Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated

PURPOSE: Meningiomas are the most frequent primary intracranial tumors. Patient outcome varies widely from benign to highly aggressive, ultimately fatal courses. Reliable identification of risk of progression for individual patients is of pivotal importance. However, only biomarkers for highly aggressive tumors are established (CDKN2A/B and TERT), whereas no molecularly based stratification exists for the broad spectrum of patients with low- and intermediate-risk meningioma. METHODS: DNA methylation data and copy-number information were generated for 3,031 meningiomas (2,868 patients), and mutation data for 858 samples. DNA methylation subgroups, copy-number variations (CNVs), mutations, and WHO grading were analyzed. Prediction power for outcome was assessed in a retrospective cohort of 514 patients, validated on a retrospective cohort of 184, and on a prospective cohort of 287 multicenter cases. RESULTS: Both CNV- and methylation family-based subgrouping independently resulted in increased prediction accuracy of risk of recurrence compared with the WHO classification (c-indexes WHO 2016, CNV, and methylation family 0.699, 0.706, and 0.721, respectively). Merging all risk stratification approaches into an integrated molecular-morphologic score resulted in further substantial increase in accuracy (c-index 0.744). This integrated score consistently provided superior accuracy in all three cohorts, significantly outperforming WHO grading (c-index difference P = .005). Besides the overall stratification advantage, the integrated score separates more precisely for risk of progression at the diagnostically challenging interface of WHO grade 1 and grade 2 tumors (hazard ratio 4.34 [2.48-7.57] and 3.34 [1.28-8.72] retrospective and prospective validation cohorts, respectively). CONCLUSION: Merging these layers of histologic and molecular data into an integrated, three-tiered score significantly improves the precision in meningioma stratification. Implementation into diagnostic routine informs clinical decision making for patients with meningioma on the basis of robust outcome prediction

Targeted Gene Expression Profiling Predicts Meningioma Outcomes and Radiotherapy Responses

Surgery is the mainstay of treatment for meningioma, the most common primary intracranial tumor, but improvements in meningioma risk stratification are needed and indications for postoperative radiotherapy are controversial. Here we develop a targeted gene expression biomarker that predicts meningioma outcomes and radiotherapy responses. Using a discovery cohort of 173 meningiomas, we developed a 34-gene expression risk score and performed clinical and analytical validation of this biomarker on independent meningiomas from 12 institutions across 3 continents (N = 1,856), including 103 meningiomas from a prospective clinical trial. The gene expression biomarker improved discrimination of outcomes compared with all other systems tested (N = 9) in the clinical validation cohort for local recurrence (5-year area under the curve (AUC) 0.81) and overall survival (5-year AUC 0.80). The increase in AUC compared with the standard of care, World Health Organization 2021 grade, was 0.11 for local recurrence (95% confidence interval 0.07 to 0.17, P \u3c 0.001). The gene expression biomarker identified meningiomas benefiting from postoperative radiotherapy (hazard ratio 0.54, 95% confidence interval 0.37 to 0.78, P = 0.0001) and suggested postoperative management could be refined for 29.8% of patients. In sum, our results identify a targeted gene expression biomarker that improves discrimination of meningioma outcomes, including prediction of postoperative radiotherapy responses

Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated.

