Using an SGI cluster, scientists at Linding Lab, part of the Biotech Research and Innovation Centre (BRIC) at the University of Copenhagen (UCPH), have discovered how genetic diseases such as cancer systematically attack the networks controlling human cells.
Scientists and researchers from the Yale, Zurich, Rome, and Tottori (Japan) collaborated with the BRIC at UCPH to develop advanced algorithms that can integrate data from quantitative mass-spectrometry and next generation sequencing of tumour samples. The researchers have been able to uncover cancer-related changes to phospho-signaling networks which could lead to the development of novel therapies and screening methods to help treat cancer patients.
Professor Dr Rune Linding, lead researcher on the projects from the Biotech Research and Innovation Centre said: ‘This new breakthrough allows researchers to identify the effects of mutations on the function of proteins in cancer for individual patients, even if those mutations are very rare. The identification of distinct changes within our tissues that help predict and treat cancer is a major step forward and we are confident it can aid in the development of novel therapies and screening techniques.’
These studies represent some of the initial work completed through the strategic collaboration between SGI and the Linding Lab. The findings have been published in two back-to-back papers in Cell journal.
This research highlights the importance of big data in cancer biology and demonstrates the increasing need for a large dynamic-range computing platforms. For this project the team's computational resources came from SGI UV server platform which are designed for compute-intensive, fast algorithm workloads such as CAE, genome assembly, and scientific simulations. Featuring Intel Xeon E5-4600 v3 processors and a NUMAlink topology with ultra-high scalability, these servers provide a greater processor-to-memory ratio – something critical to compute intensive applications such as exploring biological systems by modelling cell behaviour.
‘There is going to be more and more data available to us, and as scientists trying to lower the cancer burden, technology like SGI’s UV system can make sense of all this data. This technology is a real game changer and these findings are a significant discovery from life sciences using a supercomputer, which we hope will make a difference for cancer patient’s worldwide,’ stated Linding.
The interpretation of these big data sets requires more advanced modelling frameworks than traditional bioinformatics approaches. In particular, models need to account for the inherent variability and heterogeneity of biological data, which can only be achieved in a rigorous manner by probabilistic Bayesian methodologies. As these methods in turn are much more computationally demanding, technologies like the SGI UV system are becoming mandatory to support scientific analysis, and to advance our understanding of, and ability to treat, complex diseases such as cancer.
‘Thanks to the power of the technology in our supercomputers, SGI supports a broad range of fascinating and history-making research projects that will leave a strong mark in the life sciences and on the medical science community,’ said Jorge Titinger, president and CEO, SGI.
The two studies are available today in an advanced online publication and will be printed in the 24 September issue of Cell. The work was supported by the European Research Council (ERC), the Lundbeck Foundation and Human Frontier Science Programme.
The Linding lab, part of the BRIC centre at the University of Copenhagen, is a big data network biology research group that is exploring biological systems through the development and deployment of algorithms which can be used to forecast cell behaviour.
The Biotech Research and Innovation Centre (BRIC) was established in 2003 by the Danish Ministry of Science, Technology and Innovation to form an elite centre for biomedical research.
