Medulloblastoma, the most common malignant pediatric brain tumor, often harbors MYC amplifications. Compared to high-grade gliomas, MYC-amplified medulloblastomas often show increased photoreceptor activity and arise in the presence of a functional ARF/p53 suppressor pathway. Here, we generate an immunocompetent transgenic mouse model with regulatable MYC that develop clonal tumors that molecularly resemble photoreceptor-positive Group 3 medulloblastoma. Compared to MYCN-expressing brain tumors driven from the same promoter, pronounced ARF silencing is present in our MYC-expressing model and in human medulloblastoma. While partial Arf suppression causes increased malignancy in MYCN-expressing tumors, complete Arf depletion promotes photoreceptor-negative high-grade glioma formation. Computational models and clinical data further identify drugs targeting MYC-driven tumors with a suppressed but functional ARF pathway. We show that the HSP90 inhibitor, Onalespib, significantly targets MYC-driven but not MYCN-driven tumors in an ARF-dependent manner. The treatment increases cell death in synergy with cisplatin and demonstrates potential for targeting MYC-driven medulloblastoma.

ALK is the most commonly mutated oncogene in neuroblastoma with increased mutation frequency reported at relapse. Here we report the loss of an ALK mutation in two patients at relapse and a paired neuroblastoma cell line at relapse. ALK detection methods including Sanger sequencing, targeted next-generation sequencing and a new ALK Agena MassARRAY technique were used to detect common hotspot ALK variants in tumors at diagnosis and relapse from two high-risk neuroblastoma patients. Copy number analysis including single nucleotide polymorphism array and array comparative genomic hybridization confirmed adequate tumor cell content in DNA used for mutation testing. Case 1 presented with an ALK F1174L mutation at diagnosis with a variant allele frequency (VAF) ranging between 23.5% and 28.5%, but the mutation was undetectable at relapse. Case 2 presented with an ALK R1257Q mutation at diagnosis (VAF = 39%-47.4%) which decreased to <0.01% at relapse. Segmental chromosomal aberrations were maintained between diagnosis and relapse confirming sufficient tumor cell content for mutation detection. The diagnostic SKNBE1n cell line harbors an ALK F1174S mutation, which was lost in the relapsed SKNBE2c cell line. To our knowledge, these are the first reported cases of loss of ALK mutations at relapse in neuroblastoma in the absence of ALK inhibitor therapy, reflecting intra-tumoral spatial and temporal heterogeneity. As ALK inhibitors are increasingly used in the treatment of refractory/relapsed neuroblastoma, our

INTRODUCTION: International consensus recognises four molecular subgroups of medulloblastoma, each with distinct molecular features and clinical outcomes. Assigning molecular subgroup is typically achieved via the Illumina DNA methylation microarray. Given the rapidly-expanding WGS capacity in healthcare institutions, there is an unmet need to develop platform-independent, sequence-based subgrouping assays. Whole genome bisulfite sequencing (WGBS) enables the assessment of genome-wide methylation status at single-base resolution. To date, its routine application for subgroup assignment has been limited, due to high economic cost and sample input requirements. Currently, no optimised pipeline exists that is tailored to handle samples sequenced at low-pass (i.e., &lt;10x depth). METHODOLOGY: Two datasets were utilised; 36 newly sequenced low-depth (10x) and 42 publicly available high-depth (30x) WGBS medulloblastoma samples (n=34), alongside cerebellar control samples (n=8), all with matched DNA methylation microarray data. We applied imputation to low-pass WGBS data, assessed inter-platform correlation and identified molecular subgroups by directly integrating WGBS sample data with pre-existing array-trained models. We developed machine learning WGBS-based classifiers and compared performance against microarray. We optimised reference-free aneuploidy detection with low-pass WGBS and assessed concordance with microarray-derived aneuploidy calls. RESULTS: We optimised a pipeline for processing, QC, and analysis of low-pass WGBS data, suitable for routine molecular subgrouping and reference-free aneuploidy assessment that achieves 96% sensitivity compared to microarray approaches. A pilot study of the suitability of FFPE was promising, and we demonstrate that WGBS data can be integrated into existing array-trained models with high assignment probabilities. Also, WGBS-derived classifier performance measures exceeded microarray-derived classifiers. CONCLUSION: We describe a platform-independent WGBS assay for molecular subgrouping of medulloblastoma. It performs equivalently to array-based methods at increasingly comparable cost ($400 vs $580) and provides a proof-of-concept for routine clinical adoption using standard WGS technology. Finally, the full methylome enabled elucidation of additional biological heterogeneity that has hitherto been inaccessible.

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Introduction International consensus recognises four molecular subgroups of medulloblastoma, each with distinct molecular features and clinical outcomes. The current gold-standard for subgroup assignment is DNA methylation microarray. There is an unmet need to develop platform-independent subgrouping assays which are both non-proprietary and compatible with rapidly-expanding WGS capacity in healthcare. Whole Genome Bisulfite Sequencing (WGBS) enables the assessment of genome-wide methylation status at single-base resolution. Previously, WGBS adoption has been limited by cost and sample quality/quantity requirements. Its application for routine detection of medulloblastoma subgroups has not previously been reported. Methodology Two datasets were utilised; 36 newly-sequenced low-depth (10x coverage) and 34 publicly-available high-depth (30x) WGBS medulloblastomas, all with matched DNA methylation microarray data. We compared platform concordance and identified molecular subgroups. Machine-learning WGBS-based subgroup classifiers were optimised and compared between platforms. Aneuploidy and mutation detection using WGBS was optimised and compared to microarray-derived estimates where possible. Finally, comprehensive subgroup-specific DNA methylation signatures were identified. Results We optimised a pipeline for processing, quality control and analysis of low-depth WGBS data, suitable for routine molecular subgrouping and aneuploidy assessment. We demonstrated the suitability of fresh-frozen and FFPE DNA for WGBS, and, using downsampling, showed that subgroup calling is robust at coverages as low as 2x. We identified differentially methylated regions that, due to poor representation, could not be detected using methylation microarrays. Molecular subgroups of medulloblastoma assigned using WGBS were concordant with array-based definitions, and WGBS-derived classifier performance measures exceeded microarray-derived classifiers. Conclusion We describe a platform-independent assay for molecular subgrouping of medulloblastoma using WGBS. It performs equivalently to current array-based methods at comparable cost ($405 vs $596) and provides a proof-of-concept for its routine clinical adoption using standard WGS technology. Finally, the full methylome enabled elucidation of additional biological heterogeneity that has hitherto been inaccessible.