NaCTeM

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

Background

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 (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). Differences in proteins in such samples between e.g., healthy people and people with a specific disease, 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 doctors respond, doctors will be able to identify which drug is the best treatment for specific patients.

Aims

The aim of the Manchester molecular pathology node (Manchester Molecular Pathology Innovation Centre- MMPathIC) is to create an environment which enables new biomarker 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. The initial focus will be on people who suffer from inflammatory disease (psoriasis, rheumatoid arthritis and lupus), given the availability of a large number of patient samples for these diseases. It is planned 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.

MMPathIC will combine the skills of experts working in several areas. Medical expertise will be complemented by the skills of researchers working in other areas. e.g., information speciciats, to allow the data procuded to be linked to genomics data, health economists, to allow informed decisions to be made by NHS officials, and text miners.

Text Mining Workstrand

Text mining (TM) will be employed to carry out automated semantic analysis of various "unstructured" textual information sources thet may contain information that is relevant to the development of biomarker tests, including biomedical literature and electronic health records. Given that each of these sources constitutes vast numbers of documents, information contained within them may be hidden and easily overlooked. TM techniques will be used in a number of ways to enhance the ease and efficiency with which unstructured textual information sources can be exploited to support the development of biomarker tests. For example:

  • To locate, structure and link together different types of information about potential biomarkers which may be dispersed across documents of different types, e.g., genetic variations that can be indicative of a particular disease, in which types of patients such variations occur, what type of changes occur in response to drugs, etc.
  • To allow the discovery of potentally unknown associations (e.g. between proteins and diseases), which could act as a stimulus of invstigating novel biomarkers.

Among the TM-related outcomes of the project are expected to be the following:

  • A set of novel/adapted TM tools and text processing pipelines that are cutomised to automatically extract and structure biomarker-related information from text. We will make use of our web-based text mining workbench, Argo, which integrates various text mining tools processing and machine learning capabilities, to allow tools to be tailored to specific tasks.
  • Customised user inferfaces, which will use the automatically extracted information to provide functionalities for semantic search, browsing annd discovery of hidden knowledge. This will help medical experts to obtain maximum value from the volumes of available textual data, in support of the development of new biomarker tests.

Project Team

Principal Investigator: Professor Anthony Freemont (Institute of Inflammation and Repair, Faculty of Medicine and Human Sciences)

Co-Investigators:
Sophia Ananiadou (School of Computer Science, The University of Manchester)

Professor Anne Barton (Centre for Musculoskeletal Research Arthritis Research UK Epidemiology Unit, The University of Manchester and Central Manchester Foundation Trust)

Professor Graeme Black (Manchester Centre for Genomic Medicine, Central Manchester University Hospitals NHS Foundation Trust

Professor Ian Bruce (Institute of Inflammation and Repair, The University of Manchester; The Kellgren Centre for Rheumatology, Central Manchester and Manchester Children's University Hospitals Trust)

Professor Iain Buchan (MRC Health eResearch Centre/ Farr Institute for Health Informatics Research, The University of Manchester; Northwest e-Health, Salford Royal Foundation NHS Trust)

Dr Richard Byers (Institute of Cancer Sciences, The University of Manchester; Manchester Royal Infirmary, Central Manchester University Hospitals NHS Foundation Trust)

Professor Caroline Dive (Cancer Research UK Manchester Institute, The University of Manchester)

Professor Royston Goodacre (School of Chemistry, The University of Manchester)

Professor Katherine Payne (Manchester Centre for Health Economics, The University of Manchester)

Professor John Radford (Institute of Cancer Sciences, The University of Manchester; Christie NHS Foundation Trust)

Professor Anthony Whetton (Institute of Cancer Sciences, The University of Manchester; MRC Clinical Proteomics Centre)

Research Fellows:
Mr. Paul Thompson (NaCTeM)
Dr. Alexander Thompson (Health Economics)
Dr. Nophar Geifman (Health and Biomedical Informatics)

Funding

This project, which runs from October 2015 until September 2019, is being funded by the MRC and EPSRC