NIHRDataInsights/HRCSData huggingface dataset:
Title: Facilitating data integration to empower Colorectal Cancer (CRC) discovery research
Funder: Medical Research Council

Abstract: One of the major bottlenecks in achieving precision medicine in colorectal cancer (CRC) is the lack of an intuitive yet powerful analysis platform which permits scientists and clinicians to independently interrogate their own datasets. Our framework will be a CRC-specific web platform that allows the integrated analysis of multi-omics data (e.g. DNA sequencing, transcriptomics and methylation) with clinical/pathological data through a RESTful Application programming interface (API) coupled with dynamic data visualisation functionalities. The framework will be polyglot persistent and facilitates the merging of both SQL and Non-SQL database management systems (DBMS). The frameworks core will implement a 'plug and play' architecture which is built to act as an umbrella for multiple loosely coupled and highly cohesive statistical application modules (apps), each of which can be built and customised to address different analytical needs using well established statistical programming languages (e.g. R, Python). The apps available within the framework will be wide ranging and consist of many of the gold standard techniques used for data processing, visualisation and feature selection. This pipeline will help identify molecular subtypes, robust gene expression signatures, map important biological pathways and perform in silico pathology characterisation within proprietary and publically available CRC patient data cohorts. The Stratification of ColoRecTal cancer (S:CORT) consortium has generated comprehensive datasets from large retrospective and clinical trial cohorts (e.g. - MRC FOCUS). In collaboration with partners, such as S:CORT, we will use our platform to interrogate these datasets in order to develop new tests that predict which patients will respond to different treatments, including chemotherapy, radiotherapy and surgery. Finally, our framework will maintain a highly flexible and scalable design structure to permit cancer-agnostic analysis in future projects.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS
Health categories: CANCER AND NEOPLASMS
Title: A Molecular Pathological Epidemiology Approach Towards Pancreatic Cancer
Funder: Medical Research Council

Abstract: Objective 1: A population-based comparative cohort study will be conducted between Pancreatic cancer (PaC) diagnosed and other patients matched by the appropriate demographics focusing on various epidemiological factors and clinical data such as demography, lifestyle, physiological , symptoms, diagnoses, medication, blood and urine tests and healthcare utilisation markers. Patients will be classified using data-driven phenotyping algorithm using their so-far-complete healthcare trajectory. Patients' linked electronic health records (EHR) including primary and secondary care data as well as socio-economic and mortality data will be utilised collected from diverse sources such as Barts Health Data Warehouse, NHS Discovery Project East London, CPRD and NCRAS. The PaC events (incidence, recurrence, death) and comparison of these to rates in matched controls will be estimated using appropriate statistical models. Association between various exposures and outcomes will be measured in terms of odds ratios, with length and dose of exposure to known risk factors as stratification categories. Objective 2: The -omics data available through cancer-specific initiatives (TCGA, GENIE) as well as molecular data repositories (GEO, ArrayExpress) will be analysed in combination with relevant literature mining to collate existing genomic characterisations of PaC in terms of gene/mutational signatures. The assessment of molecular characteristics of PaC associated to clinical history will be conducted on data derived from PCRF Tissue Bank. Consensus clustering algorithm will be applied to obtain new stratifications of PaC along the themes of disease onset, progression, survival and response to treatment. The association between the expression/mutational status of specific PaC genes and risk/prognostic factors, longitudinal data and response to treatment will be tested. All the data obtained through the project will be published in a mine-able web-based bioinformatics infrastructure.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: CANCER AND NEOPLASMS
Title: Optimal screening and surveillance regimes for early diagnosis of cancer and precision medicine using mathematical modelling
Funder: Medical Research Council

Abstract: Current screening strategies for early cancer detection aim to identify individuals with premalignant tissue changes that signify an important first step in the progression to cancer, a manifestation of field cancerisation that occurs in many tissues, and when clinical intervention may be possible. However, many current approaches for prevention by screening and surveillance programs have achieved minimal success in reducing mortality at a high cost to healthcare services. Within the MRC priority area Precision Medicine and Diagnostics, my research project will develop and apply mechanistic mathematical models that describe how normal tissues develop into cancer to predict how surveillance (and thus early detection) may be optimised. Through simulation and mathematical analysis of the pattern and pace of cancer development, I will calculate the likely position of any individual on a developmental pathway towards cancer. By then creating a virtual population of patients, I will be able to judge the effectiveness of a screening/surveillance regime across a population. I will parameterise the biological details of these models with data spanning molecular to population scales from Health Data Research UK and a growing network of expert collaborators. I will then use this framework to predict the optimal times to screen at-risk individuals and to tailor a surveillance regime to individual patients, and validate these predictions with epidemiological data. The ultimate aims are to: 1) Rigorously assess and inform the best screening strategies to benefit public health recommendations 2) Translate this work into risk assessment, biomarker discovery, and patient stratification in the healthcare setting. My novel methodologies integrate bioinformatics using Big Data, survival analysis using public health record data, stochastic modelling, and classical biostatistics to address cancer prevention problems.


