NIHRDataInsights/HRCSData huggingface dataset:
Title: Evidence-informed decision aids for clinical trial participation: a methodological investigation of core components and outcome measures.
Funder: Medical Research Council

Abstract: When invited to participate in a clinical trial people are provided with an information leaflet and given the opportunity to have any questions answered. Despite this, there is evidence that some trial participants misunderstand the trial rationale and/or procedures, putting into question the adequacy of the informed consent process. Although provision of information aimed at improving knowledge may assist decision making, other factors, including the influence of others, have been shown to be important. In work on treatment decision making, there has been development of tools ("decision aids") for patients. Decision aids are designed to promote a deliberative process of decision making allowing people to make choices by determining what matters most to them. Preliminary studies have explored a possible role of decision aids to facilitate the informed consent process for trials. However, there is a lack of evidence about content, delivery and evaluation of these tools in the context of trial participation. This research aims to inform the design and assessment of trial participation decision aids by answering the following research questions: 1. What are the potential implications of including patient stories in trial decision aids? This will be explored using interviews to develop vignettes followed by think aloud analysis. 2. What are effective methods for presenting probabilistic trial information within trial decision aids? This will be investigated using questionnaires alongside think aloud analysis. 3. Which outcomes should be considered 'core' for the evaluation of trial decision aids? This will be determined using a systematic review of process outcomes of interventions to improve informed consent for trials. Followed by a Delphi process to determine stakeholders' views of outcome measurements appropriate in this context. Finally, guidance documents will be developed on key content and outcome measures of trial participation decision aids.


Research activity codes: 8.3 POLICY, ETHICS AND RESEARCH GOVERNANCE
Health categories: GENERIC HEALTH RELEVANCE
Title: Multiple imputation methods for valid causal treatment effects estimation after departures from protocol
Funder: Medical Research Council

Abstract: The aim is to develop methods based on multiple imputation (MI) for handling deviations from protocol, defined as non-compliance and drop-outs, in time-varying complex randomised clinical trial data where current methods are not applicable (hierarchical data, binary outcomes with continuous non-compliance and bivariate outcomes). This will be achieved by extending a novel method (called CRK henceforth), based on pattern-mixture models. It uses MI to handle drop-outs and non-compliance in a unified manner, and estimates efficacy (called de jure), by imputing post-deviation data from a distribution whose parameters have been estimated using the observed data from those subjects who comply in their corresponding allocated treatment arm. Firstly, I will relate this approach to the existing causal inference methods. I will establish relationships between the different assumptions used by each method for parameter identification. Secondly, a Principal Stratification (PS) approach that uses MI estimation for the complier-average causal effect (CACE) will be extended to time-varying binary non-compliance settings. PS divides the trial population into compliance classes. As the number of possible compliance histories grows exponentially with the number of follow-up visits, CACE estimation becomes high-dimensional. A latent-class representing the time-invariant super-compliance class is used as a pragmatic solution. Thirdly, I will extend the CRK approach to handle time-varying non-compliance of bivariate outcomes (e.g. cost-effectiveness) and hierarchical data (e.g. data from a cluster randomised trial). Finally, I will extend the method to binary outcomes in the time-varying non-compliance setting and develop a framework for sensitivity analysis to study the robustness of results when there are departures from assumptions. Throughout, newly-developed methods will be implemented in statistical packages for R and STATA.


Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Classification and functional annotation of endogenous siRNAs and other small RNAs
Funder: Medical Research Council

Abstract: I will generate genome wide data sets in the laboratory that will provide the basis for developing novel computational tools to detect and, later, predict the function and regulatory targets of endogenous siRNAs (endo-siRNAs). The initial experiments consist of a combination of dsRNA enriched sequencing data, prepared using the J2 antibody, p19 enriched dsRNA siRNA sequencing libraries and PEG enriched small RNA sequencing libraries prepared from wild type and Dicer knockdown backgrounds. These data will then be combined with information from public resources such as Ensembl and ENCODE. Machine learning will be used to identify a set of features associated with Dicer dependent endo-siRNA loci. Clustering techniques will also be used to identify features that can distinguish distinct sets of small RNA producing loci. These analyses will be used as the basis for producing methods for interpreting sRNA sequencing data. These methods will be applied to publically available sRNA sequencing data for Dicer knockout systems and other tissues and cell lines to assess performance and generate more general expression profiles. I will use endo-siRNA perturbations, ChIP-seq and public Ago-CLIP datasets to identify potential targets of transcriptional gene silencing (TGS). I will examine the attributes of target sites for features that will allow development of a predictive method. There is growing evidence that Dicer processing of dRNAs, endo-siRNAs and TGS may play a role in a range developmental and medical systems, including learning, geographic atrophy and breast cancer. This proposal will address a pressing need for tools that can enable research in a rapidly expanding niche of cellular biology.


Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: MRC-PHE Centre for Environment and Health
Funder: Medical Research Council

Abstract: The environmental sciences are at the brink of a new era in which investigators move from using relatively crude estimate of exposure (e.g., distance based methods, questionnaires, diaries) to sophisticated modelling techniques, biomarkers and personalised 24h measures of exposure from a range of hazards. This advance through the utilisation of high-throughput omic technologies, new miniaturised exposure sensors and application of advanced hybrid exposure models across the lifecourse will be a central focus of the Centre's activity over the next 5 years. As we move to the next phase of Centre funding we will take advantage of the increasingly rich metadata that are available for our cohorts and we will link these with primary care data records as they become available through the CPRD network. We will further exploit the rich and unique data resources available to Centre researchers afforded by the UK Small Area Health Statistics Unit (SAHSU) at Imperial College and the Environmental Research Group (ERG) at King's College, and utilise the improved exposure assessment methods and application of omic technologies for a more integrated approach to environmental health research. This approach across our work programmes will allow us to focus activity around a conceptual framework that examines links from the molecular level through man to populations across the lifecourse and provide outputs to advance understanding and better inform environment and health policy.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;2.2 FACTORS RELATING TO PHYSICAL ENVIRONMENT;2.6 RESOURCES AND INFRASTRUCTURE (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE
Title: University of Oxford Big Data Institute: Development & dissemination of efficient analysis methods for large, complex, heterogeneous clinical datasets
Funder: Medical Research Council

Abstract: Advances in Big Data offer new ways to conduct health research, with advantages in speed, cost and scope of scientific enquiry. In particular, major advances will come from the development of new methods to integrate and interrogate multiple, complementary datasets simultaneously. The University of Oxford Big Data Institute (BDI) will develop flexible and efficient systems, tools and methods for generating and analysing research-optimized data sets that incorporate relevant clinical information (including bespoke research data and routine clinical information), extensive phenotypic measurements (including imaging, physical and function assessments) and genomic and other laboratory data. Evaluation of clinical phenotype: The BDI will bring together bioinformaticians, clinical specialists and research users to develop validated algorithms that combine multiple data sources to confirm, clarify and classify clinically and biologically relevant phenotypes in very large studies. Interpretation of genotype: Current statistical methodologies for assessment of genetic influences on disease (many of which were developed in Oxford) fail in regions of high structural or sequence diversity (e.g. around human HLA and KIR loci, and in many bacterial and eukaryotic pathogens). The BDI will develop novel methods that integrate information from multiple reference sequences, thus extending the ability for researchers to investigate genetic influences on disease pathways. Combining multi-dimensional phenotypic and genomic data: Building on our experience in clinical informatics and genetic analyses, we will address gaps in the current standards and ontologies. We will develop appropriate statistical methods for the analysis of high-dimensional and highly correlated data in order to allow biologically meaningful associations to be identified robustly. We will also establish an infrastructure that supports appropriate sharing and analysis of biomedical Big Data.


Research activity codes: 1.5 RESOURCES AND INFRASTRUCTURE (UNDERPINNING)
Health categories: GENERIC HEALTH RELEVANCE
Title: Infrastructure for collaboration: Leeds MRC Medical Bioinformatics Centre
Funder: Medical Research Council

Abstract: The capital expenditure for the Leeds MRC Medical Bioinformatics Centre will complete an integrated Research Campus linking the University campus to the local Leeds Teaching Hospitals Trust (LTHT), The Phoenix Partnership Ltd (TPP) and HSCIC. This requires upgrades of some existing network links to 100 Gb/s, and extra data storage capability on the University Campus within the Yorkshire and Hull Metropolitan Area Network shared data centre. This is linked to matched funding for infrastructure within LTHT and TPP. Computational processing will be carried out on the University high-performance computing facility, free of direct cost to this application. Capital funds will also be used to refurbish part of a building (Worsley) to house the Centre and associated researchers. Non-capital costs will be met partly from University matched funding and are associated mainly with data scientists and high calibre research fellows. These staff will create and stress test Virtual Research Environment software specialised to the linkage of high-volume molecular (e.g. sequence) data and phenotypic data (e.g. pathology images) to appropriate anonymized health and clinical records. Stress testing will use exemplar projects in disease areas (inherited disease, melanoma, rectal cancer and renal injury) where we already have consented cohorts with significant genomic, proteomic and phenotypic data sets. A key aim of the Centre will be scientific and financial sustainability through the continued creation and funding of new projects based on the infrastructure and capability in place. Other important aspects of the centre will be work on ethics and appropriate electronic consent processes, patient and clinician communication, and engagement and education and training. We will set up and deliver a new MSc programme, a PhD training platform and engage with undergraduate teaching.


