Integrated cross-species transcriptional network analysis of metastatic susceptibility

Ying Hu, Gang Wu, Michael Rusch, Luanne Lukes, Kenneth H. Buetow, Jinghui Zhang, Kent W. Hunter

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


Metastatic disease is the proximal cause of mortality for most cancers and remains a significant problem for the clinical management of neoplastic disease. Recent advances in global transcriptional analysis have enabled better prediction of individuals likely to progress to metastatic disease. However, minimal overlap between predictive signatures has precluded easy identification of key biological processes contributing to the prometastatic transcriptional state. To overcome this limitation, we have applied network analysis to two independent human breast cancer datasets and three different mouse populations developed for quantitative analysis of metastasis. Analysis of these datasets revealed that the gene membership of the networks is highly conserved within and between species, and that these networks predicted distant metastasis free survival. Furthermore these results suggest that susceptibility to metastatic disease is cell-autonomous in estrogen receptor-positive tumors and associated with the mitotic spindle checkpoint. In contrast, nontumor genetics and pathway activities-associated stromal biology are significant modifiers of the rate of metastatic spread of estrogen receptor-negative tumors. These results suggest that the application of network analysis across species may provide a robust method to identify key biological programs associated with human cancer progression.

Original languageEnglish (US)
Pages (from-to)3184-3189
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number8
StatePublished - Feb 21 2012
Externally publishedYes


  • Gene expression
  • Mouse models

ASJC Scopus subject areas

  • General


Dive into the research topics of 'Integrated cross-species transcriptional network analysis of metastatic susceptibility'. Together they form a unique fingerprint.

Cite this