous variability in tumor behavior and slow growth rates complicate their diagnosis and treatment. A deeper comprehension of the molecular pathways and cellular microenvironment factors implicated in meningioma survival and pathology is needed. This review summarizes the known genetic and epigenetic aberrations involved in meningiomas, with a focus on neurofibromatosis type 2 (NF2) and non-NF2 mutations. Novel potential biomarkers for meningioma diagnosis and prognosis are also discussed, including epigenetic-, RNA-, metabolomics-, and protein-based markers. Finally, the landscape of available meningioma-specific animal models is overviewed. Use of these animal models can enable planning of adjuvant treatment, potentially assisting in pre-operative and post-operative decision making. Discovery of novel biomarkers will allow, in combination with WHO grading, more precise meningioma grading, including meningioma identification, subtype determination, and prediction of metastasis, recurrence, and response to therapy. Moreover, these biomarkers may be exploited in the development of personalized targeted therapies that can distinguish between the 15 diverse meningioma subtypes.

Paths of Evolution of Progressive Anaplastic Meningiomas: A Clinical and Molecular Pathology Study

Grade 3 meningiomas are rare malignant tumors that can originate de novo or from the progression of lower grade meningiomas. The molecular bases of anaplasia and progression are poorly known. We aimed to report an institutional series of grade 3 anaplastic meningiomas and to investigate the evolution of molecular profile in progressive cases. Clinical data and pathologic samples were retrospectively collected. VEGF, EGFR, EGFRvIII, PD-L1; and Sox2 expression; MGMT methylation status; and TERT promoter mutation were assessed in paired meningioma samples collected from the same patient before and after progression using immunohistochemistry and PCR. Young age, de novo cases, origin from grade 2 in progressive cases, good clinical status, and unilateral side, were associated with more favorable outcomes. In ten progressive meningiomas, by comparing molecular profile before and after progression, we identified two subgroups of patients, one defined by Sox2 increase, suggesting a stem-like, mesenchymal phenotype, and another defined by EGFRvIII gain, suggesting a committed progenitor, epithelial phenotype. Interestingly, cases with Sox2 increase had a significantly shortened survival compared to those with EGFRvIII gain. PD-L1 increase at progression was also associated with worse prognosis, portending immune escape. We thus identified the key drivers of meningioma progression, which can be exploited for personalized treatments.