The Settlement of Lesnoy, Belarus

First CORRECT Study of Minimal Residual Disease (MRD) Detection in Colorectal Cancer

CirrhoCare- Using Smart-phone Technology to Enhance Care and Access to Treatment for Cirrhosis

Cirrhosis, progressive scaring of the liver- has many causes, principally, excessive alcohol intake, fatty-liver and viral infections. Unlike many chronic diseases, cirrhosis deaths are increasing rapidly year-on-year. It is the third commonest cause of premature, UK working-age deaths, with 62,000 years of working-life lost each year and NHS care costs of £4.53bn annually. One quarter of all UK cirrhosis patients are at-risk of acute decompensation, whereby complications such as fluid-overload, confusion and infections arise, requiring hospital-emergency treatment. Currently, decompensated cirrhosis patients require regular hospital clinical assessments to detect these new complications. Even following hospital discharge, readmission with new decompensating complications approaches 37% in 4 weeks. This disease burden, compounded by increasing alcohol and obesity-driven liver disease, means demand for specialist liver services outweighs current capacity in a resource-stretched healthcare system. Moreover, regional variation of specialist liver services also impacts on illness and deaths, leading to a postcode lottery of care access and geographical inequity. The CirrhoCare trial, addresses this urgent clinical-need through an innovative cirrhosis management system, including home-monitoring of decompensated cirrhosis patients, measuring vital signs such as heart rate and blood pressure (using low cost, sensing technology), assessing weight (smart-scale) and mental ability (smartphone app), all of which are impacted as cirrhosis progresses. By efficiently and securely collecting data on CyberLiver's management-system (platform), CirrhoCare provides a decision-facilitating tool, incorporating individual-patient data, helping liver-physicians to optimise and personalise treatment in the community. The CirrhoCare trial investigators also plan to assess clinical and cost effectiveness of CirrhoCare management and seek regulatory approvals. This innovative aspect of cirrhosis management will be more acceptable and convenient for patients. It will also deliver community care with environmental, sustainable benefits, through reduced hospital visits, despite increasing service demands. The cost- effectiveness analysis will generate value-for-money evidence of CirrhoCare management, and the clinical evidence needed to inform future adoption into the NHS.

Study of Zanzalintinib (XL092) + Pembrolizumab vs Pembrolizumab in Subjects With PD-L1 Positive Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma

A Study of Ficlatuzumab in Combination With Cetuximab in Participants With Recurrent or Metastatic (R/M) HPV Negative Head and Neck Squamous Cell Carcinoma

This multicenter, randomized, double-blind, placebo-controlled Phase 3 study is designed to compare the efficacy and safety of two dose levels of ficlatuzumab combined with cetuximab (Arm 1 or Arm 2) to a control arm of placebo plus cetuximab (Arm 3) in participants with R/M human papilloma virus (HPV)-negative HNSCC. Eligible participants must have failed prior therapy with an anti-PD-1 [programmed cell death protein 1] or PD-L1 [programmed death ligand 1] immune checkpoint inhibitor (ICI) and with platinum-based chemotherapy, administered in combination or sequentially. Failure of prior treatment may be due to progression of disease or intolerance to treatment. It is anticipated that the study will enroll approximately 410 participants across 3 arms.

Relapsed Follicular Lymphoma Randomised Trial Against Standard ChemoTherapy

In the REFRACT trial patients with relapsed or refractory follicular lymphoma (rrFL) will be randomised (randomly allocated) to receive a new treatment (experimental treatment) or standard treatment which will be chosen by their doctor prior to entering the trial (called investigator choice standard therapy (ICT)). There are 3 treatment rounds which will happen one after another, testing 3 different experimental treatments. The experimental treatment in each round will be compared to ICT. ICT will be a choice of 1 of 5 standard treatment options including RCHOP, RCVP, lenalidomide and rituximab, bendamustine and rituximab or obinutuzumab and bendamustine. Patients in Round 1 (R1) will be randomised using a 1:1 allocation ratio (meaning patients have a 50/50 chance of receiving the experimental treatment). In Round 1 the experimental treatment is epcoritamab combined with lenalidomide. Patients randomised to epcoritamab and lenalidomide will receive up to 12 28-day cycles of therapy; epcoritamab will be delivered as a subcutaneous injection weekly for cycles 1 and 2 and on day 1 of cycles 3-12. Lenalidomide will be taken orally on days 1-21 of each cycle. Patients in Rounds 2 (R2) and 3 (R3) (experimental treatments yet to be determined) will be randomised using a 1:4 allocation ratio in favour of the experimental treatment (meaning patients are more likely to receive the experimental treatment). The study will recruit 284 patients with rrFL over 5 years. The aim is to identify new therapies which have better outcomes compared to ICT based on patients response to treatment (tested by PET scan) after 24 weeks of therapy. Following treatment patients will be followed up yearly until the end of the trial (up to 10 years).

