title: Predicting high-risk disease using tissue biomarkers.
abstract: OBJECTIVE
  For men newly diagnosed with prostate cancer, there are limited tools to understand the risk of disease progression and guide the treatment decision process. We will provide an overview of current prostate cancer biomarker discovery and validation strategies that are geared toward identifying aggressive, clinically significant disease at the time of diagnosis.
  
  
  RESULTS
  The prostate gland exhibits multiple genetic events leading to both latent and clinically significant prostate cancer. Recent evidence from clinical translational studies has implicated the role of aneuploidy and copy-number variation as significant predictors of aggressive disease. Furthermore, the regulation of NKX3.1 by Pim-1 has provided a novel mechanism for the balance between indolence and disease course. Although promising, there are no routine clinically used tissue-based biomarkers for identifying risk of prostate cancer progression at diagnosis. The TMPRSS2-ERG gene fusion has provided insight into the early development of prostate cancer but has not been unequivocally associated with aggressive disease. Importantly, the only platform relying on intact tissue profiles is the systems pathology analysis program that includes histomorphometry and quantitative multiplex biomarker assessment (including the evaluation of the prostate cancer stem cell) to construct prognostic algorithms for pretreatment and post-treatment assessment.
  
  
  CONCLUSIONS
  Our objective for this review was to explore the effective use of prostate tissue samples, including fluids, to identify relevant markers of clinically significant disease. We believe that the inherent molecular heterogeneity in prostate cancer requires a multimodal approach, in the context of a systems pathology platform, to create the personalized tools for future diagnostic treatment algorithms.
authors.affiliation: Department of Pathology, Mt. Sinai School of Medicine, New York City, NY 10029-6574, USA. Michael.Donovan@mssm.edu
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Donovan
authors.forename: Carlos
authors.initials: C
authors.lastname: Cordon-Cardo
mesh_terms: D000465:Algorithms; D000818:Animals; D014408:Biomarkers, Tumor / Q000032:analysis / Q000235:genetics; D001706:Biopsy; D018450:Disease Progression; D006801:Humans; D008297:Male; D018579:Patient Selection; D011237:Predictive Value of Tests; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000737:chemistry / Q000175:diagnosis* / Q000235:genetics / Q000473:pathology / Q000628:therapy; D018570:Risk Assessment; D012307:Risk Factors; D049490:Systems Biology; D013997:Time Factors; D057832:Watchful Waiting
chemical_list: D014408:Biomarkers, Tumor