Filser Mathilde, Torrejon Jacob, Merchadou Kevin, Dufour Christelle, Girard Elodie, Bourneix Christine, Lemaître Elisa, Gharsalli Tarek, Brillet Riwan, Wong Jennifer, Gentien David, Rapinat Audrey, Servant Nicolas, Vasiljevic Alexandre, Bertozzi Anne Isabelle, Raimbault Sandra, Tauziede Espariat Arnault, Lhermitte Benoit, Faure-Conter Cécile, Icher Céline, Berger Claire, Maurage Claude Alain, Bodet Damien, Meyronet David, Uro-Coste Emmanuelle, De Carli Emilie, Forest Fabien, Palenzuela Gilles, Chotard Guillaume, Gauchotte Guillaume, Sudour Helene, Mansuy Ludovic, Deparis Marianna, Tallegas Matthias, Faisant Maxime, Entz-Werle Natacha, Varlet Pascale, Leblond Pierre, Michalak-Provost Sophie, Proust Houdemont Stéphanie, Rigau Valérie, Doz François, Delattre Olivier, Bourdeaut Franck, Ayrault Olivier, Masliah-Planchon Julien
PSL Research University, Paris, France.
Genetics Department, Institut Curie, Paris, France.
Neuro Oncol. 2025 Jun 21;27(5):1313-1324. doi: 10.1093/neuonc/noae279.
Medulloblastoma (MB) is one of the most prevalent embryonal malignant brain tumors. Current classification organizes these tumors into 4 molecular subgroups (WNT, SHH, Group 3, and Group 4 MB). Recently, a comprehensive classification has been established, identifying numerous subtypes, some of which exhibit a poor prognosis. It is critical to establish effective subtyping methods for accurate diagnosis and patient's management that strikes a delicate balance between improving outcomes and minimizing the risk of comorbidities.
We evaluated the ability of Nanopore sequencing to provide clinically relevant methylation and copy number profiles of MB. Nanopore sequencing was applied to an EPIC cohort of 44 frozen MB, benchmarked against the gold standard EPIC array, and further evaluated on an integrated diagnosis cohort of 116 MB.
Most MB of both cohorts (42/44; 95.5% and 106/116; 91.4%, respectively) were accurately subgrouped by Nanopore sequencing. Employing Flongle flow cells for 18 MB allowed a more rapid and cost-effective analysis, with 94.4% (17/18) being correctly classified. Nanopore sequencing enabled us to accurately subtype 28/30 (93.3%) MB.
This study, conducted on the largest cohort of MB analyzed with Nanopore sequencing to date, establishes the proof of concept that this modern and innovative technology is well-suited for MB classification. Nanopore sequencing demonstrates a robust capacity for precise subtyping of MB, a critical advancement that holds significant potential for enhancing patient stratification in future clinical trials. Its ability to deliver quick and cost-effective results firmly establishes it as a game-changer in the field of MB classification.
髓母细胞瘤(MB)是最常见的胚胎性恶性脑肿瘤之一。目前的分类将这些肿瘤分为4个分子亚组(WNT、SHH、3组和4组MB)。最近,一种综合分类方法已经确立,识别出众多亚型,其中一些预后较差。建立有效的亚型分类方法对于准确诊断和患者管理至关重要,这需要在改善治疗效果和将合并症风险降至最低之间找到微妙的平衡。
我们评估了纳米孔测序提供MB临床相关甲基化和拷贝数图谱的能力。纳米孔测序应用于44例冷冻MB的EPIC队列,以金标准EPIC阵列作为基准,并在116例MB的综合诊断队列中进一步评估。
两个队列中的大多数MB(分别为42/44;95.5%和106/116;91.4%)通过纳米孔测序被准确地分为亚组。对来自18例MB的样本使用Flongle流动槽进行分析可实现更快速且经济高效的分析,其中94.4%(17/18)被正确分类。纳米孔测序使我们能够准确地将28/30(93.3%)的MB进行亚型分类。
本研究是迄今为止对最大队列的MB进行纳米孔测序分析,确立了这一现代创新技术非常适合MB分类的概念验证。纳米孔测序显示出对MB进行精确亚型分类的强大能力,这一关键进展在未来临床试验中增强患者分层方面具有巨大潜力。其提供快速且经济高效结果的能力使其在MB分类领域成为一个变革者。