Cancer is a leading cause of death worldwide and there is great need to define the molecular mechanisms driving the development and progression of individual tumors. The Hallmarks of Cancer has provided a framework for a deeper molecular understanding of cancer, and the focus so far has been on the genetic alterations in individual cancers, including genome rearrangements, gene amplifications, and specific cancer-driving mutations.
Now, Swedish scientists at the Royal Institute of Technology led by Mathias Uhlén, have launched a unique Pathology Atlas for cancer built on gene expression data that includes quantitative transcriptomics data (RNA-Seq) and spatial proteomics data (immunohistochemistry on tissue microarrays).
The difference in expression patterns of individual cancers observed in the study strongly reinforces the need for personalized cancer treatment based on precision medicine. In addition, the systems level approach used to construct the Pathology Atlas demonstrates the power of “big data” to change how medical research is performed.
This new pathology atlas is a major step forward for the dream of personalised cancer treatment. The analyses of data from 8,000 patients and five million pathology-based images cover all human genes involved in all common forms of cancer (17 different cancer types) and show the consequences of
their corresponding protein levels for patient survival.The researchers made a systems-based analysis of the transcriptome and corresponding proteins in 17 different types of cancer, including cancer of the lung, kidney, breast, pancreas and skin. The analysis, which resulted in 900,000 individual survival plots, required 200,000 core hours in supercomputing time.
The analysis identified candidate prognostic genes associated with clinical outcome for each tumor type. And while no single general prognostic gene could apply to all 17 forms of the disease that were examined, the researchers reported that shorter patient survival was associated with increased cell signaling activity, or up-regulation, among genes involved in cell growth. This pattern also involved down-regulation of genes involved in cellular differentiation.
“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,” Uhlén says.
Uhlén says the study shows the power of big data mining to transform medical research. “It also highlights 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,” he says.
The pathology atlas is now being opened up to researchers around the world. “We are pleased to provide a stand-alone open-access resource for cancer researchers worldwide, which we hope will accelerate their efforts to find the biomarkers needed to develop personalised cancer treatments,” says Fredrik Pontén, Professor of Clinical and Experimental Pathology at the Department of Immunology, Genetics and Pathology, Uppsala University.
