We introduce Pathifier, an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample. We demonstrate the algorithm's performance on three colorectal cancer datasets and two glioblastoma multiforme datasets and show that our multipathway-based repre- sentationisreproducible, preserves muchofthe original information, and allows inference of complex biologically significant information. We discovered several pathways that were significantly associated with survival of glioblastoma patients and two whose scores are predictive of survival in colorectal cancer: CXCR3-mediated signaling and oxidative phosphorylation. We also identified a subclass of pro-neural and neural glioblastoma with significantly better survival, and an EGF receptor-deregulated subclass of colon cancers.
|Original language||American English|
|Number of pages||6|
|Journal||Proceedings of the National Academy of Sciences of the United States of America|
|State||Published - 16 Apr 2013|
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