Biology subjects

Stead, L. F.

Publications and source records attributed to Stead, L. F..

13 recordsLinked to original sources

bioRxiv2026

Sexual Dimorphism of Cancer-Associated Fibroblasts Governs Matrix and Vascular Organisation in Breast Cancer

Breast cancer (BC) shows greatest sexual diversity. Increased diagnosis and poorer outcomes in men highlights the need to better understand its biology. We hypothesised that cancer-associated fibroblasts (CAFs), the most abundant cell type in the tumour microenvironment, might define sex-related differences. Using phenotypically matched male and female CAFs generated from breast cancer tissues, we demonstrate distinct transcriptional programmes and functional behaviours associated with extracellular matrix remodelling, cell adhesion, migration and vascular development. Compared to CAFs generated from females BC, those from males generated denser, more complex matrices promoting stronger tumour and endothelial cell adhesion, vascular growth, but less organised capillary network formation. Findings reveal fundamental sex-related variations in CAF phenotype and biology in BC. These findings highlight the need to integrate biological sex into precision oncology to identify opportunities for sex-specific therapeutic strategies in BC.

cancer biology↗

bioRxiv2025

Spatial profiling of longitudinal glioblastoma reveals consistent changes in cellular architecture, post-treatment

Glioblastoma (GBM), the most aggressive adult brain cancer, comprises a complex tumour microenvironment (TME) with diverse cellular interactions driving progression and pathobiology. How these spatial patterns and interactions evolve with treatment remains unclear. Here, we apply imaging mass cytometry to analyse protein-level changes in paired pre- and post-treatment GBM samples from five patients. We find a significant post-treatment increase in normal brain cells alongside a reduction in vascular cells. Moreover, despite minimal overall change in cellular diversity, interactions among astrocytes, oligodendrocytes, and vascular cells increase post-treatment, suggesting reorganisation of the TME. The GBM TME cells form spatially organized layers driven by hypoxia pre-treatment, but this influence diminishes post-treatment, giving way to less organised layers with organisation driven by reactive astrocytes and lymphocytes. These findings provide insight into treatment-induced shifts in GBMs cellular landscape, highlighting aspects of the evolving TME that appear to facilitate recurrence and are, therefore, potential therapeutic targets. Key pointsO_LISpatial organisation in primary GBM consist of layers driven by the presence of hypoxia C_LIO_LIThe layers in recurrent GBM appear are driven more by the presence of reactive astrocytes C_LIO_LIIncreased cellular cross-talk in recurrent GBM presents novel therapeutic targets C_LI

cancer biology↗

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↗

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

SpheroidAnalyseR - an online platform for analysing data from 3D spheroids or organoids grown in 96-well plates

Spheroids and organoids are increasingly popular three-dimensional (3D) cell culture models. Spheroid models are more physiologically relevant to a tumour compared to two-dimensional (2D) cultures and organoids are a simplified version of an organ with similar composition. Both can be used in cell migration studies, disease modelling and drug discovery. A drawback of these models is, however, the lack of appropriate analytical tools for high throughput imaging and analysis over a time course. To address this, we have developed an R Shiny app called SpheroidAnalyseR: a simple, fast, effective open-source app that allows the analysis of spheroid or organoid size data generated in a 96-well format. SpheroidAnalyseR processes and analyses datasets of image measurements that can be obtained via a bespoke software, described herein, that automates spheroid imaging and quantification using the Nikon A1R Confocal Laser Scanning Microscope. However, templates are provided to enable users to input spheroid image measurements obtained by user-preferred methods. SpheroidAnalyseR facilitates outlier identification and removal followed by graphical visualisation of spheroid measurements across multiple predefined parameters such as time, cell-type and treatment(s). Spheroid imaging and analysis can, thus, be reduced from hours to minutes, removing the requirement for substantial manual data manipulation in a spreadsheet application. The combination of spheroid generation in 96-well ultra-low attachment microplates, imaging using our bespoke software, and analysis using SpheroidAnalyseR toolkit allows high throughput, longitudinal quantification of 3D spheroid growth whilst minimising user input and significantly improving the efficiency and reproducibility of data analysis. Our bespoke imaging software is available from https://github.com/GliomaGenomics. SpheroidAnalyseR is available at https://spheroidanalyser7.azurewebsites.net, and the source code found at https://github.com/GliomaGenomics. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/481039v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@1f779fborg.highwire.dtl.DTLVardef@698103org.highwire.dtl.DTLVardef@94f252org.highwire.dtl.DTLVardef@b1180e_HPS_FORMAT_FIGEXP M_FIG Graphical abstract - SpheroidAnalyseR main steps. A summary of the process followed within the SpheroidAnalyseR app. Dotted boxes are the steps required to produce the input data for SpheroidAnalyseR. Solid boxes show the 5 main steps of SpheroidAnalyseR: Data input, Preview, Outlier removal, Merging and Plotting. C_FIG

cell biology↗

bioRxiv2021

The epigenetic evolution of gliomas is determined by their IDH1 mutation status and treatment regimen

