NIHRDataInsights/HRCSData huggingface dataset:
Title: TOPICAL: The impact of air pollution upon cancer initiation in the lung
Funder: Cancer Research UK

Abstract: Background Whilst low-dose CT screening has advanced the early detection of lung cancer in individuals with a history of cigarette smoking, there is no early diagnosis strategy for lung cancer in never-smokers (LCINS), the 7th leading cause of cancer death worldwide. There is therefore a need to understand the underlying biology of early disease in LCINS to develop methods for early detection. Almost one third of LCINS patients harbour driver mutations in EGFR (EGFRmut). A risk factor for lung cancer is ambient air pollution, including particulate matter with a diameter less than 2.5 µm (PM2.5). This project investigates microenvironmental remodelling and epigenetic reprogramming of EGFRmut tumour initiating cells in response to pollution to uncover the mechanisms underpinning epidemiological associations between pollution and LCINS. Aims 1. To determine the influence of immunity and stromal remodelling upon pollution-induced EGFRmut clonal expansion 2. Defining pollution-induced epigenetic reprogramming of EGFRmut tumour initiating cells Outcomes Our project will define cell intrinsic and extrinsic mechanisms by which PM triggers the initiation of tumourigenesis in vivo. Future work will combine mechanistic insights and patient data to lay the groundwork for the development of interception and early detection strategies in LCINS.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: CANCER AND NEOPLASMS
Title: Early detection of gastric cancer by the identification of functional markers driving pre-malignant alterations of the tissue.
Funder: Cancer Research UK

Abstract: Background Atrophic gastritis and intestinal metaplasia are conditions of the stomach at high risk to develop into adenocarcinoma. They are often found accidentally, suggesting that some cases remain undiagnosed. To detect these gastric pre-cancerous conditions, patients are undergoing an endoscopic examination where tissue biopsies are taken and analysed by a pathologist. Atrophic gastritis is characterised by a decreased thickness of the mucosa and by a reduced number of cells involved in digestive functions. The deep invaginations, called glands in the stomach, become shorter in atrophic gastritis. Intestinal metaplasia is instead characterised by the appearance of cells producing an intestinal type of mucus. These tissue features are analysed and quantified in different biopsies from different areas of the stomach to determine a score that is predictive for the onset of cancer. Tissue shape depends on a set of local molecular instructions that are driving cells to regenerate, differentiate, proliferate or re-program in the case of metaplasia. The signals affecting tissue shape are called “morphogens”. We have recently discovered that EGF, BMP and Noggin are morphogens in healthy human gastric glands. As the shape and cell composition of the glands change during disease progression we hypothesize that morphogens change their expression too. The detection of a specific combination of morphogens can be used as surrogate biomarkers to define atrophic gastritis and intestinal metaplasia. Aims We aim to map morphogens, receptors and their signalling related proteins in the stomach by using spatial transcriptomics. We aim to find a combination of morphogen signalling biomarkers that is unique for atrophic gastritis or for intestinal metaplasia. Methods We are using spatial transcriptomics to obtain a map of gene expression in tissue from healthy, and precancerous conditions of the stomach. To test the function of the activated morphogen pathways we are using our recently develop mucosoid cultures. In this new stem-cell driven culture system, epithelial cells can regenerate, proliferate and differentiate upon stimulation. Recombinant morphogens and their pathway inhibitors can be tested by adding them to the mucosoid cultivation cocktail. How the results of this research will be used This preliminary study promises to provide new insights into tissue dynamics in gastric carcinogenesis and early detection. Our plan is to validate our new set of biomarkers by using less invasive methodologies to harvest cells in the stomach or by exploring the presence of related antibodies in the blood of patients.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS
Title: PRECISION - supplementary award
Funder: Cancer Research UK

