NIHRDataInsights/HRCSData huggingface dataset:
Title: Alcohol Health Alliance Advocacy Project
Funder: Cancer Research UK

Abstract: Background Alcohol causes significant harm: more than 70 people die every day across the UK due to alcohol. Alcohol is the leading risk factor for early death, ill-health and disability for 15-49-year-olds. Every day, 33 people are diagnosed with alcohol-related cancer. Alcohol also contributes to health inequalities, with people from deprived groups more likely to feel the adverse consequences. Reducing alcohol harm has never been more urgent, as deaths from alcohol rose to record levels in 2020. Aims This proposal seeks continued support for the Alcohol Health Alliance Advocacy Project. This work will be match-funded by the Institute of Alcohol Studies. The project involves advocacy to reduce and prevent alcohol harm. We are calling on the UK Government to introduce a comprehensive evidence-based alcohol strategy, which includes policies to reduce the affordability and restrict the promotion of alcohol. To secure new policies, we need the Government to recognise that effective alcohol policies are needed, workable and wanted. To achieve this, we will need increased awareness of alcohol harm and policy solutions amongst individuals (Parliamentarians and general population) and for them to increase pressure on Government to act. Intermediate outcomes we aim to bring about through advocacy activities therefore include: - Alcohol harm is raised in the media; - Constituents take AHA-led campaigner actions to talk to their MPs about alcohol harm; - Parliamentarians speak about alcohol harm in Parliament and take actions to make a difference. Methods The AHA’s strength lies in its members’ collective expertise and commitment. We bring together more than 60 organisations of different sizes, with different interests and different types of expert knowledge. We create a shared voice and ensure resources are pooled and activities coordinated. To achieve policy change, we show that policy action is needed, workable and wanted. Leveraging the unique strength of the AHA – our large and diverse membership – we: - Collect and generate data on alcohol harm and policy solutions and present these in engaging forms for different audiences; - Influence policy development by engaging with decision makers, responding to consultations and influencing legislative processes; - Work with Parliamentarians to raise alcohol harm and policy solutions in Parliament; - Strengthen and engage our campaigners’ network to contact their local MPs on alcohol issues; - Secure local and national media coverage and develop online content to increase awareness among the general public.


Research activity codes: 3.1 PRIMARY PREVENTION INTERVENTIONS TO MODIFY BEHAVIOURS OR PROMOTE WELL-BEING
Health categories: CANCER AND NEOPLASMS
Title: Exosome-based diagnostic module for early cancer detection
Funder: Cancer Research UK

Abstract: Early detection is crucial for successful cancer treatment and survival rates increase dramatically the earlier the cancer is detected. The biggest challenge with early detection is the lack of accurate markers of cancer and the devices to sensitively detect their presence. Recent studies show the potential of exosomes to act as accurate markers of cancer detection at its early stages. Exosomes are small, spherical structures secreted by all cell types in the body. They carry signatures specific to the parent cell, making them excellent markers for detecting the presence of cancerous cells. They are present in various bodily fluids even at the early stages of cancer but currently, there is no clinical device available to accurately detect their presence in patient samples for early cancer screening. We aim to develop a screening chip, the ‘CanScreen’, that can screen for and detect ovarian cancer at its early stages. CanScreen will achieve this by (1) size sorting and enriching the different sub-populations of exosomes followed by (2) capturing and detecting the presence of exosomes carrying ovarian cancer-specific signatures and finally (3) calculating the percentage of cancerous exosomes to the total exosome population. Isolation and size-sorting of target exosome sub-populations will be achieved by integrating nano-filters of various pore sizes within CanScreen. This will enable concentrating the target exosomes of interest that are present in minimal quantities, especially in the early stages of cancer and hence get a more accurate screening result. Downstream of the isolation, CanScreen has biomolecules that can capture the target (cancerous) exosomes and emit a fluorescent signal. This signal will be measured to detect the presence of early ovarian cancer. By calculating the ratio of cancerous to total exosome numbers, we can obtain an indirect measure of cancerous cell numbers and hence disease progression. In this project, the CanScreen technology will be validated using early ovarian cancer as the disease model which can then be extended to a multitude of other cancer types for early diagnosis. By establishing the fabrication protocol, it will then be straightforward to adapt the detection component to different markers specific to other cancer types.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;4.5 RESOURCES AND INFRASTRUCTURE (DETECTION)
Health categories: CANCER AND NEOPLASMS
Title: Implementation, delivery and perceived effectiveness of a peer intervention for smoke-free living and health improvement for people in custody
Funder: Cancer Research UK

