title: Serum proteomic patterns for detection of prostate cancer.
abstract: Pathologic states within the prostate may be reflected by changes in serum proteomic patterns. To test this hypothesis, we analyzed serum proteomic mass spectra with a bioinformatics tool to reveal the most fit pattern that discriminated the training set of sera of men with a histopathologic diagnosis of prostate cancer (serum prostate-specific antigen [PSA] > or =4 ng/mL) from those men without prostate cancer (serum PSA level <1 ng/mL). Mass spectra of blinded sera (N = 266) from a test set derived from men with prostate cancer or men without prostate cancer were matched against the discriminating pattern revealed by the training set. A predicted diagnosis of benign disease or cancer was rendered based on similarity to the discriminating pattern discovered from the training set. The proteomic pattern correctly predicted 36 (95%, 95% confidence interval [CI] = 82% to 99%) of 38 patients with prostate cancer, while 177 (78%, 95% CI = 72% to 83%) of 228 patients were correctly classified as having benign conditions. For men with marginally elevated PSA levels (4-10 ng/mL; n = 137), the specificity was 71%. If validated in future series, serum proteomic pattern diagnostics may be of value in deciding whether to perform a biopsy on a man with an elevated PSA level.
authors.affiliation: Food and Drug Administration (FDA)-National Cancer Institute (NCI) Clinical Proteomics Program, Department of Therapeutic Proteins/Center for Biologics Evaluation and Research (CBER), Bethesda, MD, USA. petricoin@cber.fda.gov
authors.forename: Emanuel F
authors.initials: EF
authors.lastname: Petricoin
authors.forename: David K
authors.initials: DK
authors.lastname: Ornstein
authors.forename: Cloud P
authors.initials: CP
authors.lastname: Paweletz
authors.forename: Ali
authors.initials: A
authors.lastname: Ardekani
authors.forename: Paul S
authors.initials: PS
authors.lastname: Hackett
authors.forename: Ben A
authors.initials: BA
authors.lastname: Hitt
authors.forename: Alfredo
authors.initials: A
authors.lastname: Velassco
authors.forename: Christian
authors.initials: C
authors.lastname: Trucco
authors.forename: Laura
authors.initials: L
authors.lastname: Wiegand
authors.forename: Kamillah
authors.initials: K
authors.lastname: Wood
authors.forename: Charles B
authors.initials: CB
authors.lastname: Simone
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Levine
authors.forename: W Marston
authors.initials: WM
authors.lastname: Linehan
authors.forename: Michael R
authors.initials: MR
authors.lastname: Emmert-Buck
authors.forename: Seth M
authors.initials: SM
authors.lastname: Steinberg
authors.forename: Elise C
authors.initials: EC
authors.lastname: Kohn
authors.forename: Lance A
authors.initials: LA
authors.lastname: Liotta
mesh_terms: D014408:Biomarkers, Tumor / Q000097:blood*; D016022:Case-Control Studies; D016009:Chi-Square Distribution; D003937:Diagnosis, Differential; D006801:Humans; D008297:Male; D013058:Mass Spectrometry; D011237:Predictive Value of Tests; D017430:Prostate-Specific Antigen / Q000097:blood; D011469:Prostatic Diseases / Q000097:blood / Q000175:diagnosis; D011471:Prostatic Neoplasms / Q000097:blood* / Q000175:diagnosis* / Q000276:immunology; D020543:Proteome / Q000032:analysis*
chemical_list: D014408:Biomarkers, Tumor; D020543:Proteome; D017430:Prostate-Specific Antigen