Abstract: Background Ductal carcinoma in situ (DCIS) now represents 20-25% of all breast neoplasia. This is due to large-scale detection by widely adopted population-based breast cancer screening programs . As a result, thousands of women are confronted with DCIS each year: more than 8,000 in the UK, 2,300 in the Netherlands, and over 50,000 in the US. Conventional management includes surgery, supplemented by radiotherapy and/or endocrine therapy, but overtreats the majority of DCIS as ~1% recur annually and breast cancer mortality is ~3% at 20 years. Uncertainty as to which DCIS lesions will progress to invasive cancer or, after excision, which will return with recurrent DCIS or invasive breast cancer drives this overtreatment. Distinguishing DCIS that may progress to lethal disease from the majority of harmless DCIS is therefore an urgent need to save thousands of women with low risk DCIS the burden of radical treatment without any survival benefit. Aim We aim to reduce the burden of overtreatment of DCIS (surgery, radiation therapy, hormonal therapies) through the development of novel tests that promote informed and shared decision-making between patients and clinicians, without compromising the excellent outcomes for DCIS management presently achieved. Methods First, three large retrospective DCIS cohorts (and supplementary sources) will be collected enabling subsequent in depth molecular studies. Second, extensive genomic characterisation, immune profiling and imaging analysis will be performed. In vivo and in vitro modelling will be performed to study the biology of DCIS in detail. Finally, all clinical, immune, and molecular data will be incorporated into a clinical risk prediction model. This risk prediction model can be validated in three prospective DCIS trials. Specifically, in the current project we will a.) collect tissue and blood samples from these trials and b.) start validation of the biomarkers detected in the retrospective series. How the results of this research will be used The discoveries from our laboratory studies, including a risk stratification model, will be cross-validated in prospective trials of DCIS active surveillance versus conventional treatment (the LORIS, LORD and COMET trials). As such, the main result of this study will be that we can identify a group of women for which active surveillance for DCIS could be a safer alternative to intensive treatment. Ultimately, this may also contribute to a more reassuring perception of risk regarding non-life threatening precancerous lesions in general, reducing anxiety and preserving quality of life.


Research activity codes: 2.6 RESOURCES AND INFRASTRUCTURE (AETIOLOGY);4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS
Title: Genomic signatures in cell-free DNA and their role in early detection of cancer
Funder: Cancer Research UK

Abstract: Cancer can be treated with curative intent when diagnosed in its early stages. Attention has focussed increasingly on the use of circulating tumour DNA (ctDNA). Work by our group and others has pushed the boundaries of information that can be obtained by analysing cell-free DNA. Detection accuracy depends on the burden of disease, with 1 tumour genome present per 5 ml of plasma for patients with early stage disease. To mitigate this, recent efforts attempt to improve detection by collecting information across multiple genomic loci. Research groups including our own have developed methods that leverage low-depth whole genome sequencing (WGS) and use patterns such as copy number changes, differences in genomic coverage, and features of DNA fragmentation (“fragmentomics”) to develop machine learning (ML) classification models. In this primer project, we aim to improve performance of classification models based on WGS data by adding information from mutation signature patterns: groups of mutations associated with cancer aetiology, risk factors and exposures. We propose to investigate mutation signatures in ctDNA using a combination of available data novel data we will generate. We will seek to identify patterns related to mutation signatures in low-depth (0.4x) data we have previously generated across hundreds of samples; public datasets at ~1x coverage; and additional WGS data we will generate at deeper coverage (~15x) through this award. We will develop algorithms to reduce background noise and extract mutational signature information. This will be used to develop ML classifiers to detect cancer DNA. Classifiers will be tested in cross-validation and on held-back data, and will be combined with other metrics we previously developed to quantify copy-number changes and fragmentation patterns in cell-free DNA. Based on learnings, we will generate a further preliminary validation dataset from approx. 100 samples, focussing on plasma samples from patients with early-stage cancer. If successful, these results can be used in other Early Detection programmes and may offer opportunities for expansion or validation as a future project proposal. In addition to improvements to ctDNA detection, this proposed project aims to support training and development of bioinformatic skills. The named researcher has a background in physics, and the primer award proposal intends to offer her an opportunity to improve her skills in machine learning and to transition into computational cancer genomics. This application therefore addresses key gaps in methods and skills that are essential for effective development and future research in early detection of cancer.


Research activity codes: 4.5 RESOURCES AND INFRASTRUCTURE (DETECTION)
Health categories: CANCER AND NEOPLASMS
Title: A label-free and real-time Graphene bioSensor for exosome-driven point-of-care detection of early CANcers (Gr-SensorCAN)
Funder: Cancer Research UK

