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 has addressed these challenges. The overall objective of the project was 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 has combined, in a single platform, omics and quantitative immunohistopathological data of cancer mouse models with analytical, modelling, predictive and visualisation computational tools. The platform performs 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 that have been 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 has taken an 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.
