NIHRDataInsights/HRCSData huggingface dataset:
Award Title: Understanding nurse retention and its impact on patient outcomes in secondary care
Funder: The Health Foundation

Abstract: The rate at which nursing and ambulance staff are leaving the NHS is increasing. The number of nurse vacancies has risen to over 40,000 – a record high. The ambulance service has recorded an 80 per cent increase in staff leaving the profession since 2010. These rates are unequally distributed across professions, specialties and geographical regions, introducing inevitable inequalities in patient care.

This Efficiency Research project aims to use this variation to detect underlying contributory factors for better or worse nurse and ambulance staff retention, and determine its effect on patient outcomes.

A research team from Staffordshire University will use their experience of applying ‘big data’ analytics and unifying large datasets from three previous studies on the effect of nurse staffing on patient safety.

There are three work programmes to explore workforce retention and configuration in health care.

The first programme will combine and align multiple large datasets from 20 NHS trusts across secondary care and mental health and 10 ambulance trusts. This will enable the analysis of multiple variables and their effect on workforce retention, and how these variables, in combination with workforce retention, subsequently impact patient outcomes.

The second work programme will involve designing and testing an infrastructure for the routine extraction, combination and analysis of these large datasets. This will enable the adoption of these techniques across the NHS. The nursing element (NuRS) will start first, with the ambulance staff (AmReS) element following approximately six months later.

A third programme will examine the effect of the COVID-19 pandemic on patient safety in terms of reporting behaviours, for example; and will explore how nursing and ambulance workforce configuration in response to a pandemic affects patient safety and quality of care.

This project is a unique opportunity to unlock the key underlying drivers of nurse and ambulance retention and determine their impact on care quality, helping to tackle the challenge of supply in the NHS and ensure that high quality, sustainable care is available to all.


Research Activity Codes: 8.5 RESOURCES AND INFRASTRUCTURE (HEALTH SERVICES)
Health Categories: GENERIC HEALTH RELEVANCE
Award Title: Understanding potential candidates for malarial transmission-blocking vaccines
Funder: Wellcome Trust

Abstract: Malaria is a deadly infectious disease caused by a parasite called Plasmodium. It has a complex life cycle that involves sexual and asexual stages and can live in both the human host and the mosquito vector. It is challenging to search for effective malarial vaccines as the Plasmodium surface proteins can adapt rapidly and hide from the host’s immune system. Hence, an effective vaccine will require multiple antigens that present on the surface of the parasites at different stages. Transmission-blocking vaccines aim to reduce the infectivity of parasites and prevent the spreading of the disease. The potential candidates for transmission-blocking vaccines are proteins located on the surface of Plasmodium gametocytes, the specialised sexual cells that can be transmitted from human host to the mosquito. We aim to see what candidates for transmission-blocking vaccines look like, using X-ray crystallography and cryo-EM. Together with biochemical and biophysical methods, we are able to investigate their interactions with monoclonal antibodies and understand how these proteins regulate the fertility of the parasites. These studies will provide structural information for developing transmission-blocking vaccines.


Research Activity Codes: 2.2 FACTORS RELATING TO PHYSICAL ENVIRONMENT
Health Categories: INFECTION
Award Title: Understanding signal modulation by the inhibitory immune checkpoints
Funder: Wellcome Trust

Abstract: Immune checkpoint proteins expressed on the surface of T-cells fine-tune signaling from the T-cell receptor by providing co-stimulatory and inhibitory signals. The manipulation of the receptors that provide the inhibitory signals (‘inhibitory receptors’), using interaction-blocking and agonistic antibodies, holds great promise for treating cancer and autoimmunity. Remarkably little is known, however, about the signaling pathways initiated by the inhibitory receptors, or how their expression at the cell surface is regulated following signaling. Important, unanswered questions therefore are: (1) what makes inhibitory receptors inhibitory - are there shared or convergent inhibitory pathways that can be targeted systematically; and (2) if we want to agonize these receptors, can their downregulation be prevented so that signaling is sustained? This proposal aims to characterize the signaling and surface-modulatory behavior of a set of five immune checkpoints using systematic genetic screens. I will use reporter cell-lines generated to study inhibitory signaling pathways in T-cells to identify the genetic regulators of inhibitory signaling and receptor down-modulation induced by native ligands and by antibody agonists, using a CRISPR/Cas9-based, genome-scale loss-of-function screening approach. The project should yield new insights into how inhibitory signals are generated, and how they might be better harnessed for treating malignancies and autoimmunity.

