MICA: Medical Bioinformatics: Data-Driven Discovery for Personalised Medicine

MICA：医学生物信息学：数据驱动的个性化医疗发现

• 批准号: MR/L016311/1
• 负责人: David Lomas
• 金额: $ 1130.97万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FL016311%2F1
• 关键词: MICA; Medical; Bioinformatics; Data; Driven

We will improve patient health and medical research by maximising the use of vast amounts of human data being generated in the NHS. But there are two obstacles: (i) inter-related clinical and research datasets are dispersed across numerous computer systems making them hard to integrate; (ii) there is a serious shortage of computational expertise as applied to clinical research. As part of the UK's healthcare strategy to overcome these limitations, we have assembled a world-class consortium of institutions and scientists, including UCL Partners (containing NHS Trusts treating >6 million patients), Francis Crick Institute, Sanger Institute and European Bioinformatics Institute. Close links with the NHS (through Farr and Genomics England) will allow information exchange for health and disease progression. We have also engaged leading companies like GSK and Intel.We will use the MRC funds for two purposes:1. Create a powerful eMedLab data centre. We will build a computer cluster that allows us to store, integrate and analyse genetic, patient and electronic health records. By co-locating in a single centre, we eliminate delays and security risks that occur when information is transmitted. Research Technologists supplied by the partners will install and maintain the infrastructure and software environment. 2. Expand scientific and technical expertise in UK Medical Bioinformatics through a Research & Training Academy. Basic and clinical scientists, and bioinformaticians will be trained to perform world-leading computational biomedical science. We will train in the whole range of skills involved in medical bioinformatics research with taught courses, seminars, workshops and informal discussion. To coordinate research activities across partners, we will establish Academy Labs, which are flexible, semi-overlapping groupings of academic and industrial researchers to share insights and plan activities in areas of common analytical challenges. The Academy will provide a mechanism for information and skills exchange across the traditional boundaries of disease types.These will enable existing projects in 3 disease domains in which we have unique strengths: rare diseases, cardiovascular diseases and cancer. Rare: We house 31/70 Nationally Commissioned Highly Specialised Services; ~0.5M of the 6M of our patients have a rare disease, including >50% of those treated at Great Ormond Street Hospital. >200 research teams generate large quantities of genetic, imaging (eg, 3D facial reconstructions), and clinical information (eg, patient records). Cardiovascular: We also lead genomic, imaging, and health informatics programmes in cardiovascular disease with contributions to projects like UK10k project and host multiple national cardiovascular registries through the National Institute for Cardiovascular Outcomes Research. These are linked to primary and hospital clinical care records through Farr@UCLP with current cohort sizes of ~2M people. Cancer: We also have particular clinical expertise in some of the most difficult to treat cancer types and we host major international data resources. These include individuals recruited to the TRACERx study of lung cancer, 8,500 women with abnormal cervical smears in whom methylation patterns of the HPV16 genome predict progression to high-grade precursor disease, and one of the largest sarcoma biobanks in the world. Ultimately, this bid will allow us to use new computational approaches to (i) link patient records and research data in order to understand the pathogenesis of disease, (ii) use genomic, imaging and clinical data to identify diagnostic, prognostic and predictive biomarkers to guide therapy, predict outcome and increase recruitment to clinical trials based on stratified populations and (iii) translate new IP by engagement with the pharmaceutical industry.

