Deconvoluting the spatial multi-omic landscape of high-grade glioma to target post-surgical residual disease

Background

High-grade gliomas are the most common cancers arising within the brain and contribute to ~190,000 brain tumour related deaths/year globally. These tumours demonstrate extraordinary intratumoural heterogeneity, however increasing understanding of genetic diversity within tumours has not yet led to tangible improvements in patient survival, which has improved little over the last 40 years. Most patients receive surgical resection followed by DNA damaging radiotherapy and temozolomide chemotherapy. However, despite standard-of-care treatment, average survival remains around 1 year, highlighting an urgent need to improve outcomes. Critically, tumours harbour diverse subpopulations of glioma stem cells (GSCs), which possess unlimited regenerative potential and enhanced DNA repair capabilities. Although recent studies clearly demonstrate regional genetic differences within individual tumours, whether these translate to functional differences in response to DNA damaging therapy remains unproven. Our team have developed a ‘living biobank’ of surgically-relevant preclinical high-grade glioma models, through sampling multiple distinct tumour regions to recapitulate the native features and heterogeneity of parental tumours. These models provide a unique capability to understand how spatial variation shapes resistance to current DNA damaging treatments, and construct patient-specific drug combinations targeting the DNA damage response, designed to tackle the range of functional diversity within a given tumour. We have recently generated whole exome sequencing (WES) and transcriptomic (RNASeq) data for a number of these models within our Biobank.

Hypothesis

Residual and resected GSCs have genetic and transcriptomic differences that could be exploited to improve the clinical management of these currently incurable tumours.

Methods

These computationally-focused studies leverage our existing and expanding database of omic data generated from SLB to uncover distinct, targetable features of the disease left-behind after surgery. In Year 1 the genomic and transcriptomic landscapes of resected and residual disease will be contrasted using ‘Cancer Hallmarks’⁽¹⁵⁾ and cell state-based^(7,20) frameworks to prioritise targetable cancer vulnerabilities before generating experimental drug-response data to iteratively refine pharmacogenomic predictions using our leading expertise in ex vivo therapeutic drug screening⁽²¹⁾. Years 2-3 will focus on bioinformatics analyses to further expand our understanding of residual GSCs, including through data integration with emerging single-cell (10x Genomics RNA+ATACseq) data generated in our laboratory and large external datasets (including TCGA and GLASS cohorts) – before final experimental evaluation of the most promising treatment strategy/strategies in our patient-derived 3D intratumoural heterogeneity models. Collectively, the studies will systematically identify and validate new treatment strategies most likely to be effective against difficult-to-treat post-surgical residual disease in patients.

Entry Requirements:

Candidates must have a first or upper second class honours degree or significant research experience. Previous experience with R and/or bioinformatics analyses is desired.

How to apply:

Please complete a University Postgraduate Research Application form available here: https://www.sheffield.ac.uk/postgraduate/phd/apply/applying

Please clearly state the prospective main supervisor in the respective box and select School of Medicine & Population Health (Oncology & Metabolism) as the department.

Enquiries:

Interested candidates should in the first instance contact Dr Spencer Collis (s.collis@sheffield.ac.uk).

Proposed start date - October 2023