01 Sep 2017   |   News   |   Sweden

Sweden: New atlas maps genes in cancer to accelerate progress in personalised medicine

A new Human Pathology Atlas launched this month by researchers at Sweden’s national Science for Life Laboratory analyses all human genes in all major cancers, showing the consequence of their corresponding protein levels for overall patient survival

In total, the Atlas is based on the analysis of 17 main cancer types using data from 8,000 patients.

A new concept for showing patient survival data is introduced, called Interactive Survival Scatter plots, with Sweden’s national supercomputer centre analysing more than 2.5 petabytes of underlying publicly available data to generate more than 900,000 survival plots, describing the consequence of RNA and protein levels on clinical survival.

The Pathology Atlas also contains five million pathology-based images generated by the Human Protein Atlas consortium.

Mathias Uhlen, leader of the Pathology Atlas work said, “This study differs from earlier cancer investigations, since it is not focused on the mutations in cancers, but the downstream effects of such mutations across all protein-coding genes. We show, for the first time, the influence of the gene expression levels demonstrating the power of big data to change how medical research is performed. It also shows the advantage of open access policies in science in which researchers share data with each other to allow integration of huge amounts of data from different sources.”

In a related paper published in Science earlier this month, the researchers report several important findings related to cancer biology and treatment. Firstly, a large fraction of genes is differentially expressed in cancers and in many cases have an impact on overall patient survival.

The research also showed that gene expression patterns of individual tumours varied considerably, and could exceed the variation observed between different cancer types.

The data allowed the researchers to generate personalised genome-scale metabolic models for cancer patients to identify key genes involved in tumour growth.

The work drew heavily on the supercomputing power available through the Science for Life Laboratory (SciLifeLab). Adil Mardinoglu, SciLifeLab fellow and leader of the systems biology effort in the project said, “We are now in possession of incredibly powerful systems biology tools for medical research, allowing, for the first time, genome-wide analysis of individual patients with regards to the consequence of their expression profiles for clinical survival.”

Uhlen et al: A Pathology Atlas of human cancer transcriptomes, Science 17 August 2017. http://dx.doi.org/10.1126/science.aan2507

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