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

Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Clinical Chemistry (Impact Factor: 7.91). 02/2008; 54(1):44-52. DOI: 10.1373/clinchem.2007.091470
Source: PubMed


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.
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.
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.
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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Available from: Elzbieta Izbicka
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    • "In recent years, proteomic techniques have achieved a rapid evolution, due to innovative experimental approaches and improvements in sensitivity, resolution and accuracy of the mass analysers. Several proteomic studies have been carried out on serum [7,8], urine [9], biopsy tissue [10] and cell lines [11,12], with the purpose of identifying promising targets for the early detection of PCa. Unfortunately, the majority of the candidate biomarkers are still awaiting validation [13,14]. "
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    ABSTRACT: Background A more specific and early diagnostics for prostate cancer (PCa) is highly desirable. In this study, being inflammation the focus of our effort, serum protein profiles were analyzed in order to investigate if this parameter could interfere with the search of discriminating proteins between PCa and benign prostatic hyperplasia (BPH). Methods Patients with clinical suspect of PCa and candidates for trans-rectal ultrasound guided prostate biopsy (TRUS) were enrolled. Histological specimens were examined in order to grade and classify the tumor, identify BPH and detect inflammation. Surface Enhanced Laser Desorption/Ionization-Time of Flight-Mass Spectrometry (SELDI-ToF-MS) and two-dimensional gel electrophoresis (2-DE) coupled with Liquid Chromatography-MS/MS (LC-MS/MS) were used to analyze immuno-depleted serum samples from patients with PCa and BPH. Results The comparison between PCa (with and without inflammation) and BPH (with and without inflammation) serum samples by SELDI-ToF-MS analysis did not show differences in protein expression, while changes were only observed when the concomitant presence of inflammation was taken into consideration. In fact, when samples with histological sign of inflammation were excluded, 20 significantly different protein peaks were detected. Subsequent comparisons (PCa with inflammation vs PCa without inflammation, and BPH with inflammation vs BPH without inflammation) showed that 16 proteins appeared to be modified in the presence of inflammation, while 4 protein peaks were not modified. With 2-DE analysis, comparing PCa without inflammation vs PCa with inflammation, and BPH without inflammation vs the same condition in the presence of inflammation, were identified 29 and 25 differentially expressed protein spots, respectively. Excluding samples with inflammation the comparison between PCa vs BPH showed 9 unique PCa proteins, 4 of which overlapped with those previously identified in the presence of inflammation, while other 2 were new proteins, not identified in our previous comparisons. Conclusions The present study indicates that inflammation might be a confounding parameter during the proteomic research of candidate biomarkers of PCa. These results indicate that some possible biomarker-candidate proteins are strongly influenced by the presence of inflammation, hence only a well-selected protein pattern should be considered for potential marker of PCa.
    Full-text · Article · Jun 2014 · Proteome Science
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    • "It can be seen that the spectra are noisy, especially among the small peaks that are close to the baseline; in addition, the two spectra have different baselines; and finally, the two spectra are not aligned—the peaks corresponding to the same bioentity do not show up at the same m/z location. Therefore, to ensure the consistency of the subsequent analyses, various data preprocessing steps such as smoothing, baseline correction, normalization and peak alignment, are required.2–6 "
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    ABSTRACT: With recent advances in mass spectrometry (MS) technologies, it is now possible to study protein profiles over a wide range of molecular weights in small biological specimens. However, MS spectra are usually not aligned or synchronized between samples. To ensure the consistency of the subsequent analysis, spectrum alignment is necessary to align the spectra such that the same biological entity would show up at the same m/z value for different samples. Although a variety of alignment algorithms have been proposed in the past, most of them are developed based on chromatographic data and do not address some of the unique characteristics of the serum or other body fluid MS data. In this work, we propose a self-calibrated warping (SCW) algorithm to address some of the challenges associated with serum MS data alignment. In addition, we compare the proposed algorithm with five existing representative alignment methods using a clinical surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) data set.
    Full-text · Article · Mar 2011 · Cancer informatics
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    • "Study design and bias have also been problematic for cancer biomarker validation and qualification. This is illustrated in recent prostate cancer early detection proteomics with surface enhanced laser desorption-ionization based mass spectrometry (SELDI-MS), where biased specimen collection and storage has hampered progress.13,14 Furthermore, despite significant activity in the private sector on cancer drug development, biomarker discovery efforts have fallen short of anticipated benefits and cost savings.6,15 "
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    ABSTRACT: The anticipated biological and clinical utility of biomarkers has attracted significant interest recently. Aging and early cancer detection represent areas active in the search for predictive and prognostic biomarkers. While applications differ, overlapping biological features, analytical technologies and specific biomarker analytes bear comparison. Mitochondrial DNA (mtDNA) as a biomarker in both biological models has been evaluated. However, it remains unclear whether mtDNA changes in aging and cancer represent biological relationships that are causal, incidental, or a combination of both. This article focuses on evaluation of mtDNA-based biomarkers, emerging strategies for quantitating mtDNA admixtures, and how current understanding of mtDNA in aging and cancer evolves with introduction of new technologies. Whether for cancer or aging, lessons from mtDNA based biomarker evaluations are several. Biological systems are inherently dynamic and heterogeneous. Detection limits for mtDNA sequencing technologies differ among methods for low-level DNA sequence admixtures in healthy and diseased states. Performance metrics of analytical mtDNA technology should be validated prior to application in heterogeneous biologically-based systems. Critical in evaluating biomarker performance is the ability to distinguish measurement system variance from inherent biological variance, because it is within the latter that background healthy variability as well as high-value, disease-specific information reside.
    Full-text · Article · Nov 2009 · Biomarker insights
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