Biology subjects

Overton, I. M.

Publications and source records attributed to Overton, I. M..

4 recordsLinked to original sources

bioRxiv2022

IRF7 impacts on prostate cancer cell survival in response to radiation

Understanding the impact of radiotherapy on the evolution of treatment resistant prostate cancer is critical for selecting effective treatment combinations. Whilst activation of Type 1 interferon signalling is a hallmark of how cells respond to viral infection, in cancer cells, multiple stresses are known to activate this same response. In this study we have evaluated for the first time the changes in the interferon response induced by culturing prostate cancer cells under sphere- forming conditions and following irradiation. We report a conserved upregulated transcript profile for both conditions that is strongly associated with therapeutic resistance and cell survival in vitro and in vivo. The profile includes and is regulated by the Type 1 interferon master regulator IRF7 which, when depleted, delays tumour re-growth following irradiation. We immuno-stained two independent prostate cohorts for IRF7 and found that increased expression, particularly in cases with low PTEN expression, correlated with poor prognosis. To more comprehensively characterise the impact of IRF7 and radiation on cells, RNA-Seq was performed on IRF7 knockdown cells at different radiation doses. We identified a number of biological processes that were IRF7-dependent, including the formation of stem-like cell populations and also therapeutic vulnerabilities. For example, irradiation sensitised surviving cells to either a combination of an IKK{varepsilon}/TBK1 and a MEK inhibitor or treatment with an inhibitor of IDO1, an IRF7- dependent gene. Translationally our work suggests that IRF7 expression can be used to stratify patients who may not benefit from receiving radiotherapy alone but rather may benefit from treatment combinations. In two cohorts treated with radical intent, strong IRF7 staining was associated with disease-specific death implicating this pathway as a convergence point for therapeutic resistance in prostate and potentially other cancer types.

cancer biology↗

bioRxiv2022

Strategies and Techniques for Quality Control and Semantic Enrichment with Multimodal Data: A Case Study in Colorectal Cancer with eHDPrep

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBO_SCPLOWACKGROUNDC_SCPLOWC_ST_ABSIntegration of data from multiple domains can greatly enhance the quality and applicability of knowledge generated in analysis workflows. However, working with health data is challenging, requiring careful preparation in order to support meaningful interpretation and robust results. Ontologies encapsulate relationships between variables that can enrich the semantic content of health datasets to enhance interpretability and inform downstream analyses. FO_SCPLOWINDINGSC_SCPLOWWe developed an R package for electronic Health Data preparation eHDPrep, demonstrated upon a multi-modal colorectal cancer dataset (n=661 patients, n=155 variables; Colo-661). eHDPrep offers user-friendly methods for quality control, including internal consistency checking and redundancy removal with information-theoretic variable merging. Semantic enrichment functionality is provided, enabling generation of new informative meta-variables according to ontological common ancestry between variables, demonstrated with SNOMED CT and the Gene Ontology in the current study. eHDPrep also facilitates numerical encoding, variable extraction from free-text, completeness analysis and user review of modifications to the dataset. CO_SCPLOWONCLUSIONC_SCPLOWeHDPrep provides effective tools to assess and enhance data quality, laying the foundation for robust performance and interpretability in downstream analyses. Application to a multi-modal colorectal cancer dataset resulted in improved data quality, structuring, and robust encoding, as well as enhanced semantic information. We make eHDPrep available as an R package from CRAN [[URL will go here]].

bioinformatics↗

bioRxiv2021

Immune cell networks uncover candidate biomarkers of cancer immunotherapy response

Therapeutic activation of antitumour immunity by immune checkpoint inhibitors (ICIs) is a significant advance in cancer medicine, not least due to the prospect of long-term remission. However, many patients are unresponsive to ICI therapy and may experience serious side effects; companion biomarkers are urgently needed to help inform ICI prescribing decisions. We present the IMMUNETS networks of gene coregulation in five key immune cell types, and application to interrogate control of nivolumab response in advanced melanoma cohorts. Results evidence a role for each of the IMMUNETS cell types in ICI response and in driving tumour clearance with independent cohorts from TCGA. As expected, immune hot status, including T cell proliferation, confers good response to first line ICI therapy. Genes regulated in NK, dendritic and B cells are the most prominent discriminators of nivolumab response in patients that had previously progressed on another ICI. Multivariate analysis controlling for tumour stage and age highlights B cell genes as candidate prognostic biomarkers. IMMUNETS provide a resource for network biology, enabling context-specific analysis of immune components in orthogonal datasets. Overall, our results illuminate the relationship between the tumour microenvironment and clinical trajectories, with potential implications for precision medicine.

systems biology↗

bioRxiv2018

Functionally Coherent Transcription Factor Target Networks Illuminate Control of Epithelial Remodelling and Oncogenic Notch

BackgroundCell identity is governed by gene expression, regulated by Transcription Factor (TF) binding at cis-regulatory modules. Decoding the relationship TF binding patterns and the regulation of cognate target genes is nontrivial, remaining a fundamental limitation in understanding cell decision-making mechanisms. Identification of TF physical binding that is biologically neutral is a current challenge. We studied cell identity in the context of Epithelial to Mesenchymal Transition (EMT), a cell programme fundamental for normal embryonic development that contributes to tumour progression and fibrosis.\n\nResultsWe developed the NetNC software for discovery of functionally coherent TF targets. NetNC was applied to analyse gene regulation by the EMT TFs Snail, Twist in early embryogenesis and also to modENCODE HOT regions. Predicted neutral binding accounted for 50% to [≥]80% of candidate target genes assigned from significant binding peaks. Novel gene functions and network modules were identified, including regulation of chromatin organisation and crosstalk with notch signalling. Orthologues of predicted TF targets discriminated breast cancer molecular subtypes and NetNC analysis predicted new gene functions; for example, evidencing networks that reshape Waddingtons landscape during EMT-like phenotype switching. Predicted invasion roles for SNX29, ATG3, UNK and IRX4 were validated using a tractable cell model.\n\nConclusionsWe found extensive neutral TF binding across the nine datasets examined and showed that NetNC performs well in identifying functionally coherent targets. HOT regions had comparatively high functional coherence. Our results illuminate conserved molecular networks that regulate epithelial remodelling in development and disease, with potential implications for precision medicine.

systems biology↗