NIHRDataInsights/HRCSData huggingface dataset:
Title: Investigating Proton and Electron Flash in novel 3D cultures
Funder: Cancer Research UK

Abstract: Background: Every patient treated for cancer experiences side-effects. These can be short term or last over many years. As cancer becomes much more treatable, helping patients to live with and beyond cancer is increasingly important. In radiotherapy (RT) damage to normal tissue is responsible for most RT side effects. Some side-effects are relatively mild, whilst others can be seriously debilitating, leading to a significant loss in quality of life. Ultra high dose rate (FLASH) RT has the potential to provide a disruptive change in the way RT is delivered, by significantly reducing RT side-effects. Worldwide interest in the transformative potential of FLASH stems from experimental results which show remarkable reductions in normal tissue damage without compromising the tumour suppressing ability of the radiation. Aims: To Understand the mechanisms which underlie the tumour and normal tissue response to FLASH RT, so that these can be harnessed, to reduce side-effects and improve patient quality of life. Methods: At the moment the way in which FLASH works is largely unknown particularly over longer timeframes where biochemical and biological mechanisms dominate. A significant number of FLASH studies, to date, have been carried out either as live animal studies: mice, rats, mini-pigs or as veterinary studies (cats and dogs). A number of studies have also been carried out on living cells (in vitro) but this always raises questions as to whether these studies can adequately represent what is being observed in living creatures. By using novel 3D tissue cultures, prepared at Cambridge, which are derived from animals and humans, there is an unrivalled opportunity to study long term effects (up to 2 years), in a large number of samples comprised of normal and tumour cells. Using these novel 3D cultures, the need for animal experiments is reduced and this excellent model system enables high throughput experiments to be conducted. The CRUK RadNet FLASH infrastructure allows this proposal to access electron FLASH in Oxford and proton FLASH in Manchester and apply it to novel 3D tissue cultures from Cambridge. Although FLASH is thought to be agnostic of treatment modality, there are very few studies that compare different modalities and use tissue cultures to emulate the animal response, as envisaged in this study. Additionally, further insights into FLASH will be gained by varying the proton and electron FLASH delivery parameters and examining the biological response of the tumour and normal tissues.


Research activity codes: 5.5 RADIOTHERAPY AND OTHER NON-INVASIVE THERAPIES
Health categories: CANCER AND NEOPLASMS
Title: ReTHOMS: Real-time High-sensitivity Optrode Metabolic Sensor for Pancreatic Cyst Fluids
Funder: Cancer Research UK

Abstract: Background Pancreatic cancer has a very poor prognosis with a 5-year survival rate of 7%, due largely to lack of early detection. One potential route to improve outcomes is the identification and management of pancreatic cysts (usually detected incidentally via CT scan) that have the potential for malignant transformation, indicated by the presence of mucin. Conventional methods of mucin detection are only 60-70% accurate at detecting malignancy, which risks over-treatment and increased morbidity in typical at-risk populations. Sensitivity and specificity may be improved by detecting a wider range of biomarkers, for example metabolites indicative of the different metabolic rates of cancer and healthy tissue, and to use a more targeted and sensitive detection approach. Reactive oxygen species (ROS) and hydrogen peroxide (H2O2), are known to be involved in cancer development and experimental data have shown that cancer cells commonly exhibit increased levels of H2O2. One way to achieve the required specificity and sensitivity, particularly considering cysts produce very small amounts of fluid, could be to use electrogenerated chemiluminescence (ECL), an electrochemical reaction between a luminophore and the analyte leading to emission of light, implemented using optrodes, optical sensors with integrated electrodes for chemical transductance. ECL and optrodes have been demonstrated to sensitively detect H2O2 and more recently, mucin. Aims We will develop a new optrode sensing platform that sensitively detects H2O2 and mucin in fluids derived from a range of diverse cysts using electrogenerated chemiluminescence as analytical tool. Methods We will first build the optrode sensing set-up. We will then use luminol (in solution or electropolymerized film on the microelectrode surface) as a luminophore with the H2O2 as coreactant in dilution series for technical validation. For biological validation, we will directly measure produced H2O2 and mucin using cell cultures with different metabolic rates controlled by activation of KRAS genes to encourage tumour-like growth. How the results of this research will be used. The availability of cyst fluid as part of standard clinical procedure presents a unique opportunity to use optrode-based ECL sensing for H2O2 and mucin to improve early detection for patients undergoing cyst aspiration. The proof-of-principle device produced in this grant could later be tested in a clinical trial of real-time analysis of pancreatic cyst fluid, which could ultimately help clinicians decide whether to pursue more invasive testing or treatment. In future, the optrode could be miniaturised and integrated within an endoscope for real-time, in-vivo detection inside the pancreas.


Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS
Title: Harnessing liquid biopsy for early detection of paediatric cancer
Funder: Cancer Research UK

Abstract: Recurrence of the childhood high-risk childhood tumours, in particular brain and non-brain solid tumours (~400 cases/yr.) is the major cause of death in children with cancer. The developmental origin of paediatric tumours, their low mutational burden and epigenetically reprogrammed genomes makes either primary or relapse screening a major challenge. We currently sequence all children who relapse with solid tumours in the UK through CRUK Stratified Medicine Paediatrics (SMP - multiomic sequencing including whole-exome, targeted high-depth tumour and plasma capture panel sequencing, RNA-Seq, low-depth WGS) and enrol them onto available early phase experimental clinical trials. However, due to the rapidly progressive nature of these aggressive malignancies, approximately 50% of children who have actionable alterations and viable treatment options detected by CRUK-SMP die prior to study enrolment. We intend to make a major impact into reducing yearly deaths in children who relapse with cancer (of known aetiology and who have potentially already been evaluated in tumour and plasma sequencing), through deployment of plasma-based early screening for cancer relapse in children with aggressive solid tumours who are currently in remission. We propose to use multiplatform interrogation and mathematical modelling to drive forward early detection in this setting using combined testing of plasma sequence variants (SNV), copy number changes and fusions (lcWGS), methylation profiles and DNA fragment size mapping (ctDNA bisulfite sequencing and nucleosome mapping). We have validated initial feasibility of these platforms through CRUK-SMP (where we have assembled and sequenced a small cohort of relapsed tumour biopsy:plasma pairs) and together with external collaborators (Lambrechts, KU Leuven, and Cortes-Ciriano, EBI- Cambridge), and here request funding to drive forward ethically approved serial plasma collections (Anderson-GOSH, Angelini-RMH, PIs) in all children with solid tumours who are currently in remission, within GOSH and RMH cancer surveillance/follow-up clinics. Collectively these patients represent a significant proportion of childhood relapse cases in the UK and are an ideal cohort within which we could validate this approach.


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
Title: Functional profiling and computational models to better understand DDR processes and guide improvements in radiotherapy
Funder: Cancer Research UK