Abstract: Background Early detection of cancers has been identified as a key global strategic challenge. Though widely used, imaging approaches such as CT and MRI are costly and not ideal for large population screening. Liquid biopsies have the potential to detect, characterize, and monitor cancers earlier than is possible with tissue biopsy-based conventional approaches. Exosomes, double layered tiny (30-200 nanometers) spherical vesicles released by cells in the circulating blood, have proved to be an exciting potential target for liquid biopsy. Tumour-derived exosomes, facilitating tumour growth and metastasis, transporting nucleic acids and proteins from tumour cells to neighboring and distant cells, are believed to be more abundant and directed non-invasive cancer biomarkers sources than circulating tumor cells and circulating cell-free DNA for early detection, due to rich information about tumour cells and high concentration in blood. Biosensors for exosome-derived detection are promising for point-of-care testing of cancers because of their low-cost and fast detection. Graphene as one of the most promising nanomaterials, exhibits attractive features such as great tunability, high mobility and optical transparency. For biosensors, graphene can be used to identify and capture biomarkers present in serum, saliva or tissues for collecting information on the state and stage of a disease. Therefore, graphene biosensor’s sensitivity for biomarker detection could lead to earlier diagnosis and better prognosis. Aims Based on initial work on graphene sensor using field effect transistor (gFET), we propose to develop miniaturized chip-based sensors with electrical signal readout, as a universal technology platform for clinical cancer diagnosis through rapid monitoring of valid proteins on cancer-derived exosomes. Our detection technology can offer accurate, ultrasensitive, selective, and label-free detection of cancerous exosome biomarkers. Methods Graphene sensor arrays will be designed and manufactured using thin film and cleanroom facilities. Sensors will then be bio-functionalized with linker chemistry to attach bio-specific antibodies for antigen capture and recognition. Microfluidic channels will be integrated onto the chip to form a sensing system for real time and label-free detection. Pancreatic cancer cell derived exosomes in buffer will be quantified using gFET sensors. Sensitivity, specificity, and detection limits will be investigated for optimum performance. Blood samples will be collected from various stages of pancreatic cancer patients and characterized using gFET sensors. Use of results Results will be published throughout referred journals. The data gained will be made available in a safe and feasible way. A patent will be filed, and possible spin-off opportunities will be explored.


Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS
Title: Extracellular Vesicle Glycoproteins for Prostate Cancer Diagnosis (EVGlyProD)
Funder: Cancer Research UK

Abstract: Background: Prostate cancer (PCa) is the second most common cancer in the UK, with ~48,500 cases diagnosed annually. Many patients have slow growing, clinically insignificant, tumours. Some men, however, present clinical symptoms only when the disease is clinically significant, requiring urgent treatment. A lack in sensitivity and specificity of current diagnostic tests results in under diagnosis of men with clinically significant disease and overtreatment of men who will not benefit from therapy. Whilst technological advancements have resulted in an increased use of magnetic resonance imaging, a lack of molecular information can necessitate the need for biopsy and subsequent histological grading (Gleason score). This is invasive, with associated risks, and even when combined with the standard blood test (PSA) lacks absolute reliability for detection of clinically significant disease. Better blood-based tests are therefore required to facilitate diagnosis of clinically significant PCa. Aims: There is much interest in the use of extracellular vesicles (EVs) as cancer biomarkers, and elevated levels of EVs reported in serum and urine from PCa patients. We have shown that glycoproteins, present on the EV surface, promote tumour growth in vivo. But the potential of EV-glycoproteins as PCa biomarkers remains unclear. Here, we aim to develop a plate-based assay for detection of EV-glycoproteins, capable of distinguishing between patients with clinically insignificant or clinically significant disease, limiting the need for invasive biopsy. Methods: We will refine our current assay concept by incorporation of lectin-nanoparticle conjugates to detect EV-glycoproteins in patient serum. We will compare 15 different lectin nanoparticle conjugates, with previously selected antibodies, for discrimination between patients with PCa or benign disease. From this we develop a streamlined multiplex plate-based assay for PCa EV detection. We will assess EV detection in archived serum samples from patients with low, intermediate, or high-risk localised PCa. Finally, we will test whether our assay can differentiate patients with clinically insignificant disease from those with clinically significant PCa using a prospective sample cohort for assay validation. Future use of results: Data generated will provide conceptual proof required for future funding applications to continue assay development. Furthermore, establishment of local sample collection will be a key asset for this. Utilising established collaborations with industry we have a route to potential assay commercialisation. This proposal presents the first use of EV-glycoproteins for diagnosis of clinically significant PCa, with potential to improve the lives of thousands of men by limiting the need for invasive biopsy.


Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS
Title: Skin Cancer and Ageing
Funder: Cancer Research UK

Abstract: Cancer is more common in elderly patients and melanoma incidence continues to rise predominantly in this population. More than 80% of melanoma deaths occur in patients who are older than 50 years of age, and mortality is specifically increasing in the elderly. Older patients are more likely to suffer from multiple melanocytic and non-melanocytic skin cancers. Melanomas in the elderly more frequently present as aggressive primary lesions, thick primary tumours that predominantly comprise the high-risk primary melanoma stages (Stage IIB-IIC). The overall survival for stage IIB-IIC patients of all ages at 5 years is 60% and 45%, respectively, despite being localised to the skin and non-metastatic at diagnosis. There is a gradual decline in 5-year survival with increasing decades of life, with an almost 20% decrease from ages 60 to 69, to ages greater than 80 years. Additional characteristics of poor prognosis, such as ulceration and elevated mitotic rate, are also more common in the elderly. However, even after taking the main prognostic factors into account there is a survival discrepancy between elderly and young patients, and age is the strongest independent adverse prognostic factor together with tumour thickness. Understanding the mechanisms that drive poor outcome in the elderly represents a critical current problem in melanoma care. We focus our research on understanding the changes in aged skin that promote melanoma and the biology of aggressive disease affecting the elderly, in order to identify new strategies of adjuvant therapy.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: CANCER AND NEOPLASMS
Title: Secreted phosphoproteome in blood as early detection and risk stratification biomarkers of glioma
Funder: Cancer Research UK

