Biology subjects

Pollock, S.

Publications and source records attributed to Pollock, S..

3 recordsLinked to original sources

bioRxiv2023

IDHwt glioblastomas can be stratified by their transcriptional response to standard treatment, with implications for targeted therapy

Glioblastoma (GBM) brain tumours lacking IDH1 mutations (IDHwt) have the worst prognosis of all brain neoplasms. Patients receive surgery and chemoradiotherapy but tumours almost always fatally recur. Using RNAseq data from 107 pairs of pre- and post-standard treatment locally recurrent IDHwt GBM tumours, we identified two responder subtypes based on therapy-driven changes in gene expression. In two thirds of patients a specific subset of genes is up-regulated from primary to recurrence (Up responders) and in one third the same genes are down-regulated (Down responders). Characterisation of the responder subtypes indicates subtype-specific adaptive treatment resistance mechanisms. In Up responders treatment enriches for quiescent proneural GBM stem cells and differentiated neoplastic cells with increased neurotransmitter signalling, whereas Down responders commonly undergo therapy-driven mesenchymal transition. Stratifying GBM tumours by response subtype may lead to more effective treatment. In support of this, modulators of gamma aminobutyric acid (GABA) neurotransmitter signalling differentially sensitise Up and Down responder GBM models to standard treatment in vitro.

cancer biology↗

bioRxiv2022

GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data

The biological and clinical impact of neoplastic and immune cell type ratios in the glioblastoma (GBM) tumour microenvironment is being realised. Characterising and quantifying cell types within GBMs at scale will facilitate a better understanding of the association between the cellular landscape and tumour phenotypes or clinical correlates. This study aimed to develop a tool that can deconvolute immune and neoplastic cells within the GBM tumour microenvironment from bulk RNA sequencing data. We developed an IDH wild-type (IDHwt) GBM specific single immune cell reference dataset, from four independent studies, consisting of B cells, T cells, NK cells, microglia, tumour associated macrophages, monocytes, mast and DC cells. We used this alongside an existing neoplastic single cell-type dataset consisting of astrocyte-like, oligodendrocyte- and neuronal-progenitor like and mesenchymal GBM cancer cells to create both marker and gene signature matrix-based deconvolution tools. We then applied single-cell resolution imaging mass cytometry (IMC) to ten IDHwt GBM samples, five paired primary and recurrent tumours, in parallel with these tools to determine which performed best. Marker based gene expression deconvolution using GBM tissue specific markers, which we have packaged as GBMdeconvoluteR, gave the most accurate results. The correlation between immune cell quantification by IMC and by GBMdeconvoluteR for primary IDHwt GBM samples was 0.52 (Pearsons P=7.8x10-3) and between neoplastic cell quantification by IMC and by GBMdeconvoluteR was 0.75 (Pearsons P=1.2x10-3). We applied GBMdeconvoluteR to bulk GBM RNAseq data from The Cancer Genome Atlas (TCGA) and were able to recapitulate recent findings from multi-omics single cell studies with regards associations between mesenchymal GBM cancer cells and both lymphoid and myeloid cells. Furthermore, we were able to expand upon this to show that these associations are stronger in patients with worse prognosis. GBMdeconvoluteR is accessible online at https://gbmdeconvoluter.leeds.ac.uk. Key pointsGBMdeconvoluteR is a glioblastoma-specific cellular deconvolution tool. When applied to bulk GBM RNAseq data, it accurately quantifies the neoplastic and immune cells in that tumour. It is available online at https://gbmdeconvoluter.leeds.ac.uk

cancer biology↗

bioRxiv2022

Cost-efficient whole genome-sequencing using novel mostly natural sequencing-by-synthesis chemistry and open fluidics platform

We introduce a massively parallel novel sequencing platform that combines an open flow cell design on a circular wafer with a large surface area and mostly natural nucleotides that allow optical end-point detection without reversible terminators. This platform enables sequencing billions of reads with longer read length ([~]300bp) and fast runs times (<20hrs) with high base accuracy (Q30 > 85%), at a low cost of $1/Gb. We establish system performance by whole-genome sequencing of the Genome-In-A-Bottle reference samples HG001-7, demonstrating high accuracy for SNPs (99.6%) and Indels in homopolymers up to length 10 (96.4%) across the vast majority (>98%) of the defined high-confidence regions of these samples. We demonstrate scalability of the whole-genome sequencing workflow by sequencing an additional 224 selected samples from the 1000 Genomes project achieving high concordance with reference data.

genomics↗