Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets

By Silpa Suthram, Joel T. Dudley, Annie P. Chiang, Rong Chen, Trevor J. Hastie, Atul J. Butte. PLoS Comput. Biol., 2010

329 citations recorded by the source corpus.

Current work in elucidating relationships between diseases has largely been based on pre-existing knowledge of disease genes. Consequently, these studies are limited in their discovery of new and unknown disease relationships. We present the first quantitative framework to compare and contrast diseases by an integrated analysis of disease-related mRNA expression data and the human protein interaction network. We identified 4,620 functional modules in the human protein network and provided a quantitative metric to record their responses in 54 diseases leading to 138 significant similarities between diseases. Fourteen of the significant disease correlations also shared common drugs, supporting the hypothesis that similar diseases can be treated by the same drugs, allowing us to make predictions for new uses of existing drugs. Finally, we also identified 59 modules that were dysregulated in at least half of the diseases, representing a common disease-state “signature”. These modules were significantly enriched for genes that are known to be drug targets. Interestingly, drugs known to target these genes/proteins are already known to treat significantly more diseases than drugs targeting other genes/proteins, highlighting the importance of these core modules as prime therapeutic opportunities.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2bd610bb20f4a8c0462a77a79605a6789e85ffa6

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Citations in the corpus

Computational Drug Repositioning: From Data to Therapeutics (2013, background): “39 Screening data 40 and off-target effects offer tantalizing opportunities to reposition drugs.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “Suthram et al. (2010) identified 59 core modules out of the 4620 modules of the human interactome, which were affected by mRNA changes in more than half of the 54 diseases examined.”
Exploiting drug-disease relationships for computational drug repositioning (2011, background): “[38] integrated a larger set of molecular profiles of diseases with protein–protein interaction (PPI) data, to infer protein functional modules and networks that were shared among many diseases.”
The Emerging Paradigm of Network Medicine in the Study of Human Disease (2012, methodology): “Adapted from Suthram et al, 77 with permission.”
NCBI GEO: archive for functional genomics data sets—10 years on (2010, background): “Despite the heterogeneity of data types and experimental designs, many users have been able to perform powerful meta-analyses across thousands of independentlysubmitted GEO Samples (20,21).”