EpiGe: A machine-learning strategy for rapid classification of medulloblastoma using PCR-based methyl-genotyping.

Molecular classification of medulloblastoma is critical for the treatment of this brain tumor. Array-based DNA methylation profiling has emerged as a powerful approach for brain tumor classification. However, this technology is currently not widely available. We present a machine-learning decision support system (DSS) that enables the classification of the principal molecular groups-WNT, SHH, and non-WNT/non-SHH-directly from quantitative PCR (qPCR) data. We propose a framework where the developed DSS appears as a user-friendly web-application-EpiGe-App-that enables automated interpretation of qPCR methylation data and subsequent molecular group prediction. The basis of our classification strategy is a previously validated six-cytosine signature with subgroup-specific methylation profiles. This reduced set of markers enabled us to develop a methyl-genotyping assay capable of determining the methylation status of cytosines using qPCR instruments. This study provides a comprehensive approach for rapid classification of clinically relevant medulloblastoma groups, using readily accessible equipment and an easy-to-use web-application.t.

MIF/CXCR4 signaling axis contributes to survival, invasion, and drug resistance of metastatic neuroblastoma cells in the bone marrow microenvironment.

BACKGROUND: The bone marrow (BM) is the most common site of dissemination in patients with aggressive, metastatic neuroblastoma (NB). However, the molecular mechanisms underlying the aggressive behavior of NB cells in the BM niche are still greatly unknown. In the present study, we explored biological mechanisms that play a critical role in NB cell survival and progression in the BM and investigated potential therapeutic targets. METHODS: Patient-derived bone marrow (BM) primary cultures were generated using fresh BM aspirates obtained from NB patients. NB cell lines were cultured in the presence of BM conditioned media containing cell-secreted factors, and under low oxygen levels (1% O2) to mimic specific features of the BM microenvironment of high-risk NB patients. The BM niche was explored using cytokine profiling assays, cell migration-invasion and viability assays, flow cytometry and analysis of RNA-sequencing data. Selective pharmacological inhibition of factors identified as potential mediators of NB progression within the BM niche was performed in vitro and in vivo. RESULTS: We identified macrophage migration inhibitory factor (MIF) as a key inflammatory cytokine involved in BM infiltration. Cytokine profiling and RNA-sequencing data analysis revealed NB cells as the main source of MIF in the BM, suggesting a potential role of MIF in tumor invasion. Exposure of NB cells to BM-conditions increased NB cell-surface expression of the MIF receptor CXCR4, which was associated with increased cell viability, enhanced migration-invasion, and activation of PI3K/AKT and MAPK/ERK signaling pathways. Moreover, subcutaneous co-injection of NB and BM cells enhanced tumor engraftment in mice. MIF inhibition with 4-IPP impaired in vitro NB aggressiveness, and improved drug response while delayed NB growth, improving survival of the NB xenograft model. CONCLUSIONS: Our findings suggest that BM infiltration by NB cells may be mediated, in part, by MIF-CXCR4 signaling. We demonstrate the antitumor efficacy of MIF targeting in vitro and in vivo that could represent a novel therapeutic target for patients with disseminated high-risk NB.

A Novel Method for Rapid Molecular Subgrouping of Medulloblastoma.

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. Clin Cancer Res; 24(6); 1355-63. ©2018 AACR.