In the last three decades, survival of radiotherapy (RT) patients has greatly improved due to technological advances in delivery of radiation to tumour volumes. However, in spite of improvements in delivery of RT, a significant number of patients still experience severe toxicity from radiation treatment, particularly when the treatment volume overlaps with organs at risk. It was recently established that ultra-high dose rate (UHDR), known as FLASH RT, leads to remarkable reduction of normal tissue toxicity while maintaining tumour control with respect to conventional dose-rate RT. This so called "FLASH effect" was demonstrated in vivo on different animal models and different organs by delivering the total amount of radiation dose in a very short time (usually <200 ms). FLASH RT could represent a paradigm shift in modern radiotherapy with significant benefits for cancer patients and healthcare providers. However, the complexity of this new technology and the limited understanding of the underpinning radiobiological mechanisms hamper clinical exploitation. Even though the literature demonstration of the FLASH effect is growing very rapidly, the published studies may lead to flawed interpretation of data due to lack of established dosimetry methods for this new radiotherapy modality. Dosimetry at UHDR is complicated and it is essential to understand the effects that impact detector response in this radiotherapy modality. Without a clear understanding of the fundamental dosimetry issues, there is potential for significant errors and misinterpretation of research results and trials. Accurate dosimetry is crucial for the safe implementation of any radiotherapy technique and ensures best practice and consistency of treatments across different radiotherapy centres. The full clinical exploitation and optimization of FLASH RT requires a multidisciplinary approach to best solve the multiple complex challenges this field faces. The major part of solving these challenges will be through the development of metrology in measurement of dose and dose-rate for FLASH radiotherapy. This will enable validation of treatment planning for FLASH RT, commissioning of the new UHDR delivery systems, demonstration of compliance with safety requirements and support accurate radiobiological investigations.

Telomeres are protein/DNA structures that protect the ends of eukaryotic chromosomes and are defective in essentially all incidents of cancer. Telomeric DNA is packaged into specialised chromatin fibres that require a distinct set of factors to be copied and reassembled each replication cycle, but how these processes take place is largely unknown. To address this question, we will combine an in vitro system for human DNA replication with a multidisciplinary experimental approach. Our work will address how the unique chromatin environment established at telomeres is maintained, providing molecular insights into the functions of proteins that drive cancer.

Worldwide kidney cancer causes over 140,000 deaths each year and its incidence of has markedly increased over the past 30 years. Despite advances in the treatment of kidney cancer many patients still have an unfavourable outlook. Cancers are caused by DNA mutations. Identifying these mutations is crucial for understanding what drives kidney cancer and devising new treatments. Studies of kidney cancer have, however, largely focused on mutations that cause changes in the proteins, without considering the remaining 98% of the cell's DNA. Some examples of cancer promoting mutations in this "non-coding DNA" have been identified but there has not been a comprehensive analysis for kidney cancer. To address this deficiency we shall therefor scrutinise the non-coding DNA cataloguing mutations and deciphering their functional consequences. This work should allow patient prognosis to be more accriyately defined and inform new targets for therapy.

