Periodic Reporting for period 1 - TranSYS (Translational SYStemics: Personalised Medicine at the Interface of Translational Research and Systems Medicine)
Período documentado: 2019-09-01 hasta 2021-08-31
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].
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].
TranSYS has recruited 15 ESRs representing a diverse, young and dynamic community of promising scientists, covering 11 countries across 4 continents (Europe, Asia, Africa and South America); 67% (10/15) are female. All ESRs are expected to enroll in an institutional doctoral program including biomedical sciences, molecular medicine, molecular biosciences, life science complexity, artificial intelligence, genetics, pharmacy, and both generic and interdisciplinary tracks.
TranSYS has held 3 main training events in this reporting period, 2 of these, the mini symposium and Training school were free to attend for external participants with post event recordings accessible through the TranSYS website.
• Mini symposium March 2020 hosted by KU Leuven
• Training school 1, Nov 2020, hosted by the University of Lubjiana
• HBDI training March 2021External training provider expert academy
• Bootcamp 1 June 2021 hosted by Max Planck Institute of Psychiatry
Other network activities:
• ESR Webinars : ESRs present his/her work, and the host beneficiary PI had the opportunity to present his/her institution, followed by discussions of the ESR’s work, within the consortium in general and/or with the TranSYS PhD supervisory teams in particular. There have been 10 TranSYS webinars, with 11 ESR presentations.
• There have been 6 TranSYS Journal Clubs in this reporting period: The TranSYS journal clubs pursue the following objectives: 1) increase, for ESRs and other TranSYS members, familiarity with the data analysis and modeling methodologies used across TranSYS; 2) create opportunities for discussion and collaboration within TranSYS, and 3) provide a safe space to ESRs in which they can work on their presentation skills and practice how to critically review scientific literature.
• Workgroup-specific activities started in October 2020 and focused on research, well-being and building bridges.
TranSYS ESRs have actively participated in several international conferences, a number of publications are available and underway.
TranSYS has held 3 main training events in this reporting period, 2 of these, the mini symposium and Training school were free to attend for external participants with post event recordings accessible through the TranSYS website.
• Mini symposium March 2020 hosted by KU Leuven
• Training school 1, Nov 2020, hosted by the University of Lubjiana
• HBDI training March 2021External training provider expert academy
• Bootcamp 1 June 2021 hosted by Max Planck Institute of Psychiatry
Other network activities:
• ESR Webinars : ESRs present his/her work, and the host beneficiary PI had the opportunity to present his/her institution, followed by discussions of the ESR’s work, within the consortium in general and/or with the TranSYS PhD supervisory teams in particular. There have been 10 TranSYS webinars, with 11 ESR presentations.
• There have been 6 TranSYS Journal Clubs in this reporting period: The TranSYS journal clubs pursue the following objectives: 1) increase, for ESRs and other TranSYS members, familiarity with the data analysis and modeling methodologies used across TranSYS; 2) create opportunities for discussion and collaboration within TranSYS, and 3) provide a safe space to ESRs in which they can work on their presentation skills and practice how to critically review scientific literature.
• Workgroup-specific activities started in October 2020 and focused on research, well-being and building bridges.
TranSYS ESRs have actively participated in several international conferences, a number of publications are available and underway.
TranSYS ESR projects are expected to deliver the following key innovations in improving the detection, diagnosis and treatment of diseases, and accelerate knowledge in preventive healthcare:
Compendia of datasets and new analysis tools: Data mining tools and analysis, including using API to access hidden data, will improve understanding of patient heterogeneity and targeted medicine. Analytics will identify newly observed genetic and clinical associations and will include clinical and wet-lab validation.
Biomarker discovery and Patient stratification methods: Data science approaches and applying biomarkers will improve identification of (multi-gene/multi-omics) disease subtypes (e.g. pancreatic cancer, IBD, melanoma and immunodeficiencies). These will identify novel, and validate promising biomarkers candidates for diagnostic, predictive and stratified medicine purposes.
Drug Response Pathways and Pharmacogenomics App: Unique large-scale studies (e.g. LifeLinesDeep cohort with deep phenotyping and multi-omics) and innovations to deal with small patient groups and hard-to-measure life-style or risk factors will elucidate downstream effects of drug metabolising/interfering genomic markers. Successful results will support the development of a Pharmacogenomics App building on previous DNA passport work.
Compendia of datasets and new analysis tools: Data mining tools and analysis, including using API to access hidden data, will improve understanding of patient heterogeneity and targeted medicine. Analytics will identify newly observed genetic and clinical associations and will include clinical and wet-lab validation.
Biomarker discovery and Patient stratification methods: Data science approaches and applying biomarkers will improve identification of (multi-gene/multi-omics) disease subtypes (e.g. pancreatic cancer, IBD, melanoma and immunodeficiencies). These will identify novel, and validate promising biomarkers candidates for diagnostic, predictive and stratified medicine purposes.
Drug Response Pathways and Pharmacogenomics App: Unique large-scale studies (e.g. LifeLinesDeep cohort with deep phenotyping and multi-omics) and innovations to deal with small patient groups and hard-to-measure life-style or risk factors will elucidate downstream effects of drug metabolising/interfering genomic markers. Successful results will support the development of a Pharmacogenomics App building on previous DNA passport work.