Lay Abstract: When specialized white blood cells recognize the signatures of pathogens and cancer, proteins expressed on their surfaces instruct the immune system to clear the threat. Other, inhibitory, proteins expressed by these cells can override these instructions, however, to prevent over-reactions. Recent years have witnessed great excitement over the therapeutic potential of these inhibitory proteins: by blocking their activity, the immune system can be primed to attack cancer cells, whereas enhancing their effects allows unwanted immune responses (e.g. colitis) to be switched off. Despite this excitement, important questions remain: what makes the inhibitory proteins inhibitory, and how is their expression regulated after they exert their inhibitory effects. Using advanced ‘gene-editing’ methods and new assays that I will develop, I will address these questions in ways that haven't been possible previously. A deeper understanding of how these inhibitory proteins work should allow us to build upon the recent, remarkable developments in immunotherapy.

Research Activity Codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING
Health Categories: CANCER AND NEOPLASMS
Award Title: Understanding the Global Gist Signal as a Risk Marker for Early Cancer Detection
Funder: Cancer Research UK

Abstract: Background
Humans are experts at the rapid perception of the broad meaning of ‘gist’ of a scene (forest, beach, etc). Medical images are the ‘scenes’ processed by expert radiologists. We have shown that expert radiologists can distinguish normal from abnormal mammograms at above chance levels after viewing them for just 500 msec even when the signs of cancer are quite subtle. We have shown that the signal is not dependent on breast density or symmetry. The gist signal is strongest in the high spatial frequencies and is detectable in images from the otherwise ‘normal’ breast contralateral to the breast with overt signs of cancer. These results suggest that radiologists are detecting a ‘global gist signal’ in the texture of the breast tissue as a whole and not simply fixating on the lesion by chance on a few images. Critical for this proposal, two of our studies show that experts can detect the signal in precancerous images taken 3 years before the onset of cancer itself.
Aims
A gist signal that is detectable in prior exams could be an imaging risk marker. Our goal is to understand the signal and establish how it might be exploited as a perceptual ‘risk factor’. There are 3 aims.

1) Understand the nature of the gist signal by isolating visual features (spatial frequency and texture descriptors) that support classification with gist and testing its relationship to ‘complexity’ measures estimated using validated quantitative computer methods.
2) Assess the use of the global gist signal as an imaging risk marker that when combined with deep learning methodology can augment deep learning breast cancer risk assessment.
3) Determine if the global gist signal reflects the phenotype of a specific subtype or subtypes of cancer. We will also explore the existence of similar gist signals in cervical, lung, and prostate cancer screening.
Method
We will use behavioural studies of radiologists and radiographers viewing mammograms. Experimental protocols will include rapid image assessment and self-paced report protocols administered to expert radiologists and radiographers. We will also use image-analysis and pattern-recognition capabilities provided by deep convolutional neuronal networks (CNN) and machine learning.
How the results of this research will be used
A reliable gist signal could serve as an early warning for breast cancer. It could be used to guide screening and to improve early diagnosis combined with CNN’s into a variety of cancer screening modalities.


Research Activity Codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health Categories: CANCER AND NEOPLASMS
Award Title: Understanding the association between adiposity and aggressive prostate cancer
Funder: Cancer Research UK

Abstract: Background: Prostate cancer accounted for over 11,000 deaths in the UK in 2016 but there are no established modifiable risk factors for the disease. There is some evidence that relates adiposity (excessive fat accumulation) to aggressive prostate cancer (fast-growing with lethal progression). However, it is unknown if adiposity or its distribution (visceral [fat around internal organs] or subcutaneous fat), promote metabolic and hormonal dysfunction that stimulates the growth of prostate cancer cells, or if late detection explains these associations. Genetic data and better measurements of body composition in large population studies and international consortia now give us the opportunity to assess these associations more reliably.    Aims: The main aim is to understand how adiposity and its distribution relate to aggressive prostate cancer. For this, I will study whether men with genetic predisposition to higher total adiposity or a specific fat distribution (alleles associated with decreased subcutaneous but increased visceral fat and insulin levels, or vice versa) have a higher risk of aggressive prostate cancer. I will also investigate potential metabolic and hormonal pathways that may underlie these associations.    Methods: I will use data from two large international consortia and from UK Biobank, the largest population study using gold standard methods to quantify body fat. I will first determine whether men with genetic predisposition to higher adiposity or a specific fat distribution (visceral or subcutaneous) have a higher risk of aggressive prostate cancer, using data from the PRACTICAL consortium. Secondly, I will assess the relationship of blood biomarkers related to adiposity (i.e. C-peptide, glucose), including their genetic determinants, with the risk of aggressive prostate cancer using individual participant data from the EHNBPCCG prostate cancer consortium and PRACTICAL. Finally, I will investigate how adiposity and its distribution (using phenotypic and genetic data) relate to other biomarkers previously linked to prostate cancer development (i.e. insulin-like growth factor-I, C-reactive protein, sex hormone-binding globulin, oestradiol, free testosterone) in UK Biobank.     How the results of this research will be used: Identification of adiposity or its distribution as the only modifiable risk factor to prevent aggressive prostate cancer should promote the development of clinically-based and public health interventions to tackle obesity. This study will inform which biological pathways should be examined in more detail to increase our understanding of this complex relationship, which may help to identify new targeted preventive interventions for men with excess adiposity and/or clinical prognostication in men with prostate cancer.


Research Activity Codes: 2.2 FACTORS RELATING TO PHYSICAL ENVIRONMENT
Health Categories: CANCER AND NEOPLASMS
Award Title: Understanding the impact of hypoxia dynamics on therapeutic response in breast cancer
Funder: Cancer Research UK

Abstract: Background: Hypoxia, resulting from an imbalance between oxygen delivery and consumption, is a common feature of most solid tumours that is clinically associated with therapeutic resistance. It is also a highly dynamic feature, with fluctuations recorded on time scales ranging from seconds to days. Current knowledge of the magnitude, timescale and spatial distribution of hypoxia dynamics is fundamentally limited by our inability to visualise hypoxia in vivo at adequate resolution and derive mechanistic insight into the relationship between haemoglobin oxygen delivery and tumour tissue hypoxia.

Aims:
Our aim is to better understand hypoxia dynamics in vivo and evaluate whether this knowledge can be used to aid precision therapy in breast cancer.

The workflow of the proposal runs through 4 objectives:
1)	Develop and validate photoacoustic imaging (PAI) assessment of hypoxia dynamics based on transient blood flow changes in vivo;
2)	Establish a computational framework for modelling hypoxia dynamics in silico;
3)	Test the relationship between the observed magnitudes and timescales of oxygenation changes and therapeutic response in vitro;
4)	Apply the integrated framework from objectives 1 – 3 to precision therapy in mouse models.

Methods:
Our multidisciplinary team brings together the required expertise of in vivo imaging, in silico modelling and in vitro cell culture studies to tackle the challenge of understanding the magnitude, timescale and spatial distribution of hypoxia dynamics and their impact on cancer therapeutic response. We propose to use observational data of tumour haemoglobin concentration and oxygenation dynamics measured using PAI to inform mathematical models that can reveal mechanistic causes of dynamic behavior and predict the resulting tissue oxygen saturation. Our predictions can then be tested and refined using in vitro and in vivo studies.

How the results of this research will be used:
The framework developed here will provide us with fundamental insight into the magnitudes, timescales and spatial distributions of hypoxia dynamics in breast cancer. It will also allow us to test the interaction of novel therapies with the hypoxic solid tumour microenvironment. The framework could, in the future, be applied to a range of other cancer models in which hypoxia is also known to be a key factor in therapeutic response, including head and neck, and colorectal cancers. Future work should examine how the identified PAI techniques could be practically adapted to clinical implementations of the modality with appropriate validation and how they might be integrated into the clinical pathway for improved patient management.


Research Activity Codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health Categories: CANCER AND NEOPLASMS
Award Title: Understanding the impact of immune tolerance within the liver microenvironment on efficacy of immune checkpoint inhibitors
Funder: Cancer Research UK

Abstract: Background  Patients with melanoma liver metastases have inferior response to checkpoint inhibitors (CPI) compared to those without, resulting in decreased survival.     Aim  To assess whether cross-talk between melanoma cells and cells within the liver microenvironment alters the milieu of inflammatory modulators/immune infiltrates and thereby efficacy of CPI.     Methods  To use reductionist in vitro models combining melanoma cells with liver stromal cells in parallel with in vivo studies and analysis of patient biopsies.    How the results of this research will be used  Data from these studies will provide rationale for drug development programmes or re-purposing of drugs to use in combination with checkpoint inhibitors in order to increase their efficacy in patients with liver metastases.


Research Activity Codes: 5.1 PHARMACEUTICALS
Health Categories: CANCER AND NEOPLASMS
Award Title: Understanding the impact of ‘near-coding’ variation in human disease
Funder: Wellcome Trust

Abstract: Rare diseases (prevalence <1/2000) collectively affect >250 million people globally. Current approaches for identifying the genetic cause of a disease focus on regions of the genome that code directly for protein, finding a disease-causing variant in only ~50% of cases.

