NIHRDataInsights/HRCSData huggingface dataset:
Title: Structural Biology of DNA Damage Response and Repair Mechanisms
Funder: Cancer Research UK

Abstract: Background Defects in genome stability lie at the heart of cancer, providing the critical activating mutations of oncogenes and/or loss-of-function mutations of tumour suppressors that initiate uncontrolled proliferation, as well as the genetic variation from which the developing tumour selects and adapts to evade host defences and escape therapeutic intervention. We wish to understand the structural basis for assembly, specificity and mechanism of the complexes that constitute the central systems of genome stability maintenance – the DNA Damage Response (DDR) – that have come to the fore as prime targets for therapeutic intervention. These studies provide the mechanistic insights essential to rationalising how failures in DDR systems contribute to carcinogenesis and understanding how they can be exploited as targets for novel cancer therapeutics Aims We will determine three-dimensional structures of complexes that mediate recognition and repair of DNA single- and double-strand breaks and gaps, and signal the presence of such damage to the cell. We will use these to identify key interactions that mediate complex formation and/or damage recognition, and characterize their functional roles in vitro and in cells in culture. Our studies will focus on: the PARP/XRCC1-scaffolded ssDNA gap recognition/repair complex and its interactions with damaged chromatin; the non-homologous end joining (NHEJ) repair complex and its interactions with DNA double-strand breaks; and the 9-1-1/TopBP1/ATR DNA damage checkpoint complex that signals the presence of extended ssDNA segments. We will also initiate gross in situ structural studies via correlative microscopy, of the ionizing radiation-induced foci (IRIFs) within which repair of DNA double-strand breaks occurs. Methods Complexes will be reconstructed using recombinant proteins purified from overexpressing insect cell or bacterial systems. Structures will be determined by single particle cryoelectron microscopy reconstruction and/or X-ray crystallography, supported by solution X-ray scattering and NMR spectroscopy. Functional studies in vitro will utilize biochemical/biophysical methodologies, such as electro-mobility shift assays, fluorescence polarization, and isothermal titration calorimetry, as well as single-molecule fluorescence techniques. Cellular studies will use selective knock-down/replacement approaches, with confocal fluorescence microscopy to characterize recruitment and co-localisation following DNA damage. Exploitation of Results Structural and mechanistic insights from these studies will greatly facilitate drug discovery targeted at DDR systems. Such drugs may have utility in cancer therapy as potentiators of existing genotoxic therapies (including radiation), and/or as synthetic-lethal therapies, exploiting a specific lack of system redundancy in genetically defined tumours with inherent DDR defects.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING
Health categories: CANCER AND NEOPLASMS
Title: Targeting the innate immune system in high grade serous ovarian cancer
Funder: Cancer Research UK

Abstract: Background High-grade serous ovarian cancer, HGSOC, is a major clinical problem. Although 75% of patients initially respond to chemotherapy, the overall 5-year survival rate is 40% reducing to 5% in patients diagnosed with Stage 4 disease. Patients usually present with disseminated peritoneal disease and relapse with increasingly chemo-resistant metastases. During the current programme, we have: • Established a unique platform of mouse and human 3D models that more accurately reflect the genetics, transcriptomics, and tumour microenvironment of human HGSOC, including relapse. • Studied the effects of chemotherapy and anti-IL-6 antibodies on the tumour microenvironment in HGSOC. Of particular interest, we found that reduction in tumour-associated macrophages (TAM) and/or phenotype is accompanied by an increase in T cells and/or T cell activation. • Completed a multi-level ‘deconstruction’ of the evolving HGSOC metastatic tumour microenvironment uncovering evidence for a prognostic matrisome response that may influence tumour immunity. Aims Our aim is to prevent relapse of HGSOC. We hypothesise that targeting cells and mediators of the innate immune system will enhance the ability of the adaptive immune system to destroy peritoneal metastases. We will: • Uncover mechanisms of reciprocal regulation of TAM and T cell populations in HGSOC tumours, as we believe this will lead to better ways of enhancing anti-tumour immune responses (Work Package 1). • Elucidate interactions between the matrisome and the host immune response (Work Package 2). • Use the results of this research to define effective treatments that will deliver long-lasting disease control and prevent relapse (Work Package 3). Methods Using our novel platform of mouse and human HGSOC models and clinical samples, we will conduct sequential and multi-level analyses of tumours as the tumour microenvironment is modified by chemotherapy or biological therapies, obtaining data on gene expression and proteomics, cytokine/chemokine levels, cellularity, extracellular matrix organization and biomechanics. We will integrate these results by univariate, multivariate and network analyses to answer our research questions. We will then conduct pre-clinical experiments in models of advanced HGSOC with the aim of eradicating peritoneal metastases and preventing relapse. How the results of the research will be used The key regulatory pathways discovered in this research will give us compelling data about best treatments and rational treatment combinations that can be tested pre-clinically before progressing to clinical trial in advanced HGSOC. The integrated tumour microenvironment data resources we generate will be publicaly available and relevant to research in other cancers.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: CANCER AND NEOPLASMS
Title: Defining metabolic mechanisms of drug resistance in colorectal cancer
Funder: Cancer Research UK

