title: Analytical validation of serum proteomic profiling for diagnosis of prostate cancer: sources of sample bias.
abstract: BACKGROUND
  This report and a companion report describe a validation of the ability of serum proteomic profiling via SELDI-TOF mass spectrometry to detect prostatic cancer. Details of this 3-stage process have been described. This report describes the development of the algorithm and results of the blinded test for stage 1.
  
  
  METHODS
  We derived the decision algorithm used in this study from the analysis of serum samples from patients with prostate cancer (n = 181) and benign prostatic hyperplasia (BPH) (n = 143) and normal controls (n = 220). We also derived a validation test set from a separate, geographically diverse set of serum samples from 42 prostate cancer patients and 42 controls without prostate cancer. Aliquots were subjected to randomization and blinded analysis, and data from each laboratory site were subjected to the decision algorithm and decoded.
  
  
  RESULTS
  Using the data collected from the validation test set, the decision algorithm was unsuccessful in separating cancer from controls with any predictive utility. Analysis of the experimental data revealed potential sources of bias.
  
  
  CONCLUSIONS
  The ability of the decision algorithm to successfully differentiate between prostate cancer, BPH, and control samples using data derived from serum protein profiling was compromised by bias.
authors.affiliation: Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
authors.forename: Dale
authors.initials: D
authors.lastname: McLerran
authors.forename: William E
authors.initials: WE
authors.lastname: Grizzle
authors.forename: Ziding
authors.initials: Z
authors.lastname: Feng
authors.forename: William L
authors.initials: WL
authors.lastname: Bigbee
authors.forename: Lionel L
authors.initials: LL
authors.lastname: Banez
authors.forename: Lisa H
authors.initials: LH
authors.lastname: Cazares
authors.forename: Daniel W
authors.initials: DW
authors.lastname: Chan
authors.forename: Jose
authors.initials: J
authors.lastname: Diaz
authors.forename: Elzbieta
authors.initials: E
authors.lastname: Izbicka
authors.forename: Jacob
authors.initials: J
authors.lastname: Kagan
authors.forename: David E
authors.initials: DE
authors.lastname: Malehorn
authors.forename: Gunjan
authors.initials: G
authors.lastname: Malik
authors.forename: Denise
authors.initials: D
authors.lastname: Oelschlager
authors.forename: Alan
authors.initials: A
authors.lastname: Partin
authors.forename: Timothy
authors.initials: T
authors.lastname: Randolph
authors.forename: Nicole
authors.initials: N
authors.lastname: Rosenzweig
authors.forename: Shiv
authors.initials: S
authors.lastname: Srivastava
authors.forename: Sudhir
authors.initials: S
authors.lastname: Srivastava
authors.forename: Ian M
authors.initials: IM
authors.lastname: Thompson
authors.forename: Mark
authors.initials: M
authors.lastname: Thornquist
authors.forename: Dean
authors.initials: D
authors.lastname: Troyer
authors.forename: Yutaka
authors.initials: Y
authors.lastname: Yasui
authors.forename: Zhen
authors.initials: Z
authors.lastname: Zhang
authors.forename: Liu
authors.initials: L
authors.lastname: Zhu
authors.forename: O John
authors.initials: OJ
authors.lastname: Semmes
mesh_terms: D000465:Algorithms; D015982:Bias; D014408:Biomarkers, Tumor / Q000097:blood*; D001800:Blood Specimen Collection; D003661:Decision Support Techniques; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011470:Prostatic Hyperplasia / Q000175:diagnosis / Q000378:metabolism; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000378:metabolism; D040901:Proteomics; D012372:ROC Curve; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
chemical_list: D014408:Biomarkers, Tumor