Pathway biomarker profiling of localized and metastatic human prostate cancer reveal metastatic and prognostic signatures

Robert L. Grubb, Jianghong Deng, Peter A. Pinto, James L. Mohler, Arul Chinnaiyan, Mark Rubin, W. Marston Linehan, Lance A. Liotta, Emanuel F. Petricoin, Julia D. Wulfkuhle

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


Reverse phase protein microarray technology was used to study key signaling pathways thought to be involved in the progression of benign epithelium to the lethal phenotype of prostate cancer. Specimens of androgen-stimulated localized prostate cancer (N = 21) and androgen-deprivation therapy-recurrent local (N = 4) or metastatic (N = 11) prostate cancer were laser capture microdissected prior to analysis. The results showed significant increases in protein expression levels in malignant epithelial cells and patient-matched stromal tissue, which included higher levels of the apoptotic proteins Bax and Smac/ Diablo and increased phosphorylation of Bcl2 (S70). The mitochondrial protein Smac/Diablo and the transcription regulatory protein STAT3 (Y705) correlated with Gleason sum and differed statistically in high Gleason grade (8-10) prostate cancers. Distinct metastasis-specific pathways were activated by caspase cleavage activation, ErbB2 phosphorylation, Bax total protein and Bcl-2 phosphorylation while phosphorylation of all three members of the MAPK family, ERK, p38, and SAP/JNK, were reduced significantly in metastatic lesions compared to primary cancers. This study, the most comprehensive pathway analysis ever performed for human prostate cancer, presents evidence of specific pathway biomarkers that may be useful for assessment of prognosis and stratification for therapy if validated in larger clinical study sets.

Original languageEnglish
Pages (from-to)3044-3054
Number of pages11
JournalJournal of Proteome Research
Issue number6
StatePublished - 5 Jun 2009
Externally publishedYes


  • Metastasis
  • Prostate cancer
  • Protein microarray
  • Proteomics
  • Reverse phase array
  • Signaling


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