Pathway-based personalized analysis of cancer.

Abstract:

: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 representation is reproducible, preserves much of the 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 proneural and neural glioblastoma with significantly better survival, and an EGF receptor-deregulated subclass of colon cancers.

authors

Drier Y,Sheffer M,Domany E

doi

10.1073/pnas.1219651110

subject

Has Abstract

pub_date

2013-04-16 00:00:00

pages

6388-93

issue

16

eissn

0027-8424

issn

1091-6490

pii

1219651110

journal_volume

110

pub_type

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