Protein-network modeling of prostate cancer gene signatures reveals essential pathways in disease recurrence

James L. Chen, Jianrong Li, Walter M. Stadler, Yves A Lussier

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

Objective: Uncovering the dominant molecular deregulation among the multitude of pathways implicated in aggressive prostate cancer is essential to intelligently developing targeted therapies. Paradoxically, published prostate cancer gene expression signatures of poor prognosis share little overlap and thus do not reveal shared mechanisms. The authors hypothesize that, by analyzing gene signatures with quantitative models of proteineprotein interactions, key pathways will be elucidated and shown to be shared. Design: The authors statistically prioritized common interactors between established cancer genes and genes from each prostate cancer signature of poor prognosis independently via a previously validated single protein analysis of network (SPAN) methodology. Additionally, they computationally identified pathways among the aggregated interactors across signatures and validated them using a similarity metric and patient survival. Measurement: Using an information-theoretic metric, the authors assessed the mechanistic similarity of the interactor signature. Its prognostic ability was assessed in an independent cohort of 198 patients with high-Gleason prostate cancer using KaplaneMeier analysis. Results: Of the 13 prostate cancer signatures that were evaluated, eight interacted significantly with established cancer genes (false discovery rate <5%) and generated a 42-gene interactor signature that showed the highest mechanistic similarity (p<0.0001). Via parameter-free unsupervised classification, the interactor signature dichotomized the independent prostate cancer cohort with a significant survival difference (p=0.009). Interpretation of the network not only recapitulated phosphatidylinositol-3 kinase/NF-kB signaling, but also highlighted less well established relevant pathways such as the Janus kinase 2 cascade. Conclusions: SPAN methodolgy provides a robust means of abstracting disparate prostate cancer gene expression signatures into clinically useful, prioritized pathways as well as useful mechanistic pathways.

Original languageEnglish (US)
Pages (from-to)392-402
Number of pages11
JournalJournal of the American Medical Informatics Association
Volume18
Issue number4
DOIs
StatePublished - Jul 2011
Externally publishedYes

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Neoplasm Genes
Prostatic Neoplasms
Recurrence
Proteins
Transcriptome
Janus Kinase 2
Phosphatidylinositol 3-Kinase
Genes
Survival
NF-kappa B
Genetic Association Studies

ASJC Scopus subject areas

  • Health Informatics

Cite this

Protein-network modeling of prostate cancer gene signatures reveals essential pathways in disease recurrence. / Chen, James L.; Li, Jianrong; Stadler, Walter M.; Lussier, Yves A.

In: Journal of the American Medical Informatics Association, Vol. 18, No. 4, 07.2011, p. 392-402.

Research output: Contribution to journalArticle

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