Research activity codes: 4.5 RESOURCES AND INFRASTRUCTURE (DETECTION)
Health categories: CANCER AND NEOPLASMS
Title: Towards quantitative image analysis of respiratory imaging data for pulmonary disease assessment.
Funder: Medical Research Council

Abstract: This research project goes substantially beyond the state-of-the-art in medical image analysis in term of: - a methodology by a novel paradigm extending current local or global formulation to a unified non-local counterpart considering all components of image registration (similarity measure, motion/deformation model, optimisation method) jointly with quantitative imaging; - a hypothesis that this unified image analysis framework will be physiologically explicitly grounded in the analysed data (potential to link with advances in machine learning, and opening up to future opportunities for collaboration on big data), and be consequently more relevant to biomedical applications. - challenge on improving the performance of motion estimation, the author believes that the presented methodological advances will eventually lead to the development of real-time (close to real-time) motion estimation for highly dimensional biomedical data. Such an advance will have immediate impact beyond personalised precise radiotherapy planning and delivery.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: CANCER AND NEOPLASMS
Title: Classification of oral lesions using deep learning for early detection of oral cancer
Funder: Medical Research Council

Abstract: About 300,000 individuals are diagnosed with oral cancer every year and the majority of these patients live in low- and middle-income countries. Up to 80% of oral cancers are preceded by early lesions called oral potentially malignant lesions (OPMD) and this presents an opportunity to identify and manage these lesions to reduce the chances of malignant transformation. However, due to the numerous types of lesions that present in the oral cavity, the identification of OPMD is not straightforward. Further, this is confounded by the limited number of trained oral medicine specialists in countries where the prevalence is at its highest. One way to overcome the lack of access to expertise for the clinical diagnosis of these lesions is to use deep learning approaches to develop an algorithm that could automatically classify images into different categories that require distinct clinical management. Such an approach has been successfully used for the classification of skin diseases. In this project, we will work within a multi-disciplinary team to use deep learning for the classification of oral lesions. In particular, we will employ two different deep learning architectures - VGG-16 and ResNet, pre-trained using ImageNet, and apply transfer learning to classify oral lesions. The outcome of this project will pave the way for further rigorous testing, development of an App incorporating this automated tool and clinical validation for the early detection of oral cancer. The development of an automated tool for the classification of oral lesions would facilitate the identification of patients most at risk of developing oral cancer so that these individuals can be managed appropriately.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS
Title: A study of the implementation of population-based bowel cancer screening in Malaysia
Funder: Medical Research Council

Abstract: We aim to develop and evaluate a screening programme to promote colorectal cancer screening awareness and increase participation and early detection. Phase 1 will review and synthesise best available evidence and implementation lessons; and assess views and preferences of stakeholder groups. A survey will be conducted of health facilities in order to describe usual practice and assess capacity to run a large scale roll-out of screening. Approximately six focus group discussions (FGDs) will be conducted with males/females aged 50 years or older with different socioeconomic backgrounds and from the three ethnic groups in Segamat (the study area) to identify barriers towards screening and investigate views about methods to increasing uptake and facilitating access. Phase 2 will co-design a culturally sensitive screening programme for Malaysians in Segamat. NI Bowel Cancer Screening Programme materials will be translated and adapted to suit the Malaysian population based on findings from Phase 1. The clarity of materials and their acceptability will be tested via a further four FGDs. Key service provider, delivery and laboratory staff will be surveyed about implementation practice, procedural issues and ways to improve the programme. Phase 3 will comprise a random sample (n=780) of eligible residents in Segamat in order to provide a precise estimate of the true percentage uptake of screening by income and ethic group. Positive iFOBT test results will be followed-up with an invitation for a colonoscopy. Surveys will elicit views about acceptability and facilitating/inhibiting factors to participating in screening. In Phase 4, data from previous phases will be analysed and synthesised to produce a logic model and a protocol for an experimental/comparative effectiveness evaluation. Routinely available data and the data from the set of investigative activities presented here will be used to conduct an initial economic evaluation and budget impact analysis.