Abstract: Background: Smokefree legislation was introduced in all Scottish prisons from 30th November 2018. Although initial implementation appears successful, building on the new legislation to help people in custody (prisoners) remain abstinent from smoking in the longer term presents new challenges and opportunities. Following the smoking ban, NHS Greater Glasgow and Clyde (GGC) are piloting a novel peer intervention for smokefree living and health improvement for people in custody. The intervention seeks to support both peer mentors and recipients of health-related activities to manage and decrease nicotine dependence in prison and make sustainable improvements to overall health and wellbeing, including long-term smoking abstinence. Peer mentors will have opportunities to develop life skills and qualifications which can strengthen individual and social capital in disadvantaged communities. The proposed study of this peer intervention will address gaps in evidence on integrated health promotion activities in institutions that have removed tobacco. The study builds on complementary studies on prison tobacco bans (NIHR funded) and e-cigarettes (CRUK TAG funded) in Scottish prisons, being conducted by joint lead applicants Ashley Brown and Kate Hunt, and co-applicant Linda Bauld, and existing close partnerships with GGC, and the Scottish Prison Service (SPS). Aims: The overall aim is to qualitatively explore the design, implementation, delivery and perceived effectiveness of the peer intervention, from the perspectives of people in custody and staff. Objectives are to: 1. investigate the design, implementation, delivery and perceived effectiveness of the peer intervention from the perspectives of people in custody and staff. 2. gather evidence to optimise ongoing delivery of the intervention in three prisons in GGC, and to support similar delivery in jurisdictions, in Scotland and elsewhere, seeking to implement peer-delivered health promotion initiatives in smokefree prisons. Methodology: One-to-one in-depth qualitative interviews with a purposely selected sample of people in custody and staff (N=27), and observations (N=9) of training of peer mentors and delivery of health-related activities as appropriate. The research team will explore the feasibility of conducting an analysis of routinely collected data to strengthen understanding of the intervention. Results: Emergent findings will be fed back to stakeholders on an ongoing basis to inform intervention delivery, and support early identification and rectification of problems. Evaluation of the intervention will inform the evidence base in the UK and internationally in respect of what can be done to fully capitalise on the potential of institutional smoking bans to support long-term health improvement and abstinence post-release.


Research activity codes: 3.1 PRIMARY PREVENTION INTERVENTIONS TO MODIFY BEHAVIOURS OR PROMOTE WELL-BEING
Health categories: CANCER AND NEOPLASMS
Title: Cancer signalling and therapeutics
Funder: Cancer Research UK

Abstract: Our lab integrates machine learning and high-throughput biochemistry to study how proteins selectively recognize their substrates, how this process is perturbed in cancer and how it can be hijacked to find highly selective and mutant-specific drugs to overcome drug resistance. Targeted therapies have significantly improved outcomes for patients and shifted the clinical and biological goal towards targeting evolutionary trajectories and overcoming resistance. To overcome these challenges, it is critical to repurpose existing cancer drugs and design news ones with higher selectivity, lower toxicity and less prone to resistance. In our lab we combine and develop technology ranging from peptide display, deep sequencing, machine learning, drug design and functional protein biochemistry with the long-term goal to make an impact in our understanding and treatment of cancer and drug resistance. Our previous studies have taught general principles in cellular signaling specificity, which we are now using to investigate unexplored cancer signaling, molecular recognition and epistasis, novel therapeutics and predict and overcome drug resistance.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;5.1 PHARMACEUTICALS
Health categories: CANCER AND NEOPLASMS
Title: LungHealth AI: Using AI to improve early detection and diagnosis of lung cancer
Funder: Cancer Research UK