Abstract: Background: The propensity of deficient DNA response and repair components in cancer cells are exploited in radiotherapy treatments. Irradiation leads to damage of cellular molecules, the most prominent being breaks in nuclear DNA which can culminate in cell death or senescence if levels are above threshold of the cell’s capacity to repair these. The DNA Damage Response (DDR) is a dynamic reconfiguring of chromatin organisation and behaviour, driven by a complex cascade of proteins and post-translational modifications (PTMs), such as phosphorylation, that are activated upon DNA damage detection. Systematic approaches to experimental map and computational model signalling networks offers a powerful capability to gain a deeper mechanistic understanding of the regulatory processes that orchestrate DDR. This information can be exploited to guide improvements in radiotherapy treatments. Aims: We will systematically characterise the molecular changes that occur in DDR deficient cells and signalling events that are activated upon IR perturbation. High content proteomics datasets will be generated and integrated on a mechanistic network model of DDR, which will be deployed to conduct virtual screens and predict clinical responses. Through this work we will establish a computational platform that can integrate diverse experimental data and produce a valuable resource for the RadNet community to use. Methods: We will draw on our combined expertise in functional genomics, proteomics and computational biology, to produce a network model of DDR. Leading edge quantitative mass spectrometry will be applied to systematically characterise proteome and phosphoproteome changes across DDR deficient cell models, at baseline and upon IR perturbation. We will apply statistical approaches to derive robust molecular signatures for DDR deficiency states and to associate the changes to pathways, genes or perturbation states. These data will be embedded in an executable computational model of DDR. We will implement iterative cycles between experimental and computational components to train and validate a mechanistic model of DDR. How research will be used: By extending our understanding of DDR processes and how deficient genetic states respond to IR, we will be able enhance the therapeutic efficacy radiotherapy. Firstly, our data driven executable network model of DDR will be used to conduct virtual drug and CRISPR screens to identify new sensitising treatments. Secondly, we will further use these predictive models to identify radio-resistance mechanisms and to select biomarkers to monitor IR therapy. Thirdly, we incorporate information on patient backgrounds to accurately model clinical data and predict patient treatment outcomes.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;5.5 RADIOTHERAPY AND OTHER NON-INVASIVE THERAPIES
Health categories: CANCER AND NEOPLASMS
Title: Nanotechnology-enabled multi-omic liquid biopsies in non-small cell lung cancer
Funder: Cancer Research UK

Abstract: Background: Cancer early detection offers patients the greatest potential for cure. ‘Liquid-biopsies' have the potential to allow a non-invasive description of a patient's tumour and to detect the onset of cancer in phenotypically healthy individuals. However, the discovery of novel circulating biomarkers in the blood of patients with low tumour burden is hindered by the large dynamic range of blood components and the low concentration of tumour-specific molecules, which mandates the development of novel analytical platforms to overcome the critical 'signal-to-noise' hurdle. Several blood-circulating entities have been distinctly explored as indicators of cancers (i.e. proteins, nucleic acids, circulating tumour cells etc.) however, ongoing biomarker development efforts indicate that multiple markers are required to enhance sensitivity and specificity for early cancer detection. Aims: The main scope of this pilot project is to develop an integrative multi-omics blood analysis platform (the ‘NanoOmics’ Integrated Platform), based on a single nanoparticle-enabled plasma sample enrichment step, with the potential for further development into a multi-omics biomarker discovery tool for cancer early detection. There is no other available technology offering multi-omic blood analysis with a single extraction process. This is the ‘blood analysis gap’ we seek to fill. Methods: Nanoparticles will be used harvesting agents in order to simultaneously enrich disease-specific proteomic and genomic information from a single peripheral blood sample. To demonstrate the transformative power and broad utility of the NanoOmics Integrated Platform in multi-omic liquid biopsies, we will analyse clinical samples banked and collected within Manchester’s lung cancer screening programmes, and specifically plasma samples obtained from Non-Small Cell Lung Cancer (NSCLC) patients (COMPASS Study). How the results of this research will be used: The output of this project will be to develop, test and validate a proteogenomic integrative blood analysis platform that could facilitate the discovery of multi-omic biomarker panels, across a range of cancer early detection unmet clinical needs. Analysis of blood samples from the COMPASS study, used as an exemplar cohort in this pilot project, will generate proteogenomic datasets that will be studied further upon completion of this pilot study in order to: a) unveil novel disease-specific patterns in blood with the potential to be further developed into tumour-specific blood tests for early detection and/or minimal residual disease monitoring of NSCLC; and b) apply mathematical modelling to untangle underlying biological processes associated with lung cancer biomarker origins.