Research activity codes: 4.4 POPULATION SCREENING
Health categories: CANCER AND NEOPLASMS
Title: A pilot study to develop an efficacious oral cancer screening strategy for India
Funder: Medical Research Council

Abstract: We propose conducting a study in North East India (Assam), to investigate the proof-of-performance of the proposed 3-step oral cancer screening strategy. First, we propose to use a risk prediction model to identify individuals at high-risk of oral pre-cancer/ early oral cancer for inclusion in the screening program. Second, we propose to rule-out disease in these high-risk individuals using a wide-field screening adjunct with high sensitivity - autofluorescence. Third, in individuals found to be positive by autofluorescence, we propose to rule-in disease using a narrow-field screening adjunct with high specificity - brush cytology. The study will be conducted in two phases. All healthy individuals aged between 30 to 65 years, with exposure to tobacco, areca nut and alcohol, will be included in the study. Logistic regression analyses will be utilized for the development of the risk-prediction model using data on 15,000 participants recruited in Phase I. The developed model will be applied to 15,000 participants recruited into Phase II for validation. Model-validation will be evaluated using calibration ratios (ratio of observed to expected number of cases) and discrimination (area under the receiver operating characteristic curve [AUC]). Successful completion of this study is anticipated to lead to the design and implementation of a large randomized controlled trial to investigate the efficacy, cost and population-level effectiveness of this oral cancer screening strategy.


Research activity codes: 4.4 POPULATION SCREENING
Health categories: CANCER AND NEOPLASMS
Title: Feasibility of a cluster randomised trial of a nutritional intervention to improve outcomes after cancer surgery in low-income countries
Funder: Medical Research Council

Abstract: In WP1, we will develop a pragmatic but robust screening tool for malnutrition. This will identify patients to be recruited to the final trial and be used to stratify severity. Systematic reviews will be registered with PROSPERO and performed/reported using PRISMA guidelines. We have good experience and a related Cochrane review already published. We have brought in qualitative/mixed methods expertise and all work will be performed using the SRQR guidelines. All focus group work will be examined using framework analysis and identified themes used to create or adapt a pre-existing tool for use in LMICs. Validation of the tool will take place along with WP3. WP2 is concerned with the identification of the most appropriate nutritional intervention. Our "expert advisory group on nutrition" will guide this work. Existing contacts in industry/logistics will ensure the intervention is appropriate, locally-sourced, and sustainable. We are particularly multidisciplinary with patients, nutritionists, surgeons, anaesthetists, oncologists, epidemiologists, methodologists and public health doctors. Our target product profile includes low volume, high energy (600 kcals), 40g protein, essential amino acids and micronutrients. WP3 will determine the feasibility of a cluster RCT. We have extensive experience in cluster trials (BCTU, ECTU, Norrie) and a step-wedged design may be deemed most appropriate. Using the CONSORT 2010 extensions (feasibility trials) we will capture patient/cluster numbers. We will determine the process of patient identification, recruitment, and administration of intervention. We will prospectively trial randomisation, blinding, outcome assessment and data collection instruments and establish longer-term follow-up, e.g. disease-free/overall survival. This will determine final study design, analysis plan and sample size. Our health economics team are essential given the importance of cost effectiveness in developing a long-term sustainable solution.


Research activity codes: 3.3 NUTRITION AND CHEMOPREVENTION;4.4 POPULATION SCREENING
Health categories: CANCER AND NEOPLASMS
Title: Understanding data and information needs for palliative cancer care to target mobile phone-based intervention development in Uganda and Zimbabwe
Funder: Medical Research Council

