Biology subjects

Tanner, G.

Publications and source records attributed to Tanner, G..

5 recordsLinked to original sources

bioRxiv2024

GBMPurity: A Machine Learning Tool for Estimating Glioblastoma Tumour Purity from Bulk RNA-seq Data

BackgroundGlioblastoma (GBM) presents a significant clinical challenge due to its aggressive nature and extensive heterogeneity. Tumour purity, the proportion of malignant cells within a tumour, is an important covariate for understanding the disease, having direct clinical relevance or obscuring signal of the malignant portion in molecular analyses of bulk samples. However, current methods for estimating tumour purity are non-specific, unreliable or technically demanding. Therefore, we aimed to build a reliable and accessible purity estimator for GBM. MethodsWe developed GBMPurity, a deep learning model specifically designed to estimate the purity of IDH-wildtype primary GBM from bulk RNA-seq data. The model was trained using simulated pseudobulk tumours of known purity from labelled single-cell data acquired from the GBmap resource. The performance of GBMPurity was evaluated and compared to several existing tools using independent datasets. ResultsGBMPurity outperformed existing tools, achieving a mean absolute error of 0.15 and a concordance correlation coefficient of 0.88 on validation datasets. We demonstrate the utility of GBMPurity through inference on bulk RNA-seq samples and reveal reduced purity of the Proneural molecular subtype attributed to increased presence of healthy brain cells. ConclusionsGBMPurity provides a reliable and accessible tool for estimating tumour purity from bulk RNA-seq data, enhancing the interpretation of bulk RNA-seq data and offering valuable insights into GBM biology. To facilitate the use of this tool by the wider research community, GBMPurity is available as a web-based tool at: https://gbmdeconvoluter.leeds.ac.uk/. Key PointsO_LIGBMPurity is a glioblastoma-specific purity estimation tool. C_LIO_LIThe model accurately estimates the purity of bulk RNA-seq data, outperforming existing tools. C_LIO_LIThe model is available online at: https://gbmdeconvoluter.leeds.ac.uk/. C_LI Importance of the StudyGlioblastoma (GBM) is a deadly brain tumour with a dismal prognosis. Research on this disease has lagged compared to other cancers, underscoring the need to streamline investigations. The cellular composition of the GBM tumour microenvironment significantly influences therapy resistance, prognosis, and the molecular state of neoplastic cells. Consequently, tumour purity (the proportion of malignant cells within a tumour) is a critical variable for understanding and contextualizing molecular and clinical analyses. We present GBMPurity (https://gbmdeconvoluter.leeds.ac.uk/), an accessible, GBM-specific tool that accurately predicts sample purity from bulk RNA-seq data. This tool can be used by the wider research community to support the interpretation of bulk omics data and accelerate the identification of more effective therapeutic strategies for treating GBM.

bioinformatics↗

bioRxiv2023

Single Cell Track and Trace: live cell labelling and temporal transcriptomics via nanobiopsy.

Single-cell RNA sequencing has revolutionised our understanding of cellular heterogeneity, but whether using isolated cells or more recent spatial transcriptomics approaches, these methods require isolation and lysis of the cell under investigation. This provides a snapshot of the cell transcriptome from which dynamic trajectories, such as those that trigger cell state transitions, can only be inferred. Here, we present cellular nanobiopsy: a platform that enables simultaneous labelling and sampling from a single cell without killing it. The technique is based on scanning ion conductance microscopy (SICM) and uses a double-barrel nanopipette to inject a fluorescent dye and to extract femtolitre-volumes of cytosol. We used the nanobiopsy to longitudinally profile the transcriptome of single glioblastoma (GBM) brain tumour cells in vitro over 72hrs with and without standard treatment. Our results suggest that treatment either induces or selects for more transcriptionally stable cells. We envision the nanobiopsy will transform standard single-cell transcriptomics from a static analysis into a dynamic and temporal assay.

genomics↗

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↗

bioRxiv2020

Primary and recurrent glioma patient-derived orthotopic xenografts (PDOX) represent relevant patient avatars for precision medicine

Patient-derived cancer models are essential tools for studying tumor biology and preclinical interventions. Here, we show that glioma patient-derived orthotopic xenografts (PDOXs) enable long-term propagation of patient tumors and represent clinically relevant patient avatars. We created a large collection of PDOXs from primary and recurrent gliomas with and without mutations in IDH1, which retained histopathological, genetic, epigenetic and transcriptomic features of patient tumors with no mouse-specific clonal evolution. Longitudinal PDOX models recapitulate the limited genetic evolution of gliomas observed in patient tumors following treatment. PDOX-derived standardized tumor organoid cultures enabled assessment of drug responses, which were validated in mice. PDOXs showed clinically relevant responses to Temozolomide and to targeted treatments such as EGFR and CDK4/6 inhibitors in (epi)genetically defined groups, according to MGMT promoter and EGFR/CDK status respectively. Dianhydrogalactitol, a bifunctional alkylating agent, showed promising potential against glioblastoma. Our study underlines the clinical relevance of glioma PDOX models for translational research and personalized treatment studies.

cancer biology↗

bioRxiv2019

JARID2 facilitates transcriptional reprogramming in glioblastoma in response to standard treatment

BackgroundGlioblastoma (GBM) is a fatal and incurable brain cancer with a dismal prognosis. In order to impact on this disease, we need to understand how infiltrating, non resectable tumour cells resist chemoradiation and facilitate disease recurrence. To this end, we generated or acquired bulk tumour RNA sequencing data from 45 paired primary and locally recurrent GBM tumours (split into original and validation cohorts) from patients that received standard treatment. We also generated DNA methylation profiles for 9 pairs and sequenced RNA from single cells isolated from a patient derived GBM spheroid model at different timepoints following in vitro chemoradiation.\n\nResultsWe have identified a set of genes with Jumonji and AT-Rich Interacting Domain 2 (JARID2) binding sites in their promoters that are universally dysregulated in post-standard treatment recurrent GBMs compared to the primary tumour. The direction of dysregulation is patient-dependent and not associated with differential promoter DNA methylation. Our in vitro experiments suggest that this dysregulation occurs dynamically following treatment as opposed to resulting from selection of cells with specific expression profiles.\n\nConclusionJARID2 is an accessory protein to a chromatin remodeling complex, responsible for histone modifications observed during cell state transitions in both normal brain and GBM. We propose that JARID2 facilitates GBM recurrence following treatment by indirect transcriptional reprogramming of surviving cells in whichever manner is needed to reproduce the phenotypic heterogeneity required for tumour regrowth in vivo. The mechanism of this reprogramming may present a therapeutic vulnerability for more effective treatment of GBM.

cancer biology↗