Research activity codes: 1.5 RESOURCES AND INFRASTRUCTURE (UNDERPINNING)
Health categories: GENERIC HEALTH RELEVANCE
Title: MICA: Medical Bioinformatics: Data-Driven Discovery for Personalised Medicine
Funder: Medical Research Council

Abstract: We request funding for: 1. a collocated, large-scale data storage and compute facility (eMedLab) and 2. a Medical Bioinformatics Research and Training Academy. These strategically vital infrastructures will enable research into new approaches to understand cancer, rare and cardiovascular diseases; establish stratified clinical trials, and discover diagnostic and prognostic biomarkers for clinical practice. 1. We have a team of 10 Research Technologists to develop and maintain eMedLab (>9,000 cores, 4PB, JANET6 network). We shall consolidate dispersed datasets in a single facility, eliminating delays and security risks involved in data transfer. Direct links to NHS Trusts will allow integration of clinical data for >6M patients. We will interface with industry-derived data and the Global Alliance for secure data sharing. We will deploy a knowledge management platform like tranSMART and sensitive information will be placed in secure areas complying with NHS requirements. Rapid developments make long-term planning difficult; but we have designed eMedLab to be flexible for future growth. 2. The Academy will be a forum for basic and clinical scientists, and bioinformaticians to train in medical bioinformatics techniques, and to interact across the traditional boundaries of disciplines and disease areas. (i) We will support leaders in the field by funding 4 Career Development Fellowships to establish new laboratories. (ii) We will coordinate training to co-supervise and mentor scientists, provide courses, seminars, and workshops. We will leverage existing Doctoral Training Programmes across partners, and we will align with the Europe-wide strategies of the ELIXIR-UK training node. (iii) We will coordinate research activities through Academy Labs - analogous to the Crick "Interest Groups" - that comprise flexible, semi-overlapping groupings of academics and industrial partners to share and plan activities in areas of common analytical challenges.


Research activity codes: 1.5 RESOURCES AND INFRASTRUCTURE (UNDERPINNING)
Health categories: GENERIC HEALTH RELEVANCE
Title: MICA: UK MEDical BIOinformatics partnership - aggregation, integration, visualisation and analysis of large, complex data (UK MED-BIO).
Funder: Medical Research Council

Abstract: UK MED-BIO brings together a multidisciplinary team in medical and computing science, statistical analysis, metabolic phenotyping and medical bioinformatics across Imperial College London (ICL, lead institution), Institute of Cancer Research, European Molecular Biology Laboratory-European Bioinformatics Institute, Universities of Oxford, Swansea and Nottingham, MRC Clinical Sciences Centre and MRC Human Nutrition Research - supported by strong industrial partnerships and collaborations. The aim is to bring together the necessary patient data, new omics technologies, computing and other resources, with dedicated infrastructure in data management, storage, integration, visualisation and analysis, to enable major advances in understanding the aetiopathogenesis of chronic human diseases. We have powerful world-leading expertise in metabolic phenotyping through the MRC-NIHR National Phenome Centre at ICL, and in innovative software infrastructure solutions through our leadership position in developing tranSMART and eTRIKS. We will provide unprecedented capacity to i) combine lifestyle/socio-demographic information, clinical data, and disease phenotypes from well characterised patient and population cohorts, with multi-omic information from analysis of linked biological samples; ii) develop powerful new approaches to integration/analysis of such large-scale, complex, multi-source medical data; hence iii) create new knowledge on disease aetiology, underlying biochemical pathways and mechanisms, leading to improved patient stratification, prevention and treatments; iv) transform the training/development of medical bioinformatics future leaders by providing a seamless interdisciplinary environment, bringing together researchers from biomedicine, physical sciences, computing and engineering. This application is strengthened by ICL's decision to establish a major interdisciplinary 'big data' centre at the Imperial West campus, ensuring sustainability over the long term.


Research activity codes: 1.5 RESOURCES AND INFRASTRUCTURE (UNDERPINNING)
Health categories: GENERIC HEALTH RELEVANCE
Title: Structure and function of ZMPSTE24, an integral membrane protease mutated in progeria
Funder: Medical Research Council

