For many diseases, mRNA or protein expression profiling are the methods of choice for identifying biomarkers able to diagnose the severity of disease and to predict future disease outcomes. In many diseases, expression-based classification has yet to achieve high accuracy, however. One reason why such diagnostics fail is that changes in expression of the few genes causing disease can be subtle compared to those of the downstream effectors which may vary considerably from patient to patient. A more effective means of marker identification may be to combine gene expression measurements over groups of genes that fall within common pathways. In past years, the Ideker lab has developed several approaches for integrating expression profiles with pathways extracted from protein interaction networks [Chuang et al. MolSyst Biol. 3:140 2007] or curated from literature [Lee et al. PLoSCompu Biol. 4(11): e1000217 2008]. Large protein-protein interaction networks have only recently become available for human, enabling new opportunities for elucidating pathways involved in major diseases and pathologies. Our methodology is to overlay a patient's expression profile onto the human protein-protein interaction map to identify pathways that are predictive of disease. This approach has shown success in diagnosis of metastatic breast cancer [Chuang et al. MolSyst Biol. 3:140 2007] as well as classification of cell fate decisions during development [Ravasi et al. Cell 140(5):744-752 2010]. We are now working together with Dr. Thomas Kipps at the UCSD Moores Cancer Center to diagnose aggressive versus indolent cases of Chronic Lymphocytic Leukemia (CLL).This project is funded by grant IIS-0803937 from the National Science Foundation.
Extracting ‘network biomarkers’ from a large protein interaction data set