Analytical and Preanalytical Biases in Serum Proteomic Pattern Analysis for Breast Cancer Diagnosis

Abstract

Currently available serum tumor markers lack sufficient specificity and sensitivity as stand-alone diagnostic or screening tests (1). Nevertheless, these assays are used extensively because of a lack of better alternatives. To accelerate the discovery of tumor markers for diagnosis and/or prognosis, there has been great enthusiasm in attempting to use mass spectrometry (MS)-based testing of serum to identify potential biomarkers or spectral patterns that can act as a fingerprints for specific diseases (1). Analysis of serum by surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) MS was recently reported to be able to predict the existence of ovarian cancer without missing a single case (2). In this method, capture of serum proteins on a biochip by use of surface chemistry is followed by MS analysis of all captured proteins, and data mining software is used to identify a pattern of spectral peaks that will predict the presence or absence of the particular disease state in question (2). Although the seminal study using this technique has been criticized by statisticians, bioinformaticians, and clinical chemists because of the likelihood of systematic bias, there has been no empirical testing of the potential for systematic bias in SELDI-TOF serum analysis (1)(3)(4)(5)(6). This study was conducted to determine whether spectral patterns generated by SELDI-TOF MS could distinguish between patients with cancer and those with benign disease among women presenting with suspicious breast abnormalities on mammography or physical examination. The study was approved by the Research Ethics Board at the University of British Columbia, and all participating patients provided informed consent. We prospectively recruited 136 consecutive consenting patients attending 3 different clinics from September 2002 to April 2004 for a core needle biopsy for histopathologic diagnosis of a suspicious breast lump. Of the 136 patients, 3 were lost to follow-up, and …