title: Protein-network modeling of prostate cancer gene signatures reveals essential pathways in disease recurrence.
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 protein-protein interactions, key pathways will be elucidated and shown to be shared.
  
  
  METHODS
  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.
  
  
  METHODS
  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 Kaplan-Meier 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-κB signaling, but also highlighted less well established relevant pathways such as the Janus kinase 2 cascade.
  
  
  CONCLUSIONS
  SPAN methodology provides a robust means of abstracting disparate prostate cancer gene expression signatures into clinically useful, prioritized pathways as well as useful mechanistic pathways.
authors.affiliation: Department of Medicine, Section of Hematology/Oncology, University of Chicago, Chicago, Illinois, USA.
authors.forename: James L
authors.initials: JL
authors.lastname: Chen
authors.forename: Jianrong
authors.initials: J
authors.lastname: Li
authors.forename: Walter M
authors.initials: WM
authors.lastname: Stadler
authors.forename: Yves A
authors.initials: YA
authors.lastname: Lussier
mesh_terms: D015415:Biomarkers; D018450:Disease Progression; D020869:Gene Expression Profiling; D023281:Genomics / Q000379:methods*; D006801:Humans; D053614:Janus Kinase 2 / Q000378:metabolism; D053208:Kaplan-Meier Estimate; D008297:Male; D058990:Molecular Targeted Therapy; D016328:NF-kappa B / Q000378:metabolism; D058539:Phosphatidylinositol 3-Kinase / Q000378:metabolism; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000188:drug therapy / Q000235:genetics*; D025941:Protein Interaction Mapping; D015398:Signal Transduction / Q000235:genetics
chemical_list: D015415:Biomarkers; D016328:NF-kappa B; D058539:Phosphatidylinositol 3-Kinase; C507924:JAK2 protein, human; D053614:Janus Kinase 2