NIHRDataInsights/HRCSData huggingface dataset:
Title: Mechanistic analysis of DNA damage induced transcriptional silencing
Funder: Medical Research Council

Abstract: Cells respond to double strand breaks (DSBs) in their DNA with actions that prevent genome instability, such as cell cycle checkpoints and apoptosis. Defects in these pathways are frequently found in cancer, and can also lead to inherited disorders. A recently described cellular response to DSBs is the rapid repression of RNA polymerase II transcription specifically in the vicinity of the break. We identified a number of new factors involved in this pathway. How these fit together with the other pathway components recently identified by several other groups is an important outstanding question. Here, we propose to investigate the molecular events taking place during this response. Importantly, chromosomal translocations are frequently observed in cancer cells. Evidence suggests that DSBs within actively transcribed genes that are in close proximity are particularly vulnerable to chromosomal translocations. We therefore plan to test the prediction that transcriptional repression in response to nearby DSBs will help to prevent translocations. Understanding more about the cellular pathways that prevent such events will help us understand the early steps of tumourigenesis.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: GENERIC HEALTH RELEVANCE
Title: Engineering novel synthetic factors to reprogram cell identity
Funder: Medical Research Council

Abstract: It is currently feasible to convert adult cells to other cell types by forcing the expression of transcription factors (TFs) in the laboratory. The key challenge is to translate this reprogramming technology to an effective method in the clinic. The prevailing strategy in the field relies on using "natural" TFs to inefficiently and partially perform this "unnatural" task. I propose that TFs have not evolved far enough to convert fully differentiated cells to other cell types. My goal is to engineer novel reprogramming factors that overcome these limitations by exploiting the molecular features of TFs that I have previously uncovered. I will primarily use the conversion of human fibroblasts (huFibs) to induced pluripotent stem cells (iPSCs) using Oct4, Sox2, Klf4 and c-Myc (OSKM), as a paradigm for TF-mediated reprogramming. I aim to define the molecular features that impart OSKM a pioneer activity, allowing access to silent genes within closed chromatin. To this end, I will combine a novel protein printing technology and biochemical assays with a suite of next-generation-sequencing based technologies to extract key mechanisms underlying cell fate conversion. I aim to map the functionally-minimal OSKM subdomains that are essential in reprogramming. To achieve this, I will employ synthetic DNA assembly to dissect the minimum OSKM subdomains that are sufficient for converting huFibs to iPSCs. I also aim to build a protein design framework to generate novel reprogramming factors. By using a novel tagging technology, I will append defined chromatin-modifying domains to the OSKM DNA-binding domains and screen for optimized reprogramming activities. My findings will expand the reprogramming factors repertoire, which is currently limited to natural TFs, to control cell fate with improved efficiency and high fidelity. Developing this technology will lead to significant advances towards repairing tissues and organs in regenerative medicine.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: GENERIC HEALTH RELEVANCE
Title: Improving Mendelian randomisation based on meta-GWAS summary statistics.
Funder: Medical Research Council

Abstract: Mendelian randomization (MR) provides a framework for assessing causal relationships in observational data by exploiting the natural randomization of genetic variants at birth. Unconfounded genetic proxies, or instruments, are constructed from variants known to predict the exposure of interest; typically single nucleotide polymorphisms, or SNPs. A recent surge in MR has been fuelled by the publication of summary statistics from a variety of high powered large-scale meta-GWAS (meta-analyses of genome wide association studies); which amass data on tens of thousands of individuals. However, leveraging these hugely valuable resources is currently hampered by a number of issues around the use of summary statistics in MR. The first issue arises due to the significant correlations that exist in the human genome. Weighted scores are often used in MR to aggregate multiple SNPs into a single instrument for improved power. Correlated SNPs require weights from multivariate regressions, however, meta-GWAS usually only publish summaries from one-at-time tests. We will explore the application of existing methods for imputing multivariate effects from one-at-a-time summary statistics for the first time in the context of MR. This will include extending a Bayesian variable selection algorithm to incorporate a loss function for pleiotropy (when alternative pathways exist between SNPs and the outcome invalidating MR assumptions). We will also explore a range of extensions to the summary statistic MR-Egger regression method, a tool to detect small study bias in meta-analysis which was recently adapted to test and adjust for bias from pleiotropy in the context of MR. We will extend this robust inference method for use with correlated SNPs, case-control data, and to multivariable MR, where several correlated risk factors are modeled simultaneously. Methods will be developed and demonstrated in a range of four topical case studies shared by collaborators at the MRC Epidemiology unit.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE
Title: System Level Safety for Interventional MRI
Funder: Medical Research Council