Abstract: Defining metabolic mechanisms of drug resistance in colorectal cancer Background: Recent transcriptional profiling of colorectal cancer (CRC) datasets has uncovered a KRAS mutant, metabolically altered subtype, the clinical significance of which remains unknown. CRC is commonly treated with anti-metabolite (5-Fluorouracil, 5FU) based single agent or combination therapy; however, heterogeneous responses and treatment failure are common due to inherent and acquired resistance mechanisms. By nature of its mechanism-of-action, 5FU significantly impacts a number of metabolic networks, and therefore sensitivity to 5FU is impacted by changes in cancer metabolism. Metabolic reprogramming is an accepted cancer hallmark, often driven by oncogenic signals. Mutant KRAS signalling rewires glycolytic, mitochondrial and fatty acid metabolism to support aberrant proliferation and tumour progression and often impacts sensitivity to therapy. Importantly, the significance of mutant KRAS-driven metabolic reprogramming in CRC is yet to be defined. Understanding the role of metabolic reprogramming in CRC has the potential to uncover more effective therapeutic approaches and improve outcomes. Aims: By profiling CRC metabolism in multiple model systems, the applicant aims to determine the role of metabolic reprogramming in colorectal tumour progression and define the networks critical for mediating resistance to 5FU-based chemotherapy and thus identify relevant metabolic pathways in genetically defined sub-groups, the targeting of which will overcome therapeutic resistance and improve patient response rates in advanced metastatic disease (stage IV CRC). Methods: Using a combination of clinical sample profiling and in vivo model systems, the applicant will generate a complete model of metabolic reprogramming in colorectal cancer. A “multi-‘Omics” approach combining RNAseq gene expression profiles and mass spectroscopy-based metabolomics analyses will be employed to identify metabolic pathways that mediate 5FU resistance, and using CRISPR screening, shRNA approaches and small molecule inhibitors, targetable metabolic axes that can overcome 5FU resistance and response will be identified. Output use: The generation of a metabolic map of CRC will define the role of metabolic reprogramming in CRC and identify metabolic biomarkers of response to 5FU-based therapy. Furthermore, by defining the metabolic programmes that promote 5FU resistance, the applicant will identify specific metabolic vulnerabilities that are targetable therapeutically using existing agents, and thereby identify novel therapeutic approaches to improve patient responses and disease outcomes in the advanced disease setting, where the 5-year survival rates are still less than 6%.


Research activity codes: 5.1 PHARMACEUTICALS;2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: CANCER AND NEOPLASMS
Title: Exploring the Relationship Between Antigen-Specific T Cells and the Tumour Microenvironment
Funder: Cancer Research UK

Abstract: Background: White blood cells, which are part of our immune system, are capable of seeking out and killing cancer cells. During the last decade, treatments have become available which boost the activity of these cells. Despite some promising results, these types of treatments, referred to as “immunotherapy” only work for a small proportion of cancer patients and only on certain types of cancer. One of the reasons for this limited success is due to the way cancers develop physical barriers which make them resistant to attack by white blood cells. In addition, cancer cells can partner with other cells of the body and cooperate with them to boost their own growth and spread. Our own studies have provided hints that white blood cells can be programmed to break down these barriers. As a result, the white cells become better at controlling growth of the cancer. Aims: This study sets out to 1) identify ways in white cells can break through cancer-created barriers and 2) determine whether empowering white blood cells with enhanced ability to breakdown these barriers, results in better control of cancer growth and spread. Methods: We will use mouse models of different cancer types including colorectal cancer, breast cancer and fibrosarcoma to examine different methods of activating the white cells of the immune system. Specifically, we will look at which methods generate the best anti-tumour effects and use microscopy to identify alterations to the cancer environment that are essential for enabling a successful immune response. We will then seek to empower white cells with the ability to effect these changes and examine their ability to destroy cancers. How the results will be used: The relevance of information gained in mouse models for human cancer will be tested using human white blood cells and cultures of human cancer cells already established in our lab. Our lab has an established track record of taking our lab-based findings through to early phase studies in patients with cancer, thus the findings of these experiments will be used to directly inform the design of novel immunotherapies.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: CANCER AND NEOPLASMS
Title: HLA-agnostic T-cell Targeting of Cancer
Funder: Wellcome Trust