A Trial of 5 Fraction Prostate SBRT Versus 5 Fraction Prostate and Pelvic Nodal SBRT

This study will look at the safety of curative radiotherapy to the prostate and lymph glands given in 5 visits, in men with high risk localised prostate cancer. The purpose of the research is to test an advanced type of external beam radiotherapy called stereotactic body radiotherapy (also known as SBRT) in 1128 participants with high risk localised prostate cancer (that is, prostate cancer that has not spread beyond the prostate gland but is at high risk of growing quickly or spreading). Importantly, this treatment delivers a potentially curative dose of radiotherapy in only 5 treatments over two weeks. Half the participants in the trial will receive radiotherapy to the prostate, the other half will have radiotherapy to the prostate as well as the surrounding lymph nodes. The investigators will follow patients in the trial for at least three and half years to see which treatment is best. The investigators will be looking at whether it is safe to give this treatment by reviewing any side-effects that occur and also assessing whether giving SBRT to the lymph nodes as well as the prostate reduces the chance of prostate cancer returning. The treatment will take place at NHS radiotherapy centres that are experienced in giving SBRT and radiotherapy to the pelvic nodes, and have been quality assured to deliver these treatments

Clinical and Immunogenetic Characterization of Giant Cell Arteritis (GCA) and Polymyalgia Rheumatica (PMR)

Giant cell arteritis (GCA), also known as temporal arteritis, is the most common form of primary systemic vasculitis, with up to 75,000 cases a year identified in the EU and US. It occurs almost exclusively in people over the age of 50 years and is considered to be a medical emergency. If not treated with high-dose glucocorticoids immediately, the thickening of the inflamed blood vessel wall can cause irreversible visual loss or stroke. GCA can lead to significant morbidity across a variety of systems, due to both the disease, and complications of treatment. Diagnosis may be confirmed with a temporal artery biopsy, imaging (e.g. USS/CT/MRA/PET-CR) or based on clinical signs (e.g. erythrocyte sedimentation rate) and symptoms (e.g. a new headache, jaw claudication, visual disturbances, temporal artery abnormality such as tenderness or decreased pulsation) . Polymyalgia rheumatica (PMR) is characterised by inflammatory limb-girdle pain with early morning stiffness, and a systemic inflammatory response demonstrated by elevated inflammatory markers. The UK GCA Consortium is a multi-centre observational study, the main arms of which recruit prospective (participants with suspected GCA) and retrospective cohorts (participants with confirmed GCA diagnosis). Analysis of data collected on these cohorts will help achieve the primary aim of finding genetic determinants of GCA and PMR susceptibility, in order to yield novel insights into disease pathogenesis. Secondary aims, and their associated analyses, are as follows: - Phenotype: characterising GCA and PMR subtypes, based on clinical features; imaging; cells; subcellular fractions and molecules in the circulation and/or arterial tissue; genetic/epigenetic/transcriptomic/proteomic or metabolomics factors, including next generation sequencing (whole exome sequencing) of selected cases. - Life impact: determining what aspects of the disease and treatments affect patients' quality of life, as assessed by patient-reported outcomes. - Long-term outcomes: characterising prognosis of GCA and PMR - both effects of the disease and its treatment - by longitudinal follow-up through electronic linkage to health records. - Exploratory analyses: exploring the potential role of environmental factors and co-morbidities on phenotype and outcomes. - Diagnosis, prognosis: improving diagnosis of GCA and PMR, and identifying factors that predict diagnosis, such as diagnostic clinical features, and prognostic and diagnostic biomarkers. - Disease activity: monitoring participants who commence a synthetic or biological disease-modifying anti-rheumatic drug (s/bDMARD). Finding a biomarker for GCA and PMR disease activity, which might be clinically useful in helping to optimise steroid and s/bDMARD treatments for individual patients.

Predicting Responsiveness in Oncology Patients Based on Host Response Evaluation During Anti Cancer Treatments

The goal of this research study is to develop an algorithm that predicts the patient's treatment outcome.This algorithm will serve as a tool for physicians when making treatment decisions, specifically for stage IV NSCLC and malignant melanoma patients receiving anti-cancer treatments. The investigators also aim to identify the metabolic pathways that could lead to better therapeutic options. The patients will be given their treatment according to the institute's standard of care. The patients will provide two blood samples and clinical data will be collected from their medical records. In the first part of the trial, the data obtained from the blood samples and the medical records of the patients will be used to develop the prediction algorithm, and in the second part of the trial, the algorithm will be validated by comparing the objective response rate of the patients to the theoretical response prediction of the algorithm.

Rituximab and Ibrutinib (RI) Versus Dexamethasone, Rituximab and Cyclophosphamide (DRC) as Initial Therapy for Waldenström's Macroglobulinaemia

MIDI (MR Imaging Abnormality Deep Learning Identification)

An automated strategy for identifying abnormalities in head scans could address the unmet clinical need for faster abnormality identification times, potentially allowing for early intervention to improve short- and long-term clinical outcomes. Radiologist shortages and increased demand for MRI scans lead to delays in reporting, particularly in the outpatient setting. Furthermore, there is a wide variation in the management of incidental findings (IFs) discovered in 'healthy volunteers.' The routine reporting of 'healthy volunteer' scans by a radiologist poses logistical and financial challenges. It would be valuable to devise automated strategies to reliably and accurately identify IFs, potentially reducing the number of scans requiring routine radiological review by up to 90%, thus increasing the feasibility of implementing a routine reporting strategy. Deep learning is a novel technique in computer science that automatically learns hierarchies of relevant features directly from the raw inputs (such as MRI or CT) using multi-layered neural networks. A deep learning algorithm will be trained on a large database of head MRI scans to recognize scans with abnormalities. This algorithm will be trained to classify a subset of these scans as normal or abnormal and then tested on an independent subset to determine its validity. If the tested neural network demonstrates high diagnostic accuracy, future research participants and patients may benefit, as not all institutions currently review their research scans for incidental findings and clinical scans may not be reported for weeks in some cases. In both research and clinical scenarios, an algorithm could rapidly identify abnormal pathology and prioritize scans for reporting. In summary, the aim is to develop a deep learning abnormality detection algorithm for use in both research and clinical settings.