Prospective, multicenter validation of a platform for rapid molecular profiling of central nervous system tumors.

前瞻性、多中心验证用于快速分子分析中枢神经系统肿瘤的平台

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作者:Patel Areeba, Göbel Kirsten, Ille Sebastian, Hinz Felix, Schoebe Natalie, Bogumil Henri, Meyer Jochen, Brehm Michelle, Kardo Helin, Schrimpf Daniel, Lomakin Artem, Ritter Michael, Göller Pauline, Kerbs Paul, Pfeifer Lisa, Hamelmann Stefan, Blume Christina, Ippen Franziska M, Berghaus Natalie, Euskirchen Philipp, Schweizer Leonille, Hultschig Claus, Van Roy Nadine, Van Dorpe Jo, Van der Meulen Joni, Loontiens Siebe, Dedeurwaerdere Franceska, Leske Henning, Halldórsson Skarphéðinn, Fox Graeme, Deacon Simon, Cahyani Inswasti, Holmes Nadine, Wibowo Satrio, Munro Rory, Martin Dan, Sharif Abid, Housley Mark, Goldspring Robert, Brandner Sebastian, Roy Somak, Hench Jürgen, Frank Stephan, Unterberg Andreas, Goidts Violaine, Jäger Natalie, Paine Simon, Smith Stuart, Herold-Mende Christel, Wick Wolfgang, Pfister Stefan M, Vik-Mo Einar O, von Deimling Andreas, Krieg Sandro, Jones David Tw, Loose Matthew, Schlesner Matthias, Sill Martin, Sahm Felix
Molecular data integration plays a central role in central nervous system (CNS) tumor diagnostics but currently used assays pose limitations due to technical complexity, equipment and reagent costs, as well as lengthy turnaround times. We previously reported the development of Rapid-CNS(2), an adaptive-sampling-based nanopore sequencing workflow. Here we comprehensively validated and further developed Rapid-CNS(2) for intraoperative use. It now offers real-time methylation classification and DNA copy number information within a 30-min intraoperative window, followed by comprehensive molecular profiling within 24 h, covering the complete spectrum of diagnostically and therapeutically relevant information for the respective entity. We validated Rapid-CNS(2) in a multicenter setting on 301 archival and prospective samples including 18 samples sequenced intraoperatively. To broaden the utility of methylation-based CNS tumor classification, we developed MNP-Flex, a platform-agnostic methylation classifier encompassing 184 classes. MNP-Flex achieved 99.6% accuracy for methylation families and 99.2% accuracy for methylation classes with clinically applicable thresholds across a global validation cohort of more than 78,000 frozen and formalin-fixed paraffin-embedded samples spanning five different technologies. Integration of these tools has the potential to advance CNS tumor diagnostics by providing broad access to rapid, actionable molecular insights crucial for personalized treatment strategies.

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