Abstract: The majority of cancer presentations in sub-Saharan Africa are advanced and incurable, with incidence of malignancies projected to increase significantly. With limited provision of palliative care (PC) in the region, it is essential to understand how existing services are supporting patients with advanced cancer through assessing their experiences and outcomes. This can be achieved through capture of patient-level data to ensure practice is evidence-based, can be replicated, and reflects the needs of the population served. Approaches to gathering these data are requisite to understanding current palliative cancer care provision and inform future development. With this pump-priming award, working in partnership with PC providers in Uganda and Zimbabwe, we will: i) instigate an active, interdisciplinary consortium focused on mHealth (the practice of medicine and public health supported by mobile devices) approaches to capturing patient-level data for palliative cancer care; ii) identify a list of factors to target through mHealth approaches with accompanying programme theory, iii) obtain essential preliminary data needed to inform more substantive funding applications; iv) initiate research capacity building on technology development in partner institutions and identify future capacity building needs; and v) develop a proposal outline to undertake piloting and evaluation of mHealth approaches across PC services initially in Uganda and Zimbabwe. This preliminary research will culminate in detailing mechanisms for optimal implementation of mHealth interventions to underpin mHealth piloting to support development of palliative cancer care in SSA. This project work is pivotal to ensure development of mHealth approaches that routinely collect and analyse data that produces information to directly inform patient care, service delivery and policymaker decision making. This will facilitate rapid development of the evidence base underpinning palliative cancer care.


Research activity codes: 7.2 END OF LIFE CARE
Health categories: CANCER AND NEOPLASMS
Title: Identifying the drivers and vulnerabilities of acral lentiginous melanoma through the study of PDX models from Latin American patients
Funder: Medical Research Council

Abstract: Although cutaneous melanoma survival rates have increased dramatically over the last decades, this success has not been realised for acral lentiginous melanoma (ALM), the most common form of the disease in some Latin American countries. This subtype of melanoma has been poorly studied due to its rarity in European-descent populations, and as a result there is a lack of pre-clinical models of the disease and a relatively low number of available ALM genomes. As a result, survival rates and response to therapy are still poor in these patients. Our long-term objective is to develop novel therapies for ALM patients. We have two Specific Aims: 1. To establish and characterise a genetically diverse patient-derived xenograft (PDX) collection from ALMs from Brazil and Mexico. We expect to establish 50 PDX models during the first year, which we will exome-sequence to determine genomic drivers and copy number alterations. We will also perform RNA-seq of all tumours and determine protein expression levels via reverse-phase protein arrays (RPPA). To search for ALM-predisposing haplotypes, we will genotype all patients with arrays specialised for admixed populations. 2. To use the PDX collection to identify and explore potential therapeutically targetable alterations and disease biomarkers. We will characterise gain of function alterations and validate their expression in ALM to identify potential drug targets. We will focus on CCND1/CDK4 dependencies. PDXs will be analysed for sensitivity to CDK4 inhibitors in vivo, and RNA and protein expression profiles of responders and non-responders will be compared. By bringing together a multi-disciplinary group with synergistic expertise, this project has the potential to identify risk factors and biomarkers to rationalise treatment for ALM patients, and will establish a valuable resource that can be shared with the research community.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;5.1 PHARMACEUTICALS
Health categories: CANCER AND NEOPLASMS
Title: Preventing cervical cancer in older Vietnamese women
Funder: Medical Research Council

Abstract: In Vietnam, as in most LMICs, about 80% of cervical cancer deaths are in women aged over 45, and their risk will not be reduced by vaccination for at least 30 years. Organised screening is not feasible and a single cytology screen has low sensitivity. In contrast, the sensitivity of a single HPV test to identify women at long-term risk of developing cervical cancer is very high. A single HPV test around menopause with radical treatment of HPV positive women is a practicable means of achieving a large reduction in lifetime risk. No other feasible intervention in LMICs could achieve such a large and rapid reduction in incidence and mortality for any cancer. Our primary aim is to develop and evaluate the methodology for a large population-based HPV screening study in collaboration with scientists at the Pasteur Institute in Ho Chi Minh City (HCMC). We shall estimate response rates by inviting a random sample of women aged 45-64 for HPV screening. Recruitment will continue until 300 have been screened. The response rate as well as knowledge of and attitudes to cervical screening for attenders and refusers will be analysed. A sample of 100 former sex workers aged 45-64 (FSWs) will also be contacted by a snowball method through FSWs already recruited to on-going studies. Collaboration with the cancer registry is essential as linkage to cancer registration will provide lifetime follow-up for cervical cancer incidence. We shall also interview a sample of at least 50 cervical cancer patients contacted through the registry for case-control comparison with the population and FSW participants. We aim to explain HCMC's unusual cervical cancer incidence patterns: The rate in women aged 60-64 was among the highest in the world in 1995-99, and has since halved both in this age-birth cohort. A possible explanation is selective out-migration of FSWs at very high risk. This would have important implications for targeting high-risk women in rural areas in national screening.


Research activity codes: 4.4 POPULATION SCREENING
Health categories: CANCER AND NEOPLASMS