Clinical trials are research studies involving patients or healthy volunteers which aim to test whether a new treatment is safe and 'better' than current treatment. Clinical trials are grouped into stages (or phases) and researchers aim to answer different questions at each of the phases. The earliest of these, Phase I, aims to test whether a treatment is safe, to investigate side effects and to and to recommend a dose of the treatment for further testing. Phase I trials involve small numbers of participants who may be healthy volunteers or patients. The first group of participants in a phase I trial are given a low dose of treatment and if shown to be safe and without many side effects, the next group of participants are given a higher dose. Further groups of participants are enrolled; with the dose, being raised for each successive group until a dose is reached that has too many negative side effects. The decision whether to give patients the same, a higher, or lower dose of treatment is carefully made before the treatment is given. These trials are called "dose-escalation" or "dose-finding" trials. Once a clinical trial is complete, the results are reported to the clinical research community and to the public. To make sure that these reports are reliable and helpful for further research, a set of guidelines of the important items to be reported, Consolidated Standards of Reporting Trials (or CONSORT for short) have been published. It sets out a standard way for authors to report how the trial is designed, analysed and interpreted and has been instrumental in promoting transparent reporting. The original CONSORT guidelines were developed for specific types of trials and their design features often differ from dose-finding trials. This is important as currently trial reports of Phase I trials often miss out important information about how they were designed and conducted, which can make it difficult for the reader to interpret and gauge the validity of the trials. This wastes time and resources, but more importantly, may unethically expose participants to ineffective or even harmful interventions. Making the best decisions in a Phase I trial, on whether to give a patient the same dose, or a higher or lower dose, is key to the success of the trial. The statistical methods used to guide these decisions have advanced significantly over the past 25 years, so that safer trials with more reliable results can be conducted. But these methods are often complex, and have additional transparency and reporting demands. To address these problems, we will develop an 'extension' to the CONSORT guidelines, which is relevant to all early phase dose-finding designs in different diseases. The main CONSORT Statement has now been extended in 7 other design areas, primarily in later phase trials (www.consort-statement.org/extensions). A CONSORT extension for dose-finding trials is long overdue. To develop an internationally agreed way to report such trials, we have brought together a multi-disciplinary, international team of experts in the design, running and reporting of early phase trials who work in academic institutions or pharmaceutical industry, and those with expertise in developing reporting guidelines of trials. Finally, to ensure that the developed CONSORT extension will be widely adopted, we will involve key groups throughout this research: the trial community, journal editors (who publish articles on dose-finding trials), peer reviewers (who assess research papers), regulators, patients and the public. We will publicise our outputs at relevant meetings and to local/national Patient and Pubic Involvement (PPI) groups, and will use social media to raise awareness. We will conduct practical workshops, highlighting common reporting flaws and how to use the new guideline. We will co-produce two lay papers with our PPI representative to effectively involve, engage and inform patients and the public about the importance of this work.

With the rapid advanced development in technologies that are accessible, wealth of biological and clinical data is generated in clinical trials; applying deep learning methods to integrate digital pathology features with omics data allow us fully to delineate these tumours and understand which molecular features may have contributed to the treatment response. The amount of multi-omics data generated provide valuable resource for trial methodologists to draw biological information and data to inform future trial designs. There will be continuous increased popularity of trials with pre-defined embedded correlative sciences, integral biomarkers, marker-enrichment designs, or marker-adaptive or treatment-adaptive designs. Randomised control arms allow for comparison of treatment arms without concern on confounding factors, a randomised study is not always feasible (or ideal), especially for rare cancer like sarcoma, or rare subgroup within breast cancer (e.g., basal-like tumour within estrogen receptor positive breast cancer) In such case, use of external controls to supplement single-arm data may be an attractive approach that can be explored. We are developing an artificial intelligence-based integrated genomics clinical tool, called COUNTERPOINT, which weave tumour genomics, microenvironment and clinicopathological data, to predict disease outcomes in breast cancer. This system is expandable to draw upon new biological findings with the goal to accelerate discovery of molecular features with potential for clinical implementation. For example, drug response/clinical outcome could be used to infer clinico-phenotypic associations by incorporating data from co-clinical PDX or organoid models in collaboration with cancer biologists (Dr Huang, Dr Sadanandam, Dr Perou (UNC at Chapel Hill)). Biological connectivity could then inform innovative trial designs e.g. by incorporating therapeutic-specific biological pathway impact scores/patterns as endpoints in biological-response adaptive designs, or by creation of matched synthetic controls for Bayesian trials, in line with the FDA's Real-World Evidence Program guidelines (Huang, Jones, Yap, Cheang, MRC/NIHR Rare Cancer Research platform). This PhD project will focus on review and apply agile software development methodology to create the roadmap of the creation of matched synthetic controls (based on biology) to design more efficient Bayesian trials based on exemplars from breast cancer (common) and sarcoma (rare cancer).