Abstract: Cardiac catheterisation is a minimally invasive procedure that is routinely performed under x-ray fluoroscopic guidance. MRI guided catheterisation is an emerging approach that avoids x-rays, offers superior soft tissue visualisation and physiological measures. Although many catheters and guide wires (collectively "wires") are non-ferrous, they are unsafe for MRI due to risk of radiofrequency (RF) heating caused by coupling to the transit coil, so special devices are used. This approach requires a whole new range of devices to be developed, which is expensive and limits the procedures that can be performed. MR compatibility often entails use of different materials and so far has resulted in inferior performance of the devices themselves. Safe operation can still require reduced RF power, limiting performance of the MRI scanner. System level safety can potentially be achieved using Parallel transmit (PTx) MRI to decouple existing standard catheterisation devices. PTx uses multiple individually controlled RF transmit coils that together produce the required RF magnetic fields. Coupling to each transmit element in the PTx array can be determined by measuring induced currents on the wire using non-contact current sensors. For N transmit channels and M current sensors, there are N-M drive modes available for the array that give zero current at all M sensors (referred to as decoupled modes, DM), and M modes that give maximal readings at these sensors (referred to as coupling modes, CM). We will develop a prototype PTx system that monitors current on the part of a wire that remains outside the patient during catheterisation to constantly maintain DM conditions. The system will interface to standard 1.5T MRI scanners so that standard catheter equipment can be safely used on existing hospital scanners that have suitable medical infrastructure around them. We will test the prototype on anatomically realistic phantoms and in pre-clinical in vivo studies to demonstrate safety.


Research activity codes: 5.3 MEDICAL DEVICES
Health categories: GENERIC HEALTH RELEVANCE
Title: Developing efficient perpetual platform trials to study multiple treatments and multiple biomarkers
Funder: Medical Research Council

Abstract: The availability of affordable biotechnology has meant that many biomarkers can be measured to predict the effect of different treatments. Novel trial designs have been proposed to test the effects of multiple treatments in multiple biomarker subgroups. Several of these are currently being implemented in practice. Many of these trials will allow new treatments and biomarkers to be added as the trial continues for logistical and administrative reasons. We refer to such trials, in which treatments are continually added and removed, as 'perpetual'. Statistical methodology has been lacking for such trials and in particular for when treatments are testing in distinct biomarker subgroups. We aim to develop methodology for improving several aspects of perpetual trials with multiple treatments and biomarkers. This includes: 1) extending current to allow optimal planning of enriched phase III studies from the results of a perpetual phase II study; 2) developing optimal decision rules for when to drop a treatment from a perpetual trial; 3) understanding the effect of adding in a new arm to a perpetual trial; 4) determining when a new arm should be introduced based on factors internal and external to the trial. We will use motivating examples of trials that our group has links with in order to inform simulation studies. These trials represent a range of trial designs. In addition to motivating the work, links with these trials provides additional routes for dissemination of the methodology.


Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Evaluation of Bayesian adaptive designs for Phase 3 effectiveness trials
Funder: Medical Research Council

Abstract: In this project we aim to evaluate Bayesian adaptive trial methodology, by re-designing and re-executing trials funded by the NIHR HTA Programme and conducted by Warwick Clinical Trials Unit (WCTU). Al of the datasets are held in anonymised format by WCTU. We plan three studies, using 1. datasets from completed trials; 2. datasets from trials that have completed recruitment but have not reported their results; 3. ongoing trials. In studies 1 and 2, we will redesign each trial using the following potential adaptive features: 1. Interim analyses and potential early termination of the trial; 2. Response adaptive randomisation; 3. Dropping of arms; 4. Bayesian final analysis. For each trial we will perform extensive simulations to understand the performance of proposed designs and to optimise parameters such as the number of interim analyses and thresholds for decisions. A group of researchers and clinicians, independent of the original trial, will discuss the design with the project researchers and select a single design will be selected. The trial will then be re-run using the original patient sequence, to evaluate whether use of the Bayesian adaptive design would lead to practical benefits in terms of the size, duration or cost of the trial, or differences in results or conclusions. We will use resampling from the trial dataset to estimate the range of possible sample sizes and results under the alternative design. In Study 3, we will carry out the same trial redesign process, but we will use data from real clinical trials in real time. The interim analyses will be carried out on the real accumulating data but the "decisions" resulting from them will not be implemented in the real trials. Results from the re-executions of these trials will be kept confidential and will not be made public until the real trial results are known.


Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: HSM: Estimation of intervention effects for adaptive enrichment design RCTs that incorporate identification of predictive biomarkers
Funder: Medical Research Council

Abstract: The aim of this work is to develop point and interval estimators following randomised clinical trials (RCTs) that are conducted using adaptive enrichment designs in the context of stratified medicine. In stratified medicine, it is acknowledged that patients with different characteristics such as different genetic biomarkers, may respond to treatments in different ways so that an intervention may only benefit a subpopulation of the full patient population. Testing of new interventions allow for this possibility. We will consider adaptive enrichment designs where a RCT includes an interim analysis. Using a predictive biomarker, that is a biomarker that determines how a patient responds to a treatment, the interim analysis is used to select the subpopulation that benefits from the new therapy. After the interim analysis, patients are recruited from the selected subpopulation. Definitive analysis includes data collected before and after interim analysis. Interim analysis data induce selection bias and so point and interval estimators need to adjust for this. Very little work exists that adjust for selection bias and so more research is required. We will develop point estimators for three settings: (1) Patient data are normally distributed and the biomarker is continuous with several candidate threshold values, (2) Same setting as above but for time to event data, and (3) A panel of biomarkers is used to make selection. Data collected after the interim analysis are unbiased and we will use them to get unbiased estimators, which we then Rao-Blackwellise to obtain unbiased estimators that have smaller variances. We will also develop two interval estimators for the first setting; one based on p-value inversion and the second based on the density of the statistic in the selected subpopulation. By enabling unbiased point and interval estimation, we will increase the use of these efficient adaptive enrichment designs.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY);8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: MICA: Delivering a production platform and atlas for next-generation biomarker discovery, validation and assay development in clinical proteomics
Funder: Medical Research Council

Abstract: We have recently demonstrated a fundamentally novel workflow for mass spectrometry (MS) discovery proteomics, which for the first time performs statistical modelling on the unprocessed full MS data for a significant increase in differential expression detection sensitivity. However, in conventional MS with Data-Dependent Acquisition, only the most intense signals are targeted for fragmentation and therefore available for identification. Consequently, potential biomarkers may not be identified. Recently, Data-Independent Acquisition (DIA) approaches have emerged where the whole mass range is systematically fragmented to produce a comprehensive digital record of the proteome. The SWATH approach from SCIEX has generated considerable clinical research interest, enabling multiplexed targeted analysis on large sets of hypothetical biomarkers, plus potential reanalysis of existing datasets with new candidate biomarkers as they arise. For this reason, the new MRC-funded Stoller Biomarker Discovery Centre (SBDC) at Manchester is centred upon a fleet of SWATH instruments. The purpose of this project is to exploit the quantitative sensitivity of our statistical workflow to deliver a robust, production quality DIA biomarker discovery software platform for routine use by the SBDC and beyond. From this, we will develop the computational methodology necessary for representing, parameterising and organising sum knowledge of protein quantitation across a history of past studies, to generate and openly disseminate a stratified probabilistic atlas of protein and peptide variation across health and disease. The atlas will enable candidate biomarker verification and peptide characterisation for clinical selected reaction monitoring assay development much earlier in the development pipeline than is currently possible, and realise further step-change improvements in the sensitivity and specificity of our discovery and validation platform through borrowing strength from the atlas.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE
Title: HEE: Developing a reference protocol for expert elicitation in health care decision making
Funder: Medical Research Council