PURPOSE: Meningiomas are the most frequent primary intracranial tumors. Patient outcome varies widely from benign to highly aggressive, ultimately fatal courses. Reliable identification of risk of progression for individual patients is of pivotal importance. However, only biomarkers for highly aggressive tumors are established (CDKN2A/B and TERT), whereas no molecularly based stratification exists for the broad spectrum of patients with low- and intermediate-risk meningioma. METHODS: DNA methylation data and copy-number information were generated for 3,031 meningiomas (2,868 patients), and mutation data for 858 samples. DNA methylation subgroups, copy-number variations (CNVs), mutations, and WHO grading were analyzed. Prediction power for outcome was assessed in a retrospective cohort of 514 patients, validated on a retrospective cohort of 184, and on a prospective cohort of 287 multicenter cases. RESULTS: Both CNV- and methylation family-based subgrouping independently resulted in increased prediction accuracy of risk of recurrence compared with the WHO classification (c-indexes WHO 2016, CNV, and methylation family 0.699, 0.706, and 0.721, respectively). Merging all risk stratification approaches into an integrated molecular-morphologic score resulted in further substantial increase in accuracy (c-index 0.744). This integrated score consistently provided superior accuracy in all three cohorts, significantly outperforming WHO grading (c-index difference P = .005). Besides the overall stratification advantage, the integrated score separates more precisely for risk of progression at the diagnostically challenging interface of WHO grade 1 and grade 2 tumors (hazard ratio 4.34 [2.48-7.57] and 3.34 [1.28-8.72] retrospective and prospective validation cohorts, respectively). CONCLUSION: Merging these layers of histologic and molecular data into an integrated, three-tiered score significantly improves the precision in meningioma stratification. Implementation into diagnostic routine informs clinical decision making for patients with meningioma on the basis of robust outcome prediction

Nachhaltige Vitalisierung des kreativen Quartiers um den Campus Berlin-Charlottenburg

Mit dem Ziel, diesen Campus national und international als attraktiven Wirtschafts- und Wissenschaftsstandort zu etablieren, wurde das Projekt „Nachhaltige Vitalisierung des kreativen Quartiers um den Campus Berlin-Charlottenburg“ (NAVI BC) mit einer Laufzeit von zwei Jahren (01.11.2008 – 31.12.2010) unter der Leitung der Adlershof Projekt GmbH initiiert. Dabei wurde eine Wachstumszone als Fördergebiet definiert, dessen Kern die wissenschaftlichen Einrichtungen beider Universitäten und weiterer außeruniversitärer Forschungseinrichtungen (z.B. Physikalisch-Technische Bundesanstalt, vier Fraun- hofer-Institute) darstellen. Die grundlegende Idee bestand darin, ausgehend von einer vorhandenen Konzentration und auf Basis der Berücksichtigung vielfältiger Interessen unterschiedlicher Akteure aus Wirtschaft, Wissenschaft und Politik, eine gemeinsame Strategie für den Standort zu formulieren. Vernetzung, Einbettung sowie interdisziplinäre Innovations- und Lernprozesse zwischen den Akteuren waren die tragenden Prinzipien

Targeting brain tumor stem cells by interfering with choline metabolism: Evidence for an EMT-choline oncometabolic network

Glioblastoma (GBM) is the most lethal primary malignant brain tumor with a median survival of less than two years. High levels of therapy resistance, strong cellular invasiveness and rapid cell growth demand aggressive multimodal therapies involving resection as well as radio-chemotherapy. Recent evidence has pointed to the existence of brain tumor stem cells (BTSCs), a subpopulation of human brain tumors which is thought to be responsible for tumor dissemination, relapse and chemo resistance. BTSCs have been associated with the expression of mesenchymal features as a result of epithelial-mesenchymal transition (EMT). Using high resolution proton nuclear magnetic resonance spectroscopy (1H NMR) we compared the intracellular metabolic composition of GBM cells after induction vs. inhibition of EMT as well as under stem cell or differentiated conditions. We identified that both EMT and enrichment for stemness induces the cholinic phenotype which is characterized by high intracellular levels of phosphocholine and total choline derivatives. Furthermore, interference with choline metabolism by targeting choline kinase alpha (CHKα) reversed EMT in GBM cells as we observed reduced invasiveness, clonogenicity, and expression of EMT associated genes. Taken together, interfering with choline metabolism is a powerful strategy to suppress EMT and thus target BTSCs. Moreover, the newly identified BTSC-oncometabolic network could be used to non-invasively monitor the invasive properties of glioblastomas and the success of anti-BTSC therapy

Efficiency impacts of the Chinese industrial transition: a quantitative evaluation of reforms in the coal industry

Technical efficiency, Transition, Reform, Coal industry, China, D21, L25, L72, P27, P28, Q38,