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: PANC-CYS-GAN: A Multimodal Longitudinal Generative Adversarial Network (GAN) to Discriminate High-risk Cysts for the Early Detection of Pancreatic Cancer
Funder: Cancer Research UK

Abstract: Background Pancreatic cancer (PC) is the deadliest common cancer. It is already the fourth leading cause of cancer death in Western countries. By 2030, it is projected to be the second leading cause of deaths due to cancer. Other common cancers such as those of breast or bowel have national screening programmes in the UK. These screening programmes can detect cancer early and potentially cure patients. Unfortunately, there is no screening programme for pancreatic cancer. Experts have not found answers on whom to test, what test to use and when the test, if available, should be used. On the other hand, pancreatic cysts: collections of fluid within pancreas are being increasingly diagnosed, for example during screening programmes for bowel cancer and those for enlarged blood vessels (aortic aneurysms). Some of these cysts may become pancreatic cancers. Aims Our group thinks that if we can correctly identify patients whose pancreatic cysts may become pancreatic cancers, we can save a large number of lives, since early diagnosis can potentially lead to cure. However, currently available routine tests cannot tell which cysts may become cancers with certainty. Methods We aim to feed in routinely collected information by doctors for pancreatic cysts to a new artificial intelligence (AI) based computer programming solution (not been previously applied to provide solutions to medical problems) to discover “hidden-patterns” linking possible factors, measurements, symptoms and signs related to pancreatic cancer. This will help us to identify with more certainty which pancreatic cysts are likely to become cancerous. This AI solution has met with a lot of success in non-medical industries. For example, used in driverless cars, and in entertainment and music industries. How the results of this research will be used We hope that such artificial intelligence solutions would provide a new method to detect cancers early and potentially lead to cure and save more lives.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS
Title: Deciphering the New Molecular Landscape in Paediatric Recurrent Ependymoma: Implications for Molecular Targeted Therapy
Funder: The Little Princess Trust

Abstract: Ependymoma (EPN) is the second most malignant paediatric brain tumour. The present clinical approach involves surgery and radiation, and for very young children chemotherapy. Despite adjuvant therapy, tumour recurrence occurs in about 50% of cases. Recurrent EPN is invariably fatal and poses a considerable challenge to clinical management with no well-defined treatment protocols or a clinical trial. Repeat surgical re-resection and re-irradiation may prolong survival in some patients, but is rarely curable. The number of active drugs for recurrent EPN is, unfortunately, very limited. The majority of research on the biology of EPN makes uses of tumour samples collected at the time of first surgery. There are very few genomic studies on recurrent EPN. In this project, we propose to comprehensively characterize the transcriptional and epigenetic landscape of a large, well-annotated cohort of recurrent EPN in an effort to better understand the molecular basis of this disease and identify new leads for therapy.

Lay abstract: Ependymoma is the second most malignant pediatric brain tumour. The primary treatment comprises surgery and radiotherapy and for very young children chemotherapy. In spite of these treatments, ependymoma comes back (recurrence) in about 50% of cases. Recurrent ependymoma is invariably fatal and can happen shortly or several years after the first treatment. Unfortunately, there is no standardized treatment strategy defined for recurrent ependymoma. Given that a significant number of children with ependymoma experience recurrences, it is critically important to understand the underlying biology of primary and recurrent disease in order to devise better treatments and minimise the risk of recurrence. The biology of recurrent ependymoma remains largely unknown and underlines the importance of this study. The overarching aim of this research is to identify molecular features that define recurrent ependymoma by capitalizing on the large number of recurrent tumour samples collected in Children’s Cancer and Leukaemia Group (CCLG) Centres in UK. To this end, we will use molecular profiles derived using powerful sequencing technologies to quantify differences between matched pre- and post treated tumour samples. This research will help to unravel novel molecular features so that we can find more effective treatment that will prolong survival of patients with recurrent ependymoma and in turn, hopefully by identifying more effective therapies reduce risk of recurrence.

Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: CANCER AND NEOPLASMS
Title: DARIUS: Dose Accumulation in Re-Irradiation Using Science
Funder: Cancer Research UK

Abstract: Background: Radiotherapy is a highly effective cancer treatment, which has seen immense advances in recent decades. That said, many patients ultimately experience disease recurrence, and these patients may be suitable for re-irradiation; that is, further radiotherapy in the same region or close to it. However, the clinical application of re-irradiation is currently limited as many clinicians lack confidence in how to deliver it safely. Concerns relate to cumulative normal tissue toxicities, which may result from overlap between former and re-irradiation radiotherapy doses and previous inability to meaningfully combine past and current radiation plans and techniques. By addressing these concerns, a major clinical need will be met, whereby patients will no longer be denied a potentially effective treatment. Aims: This project aims to demonstrate the feasibility assessing cumulative radiation doses for re-irradiation in the multi-centre setting, to correlate these with toxicity outcomes, and to create a multi-centre curated dataset for technical development of re-irradiation. Methods: The project will employ a recently developed methodology, in the form of a software tool incorporating deformable image registration (to take account of anatomical change) and fractionation correction (to allow anatomically and radiobiologically appropriate per-voxel assessment of cumulative doses). This tool will be used on a prospective multi-centre cohort of patients treated with re-irradiation within the NHS Commissioning through Evaluation (CtE) programme. By assessing anatomically appropriate and radiobiologically meaningful cumulative normal tissue doses, and combining them with prospectively collected toxicity data, we will produce preliminary guidance on safe cumulative normal tissue constraints for re-irradiation in the pelvis. Finally, in-silico simulation studies examining the impact of re-irradiation with Proton Beam Therapy (PBT) on cumulative normal tissue dose will produce pilot data to guide patient selection for future clinical trials of novel technologies for re-irradiation. How the results of this research will be used: This project will create a robust pathway that integrates the necessary science into cumulative dose assessment in re-irradiated patients. If this approach proves feasible, it may be extended to UK national trial cohorts and multi-centre cohorts treated with re-irradiation in routine clinical practice. Ultimately, this work will contribute to increased clinician confidence and, in turn, improve patient access to re-irradiation.


Research activity codes: 6.5 RADIOTHERAPY AND OTHER NON-INVASIVE THERAPIES
Health categories: CANCER AND NEOPLASMS
Title: Lozenges for Early Detection of Cancer
Funder: Cancer Research UK

Abstract: Background: Oral cancer affects around 12,000 people in the UK every year of which 95% arise from the squamous epithelium of oral mucosa, and are classified as head and neck squamous cell carcinoma (HNSCC). Currently reported 5-year survival rates of HNSCC are 28-67% because 62% of cases are diagnosed at stages 3-4. Early detection and treatment implementation are key for improving the HNSCC survival rates. Current state-of-art method for confirming pre-cancerous lesions is tissue biopsy, which is painful, requires experience practitioners, and is time consuming. Tissue biopsies can be avoided by measuring multiple biomarkers in saliva, but has been challenging because biomarkers are present at low levels in saliva. Aims: This proposal aims to address these challenges by developing lozenges for capturing and concentrating protein biomarkers in saliva, followed by their detection using fluorescence microplate readers and eventually mobile phones. Methods: The technology will be benchmarked against state-of-art method for measurement of proteins i.e., enzyme-linked immunosorbent assay (ELISA). The proposed technology can be adapted to measure any protein biomarkers in any biological fluids, and hence its potential applications are extensive with the power to be a transformative technology in a number of clinical pathways for early detection of cancer. This 12-month project will demonstrate the feasibility of the proposed technology using artificial and human saliva samples, which will be available via the Human Biomaterials Resource Centre (HBRC) at University of Birmingham. How the results of this research will be used: The results of this research will form the basis of subsequent clinical studies and translation of the technology to benefit cancer patients by early detection and diagnosis.


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