Report

Teaser, summary, work performed and final results

Periodic Reporting for period 3 - CanPathPro (Generation of the CanPath prototype - a platform for predictive cancer pathway modeling)

Teaser

Cancer is the result of mutations and other alterations in multiple genes, which give rise to the disease’s enormous molecular complexity affecting multiple signalling pathways and their cross talk. An influx of tumour “omics” data (genomic, transcriptomic, proteomic)...

Summary

Cancer is the result of mutations and other alterations in multiple genes, which give rise to the disease’s enormous molecular complexity affecting multiple signalling pathways and their cross talk. An influx of tumour “omics” data (genomic, transcriptomic, proteomic) has allowed the profiling of this disease in unprecedented detail. Nonetheless, the challenge remains to translate this knowledge into clear benefits for better treatment, drug development and for an enhanced understanding of the molecular basis and progression of cancer.

Genetically engineered cancer mouse models are one of the main tools for functional analysis of cancer alterations. The influx of tumour omics data has increased the need for new mouse models that recapitulate the newly discovered molecular profiles of the human disease. However, pragmatic limitations in financial and time resources preclude massively parallel experimentation, thus slowing down the progress of discovery. In the meantime, omics datasets are increasing, descriptive of numerous health and disease states, but nonetheless remain underexploited.

The main bottleneck in these efforts is a lack of efficient, validated tools which could integrate and analyse the omics datasets. Current approaches are mainly confined to statistical analysis, molecular pattern recognition through machine learning or -at best- modelling of single pathways. These approaches do not consider the complex pathways and their cross-talk, which ultimately determines cancer initiation, progression and drug response.

CanPathPro addresses these challenges. The overall objective of the project is to build and validate a combined experimental and systems biology platform, to be utilised in testing and generating new cancer signalling hypotheses, in biomedical research. It will combine, in a single platform, omics and quantitative immunohistopathological data of cancer mouse models with analytical, modelling, predictive and visualisation computational tools. The platform will perform data integration and predictive modelling (i.e. in silico predictions based on computational and mathematical modelling using large-scale datasets) of the relevant signalling networks, leading to an output of testable hypotheses.

The components used in the development of the CanPathPro platform comprise highly defined mouse and organotypic experimental systems, next generation sequencing, SWATH-based proteomics and a systems biology computational model for data integration, visualisation and predictive modelling.
The project takes a unique approach, combining classic cancer research with omics data and computational modelling to develop and validate a new biotechnological application: a platform for generating and testing cancer signalling hypotheses in biomedical research.

Work performed

Work so far has provided a strong foundation for the generation of the CanPathPro prototype by the end of the project. Efforts have been focused on the generation of mouse cancer models and tumour sample collection, and the further development and improvement of our computational model. Computational methods for model simulation, parameter estimation, uncertainty and identifiability analysis have been improved and extended, and approaches for visualization of data and model systems have been developed. In tandem, access to the necessary computing resources has been obtained and software adapted for their efficient use. Using these models we are starting to build up a clearer picture of the effect of specific molecular alterations on cell signalling networks, using predictive modelling to generate hypotheses that can be validated experimentally. Exploitation of the platform is being investigated with the development of business and commercialisation plan. Moreover, CanPathPro’s visibility and communication efforts have been heightened, with an improved online and social media presence and new communication materials (see our Explainer video – www.canpathpro.eu/news/).

Final results

CanPathPro is developing and validating a bioinformatics concept and the associated computational tools required for the translation of highly complex and heterogeneous omics data into predictive modelling of cancer signalling.

The modelling system at the centre of CanPathPro – ModCellTM, a large mathematical model of cancer-related signal transduction pathways represents progress beyond the state of the art, because it provides exceptionally high-level signal transduction analysis (incorporating the functional effects of perturbations on components of thousands of biochemical reactions and identifying cross-talk between pathways) on a quantitative basis. In addition, the extensive refinement of the computational model (via parameter optimisation as well as iterative rounds of in silico modelling and in vivo validation) represents progress beyond state-of-the-art among systems biology approaches delineating cancer signalling.

The main expected result of the project is the CanPathPro prototype: A tripartite tool for the (i) generation and (ii) integration of quantitative mouse omics data, followed by (iii) predictive in silico modelling of cancer signalling pathways and networks in mouse models. The CanPathPro prototype will be built as a commercial platform; its development represents the main aim of the project and its establishment is the endpoint of the project. Additional expected results include the delineation of four breast and lung signalling pathways, which are analysed in CanPathPro during model development, and the optimisation of associated methodologies (organotypic systems, omics platforms, HCA assays, etc.) relevant for cancer research.

Robust and accurate functional predictions of the behaviour of complex biological systems, based on a validated system will have broad and significant 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, in interpretation and analysis of omics data. Thus, in the long run, the project will contribute significantly to improving outcomes for cancer patients, beyond breast and lung cancer sufferers. Additional impacts include bridging of knowledge from different disciplines (functional genomics, cancer biology, oncology, mathematics, systems biology); generation of new hypotheses for diagnosis and treatment of cancer and their in silico testing, by rapid simulation of millions of experimental conditions. The latter can facilitate prioritisation of experimental validation, effectively reducing animal experiments while enhancing the efficient use of time and financial research resources.

Website & more info

More info: http://www.canpathpro.eu.