Omics technologies are generating complex molecular datasets that are exponentially increasing the cancer knowledge base. However, the great molecular complexity and heterogeneity exhibited by most cancers, which is reflected in their omics characterisation, necessitates a systems biology approach for analysis and interpretation. This is essential for preclinical, clinical and biotechnological applications.
Current approaches to data analysis and exploration in large cancer studies are often confined to discovering novel correlations and trends using statistical and pattern recognition techniques, or at best modelling of a single cellular signalling pathway. Such approaches, however, often cannot take into account the complexity of living organisms, comprising numerous pathways and their cross-talk; features which are of paramount significance in cancer, where signalling complexity is the major determinant of disease progression and drug response. New solutions to optimally exploit this wealth of data for basic research, better treatment and stratification of patients, as well as more efficient targeted drug development are required.
CanPathPro will address the challenge of predictive modelling of biological data by developing and refining bioinformatic and experimental tools for the evaluation and control of systems biology modelling predictions. Components will comprise highly defined mouse and organotypic experimental systems, next generation sequencing, quantitative proteomics and a systems biology computational model for data integration, visualisation and predictive modelling.
These optimised tools will be assembled together within the CanPathPro prototype - a combined experimental and systems biology platform; a highly innovative biotechnological 'toolkit' that will allow users to integrate private or public data sets to predict the activation status of individual pathways, thus enabling in silico identification of cancer signalling networks critical for tumour development, as well as the generation of hypotheses about biological systems, which can be experimentally validated.
In the project, Simula will play a central role in development efficient numerical algorithms for accurate simulation of highly-complex genome-scale models. Simula will also lead the development of novel methods for visualization of large heterogenous datasets. Furthermore, we will work on data-driven dynamical modelling for model-based integration of different datasets, critical assessment of available information, and comparison of competing biological hypotheses.
The CanPathPro project will build and validate a combined experimental and systems biology platform, which will be utilised in testing cancer signalling hypotheses, in biomedical research. The project will have a broad impact on diverse areas, from cancer research and personalised medicine to drug discovery and development. The in silico modelling and high-performance computing tools will provide completely new solutions for researchers, SMEs and industry for interpretation and analysis of omics data. Thus, in the long run, the project will contribute significantly to improving outcomes for the majority of cancer patients.
This project is funded by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 686282. (BIOTEC-2-2015: New bioinformatics approaches in service of biotechnology)
- Alacris Theranostics GmbH (Germany)
- Simula (Norway)
- Institut Clinique de la Souris (France)
- Netherlands Cancer Institute (The Netherlands)
- Leibniz Institute on Aging (Germany)
- Helmholtz Zentrum München (Germany)
- Spanish National Research Council (Spain)
- Biognosys (Switzerland)
- Finovatis (France)
Alacris Theranostics GmbH (Germany)