Abstract: Decisions about the use of health care interventions are made in the UK. At the forefront of these decisions is the aim of maximising health, calling for judgements about the costs and effects of the interventions. Due to uncertainty in the evidence, the expected cost-effectiveness of an intervention is often uncertain. To improve the accountability of the decision making process, formal methods to quantify these judgements with uncertainty can be used (expert elicitation). The aim of this project is to establish a reference protocol for the elicitation of experts' judgements to inform health care decision making. This protocol will bring clarity over the methods for collecting and using experts' judgements within the assessment of cost-effectiveness and will demonstrate where alternative methodology may be required due to a specific context, time or costs. We plan a series of work packages, the starting point being a critical review of existing expert elicitation protocols developed outside of health care. We then propose further in-depth research guiding choices on aspects of the protocol, broadly focusing on: evaluating alternative methods of elicitation in light of the need to consider uncertainty and evaluating alternative methods of aggregation in light of the need to reflect between-expert variation due to underlying heterogeneity. In addition we will use the protocol to inform a decision making process in real time. This will achieve a number of things: 1) Establish preferred approaches for elicitation 2) Develop a guide on what quantities to elicit to inform specific parameters such as diagnostic accuracy 3) Determine which elicitation methods allow experts to express parameter uncertainty 4) Establish the accuracy of consensus-based methods in generating representations of uncertainty 5) Establish the accuracy of alternative methods of pooling the individual judgements of experts 6) Determine the applicability/usefulness of the protocol


Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Making connections with GO: an integrative approach to highlighting medically relevant Drosophila data
Funder: Medical Research Council

Abstract: The aim of this research proposal is to enhance the utility and accessibility of data from Drosophila to the biomedical research community. Our approach will center on the Gene Ontology (GO), a unifying vocabulary for describing the attributes of gene products across species and databases. We will focus our efforts on improving the annotation of pathways and protein complexes relevant to human disease. In parallel, we shall undertake a comprehensive annotation of non-coding RNAs - an important emerging area in human health. Whilst achieving these aims, we will work with the GO consortium to develop the ontology itself and adopt their enhanced annotation framework. Furthermore, in collaboration with FlyBase, we shall present pathways and complex components as easily accessible lists that are highly integrated with all other data in FlyBase. Subsequently, we will enhance these pages by adding graphical models of pathways that will highlight intersections with human disease gene orthologs and those genes studied in Drosophila disease models. Overall, our approach will improve the functional annotation of Drosophila genes related to human disease, and present these data in intuitive and accessible ways to the wider research community to maximise the impact of fly research on biomedical advances.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;2.6 RESOURCES AND INFRASTRUCTURE (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE
Title: High-resolution imaging and time-resolved proteomic profiling of COPII-dependent procollagen packaging.
Funder: Medical Research Council

Abstract: The mechanisms of secretion of the most abundant protein in animals remain unclear. While much is known about the core machinery that drives export of procollagen from the ER, and several high profile papers have defined additional components, we still simply do not know how the major collagens are exported from the ER. The majority of the available mechanistic data either relates specifically to one procollagen isotype (type VII), or provides little insight beyond the initial finding (in the case of ubiquitylation of Sec31), or relate to the downstream processes of tethering and fusion. We have developed some unique methodologies to define this step for the most abundant and important collagens, types I and II. We have generated cells to express engineered constructs to enable temporally- and spatially-resolved imaging as well as proteomics of procollagen I during exit from the ER. The key advance here is the use of a biotin- and ascorbate-dependent procollagen construct to control export from the ER, imaging using a monomeric GFP. Imaging experiments will use correlative light electron microscopy, exploiting the unique expertise and previous RCUK investment in these areas within Bristol. Complementary methods will exploit the proximity-based labelling method of engineered ascorbate peroxidase (APEX2) to define those proteins within close proximity to procollagen on both sides of the ER membrane during its export. Perhaps most significantly, we have shown that the biotin-phenol substrate used for APEX2 will, like biotin itself, drive export of tagged constructs from the ER. This means we can combine these approaches as we seek to correlate super-resolution light microscopy, higher resolution electron microscopy, and proteomic profiling. These experiments will provide a major advance in our understanding of this essential process that underpins key aspects of development, wound repair, fibrosis, and cancer biology.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: GENERIC HEALTH RELEVANCE