A novel method for rapid molecular subgrouping of medulloblastoma

• Verfasst von: Gómez, Soledad [VerfasserIn]
• Verfasst von: Johann, Pascal-David [VerfasserIn]
• Verfasst von: Jones, David T. W. [VerfasserIn]
• Verfasst von: Pfister, Stefan [VerfasserIn]
• Titel: A novel method for rapid molecular subgrouping of medulloblastoma
• Verf.angabe: Soledad Gómez, Alícia Garrido-Garcia, Laura Garcia-Gerique, Isadora Lemos, Mariona Suñol, Carmen de Torres, Marta Kulis, Sara Pérez-Jaume, Ángel M. Carcaboso, Betty Luu, Mark W. Kieran, Nada Jabado, Alexey Kozlenkov, Stella Dracheva, Vijay Ramaswamy, Volker Hovestadt, Pascal Johann, David T.W. Jones, Stefan M. Pfister, Andrés Morales La Madrid, Ofelia Cruz, Michael D. Taylor, Jose-Ignacio Martin-Subero, Jaume Mora, and Cinzia Lavarino
• E-Jahr: 2018
• Jahr: March 2018
• Umfang: 9 S.
• Fussnoten: Gesehen am 10.05.2019
• Titel Quelle: Enthalten in: Clinical cancer research
• Ort Quelle: Philadelphia, Pa. [u.a.] : AACR, 1995
• Jahr Quelle: 2018
• Band/Heft Quelle: 24(2018), 6, Seite 1355-1363
• ISSN Quelle: 1557-3265
• DOI: doi:10.1158/1078-0432.CCR-17-2243
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1665162457

Abstract

Purpose: The classification of medulloblastoma into WNT, SHH, group 3, and group 4 subgroups has become of critical importance for patient risk stratification and subgroup-tailored clinical trials. Here, we aimed to develop a simplified, clinically applicable classification approach that can be implemented in the majority of centers treating patients with medulloblastoma. - Experimental Design: We analyzed 1,577 samples comprising previously published DNA methylation microarray data (913 medulloblastomas, 457 non-medulloblastoma tumors, 85 normal tissues), and 122 frozen and formalin-fixed paraffin-embedded medulloblastoma samples. Biomarkers were identified applying stringent selection filters and Linear Discriminant Analysis (LDA) method, and validated using DNA methylation microarray data, bisulfite pyrosequencing, and direct-bisulfite sequencing. - Results: Using a LDA-based approach, we developed and validated a prediction method (EpiWNT-SHH classifier) based on six epigenetic biomarkers that allowed for rapid classification of medulloblastoma into the clinically relevant subgroups WNT, SHH, and non-WNT/non-SHH with excellent concordance (>99%) with current gold-standard methods, DNA methylation microarray, and gene signature profiling analysis. The EpiWNT-SHH classifier showed high prediction capacity using both frozen and formalin-fixed material, as well as diverse DNA methylation detection methods. Similarly, we developed a classifier specific for group 3 and group 4 tumors, based on five biomarkers (EpiG3-G4) with good discriminatory capacity, allowing for correct assignment of more than 92% of tumors. EpiWNT-SHH and EpiG3-G4 methylation profiles remained stable across tumor primary, metastasis, and relapse samples. - Conclusions: The EpiWNT-SHH and EpiG3-G4 classifiers represent a new simplified approach for accurate, rapid, and cost-effective molecular classification of single medulloblastoma DNA samples, using clinically applicable DNA methylation detection methods.