Abstract: Adoptive transfer of patient T-cells expressing cancer-targeting chimeric antigen receptors (CARs) has achieved remarkable success with some soluble tumours. Unfortunately, CAR-T therapy cannot treat solid tumours. Contrastingly, tumour-infiltrating lymphocyte and checkpoint inhibitor therapies demonstrate that natural T-cells can eradicate end-stage solid cancers in some patients raising interest in engineering T-cells with T-cell receptors (TCRs) for “TCR-T” therapy. Conventional anticancer TCRs recognise endogenous proteinaceous antigens as short peptides presented by human leukocyte antigen class I (HLA-I) allowing killer T-cells to scan the internal proteome and eliminate cells bearing anomalies associated with cancerous transformation. Unfortunately, even the best conventional TCR approaches are only applicable in a minority of patients due to substantial variation in HLA across the population. The ultimate TCR-T therapy would bypass HLA-restriction to enable targeting of shared cancer antigens in all individuals. Our recent discoveries that some T-cells can recognise multiple types of cancer without the need for HLA could represent a major advance for immunotherapies. I want to understand the molecular mechanisms by which such ‘HLA-agnostic’ T-cells recognise cancer and utilise these remarkable cells to identify the cell surface changes that distinguish normal from cancerous cells. These potential cancer-biomarkers and cognate TCRs could underpin novel, broad-spectrum immunotherapies.

Lay abstract: Cancer immunotherapy was awarded the nobel Prize for Medicine in 2018 and is now a first line treatment for an increasing number of cancers. Cancer immunotherapy can result in rapid, complete and durable remission of disease (cure) in some patients. The key to these successes lies in the immune system’s T-cells which can ‘seek and destroy’ cancer cells with remarkable sensitivity and accuracy. We have been studying patients who were cured of end-stage cancer by immunotherapy and found that they have T-cells in their blood that can kill their own cancer line, cancer from other patients and other cancer types using mechanisms new to science. I will dissect and understand how these T-cells distinguish between cancer cells and healthy cells. These studies will be used to generate new cancer biomarkers and treatments.

Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: CANCER AND NEOPLASMS
Title: Systemic and local influences of cancer on myeloid cell biology promote tumor malignancy.
Funder: Cancer Research UK

Abstract: Background Cancers comprise of mutated malignant cells and a plethora of normal diploid cell-types. Immune-cells, including macrophages, are often abundant in the tumour-niche. We have shown that tumour-associated-macrophages (TAMs) promote tumour progression and metastasis in mouse models of breast cancer. These TAMs originate from circulating monocytes that in themselves play a role in promoting metastasis. Importantly, monocytes gain an immunosuppressive phenotype as they differentiate and this contributes to the survival of tumour-cells. Our studies in human endometrial- and breast-cancer patients demonstrated that monocytes and TAMs have altered transcriptional profiles compared to analogous cells in healthy people. We believe that monocytes “sense” the presence of cancer and that their progeny TAMs uniquely reflect the cancer environment and that these myeloid cells enhance malignancy. Aims The central hypothesis is that cancers cause polarization of the myeloid cell compartment both systemically and locally in a manner that promotes tumour progression and metastasis. We also hypothesize these cells inhibit effective immunotherapy. We aim to dissect the mechanism(s) behind changes in mononuclear phagocytic biology and propose that this may open new therapeutic opportunities. 1. We hypothesise that monocytes act as sensor cells and this causes changes in their epigenetic landscape and binding of specific transcription-factors (TFs). Identifying these changes will result in genetic experiments in mice to determine the function of specific TFs. 2. We will determine how cancers alter haematopoiesis to enhance myelopoiesis through examination of this process in the bone marrow, spleen and blood. 3. We will test whether human TAMs act in a similar fashion to mouse TAMs to promote angiogenesis, tumour invasion and immune evasion. Methods We will use innovative methods to dissect the epigenetic landscape of monocytes from breast cancer patients and healthy controls. These will include ATAC-seq, ChlP-seq and Gro-Seq as well as advanced bioinformatics to identify TF binding. To determine function conditional genetics will be performed in mouse models of breast-cancer using myeloid specific cre recombinase strains. To measure myelopoiesis we will enumerate progenitors by FACS and determine their output using colony assays. To study human TAM function, we have developed new assays exploiting induced-pluripotent-stem-cell-derived macrophages differentiated in tumor cell conditioned media. How results will be used Our results will indicate novel roles of monocytes and TAMs in human breast cancer. They will suggest means to therapeutically target sub-population of TAMs to promote immunotherapy and to sustain effects of radio- or chemo-therapy. Outputs will be widely disseminated.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: CANCER AND NEOPLASMS
Title: MECCA: Metastatic Evolutionary dynamics of Colorectal CAncer
Funder: Cancer Research UK