我们将通过最大限度地利用NHS中产生的大量人类数据来改善患者健康和医学研究。但有两个障碍：（i）相互关联的临床和研究数据集分散在众多的计算机系统中，使其难以整合;（ii）应用于临床研究的计算专业知识严重短缺。作为英国克服这些限制的医疗保健战略的一部分，我们组建了一个世界级的机构和科学家联盟，包括UCL Partners（包含治疗超过600万患者的NHS信托基金），弗朗西斯克里克研究所，桑格研究所和欧洲生物信息学研究所。与NHS的密切联系（通过Farr和Genomics England）将允许健康和疾病进展的信息交流。我们亦与葛兰素史克及英特尔等大公司合作。创建强大的eMedLab数据中心。我们将建立一个计算机集群，使我们能够存储、整合和分析基因、病人和电子健康记录。通过在一个单一的中心集中办公，我们消除了信息传输时出现的延迟和安全风险。由合作伙伴提供的研究技术人员将安装和维护基础设施和软件环境。2.通过研究和培训学院扩大英国医学生物信息学的科学和技术专业知识。基础和临床科学家以及生物信息学家将接受培训，以执行世界领先的计算生物医学科学。我们将通过授课课程，研讨会，研讨会和非正式讨论来培训医学生物信息学研究所涉及的全部技能。为了协调合作伙伴之间的研究活动，我们将建立学院实验室，这是一个灵活的、半重叠的学术和工业研究人员分组，以分享见解，并在共同的分析挑战领域规划活动。该学院将提供一个跨越疾病类型传统界限的信息和技能交流机制，这将使我们拥有独特优势的3个疾病领域的现有项目得以开展：罕见疾病，心血管疾病和癌症。罕见：我们拥有31/70的国家委托的高度专业化服务;我们的600万患者中约有050万患有罕见疾病，其中包括在大奥蒙德街医院接受治疗的患者中的50%以上。超过200个研究团队产生了大量的遗传、成像（如3D面部重建）和临床信息（如患者记录）。心血管疾病：我们还领导心血管疾病的基因组，成像和健康信息学项目，为UK 10 k项目等项目做出贡献，并通过国家心血管结局研究所主持多个国家心血管登记。通过Farr@UCLP将这些与初级和医院临床护理记录相关联，目前队列规模约为200万人。癌症：我们在一些最难治疗的癌症类型方面也拥有特殊的临床专业知识，我们拥有主要的国际数据资源。其中包括招募到肺癌TRACERx研究的个体，8，500名患有异常宫颈涂片的女性，其中HPV 16基因组的甲基化模式预测进展为高级前驱疾病，以及世界上最大的肉瘤生物库之一。最终，这一出价将使我们能够使用新的计算方法来（i）将患者记录和研究数据联系起来，以了解疾病的发病机制，（ii）使用基因组，成像和临床数据来识别诊断，预后和预测生物标志物，以指导治疗，根据分层人群预测结果并增加临床试验的招募，以及（iii）通过与制药行业的合作来翻译新的知识产权。

项目成果

P3-420: AN EVENT BASED MODEL OF ALZHEIMER'S DISEASE IN APOE+ SUBJECTS USING ROBUST BIOMARKERS OF VOLUMETRIC CHANGE IN REGIONAL BRAIN STRUCTURE

P3-420：使用区域脑结构体积变化的鲁棒生物标志物建立 APOE 受试者阿尔茨海默病的基于事件的模型

• DOI: 10.1016/j.jalz.2018.06.1783
• 发表时间: 2018
• 期刊: Alzheimer's & Dementia
• 作者: Aksman L

Intergenic RNA mainly derives from nascent transcripts of known genes.

• DOI: 10.1186/s13059-021-02350-x
• 发表时间: 2021-05-05
• 期刊: Genome biology
• 影响因子: 12.3
• 作者: Agostini F;Zagalak J;Attig J;Ule J;Luscombe NM
• 通讯作者: Luscombe NM

Statin Prescribing and Dosing-Failure Has Become an Option-Reply.

他汀类药物处方和剂量失败已成为一种选择-答复。

• DOI: 10.1001/jamacardio.2021.0838
• 发表时间: 2021
• 期刊: JAMA cardiology
• 影响因子: 24
• 作者: Adusumalli S

A data-driven study of Alzheimer's disease related amyloid and tau pathology progression.

• DOI: 10.1093/brain/awad232
• 发表时间: 2023-12-01
• 期刊: Brain : a journal of neurology