Abstract: Background Lung cancer is the 3rd most common cancer in the UK, accounting for 13% of all new cancer cases. According to Cancer Research UK (CRUK), around 41,500 people are diagnosed with lung cancer in each year in the UK and almost 35,000 people die from the disease. Early lung cancer is largely asymptomatic, and patients usually live with this malignancy for many years before a clear clinical manifestation, although they often display non-specific symptoms closer to diagnosis. Low-dose CT screening can detect cancer early and identify patients for curative treatment. Ideally, all individuals would be screened for lung cancer, however it is not clinically pragmatic under the current public health circumstances and never or light smokers are excluded (despite accounting for 15% of lung cancers). This emphasizes the unmet need to identify the population at sufficient risk of disease to optimise the benefit/cost ratio, minimising the unnecessary investigations for those at low risk, but better identifying risk for never smokers (e.g. using medical history). Aims We think that if we can correctly identify risk groups in a non-invasive and cost-effective manner, we can save many lives, since early diagnosis can potentially lead to cure. However, current screening protocols are based on age and smoking history, which are fairly crude measures of lung cancer risk, or simplistic risk models that do not fully take account of previous medical history and fail to include never smokers. Our team aims to address this by developing a data-driven innovative AI solution to advance personalised risk assessment and stratification. Methods We will feed data routinely collected by the healthcare system into a new artificial intelligence (AI) based computer programming solution (not previously applied to medical problems) to discover “hidden-patterns” linking possible factors, measurements, symptoms and signs related to lung cancer. This will help us to identify with more certainty who are more likely to develop lung cancers at what age. This AI pattern recognition solution has met with a lot of success in non-medical industries, e.g. used in driverless cars, social networks and in entertainment and music industries. How the results of this research will be used We hope that such an AI solution would provide a new way (non-invasive, cost-effective and easy-to-use) to aid lung cancer early detection by identifying those at greatest risk. Such AI could be easily adapted to other types of cancers (e.g., Colon, Bowel, Pancreatic, etc.).


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS
Title: Exploiting the Immune System for Early Cancer Detection
Funder: Cancer Research UK

Abstract: Background Early stage tumours represent ~0.001% of the body’s volume. For such small tumours, blood-based biomarkers are diluted to levels low enough to fundamentally limit sensitivity. It is therefore crucial to explore biomarkers with the potential to overcome these limitations. The immune system is an exquisitely sensitive and highly specific in-built diagnostic system. Because T-cells recognise and respond to nascent tumours, if T-cell responses could be “read out” in pre-diagnosis blood samples, there is potential to exploit them as a powerful early warning system for cancer. This vision requires analysing TCR repertoires in longitudinal blood samples collected from individuals before they develop an incident cancer. Two large-scale longitudinal studies, the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) and Epidemiological Study of Familial Breast Cancer (EMBRACE), have blood samples collected over decade-long timescales in which thousands of incident cancers arise. These samples will enable us to explore the enormous potential of this idea for early cancer detection but proof of principle for high quality data needs to be established first. Aims Establish that pre-Dx samples from UKCTOCS and EMBRACE yield high quality TCR sequencing data. Proof-of-principle that a tissue bank of incident tumours and matched pre-Dx bloods can be established. Methods We will perform ultra-deep TCR-sequencing on genomic DNA from ~200 UKCTOCS and ~50 EMBRACE cancer-free control samples using a multiplexed PCR kit (Adaptive biotechnologies). This will generate between ~50,000-300,000 unique CDR3 sequences per sample. To assess the quality of the TCRb data generated and its suitability for longitudinal monitoring of immune response, we will use a statistical framework we have developed that can quantify levels of technical noise and identify sequences that exhibit interesting biological changes above levels of technical noise. We will also use Hospital Episodes Statistics (HES) data from both studies to link incident cancers to the hospitals where biopsies were taken to establish proof of principle for the development of a tissue bank with tumour samples and matched pre-diagnosis blood samples. How the results of this research will be used. Data from this pilot study will be used to argue that samples from both UKCTOCS and EMBRACE provide high quality TCR repertoire data. This data will then justify establishing a larger study where large numbers of pre-diagnosis blood samples are analysed alongside matched incident tumours. This larger study will be able to critically assess blood-based immune-repertoire biomarkers for early cancer detection.


Research activity codes: 4.5 RESOURCES AND INFRASTRUCTURE (DETECTION)
Health categories: CANCER AND NEOPLASMS
Title: Real world evidence and co-morbidity in lung cancer dataset
Funder: Cancer Research UK

