Transforming cancer molecular diagnostics: Molecular subgrouping of medulloblastoma via lowdepth whole genome bisulfite sequencing

Thompson, Dean (2022) Transforming cancer molecular diagnostics: Molecular subgrouping of medulloblastoma via lowdepth whole genome bisulfite sequencing. Doctoral thesis, Northumbria Univerrsity.

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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 and currently no optimised pipeline exists that is tailored for handling samples sequenced at low-pass (i.e., 1-10x depth).

METHODOLOGY: Two datasets were utilised; 36 newly-sequenced low-depth (10x) and 42 publicly available high-depth (30x) WGBS medulloblastoma and cerebellar samples, 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 preexisting 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 and analysis of low-pass WGBS data, suitable for routine molecular subgrouping and aneuploidy assessment. Using down-sampling, we showed that subgroup assignment remains robust at low depths and identified additional regions of differential methylation that are not assessed by methylation microarray. WGBS data can be integrated into existing array-trained models with high assignment probabilities, and 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 (currently ~$396 vs ~$584) and provides 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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