Analytical validation of serum proteomic profiling for diagnosis of prostate cancer: Sources of sample bias

Dale McLerran, William E. Grizzle, Ziding Feng, William L. Bigbee, Lionel L. Banez, Lisa H. Cazares, Daniel W. Chan, Jose Diaz, Elzbieta Izbicka, Jacob Kagan, David E. Malehorn, Gunjan Malik, Denise Oelschlager, Alan Partin, Timothy Randolph, Nicole Rosenzweig, Shiv Srivastava, Sudhir Srivastava, Ian M. Thompson, Mark ThornquistDean Troyer, Yutaka Yasui, Zhen Zhang, Liu Zhu, O. John Semmes

Research output: Contribution to journalArticlepeer-review

105 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)44-52
Number of pages9
JournalClinical chemistry
Issue number1
StatePublished - Jan 1 2008

ASJC Scopus subject areas

  • Medicine(all)


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