Tumor adaptation or selection is thought to underlie therapy resistance of gliomas. To investigate the longitudinal epigenetic evolution of gliomas in response to therapeutic pressure, we performed an epigenomic analysis of 143 matched initial and recurrent patients with IDH-wildtype (IDHwt) and IDH-mutant (IDHmut) gliomas. IDHwt gliomas showed a longitudinally stable epigenome with relatively low levels of global methylation, whereas the epigenome of IDHmut gliomas showed initial high levels genome-wide of DNA methylation that was progressively reduced to levels similar to those of IDHwt tumors. By integrating DNA methylation and gene expression data, adaptive changes of putative master regulators of the cell cycle and of differentiation were seen in IDHmut recurrent tumors. Furthermore, relapses of IDHmut tumors were accompanied by histological progression which in turn influenced survival, as validated in an independent cohort. Finally, the initial cell composition of the tumor microenvironment differed between IDHwt and IDHmut tumors and changed differentially following treatment, suggesting increased neo-angiogenesis and T-cell infiltration upon treatment for IDHmut gliomas. Our study provides one of the largest cohorts of paired glioma samples profiled with epigenomics, transcriptomics and genomics; and our results demonstrate that the treatment of IDHmut gliomas reshapes the epigenome towards an IDHwt-like phenotype. Accordingly, the prevalent practice of early genotoxic treatment in this patient population may need to be revisited.

genomics↗

bioRxiv2021

Longitudinal analysis of diffuse glioma reveals cell state dynamics at recurrence associated with changes in genetics and the microenvironment

To interrogate the factors driving therapy resistance in diffuse glioma, we collected and analyzed RNA and/or DNA sequencing data from temporally separated tumor pairs of 292 adult patients with IDH-wild-type or IDH-mutant glioma. Tumors recurred in distinct manners that were dependent on IDH mutation status and attributable to changes in histological feature composition, somatic alterations, and microenvironment interactions. Hypermutation and acquired CDKN2A deletions associated with an increase in proliferating stem-like malignant cells at recurrence in both glioma subtypes, reflecting active tumor growth. IDH-wild-type tumors were more invasive at recurrence, and their malignant cells exhibited increased expression of neuronal signaling programs that reflected a possible role for neuronal interactions in promoting glioma progression. Mesenchymal transition was associated with the presence of a specific myeloid cell state defined by unique ligand-receptor interactions with malignant cells. Collectively, our results uncover recurrence-associated changes that could be targetable to shape disease progression following initial diagnosis.

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

Expression profiling of single cells and patient cohorts identifies multiple immunosuppressive pathways and an altered natural killer cell phenotype in glioblastoma

Glioblastoma (GBM) is an aggressive cancer with a very poor prognosis. Generally viewed as weakly immunogenic, GBM responds poorly to current immunotherapies. To better understand this problem we used a combination of NK cell functional assays together with gene and protein expression profiling to define the NK cell response to GBM and explore immunosuppression in the GBM microenvironment. In addition, we used transcriptome data from patient cohorts to classify GBM according to immunological profiles. We show that glioma stem-like cells, a source of post-treatment tumour recurrence, express multiple immunomodulatory cell surface molecules and are targeted in preference to normal neural progenitor cells by natural killer (NK) cells ex vivo. In contrast, GBM-infiltrating NK cells express reduced levels of activation receptors within the tumour microenvironment, with hallmarks of TGF-{beta} mediated inhibition. This NK cell inhibition is accompanied by expression of mutiple immune checkpoint molecules on T cells. Single cell transcriptomics demonstrated that both tumour and haematopoietic-derived cells in GBM express multiple, diverse mediators of immune evasion. Despite this, immunome analysis across a patient cohort identifies a spectrum of immunological activity in GBM, with active immunity marked by co-expression of immune effector molecules and feedback inhibitory mechanisms. Our data show that GBM is recognised by the immune system but that anti-tumour immunity is restrained by multiple immunosuppressive pathways, some of which operate in the healthy brain. The presence of immune activity in a subset of patients suggests that these patients will more likely benefit from combination immunotherapies directed against multiple immunosuppressive pathways.

immunology↗

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↗

bioRxiv2018

Chemically-induced Neurite-like Outgrowth Reveals Multicellular Network Function in Patient-derived Glioblastoma Cells

Tumor stem cells and malignant multicellular networks have been separately implicated in the therapeutic resistance of Glioblastoma Multiforme (GBM), the most aggressive type of brain cancer in adults. We show that small molecule inhibition of RHO-associated serine/threonine kinase (ROCKi) significantly promoted the outgrowth of neurite-like cell projections in cultures of heterogeneous patient-derived GBM stem-like cells. These projections formed de novo-induced cellular network (iNet) webs, which regressed after withdrawal of ROCKi. Connected cells within the iNet web exhibited long range calcium signal transmission, and significant lysosomal and mitochondrial trafficking. In contrast to their less-connected vehicle control counterparts, iNet cells remained viable and proliferative after high-dose radiation. These findings demonstrate a link between ROCKi-regulated cell projection dynamics and the formation of radiation-resistant multicellular networks. Our study identifies means to reversibly induce iNet webs ex vivo, and may thereby accelerate future studies into the biology of GBM cellular networks.

cancer biology↗