Abstract: Premature ageing diseases provide valuable insights into the mechanisms of normal ageing. Several premature ageing diseases are caused by failure in the post-translational processing of prelamin A, by an integral membrane zinc metalloprotease, ZMPSTE24. Mutations in the gene for ZMPSTE24 have been linked to metabolic syndrome, normal ageing and cancer. Our recent crystal structure of ZMPSTE24 (Quigley et al., Science, 2013) showed a 7 transmembrane helix barrel surrounding a huge chamber, capped by a zinc metalloprotease domain. The objective of this grant is to understand the underlying molecular mechanisms of prelamin A cleavage by ZMPSTE24 and how this cleavage reaction is disrupted in disease. There are four key components to the processing of prelamin A by ZMPSTE24: substrate entry, binding, cleavage and product release. I will investigate each step using site-directed mutagenesis, enzymology, X-ray crystallography, in silico simulations, photo-affinity labelling, protein footprinting and native mass spectrometry. I will then investigate how ZMPSTE24 achieves its exquisite substrate selectivity, cleaving only one of the 300+ isoprenylated proteins in cells, by analysing its activity on libraries on libraries of substrate peptides. I will also test the theory that role of the intramembrane chamber may be to restrict access to the active site. Once we have a clear understanding of the molecular mechanism by which ZMPSTE24 cleaves prelamin A, I will then have the tools to understand how mutations in ZMPSTE24 reduce its activity and how mutations in prelamin A prevent cleavage. My proposed studies will give us a clear picture of the molecular mechanisms for substrate entry, recognition and cleavage, and an understanding of the effects of disease-causing mutations, potentially paving the way for future therapeutic interventions for both progeria and physiologic ageing.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: GENERIC HEALTH RELEVANCE
Title: Tracking the embryonic origin of the adult haematopoietic system
Funder: Medical Research Council

Abstract: The embryonic origin of haematopoietic stem cells (HSCs) is a controversial issue and HSC development remains poorly understood. This issue is of potentially high importance for medical treatment of haematopoietic disorders. The analysis of HSC development in the mammalian embryo is hampered by intra-uterine development and the main objective of this research proposal is to identify the HSC origin in amniotes. Chick embryos develop outside the mother and can thus be manipulated and observed without significant disruption of development. Using recently developed transgenic chicken technology, we will perform orthotopic grafting of tissue fragments from ubiquitously expressing GFP-positive embryos to determine the location of embryonic HSC precursors in the primitive streak and lateral mesoderm. We will establish whether the transitory yolk sac and adult (permanent) haematopoietic hierarchies are separate at early stages of embryogenesis. We will also generate two novel transgenic chicken models, Flk1-GFP and Runx1-tdTomato which will allow us to monitor and map progressive development of the mesoderm/ endothelium/ blood in space and time. Well recognisable two-colour pattern evolving in compound [Flk1-GFP;:Runx1-tdTomato] embryos will allow us to perform fine systematic isolation and orthotopic grafting of small groups of cells. By this, the origin of the developing yolk sac haematopoietic wave and of the HSC lineage will be mapped with high definition. In the early embryo the cell fates may not be fixed and remain rather plastic. By heterotopic transplantations we will investigate whether this is the case. Live imaging of transitory and permanent HSC hierarchies will allow us to define a potentially complex series of trajectories of migration of HSC precursors. Observing these intermediate staging-posts and correlating them with the known locations of extracellular signalling molecules is essential to define the niches for HSC development.


Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING;5.2 CELLULAR AND GENE THERAPIES
Health categories: GENERIC HEALTH RELEVANCE
Title: Dynamic transcription factor function in control of pluripotent cell sub-states
Funder: Medical Research Council

Abstract: Pluripotent stem cell self-renewal is governed by a pluripotency gene regulatory network (PGRN) centred on the transcription factors (TFs) Oct4, Sox2 and Nanog. Despite considerable advances in identifying additional PGRN components, a detailed distinction between self-renewal and differentiation mechanisms remains elusive. Nanog is a key switch between states. We have identified 64 Nanog-sensitive genes and will ask how these contribute to Nanog function and how they are regulated by Nanog, via three related aims. Aim 1: Identify Nanog-sensitive genes that fully complement Nanog function. A prominent Nanog-sensitive target, Esrrb, only partially complements Nanog function. Therefore, we will test the ability of additional targets to fully compensate for loss of Nanog in combinatorial assays. We will identify genes showing co-ordinate responsiveness and determine whether specific co-dependencies distinguish pre- and post- implantation PGRN configurations, making use of new bioinformatic approaches. Aim 2: Determine the chromatin function of pluripotency TFs. Nanog protein represses Nanog, and this may cause Nanog fluctuations. We will use ChIP to determine the context in which Nanog fluctuations arise and which regulators control Nanog. We will compare regulation of Nanog with other negatively/positively regulated targets to determine how Nanog regulates distinct gene cohorts. We will investigate a novel robust pluripotent state defined by reduced Oct4. Aim 3: Examine pluripotency TF function in vivo, in EpiSCs and in human cells. We will determine whether dependencies seen in culture occur in vivo. We will investigate how TFs control transitions between pluripotent sub-states, during exit from pluripotency and will determine whether a more naïve human pluripotent cell type can be isolated using lessons learnt from our mouse cell studies. Our work will deliver a mechanistic insight into pluripotency TF action in mouse and human cells in culture and in vivo.


Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING
Health categories: GENERIC HEALTH RELEVANCE