Hermelo Ismaïl, Haapala Ilkka, Mäkelä Meri, Jacome Sanz Dafne, Kontunen Anton, Karjalainen Markus, Müller Philipp, Lehtimäki Kai, Nykter Matti, Frösén Juhana, Haapasalo Hannu, Roine Antti, Oksala Niku, Nordfors Kristiina, Vehkaoja Antti, Haapasalo Joonas
Prostate Cancer Research Center, Faculty of Medicine and Health Technology, Tampere University and Tampere University Hospital, Tampere, Finland.
Department of Neurosurgery and Tays Cancer Center, Tampere University Hospital and Tampere University, Tampere, Finland.
J Neurooncol. 2025 Feb;171(3):691-703. doi: 10.1007/s11060-024-04891-0. Epub 2024 Nov 23.
Extent of brain tumor resection continues to be one of the central decisions taken during standard of care in glioma patients. Here, we aimed to evaluate the most essential molecular factors, such as IDH (isocitrate dehydrogenase) mutation in gliomas classification with patient-derived glioma organoids (PGOs) using differential mobility spectrometry (DMS).
we prospectively recruited 12 glioma patients, 6 IDH-mutated and 6 IDH wild-type tumors, from which PGOs were generated ex-vivo. Altogether, 320 PGOs DMS spectra were analyzed with a classifier algorithm based on linear discriminant analysis (LDA).
LDA model classification accuracy (CA) obtained between IDH-mutant and IDH wild-type PGOs was 90% (91% sensitivity and 89% specificity). Furthermore, 1p/19q codeletion classification within IDH mutant PGOs reached 98% CA (93% sensitivity and 99% specificity), while CDKN2A/B homozygous loss status had 86% CA (63% sensitivity 93% specificity).
DMS suitability to differentiate IDH-mutated PGOs was thus validated in ex vivo cultured samples, PGOs. Preliminary results regarding 1p/19q codeleted PGOs and CDKN2A/B loss PGOs identification endorse testing in a prospective intraoperative glioma patient cohort. Our results reveal a sample classification set-up that is compatible with real-time intraoperative surgery guidance.
脑肿瘤切除范围仍然是胶质瘤患者标准治疗过程中做出的核心决策之一。在此，我们旨在使用差分迁移谱法（DMS），通过患者来源的胶质瘤类器官（PGO）评估胶质瘤分类中最关键的分子因素，如异柠檬酸脱氢酶（IDH）突变。
我们前瞻性招募了12例胶质瘤患者，其中6例IDH突变肿瘤和6例IDH野生型肿瘤，从中体外培养生成PGO。总共对320个PGO的DMS光谱进行了基于线性判别分析（LDA）的分类算法分析。
IDH突变型和IDH野生型PGO之间的LDA模型分类准确率（CA）为90%（敏感性91%，特异性89%）。此外，IDH突变型PGO内1p/19q共缺失分类的CA达到98%（敏感性93%，特异性99%），而CDKN2A/B纯合缺失状态的CA为86%（敏感性63%，特异性93%）。
因此，在体外培养样本PGO中验证了DMS区分IDH突变型PGO的适用性。关于1p/19q共缺失PGO和CDKN2A/B缺失PGO识别的初步结果支持在前瞻性术中胶质瘤患者队列中进行检测。我们的结果揭示了一种与实时术中手术指导兼容的样本分类设置。
