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H2020

CanPathPro Report Summary

Project ID: 686282
Funded under: H2020-EU.2.1.4.

Periodic Reporting for period 1 - CanPathPro (Generation of the CanPath prototype - a platform for predictive cancer pathway modeling)

Reporting period: 2016-03-01 to 2017-02-28

Summary of the context and overall objectives of the project

Cancer is the result of mutations 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 of cancer.

Genetically engineered cancer mouse models are one of the main tools for functional analysis of cancer mutations. 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 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 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 from the beginning of the project to the end of the period covered by the report and main results achieved so far

During the first 12 months of the project, the CanPathPro consortium has performed 3 main tasks.

It established the scientific tools required for data acquisition, analysis and validation throughout the project. For the in silico part of the project, this includes development of mechanistic computational models, which have been adapted to the mouse genome and associated signalling. The models represent 45 cancer-associated signalling pathways and can incorporate the functional effects of perturbations on pathway components. The in vivo experimental tools include generation of 14 genetically engineered mouse models that recapitulate the molecular and histopathological characteristics of human breast and lung cancers; generation of organotypic cultures of the mammary gland and lung; establishment of in-depth tumour analysis assays (histopathology, quantitative immunohistochemistry, genomic, transcriptomic, SWATH-based phospho/proteomic profiling); establishment of High Content Analysis microscopy assays for high-throughput quantitative characterisation of cancer cell features.

Second, the consortium established the administrative structures required for project coordination, for consortium-internal communication and for dissemination and exploitation of project findings. Among these structures, the project website (www.CanPathPro.eu) provides a publically-accessible hub of information on consortium activities and a first point of contact to CanPathPro for scientists and members of the public.

Third, during this period, the first datasets have been acquired, analysed and modelled. This work has already delivered ensembles of optimised computational models, based on CanPathPro-generated experimental data and iterative rounds of in silico prediction and in vivo validation of these predictions.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

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.

Although still at the beginning of CanPathPro, the basis of the project - a large mathematical model of cancer-related signal transduction pathways (ModCellTM) - already 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 to build the validated bioinformatics concept into the CanPathPro prototype (see Figure): 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.

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