Abstract: Lung cancer is the leading cause of cancer death in the UK. Only 19% of patients diagnosed with advanced stage disease survive for 1 year (5-year survival for stage VI patients is 3%). Immunotherapy has transformed the treatment of advanced lung cancer in the last 10 years and offers durable benefit to some patients. However, not all patients respond and there is an unmet need to understand the biomarkers and other factors that influence outcome which can be used to better individualise treatment in this setting. The CRUK Manchester Radnet programme focusses on the role underlying comorbidities, polypharmacy, and socioeconomic deprivation might play. It is well-known that patients from these sub-groups are under-represented in conventional clinical trials and as such the current evidence about immunotherapy is unlikely to extend to these complex groups that form a significant proportion of the lung cancer population in Greater Manchester. We have data from >400 advanced stage NSCLC patients considered for immunotherapy treatment (with complete PD-L1 biomarker analysis data) since 2017. These patients have the following data available in The Christie EHR: - Baseline patient characterises (including comorbidity burden) - Tumour staging and histology - Biomarker analyses (e.g., PDL-1, EGFR, ROS1, ALK, KRAS, BRAF, MET) - Longitudinal diagnostic and follow-up imaging data - Longitudinal routine patient blood scores - Chemotherapy, radiotherapy and biological therapy treatment details - Survival, relapse and treatment toxicity - Patient reported symptom burden and quality of life scores (longitudinal). The purpose of the DIA funding request is to make these real-world data assets as close to research (clinical trial) quality as possible and make them available for use by approved research partners. Subsequently, the potential uses of this dataset will be: Machine learning analyses to identify the complex interactions between patient comorbidity, polypharmacy and patient-reported outcomes vs immunotherapy efficacy and safety Use of AI approaches for imaging biomarkers predictive of response to immunotherapy Developing assisted decision making for patients in clinic to personalise and optimise treatment care plans and pathways


Research activity codes: 4.5 RESOURCES AND INFRASTRUCTURE (DETECTION)
Health categories: CANCER AND NEOPLASMS
Title: Quantifying immunity to high grade serous ovarian carcinoma for biomarkers for early detection
Funder: Cancer Research UK

Abstract: High-grade serous carcinoma (HGSC) has a very high mortality-to-incidence ratio. Early detection of HGSC is infrequent but low stage patients have significantly better outcomes. We discovered decidual-like (dl)NK cell subpopulations in chemo-naïve HGSC tumours using CyTOF mass cytometry. In cancer, dl-NK cells have pro-angiogenic and reduced cytotoxic functions. When co-cultured with HGSC cell lines, NK cells acquired CD9 by trogocytosis and decreased anti-tumour cytokine production and cytotoxicity. CD9 is highly expressed in HGSC cell lines, primary tumors and fallopian tube cells. CD9‐blocking antibodies restored the killing activity of CD9⁺‐NK‐92 cells. These findings identify a previously unrecognized mechanism of immune suppression in HGSC driven by CD9 trogocytosis. We will test the hypothesis that CD9 trogocytosis by NK cells can be exploited as a biomarker of early disease by (1) determining whether CD9‐NK cells can be detected in peripheral blood from advanced stage HGSC patients using CyTOF analysis (2) screening for other proteins that are trogocytosed by NK cells as potential biomarkers of cancer‐driven immune suppression for CyTOF characterization in patients and (3) use immunofluorescence to enumerate intra‐tumoral dl‐NK cells in matched tumour samples. These studies will establish feasibility for developing a sensitive and specific blood assay for early detection of HGSC.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS
Title: Cancer Research UK Clinical Trial Fellowship in Paediatric Oncology
Funder: Cancer Research UK

Abstract: My long-term career aspiration is to be a world leader in paediatric oncology, particularly in the field of sarcoma research and early phase clinical trials. Soft tissue and bony sarcomas continue to have some of the poorest outcomes of all paediatric malignancies, particularly for patients who present with metastatic disease. Early phase clinical trials are a vital component in developing new drugs to treat children with cancer and I want to be at the forefront of this fast-evolving area of paediatric oncology. This clinical trials fellowship will equip me with knowledge, experience and contacts necessary to be comfortable designing and running my own clinical trials. Through the extensive portfolio of trials at the Birmingham CTU, I will be able to work on a range of paediatric trials from those in development through to the analysis and publication of results. I am particularly interested in early phase clinical trials and sarcomas. I will combine my interest in both of these by working on two large multi-national trials (rEECUR and FaR-RMS) as well as the basket trial ESMART. I will combine my time at the Birmingham CTU with my role as an oncology registrar at Birmingham Children’s Hospital. The close working relationship between the two units will enable me to maximise my clinical and research opportunities through relationships already established over several years. My application fits well with the CRUK research strategy which aims to tackle cancers of unmet need including children’s cancer, particularly those which have low survival such as relapsed disease. I am already involved in the Experimental Cancer Medicine Centres network as a member of the steering committee for the Junior Investigators Network Group annual meeting. I will be able to use these contacts as well as those with various members of the Innovative Therapies for Children with Cancer consortium to further my experience and help me become a future clinical trials leader in paediatric oncology.


Research activity codes: 6.9 RESOURCES AND INFRASTRUCTURE (TREATMENT EVALUATION)
Health categories: CANCER AND NEOPLASMS