PATH-06. Molecular subgrouping of medulloblastoma via low-depth whole genome bisulfite sequencing. (3rd June 2022) - Thompson, Dean; Castle, Jemma; Hicks, Debbie; Schwalbe, Ed; Clifford, Steve
Abstract: 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., <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.