Development of computational analysis platform for oncology
The development of a novel high-performance computational analysis platform for data analytics of –omics projects in the field of oncology
With the rise of the different types of omics and large-scale consortia projects, biological systems are being investigated at unprecedented scale and speed. The challenge of generating, storing, and analysing these large heterogeneous and multi-dimensional data sets requires the development of novel data integration methodologies, computational and experimental approaches. Within the Cancer Center Amsterdam (CCA), in which oncology research of VUmc and AMC is bundled, the potential information enclosed in the integration of these large data sets provides new leads for improvement of treatment strategies for patients with cancer, ultimately leading to overall survival benefit and reduced disease burden caused by ineffective treatment. Integrating, interpreting, and making use of large and heterogeneous datasets does not only represent a conceptual challenge but also a practical hurdle in the daily analysis of omics data.
Within CCA, multiple projects are generating large amounts of data that can result in promising leads to novel oncological diagnostics and therapeutics. To further advance the analysis of multiple data of different kinds from patients with cancer, there is an urgent need for the development of a professional and advanced high-performance computational analysis platform. Therefore, CCA sought collaboration with SAS, a market leader in the field of data analytics with strong expertise in the development of novel algorithms for data analysis. Together, CCA and SAS selected several omics-projects as pilots, including radiomics (extraction of quantitative features from medical images using data-characteristic algorithms) and proteomics (large-scale protein analysis).
The key challenge of setting up a high-performance computational analysis platform is to develop highly specific algorithms that allow for fast and accurate analysis of the generated data. Since a novel high-performance computational analysis platform will result in the potential identification of novel oncological targets and biomarkers, this project will strongly contribute to the goals of the LSH Top Sector.