Abstract: BACKGROUND People with metastatic colorectal cancer (mCRC) are unlikely to survive for 5 years following their diagnosis. Metastasis is an evolutionary process, but the lack of understanding of the dynamics and drivers of mCRC evolution hinders efforts to better prognose and treat the disease. Our work over the past decade has delineated evolutionary dynamics in primary CRC (pCRC), identifying key driving roles for copy number and epigenetic alterations, plasticity and tumour-immune interactions. It is unknown how these processes alter when cells metastasise to other organs. AIMS We aim to determine the evolutionary dynamics of CRC metastasis, and how these dynamics are governed by molecular changes within tumour cells and interactions with the microenvironment. Specifically: 1. Determine the evolutionary paths and tumour cell-intrinsic molecular mechanisms controlling CRC metastasis. 2. Quantitatively explore the role of the changing tumour microenvironment, and its influence on tumour cell plasticity, in metastasis. 3. Determine the translational value of measures of metastasis evolution via interrogation of existing very large datasets. METHODS We have assembled a large cohort of CRC metastasis samples and matched primaries that represent the diversity of clinical groups. We will pursue an interdisciplinary approach led by mathematical modelling to decipher the multi-omic data we will generate from these samples. Specifically: timing, clonality and routes of metastatic dissemination will be inferred using phylogenetic methods applied to genome sequencing, and mathematical modelling will be used to detect evidence of ongoing selection. We will uncover epi(genetic) mechanisms that dictate gene expression in metastases, in a tissue-specific, disease course and selection-associated manner. We will learn the dynamics of tumour-microenvironment (TME) interactions by creating stochastic models of the TME, followed by parameterisation and refinement of the models by fitting to high dimensional spatial multiplexed imaging data. We will detect plasticity by comparing genotype-phenotype data to expectations from mathematical modelling. We will use existing molecular and clinical data assembled from ~5000 CRCs to determine the translational relevance of measures of metastasis evolution. HOW THE RESULTS OF THIS RESEARCH WILL BE USED Our mathematics-led analysis will enable measurement of the dynamics of metastatic spread, tumour-microenvironment interactions and tumour cell plasticity directly from patient samples. The large repository of multi-omic data generated will become a disease-defining dataset. Knowledge of the role of the epigenome and tumour-microenvironment interactions driving CRC metastasis has the potential to open up new therapeutic opportunities and allow early identification of high-risk pCRCs.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: CANCER AND NEOPLASMS
Title: Adaptive Data-driven Radiation Oncology
Funder: Cancer Research UK

Abstract: Background The strong trend towards the implementation of Stereotactic Body Radiotherapy (SBRT) and hypo-fractionated treatments for most common localized cancer types makes the control of geometrical uncertainties a central issue for the safe and successful practice of modern radiotherapy (RT). This geometrical accuracy becomes even more important for reliable targeting of smaller biological tumour sub-volumes in which substantially increased radiation boost doses are delivered. Aims Our research will use advances in physics, engineering and computer science to develop innovative dose delivery and patient imaging technologies aiming to provide the highest quality of precision RT treatments. Furthermore, we will use Big-Data analytics to derive personalized RT strategies aiming to predict local recurrences and potential RT side-effects by correlating diagnostic and patient data acquired during treatment with clinical outcomes. Methods Our research is organized in three mutually connected work packages: 1. Development of real-time adapted therapy We will focus on the further development of real-time adapted RT aiming to improve the geometrical accuracy of RT treatments. This includes research on imaging devices for dose delivery with C-arm linacs based on novel ultrasound technologies, 3D CT-imaging during therapy for robotic dose delivery and the design of a software platform to manage the real-time adaptive workflow. 2. Evolution of MRI guided radiotherapy (MRgRT) We will develop novel adaptive dose delivery techniques with the MR-Linac platform like helical dose delivery for extended treatment fields and tumour tracking to mitigate the deteriorating effects of organ motion on high precision RT. Furthermore, we will focus on exploiting the potential of high-field MRgRT at 1.5 T through the development of functional MRI to enable local dose intensification and therapy response monitoring. 3. Predictive modelling of patient data for personalized radiotherapy First, we will focus our modelling activities towards stratification of individual patients by developing machine learning (ML) and artificial intelligence (AI) models to improve patient RT by exploiting the vast amount of high quality clinical trials data at ICR/RMH. Second, we will use state-of-the-art image analysis techniques like ‘radiomics’ to monitor and predict treatment response of MRgRT. How the results will be used Our strategy is to exploit the outlined research in physics and data science for the benefit of patients via implementation in clinical studies.


Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES;5.5 RADIOTHERAPY AND OTHER NON-INVASIVE THERAPIES
Health categories: CANCER AND NEOPLASMS