Manchester Molecular Pathology Innovation Centre (MMPathIC): bridging the gap between biomarker discovery and health and wealth

曼彻斯特分子病理学创新中心（MMPathIC）：弥合生物标志物发现与健康和财富之间的差距

• 批准号: MR/N00583X/1
• 负责人: Anthony Freemont
• 金额: $ 372.55万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FN00583X%2F1
• 关键词: Manchester; Molecular; Pathology; Innovation; Centre

Stratified medicine (which is allied to personalised or precision medicine) is an approach to treating patients through categorising them into groups based on their risk of developing a particular disease, or how they are likely to respond a particular drug or therapy.It is key that the correct tests and techniques are available which can put individuals into groups (stratify patients), depending on their exact disease type and likely response to particular treatments. One way in which this might be possible is by application of molecular pathology, a specific type of pathology ( which is the study of disease), focused on the diagnosis and repeated characterisation of disease through the examination of molecules within organs, tissues or bodily fluids, such as blood, urine or synovial fluid (the fluid found in joints).The aim of the Manchester molecular pathology node (Manchester Molecular Pathology Innovation Centre- MMPathIC) is to create an environment which enables new tests, based on molecular pathology techniques, to be developed. These can then be used to stratify patients, to allow more accurate diagnosis or prediction of the best treatments to use. As we already have significant groups of patient samples from people who suffer from inflammatory disease (psoriasis, rheumatoid arthritis and lupus), we will focus on these diseases in the first instance. These diseases are also important as, between them, they affect a large part of our population, treatment can be expensive (and often doesn't work first time, meaning patients have to try a number of expensive drugs before getting any relief from their symptoms), and these diseases often severely affect a person's quality of life due to pain and discomfort. In addition, we are going to build upon the lessons already learned in this area from our established expertise in cancer molecular pathology, which is far further developed in the provision of targeted therapies.We will look at samples from patients with these inflammatory diseases, using a new technique that supports the measurement of many proteins within a minimally invasive sample (such as blood, urine or tissue). This will allow differences between samples from, for example, healthy people and people with a specific disease, to be examined- differences in certain proteins may prove useful as biomarker tests which can be used to diagnose a disease. In addition, by examining the differences in the levels of particular marker proteins from patients who respond to a drug compared to those who don't respond, doctors will be able to identify which drug is the best treatment for specific patients. This will hopefully have economic benefit as drugs will not be used on patients who will receive no benefit from them, but MMPathIC will ensure there is economic benefit through undertaking health economic analysis of potential markers- this will also allow informed decisions to be made by NHS officials who have to make decisions about which tests are viable for introduction into the health service.As we can measure these proteins, we also propose ensuring that this data is linked to genomic data (the blueprint for these building blocks that are proteins) and health records- this integration will be facilitated by MMPathIC's staff, which will include skilled information specialists who can make sense of the data produced and data which already exists (data mining).In collaboration with industry (who have the expertise to commercialise new tests, and navigate the necessary regulatory hurdles), we aim to produce at least 6 new tests which are ready to be commercialised, or ready to be used in hospital pathology laboratories in the first 3 years of the grant

分层医学（与个体化或精准医疗相关联）是一种治疗方法，根据患者患某种疾病的风险或对某种药物或疗法的反应将患者分组。关键是要有正确的测试和技术，可以将个人分组（对患者进行分层），这取决于他们的确切疾病类型和对特定治疗的可能反应。其中一种可能的方法是应用分子病理学，一种特殊类型的病理学（这是疾病的研究），侧重于通过检查器官，组织或体液（如血液）内的分子来诊断和重复表征疾病，尿液或滑液（关节中发现的液体）。曼彻斯特分子病理学节点的目的（曼彻斯特分子病理学创新中心- MMPathIC）是创造一个环境，使新的测试，基于分子病理学技术，被开发。然后，这些可以用于对患者进行分层，以便更准确地诊断或预测最佳治疗方法。由于我们已经有了来自患有炎症性疾病（银屑病，类风湿性关节炎和狼疮）的患者的大量患者样本，因此我们将首先关注这些疾病。这些疾病也很重要，因为它们影响了我们人口的很大一部分，治疗可能很昂贵（并且通常第一次不起作用，这意味着患者必须尝试一些昂贵的药物才能从症状中得到任何缓解），这些疾病通常会严重影响一个人的生活质量由于疼痛和不适。此外，我们还将借鉴我们在癌症分子病理学领域的经验，进一步发展靶向治疗。我们将研究这些炎症性疾病患者的样本，使用一种新技术，支持在微创样本（如血液、尿液或组织）中测量多种蛋白质。这将允许检查来自例如健康人和患有特定疾病的人的样本之间的差异-某些蛋白质的差异可能被证明是有用的生物标志物测试，可用于诊断疾病。此外，通过检查对药物有反应的患者与没有反应的患者的特定标志物蛋白水平的差异，医生将能够确定哪种药物是特定患者的最佳治疗方法。这将有望产生经济效益，因为药物不会用于那些不会从中受益的患者，但MMPathIC将通过对潜在标志物进行健康经济分析来确保经济效益-这也将允许NHS官员做出明智的决定，他们必须决定哪些测试是可行的，以引入卫生服务。我们还建议确保这些数据与基因组数据相关联，（这些蛋白质的构建模块的蓝图）和健康记录-这种整合将由MMPathIC的工作人员促进，其中包括熟练的信息专家，他们可以理解产生的数据和已经存在的数据（数据挖掘）。与工业界合作，（他们拥有将新检测商业化的专业知识，并能够克服必要的监管障碍），我们的目标是生产至少6种准备商业化的新检测，或在拨款期的首3年内可供医院病理化验室使用

项目成果

Translating research into clinical practice: quality improvement to halve non-adherence to methotrexate.

• DOI: 10.1093/rheumatology/keaa214
• 发表时间: 2021-01-05
• 期刊: Rheumatology (Oxford, England)
• 作者: Barton A;Jani M;Bundy C;Bluett J;McDonald S;Keevil B;Dastagir F;Aris M;Bruce I;Ho P;McCarthy E;Bruce E;Parker B;Hyrich K;Gorodkin R
• 通讯作者: Gorodkin R

Mapping Phenotypic Information in Heterogeneous Textual Sources to a Domain-Specific Terminological Resource.

• DOI: 10.1371/journal.pone.0162287
• 发表时间: 2016
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Alnazzawi N;Thompson P;Ananiadou S
• 通讯作者: Ananiadou S

A Deep Learning Approach to Refine the Identification of High-Quality Clinical Research Articles From the Biomedical Literature: Protocol for Algorithm Development and Validation (Preprint)

一种从生物医学文献中细化高质量临床研究文章识别的深度学习方法：算法开发和验证协议（预印本）

• DOI: 10.2196/preprints.29398
• 发表时间: 2021
• 作者: Abdelkader W

Features of lineage-specific hematopoietic metabolism revealed by mitochondrial proteomics.

线粒体蛋白质组学揭示谱系特异性造血代谢的特征。

• DOI: 10.1002/pmic.201700053
• 发表时间: 2017
• 期刊: Proteomics
• 影响因子: 3.4
• 作者: Billing C

Natural Language Processing for Biomedicine

生物医学自然语言处理

• DOI: 10.11159/cist21.002
• 发表时间: 2021
• 作者: Ananiadou S