Pathway-based personalized analysis of cancer

被引:233
作者
Drier, Yotam [1 ]
Sheffer, Michal [1 ]
Domany, Eytan [1 ]
机构
[1] Weizmann Inst Sci, Dept Phys Complex Syst, IL-76100 Rehovot, Israel
基金
以色列科学基金会;
关键词
computational biology; systems biology; oncogenomics; principal curve; DISEASE PROGRESSION; BREAST-CANCER; EXPRESSION; GLIOBLASTOMA; INSTABILITY; ASSOCIATION; INFORMATION; PROGNOSIS; GENES; EGFR;
D O I
10.1073/pnas.1219651110
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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 representationis 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.
引用
收藏
页码:6388 / 6393
页数:6
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