title: Systems pathology approach for the prediction of prostate cancer progression after radical prostatectomy.
abstract: OBJECTIVE
  For patients with prostate cancer treated by radical prostatectomy, no current personalized tools predict clinical failure (CF; metastasis and/or androgen-independent disease). We developed such a tool through integration of clinicopathologic data with image analysis and quantitative immunofluorescence of prostate cancer tissue.
  
  
  METHODS
  A prospectively designed algorithm was applied retrospectively to a cohort of 758 patients with clinically localized or locally advanced prostate cancer. A model predicting distant metastasis and/or androgen-independent recurrence was derived from features selected through supervised multivariate learning. Performance of the model was estimated using the concordance index (CI).
  
  
  RESULTS
  We developed a predictive model using a training set of 373 patients with 33 CF events. The model includes androgen receptor (AR) levels, dominant prostatectomy Gleason grade, lymph node involvement, and three quantitative characteristics from hematoxylin and eosin staining of prostate tissue. The model had a CI of 0.92, sensitivity of 90%, and specificity of 91% for predicting CF within 5 years after prostatectomy. Model validation on an independent cohort of 385 patients with 29 CF events yielded a CI of 0.84, sensitivity of 84%, and specificity of 85%. High levels of AR predicted shorter time to castrate prostate-specific antigen increase after androgen deprivation therapy (ADT).
  
  
  CONCLUSIONS
  The integration of clinicopathologic variables with imaging and biomarker data (systems pathology) resulted in a highly accurate tool for predicting CF within 5 years after prostatectomy. The data support a role for AR signaling in clinical progression and duration of response to ADT.
authors: Aureon Laboratories Inc, 28 Wells Ave, Yonkers, NY 10701, USA. mdonovan@pathocon.com
authors: Michael J
authors: MJ
authors: Donovan
authors: Stefan
authors: S
authors: Hamann
authors: Mark
authors: M
authors: Clayton
authors: Faisal M
authors: FM
authors: Khan
authors: Marina
authors: M
authors: Sapir
authors: Valentina
authors: V
authors: Bayer-Zubek
authors: Gerardo
authors: G
authors: Fernandez
authors: Ricardo
authors: R
authors: Mesa-Tejada
authors: Mikhail
authors: M
authors: Teverovskiy
authors: Victor E
authors: VE
authors: Reuter
authors: Peter T
authors: PT
authors: Scardino
authors: Carlos
authors: C
authors: Cordon-Cardo
mesh_terms: D000465:Algorithms; D015331:Cohort Studies; D018450:Disease Progression; D005455:Fluorescent Antibody Technique; D006801:Humans; D008297:Male; D015233:Models, Statistical; D009364:Neoplasm Recurrence, Local / Q000473:pathology*; D011237:Predictive Value of Tests; D017430:Prostate-Specific Antigen / Q000378:metabolism; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000378:metabolism / Q000473:pathology* / Q000601:surgery*; D011944:Receptors, Androgen / Q000378:metabolism; D016896:Treatment Outcome
chemical_list: D011944:Receptors, Androgen; D017430:Prostate-Specific Antigen