Abstract: Background: The current proposal plans to utilise quantitative high-throughput mass spectrometry to identify early detection and/or risk stratification biomarker(s) in brain cancer (glioma) patient blood and optimize a preliminary quantitative detection assay in 1000+ patient samples. Multiple plasma proteins are remarkably elevated in the blood of glioma patients while their levels fall post-surgery. Although differential expression of some of those proteins are pan-cancer phenomena, specific post-translational modifications on those proteins are potentially glioma specific. This is because the single enzyme responsible for modifying the plasma proteins is remarkably overexpressed in glioma. The enzyme is overexpressed both in low-grade and high-grade gliomas. Patients with low-grade gliomas are at a very high risk of progressing to high-grade eventually. Unfortunately, assessing brain tumour grade requires invasive neurosurgery and that too if the tumour is deemed operable in the first place. Thus, it is imperative to develop a biomarker detection test in liquid biopsies of patients to track glioma progression. Thus, the plan is to track and compare the activity of the enzyme between normal donor, low- and high-grade glioma patient blood and thereby identify those specific substrates of the enzyme which exhibits maximum glioma-specific alterations. Crispr mediated depletion of the enzyme alone remarkably promotes survival in glioma bearing mouse models. Overexpression of the enzyme seems to be an early event in glioma initiation and hence quantitative capture of the differentially modified plasma proteins can predict the possibility of a new brain tumour while also serving as a risk stratification test for pre-existing low-grade glioma patients. Aims 1. Identification of maximally modified plasma proteins differentially upregulated in low- and high-grade glioma. 2. Development of modification-specific antibodies for the biomarkers. 3. Establishing and optimizing a preliminary blood test with 1000+ glioma patient plasma. Methods 1. We will utilise quantitative mass spectrometry comparing normal, low-grade and high-grade glioma patient plasma to identify differentially modified proteins. 2. We will purify and optimize modification-specific antibody tools for biomarker detection. 3. We will use the optimised antibodies to develop an ELISA-assay for quick detection and quantification of the plasma biomarkers in the 1000+ patient blood samples. How the results of this research will be used: Successful completion of these aims will establish a preliminary blood-test to predict brain cancer development and thereby provide a risk assessment option to the ~7 million brain cancer patients worldwide.


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: Prediction of Early Progression in Follicular Lymphoma
Funder: Cancer Research UK

Abstract: Background Follicular lymphoma is a common haematological cancer with more than 2000 new cases each year in the UK population. It is an indolent but presently incurable malignancy. Many patients do not require treatment or require only limited treatment for many years, and around 40% of newly diagnosed patients are placed on the 'Watch&Wait' regime (no active treatment). Nevertheless, a sub-group of patients experience early disease progression, and some of these tumours will transform to other more aggressive forms of lymphoma, like diffuse large B cell lymphoma (DLBCL), requiring more powerful treatments. Aims We aim to produce a prediction method for early disease progression focusing on POD24 (Progression of Disease within 24 months), applicable at diagnosis or at any time point within a patient journey. Recent studies have shown that the prediction of progression is difficult, with methods based on both clinical features of disease and tumour genetics showing only limited predictive power. Methods We hypothesize that more information is required to predict early progression accurately, and that features of the tumour microenvironment and tumour cell phenotype are likely to add powerful predictive information. We will base our prediction methods on clinical features, tumour genetics, the tumour transcriptome and digital pathology information. Our plans have a number of novel aspects. The inclusion of transcriptome information adds information on both tumour cell gene expression phenotype and cells of the microenvironment. Digital pathology provides information on cellular morphology and the presence of stromal and immune cells. We plan to use artificial intelligence based on recent developments in deep neural networks with transfer learning to analyse digital pathology images, and to integrate information for all data types into a single predictive method. Initial studies will be based on a high quality retrospective data set of around 500 patients diagnosed with follicular lymphoma within the Haematological Malignancy Research Network since 2004. This data set contains a full standard pathology assessment, relevant clinical variables and information on treatment and outcome. Future projects will assess prospective evaluation. How the results of the research will be used The prediction method will be used to help patients and clinicians make treatment decisions. While patients placed initially on Watch&Wait do not have worse outcomes, there is a substantial psychological burden associated with having a diagnosed and untreated cancer. Accurate predictions could provide substantial re-assurance. In the longer term trials would assess the value of the predictions in treatment choices.


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