TranSYS · Translational SYStemics: Personalised Medicine at the Interface of Translational Research and Systems Medicine

Translational SYStemics: Personalised Medicine at the Interface of Translational Research and Systems Medicine

TranSYS addresses the skills and knowledge gap in Systems Health: This ITN targets the current skills gap in the emerging fields of Systems and Precision Medicine. TranSys will deliver a pioneering high-level multidisciplinary training programme to a new generation of early stage researchers (ESRs). This breaks down current research and training “silos” and cuts across life and data sciences. Fifteen ESR research projects are complimented by training covering technical (genomics, bioinformatics, health informatics, statistics, data mining, systems medicine and ethics) and key soft skills relevant to high-level research career paths as future leaders for the Precision Medicine revolution. Precision Medicine has the potential to dramatically improve the efficiency and cost-effectiveness of healthcare services delivered to society. Instead of a ‘one-size-fits-all’ approach, the promise of personalised medicine is to build treatments that target a specific disease and take into account characteristics of individual patients including their genes and lifestyle. To stratify heterogeneous populations into a subset of patients with distinct disease profiles or similar responses to treatment is highly multidisciplinary, combining Big Data analytics, bioinformatics, high-throughput technologies, and omics with clinical approaches, at the same time requiring ethics and regulatory expertise. A cornerstone of personalised medicine is comprehensive knowledge retrieval about the targeted individuals. Molecular identification and characterization of patients is a high priority. Existing data in today’s digital universe offer tremendous opportunities to achieve this but extracting insights and information from multiple heterogeneous and interdependent data in personalised medicine, requires new systems analytics, moving beyond classical algorithmic or mechanical processes and validation against disease models. TranSYS research projects address this challenge by focusing on “Integrating Big Data and ICT Solutions” and “Translating Basic to Clinical Research”. Clinical applications of personalised medicine target an increasing range of diseases including cancers, chronic conditions, diabetes, neurology, cardiology and rare diseases. This is improving understanding of disease processes, enabling new targets for treatments and biomarkers to be identified, and allowing for treatments to be tested in a more targeted way and applied in the most appropriate patient groups. TranSYS Objectives: TranSYS aims to train highly skilled professionals on using advanced precision medicine strategies, at the same time addressing reported reasons for drug development or clinical trials failure. Three key research objectives are: 1. Narrowing the gap between preclinical performance and treatment benefit: Preclinical wet-lab observations and in-silico modelling [WP1] will be combined with advanced systems analysis methods [WP2] to develop mechanistic insight into diseases. This will advance disease progression models and the discovery and verification of biomarkers [WP1,2] 2. Developing integrative strategies and corresponding integrated work flows: State-of-the-art data analysis approaches and novel tools will be used to analyse complex multi-level datasets, including hidden data [WP2] utilising high quality, state of the art data resources in seven disease areas (inflammatory and autoimmune diseases, cancer, non-alcoholic fatty liver disease, neurodegenerative diseases, psychiatric disease, cardiovascular disease, rare diseases) [WP1,3] 3. Improving understanding of patient heterogeneity and developing economically viable patient stratification strategies: ESRs will use state-of-the-art methods and develop new tools to pre-process and analyse very complex datasets available to the consortium [WP2]. The investigation of (patient) population heterogeneity will identify differences in disease progression and treatment responses as a basis to develop criteria for patient stratification in target complex disease areas [WP3].

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз