title: Genomic prostate cancer classifier predicts biochemical failure and metastases in patients after postoperative radiation therapy.
abstract: OBJECTIVE
  To test the hypothesis that a genomic classifier (GC) would predict biochemical failure (BF) and distant metastasis (DM) in men receiving radiation therapy (RT) after radical prostatectomy (RP).
  
  
  METHODS
  Among patients who underwent post-RP RT, 139 were identified for pT3 or positive margin, who did not receive neoadjuvant hormones and had paraffin-embedded specimens. Ribonucleic acid was extracted from the highest Gleason grade focus and applied to a high-density-oligonucleotide microarray. Receiver operating characteristic, calibration, cumulative incidence, and Cox regression analyses were performed to assess GC performance for predicting BF and DM after post-RP RT in comparison with clinical nomograms.
  
  
  RESULTS
  The area under the receiver operating characteristic curve of the Stephenson model was 0.70 for both BF and DM, with addition of GC significantly improving area under the receiver operating characteristic curve to 0.78 and 0.80, respectively. Stratified by GC risk groups, 8-year cumulative incidence was 21%, 48%, and 81% for BF (P<.0001) and for DM was 0, 12%, and 17% (P=.032) for low, intermediate, and high GC, respectively. In multivariable analysis, patients with high GC had a hazard ratio of 8.1 and 14.3 for BF and DM. In patients with intermediate or high GC, those irradiated with undetectable prostate-specific antigen (PSA ≤0.2 ng/mL) had median BF survival of >8 years, compared with <4 years for patients with detectable PSA (>0.2 ng/mL) before initiation of RT. At 8 years, the DM cumulative incidence for patients with high GC and RT with undetectable PSA was 3%, compared with 23% with detectable PSA (P=.03). No outcome differences were observed for low GC between the treatment groups.
  
  
  CONCLUSIONS
  The GC predicted BF and metastasis after post-RP irradiation. Patients with lower GC risk may benefit from delayed RT, as opposed to those with higher GC; however, this needs prospective validation. Genomic-based models may be useful for improved decision-making for treatment of high-risk prostate cancer.
authors.affiliation: Kimmel Cancer Center, Jefferson Medical College of Thomas Jefferson University, Philadelphia, Pennsylvania. Electronic address: Robert.Den@jeffersonhospital.org.
authors.forename: Robert B
authors.initials: RB
authors.lastname: Den
authors.affiliation: University of Michigan, Michigan Union, Michigan.
authors.forename: Felix Y
authors.initials: FY
authors.lastname: Feng
authors.affiliation: University of Virginia School of Medicine, Charlottesville, Virginia.
authors.forename: Timothy N
authors.initials: TN
authors.lastname: Showalter
authors.affiliation: University of Maryland Medical Center, Baltimore, Maryland.
authors.forename: Mark V
authors.initials: MV
authors.lastname: Mishra
authors.affiliation: Kimmel Cancer Center, Jefferson Medical College of Thomas Jefferson University, Philadelphia, Pennsylvania.
authors.forename: Edouard J
authors.initials: EJ
authors.lastname: Trabulsi
authors.affiliation: Kimmel Cancer Center, Jefferson Medical College of Thomas Jefferson University, Philadelphia, Pennsylvania.
authors.forename: Costas D
authors.initials: CD
authors.lastname: Lallas
authors.affiliation: Kimmel Cancer Center, Jefferson Medical College of Thomas Jefferson University, Philadelphia, Pennsylvania.
authors.forename: Leonard G
authors.initials: LG
authors.lastname: Gomella
authors.affiliation: Kimmel Cancer Center, Jefferson Medical College of Thomas Jefferson University, Philadelphia, Pennsylvania.
authors.forename: W Kevin
authors.initials: WK
authors.lastname: Kelly
authors.affiliation: Kimmel Cancer Center, Jefferson Medical College of Thomas Jefferson University, Philadelphia, Pennsylvania.
authors.forename: Ruth C
authors.initials: RC
authors.lastname: Birbe
authors.affiliation: Kimmel Cancer Center, Jefferson Medical College of Thomas Jefferson University, Philadelphia, Pennsylvania.
authors.forename: Peter A
authors.initials: PA
authors.lastname: McCue
authors.affiliation: GenomeDx Biosciences Inc., Vancouver, British Columbia, Canada.
authors.forename: Mercedeh
authors.initials: M
authors.lastname: Ghadessi
authors.affiliation: GenomeDx Biosciences Inc., Vancouver, British Columbia, Canada.
authors.forename: Kasra
authors.initials: K
authors.lastname: Yousefi
authors.affiliation: GenomeDx Biosciences Inc., Vancouver, British Columbia, Canada.
authors.forename: Elai
authors.initials: E
authors.lastname: Davicioni
authors.affiliation: Kimmel Cancer Center, Jefferson Medical College of Thomas Jefferson University, Philadelphia, Pennsylvania.
authors.forename: Karen E
authors.initials: KE
authors.lastname: Knudsen
authors.affiliation: Kimmel Cancer Center, Jefferson Medical College of Thomas Jefferson University, Philadelphia, Pennsylvania.
authors.forename: Adam P
authors.initials: AP
authors.lastname: Dicker
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D006801:Humans; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D060787:Neoplasm Grading; D009361:Neoplasm Invasiveness; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D017430:Prostate-Specific Antigen / Q000097:blood; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000097:blood / Q000737:chemistry* / Q000235:genetics / Q000473:pathology / Q000532:radiotherapy*; D012334:RNA, Neoplasm / Q000032:analysis*; D012372:ROC Curve; D018714:Radiotherapy, Adjuvant; D012044:Regression Analysis; D016879:Salvage Therapy; D017211:Treatment Failure
chemical_list: D012334:RNA, Neoplasm; D017430:Prostate-Specific Antigen