My aim is to identify novel genetic variants outside of these protein-coding regions that lead to disease, and the mechanisms through which they do so.

I will do this by applying computational and statistical methods to pioneering large-scale genomic datasets, totalling ~700,000 individuals. 

My approach will focus on ‘near-coding’ regions, defined as those directly adjacent to protein-coding sequence that have important functions regulating protein expression. I will use complementary approaches that (a) look for near-coding variants in rare disease patients without a causative coding variant and (b) assess the strength of negative selection acting on categories of near-coding variants to predict which are deleterious and cause disease.

The findings from my work will be translated into clinical care to enable more patients to receive a valuable genetic diagnosis. In addition, increasing our understanding of the near-coding variant types that are deleterious will inform on genetic mechanisms underlying disease and suggest novel therapeutic strategies.

Lay Abstract: Over 250 million people globally have a rare disease. For these individuals, and their families, identifying the exact genetic variant that causes the disease has enormous value for diagnosis, screening and personalised treatment. This knowledge can also reveal the biology behind why the disease developed, suggesting new therapeutic strategies.

Current clinical screening approaches are limited in the regions of DNA they assess, only looking at those that directly encode proteins. These methods identify disease-causing variants in only ~50% of patients.

I aim to improve our understanding of genetic variants outside of these protein-coding regions to identify those that cause rare disease. I will do this by applying state-of-the-art statistical and computational approaches to large datasets of individuals with and without rare disease.

This work will inform clinical genetic testing guidelines, enable more patients to receive a valuable genetic diagnosis, and improve our understanding of the genetic mechanisms leading to disease.

Research Activity Codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health Categories: GENERIC HEALTH RELEVANCE
Award Title: Understanding the mechanisms how long noncoding RNAs control genome stability in cancer
Funder: Cancer Research UK

Abstract: Genome stability is paramount to cellular homeostasis throughout the human lifespan. Cells have developed several surveillance mechanisms to protect the genome from mutations and ensure faithful duplication and transmission of the genetic material. Defects in any of these mechanisms leads to genome instability, which drives cancer evolution and contributes to tumour heterogeneity, drug resistance and poor prognosis. Protein-mediated mechanisms controlling genome stability are well described, however, the biological and regulatory function of RNA-based mechanisms in this context, and in particular the contribution of long noncoding RNAs (lncRNAs), is largely unknown.  With an RNAi-based phenotypic screen, I identified novel lncRNAs linked to chromosome mis-segregation, a process common to different types of cancer. I will focus on two nuclear-localised lncRNAs whose expression is altered in renal cell carcinoma (RCC), and investigate the mechanisms through which lncRNAs regulate genome stability and their relevance to cancer.    

Aims   

(1) Functionally analyse the lncRNA loci (DUBR, PP7080) involved in chromosome segregation fidelity and determine the nature of chromosome segregation errors (CSE) in lncRNA-depleted cells;  

(2) Identify the mechanisms underlying the role of these lncRNAs in genome stability;  

(3) Determine whether lncRNA dysregulation contributes to genome instability in RCC.    

Methods  

Aim 1.   I will employ different loss-of- and gain-of-function methods to functionally analyse lncRNA loci and ascertain whether CSE in lncRNA-depleted cells arise from pre-mitotic and/or mitotic events. I will investigate the impact of lncRNA absence on aneuploidy (unbalanced karyotype) and genome instability by analyzing p53 activity, DNA damage response and chromosome alterations in normal diploid human cell lines.     

Aim 2.   I will use complementary systems-wide approaches to identify the role of lncRNA-regulatory networks in genome stability. I will profile the transcriptome of lncRNA-depleted cells and identify lncRNA-DNA and lncRNA-protein interactions. I will investigate the functions of lncRNA interactors through rescue experiments and determine long-term consequences of lncRNA depletion on genome stability.    

Aim 3.   I will test the role of DUBR and PP7080 in maintaining genome stability in normal renal cell lines, and translate in vitro findings into a mechanistic understanding how lncRNAs contribute to genome stability in RCC.    How the results of this research will be used    My research will provide fundamental mechanistic insights into the role of lncRNAs as important yet unexplored guardians of genome integrity. Understanding how defects in RNA-mediated mechanism contribute to genome instability in cancer will pave the way for novel RNA-based strategies to improve diagnosis and treatment of cancer patients.


Research Activity Codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health Categories: CANCER AND NEOPLASMS