Oncogenic Pathway Combinations Predict Clinical Prognosis in Gastric Cancer
Name
Ooi-2009-Oncogenic Pathway Combinations Predict Clinical Prognosis in Gastric Cancer.pdf
Size
1.29 MB
Format
Adobe PDF
Checksum (MD5)
daaed71303defa49016a15e0f504d9bd
Author(s)
Ooi, Chia Huey
Ivanova, Tatiana
Wu, Jeanie
Lee, Minghui
Tan, Iain Beehuat
Tao, Jiong
Ward, Lindsay
Koo, Jun Hao
Gopalakrishnan, Veena
Zhu, Yansong
Date Issued
October 2009
Journal
PLoS Genetics
Publisher
Public Library of Science
Citation
Ooi, Chia Huey et al. “Oncogenic Pathway Combinations Predict Clinical Prognosis in Gastric Cancer.” Ed. Jason G. Mezey. PLoS Genetics 5.10 (2009) : e1000676.
Version
Final published version
Abstract
Many solid cancers are known to exhibit a high degree of heterogeneity in their deregulation of different oncogenic pathways. We sought to identify major oncogenic pathways in gastric cancer (GC) with significant relationships to patient survival. Using gene expression signatures, we devised an in silico strategy to map patterns of oncogenic pathway activation in 301 primary gastric cancers, the second highest cause of global cancer mortality. We identified three oncogenic pathways (proliferation/stem cell, NF-κB, and Wnt/β-catenin) deregulated in the majority (>70%) of gastric cancers. We functionally validated these pathway predictions in a panel of gastric cancer cell lines. Patient stratification by oncogenic pathway combinations showed reproducible and significant survival differences in multiple cohorts, suggesting that pathway interactions may play an important role in influencing disease behavior. Individual GCs can be successfully taxonomized by oncogenic pathway activity into biologically and clinically relevant subgroups. Predicting pathway activity by expression signatures thus permits the study of multiple cancer-related pathways interacting simultaneously in primary cancers, at a scale not currently achievable by other platforms.
MIT Department
Singapore-MIT Alliance in Research and Technology (SMART)
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pgen.1000676
