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Analytical Validation of Serum Proteomic Profiling for Diagnosis of Prostate Cancer: Sources of Sample Bias

¹ Fred Hutchinson Cancer Research Center, Seattle, WA, ² University of Alabama at Birmingham, Birmingham, AL, ³ University of Pittsburgh Cancer Institute, Pittsburgh, PA, ⁴ Center for Prostate Disease Research, Uniformed Services University of the Health Sciences, Rockville, MD, ⁵ Virginia Prostate Center, Eastern Virginia Medical School, Norfolk, VA, ⁶ Johns Hopkins Medical Institute, Baltimore, MD, ⁷ Institute of Drug Development, San Antonio Cancer Institute, San Antonio, TX, ⁸ National Cancer Institute, Rockville, MD, ⁹ University of Texas Health Science Center at San Antonio, San Antonio, TX, ¹⁰ Department of Public Health Services, University of Alberta, Edmonton.

^aAddress correspondence to this author at: O. John Semmes, Ph.D., The Virginia Prostate Center, Eastern Virginia Medical School, Lewis Hall 3110, Norfolk, Virginia 23506. e-mail semmesoj{at}evms.edu.

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.

Conclusion: 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.

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