MALDI/SELDI protein profiling of serum for the identification of cancer biomarkers.

George L. Wright Jr. Center for Biomedical Proteomics, Eastern Virginia Medical School, Norfolk, VA, USA.
Methods in Molecular Biology (Impact Factor: 1.29). 02/2008; 428:125-40. DOI: 10.1007/978-1-59745-117-8_7
Source: PubMed

ABSTRACT The ability to visualize the full depth of the serum proteome in a high-throughput manner is a major goal of clinical proteomics. Methodologies, which combine higher throughput with the ability to observe differential protein expression levels, have been applied to this goal. An example of such a system is the coupling of robotic sample processing to matrix-assisted laser desorption time of flight mass spectrometry (MALDI-TOF-MS). Within this paradigm is a modification of MALDI-TOF termed surface-enhanced laser desorption/ionization-TOF (SELDI-TOF). Both conventional MALDI and SELDI have been used to generate protein expression profiles reflective of potential peptide changes in serum. This information can be used to identify proteins, which may enable new diagnostic and therapeutic strategies.

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Available from: Oliver John Semmes, Sep 26, 2015
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    • "Groups of peaks (features) may have similar, but not identical m/z values, appearing in spectra acquired at different laser energies, from different chromatographic fractions of samples, or even at mass multiples that might indicate different ionizations or protein aggregates. In addition there could be biological correlations such as proteins without and with post-translational modifications [4-6]. We have previously developed a clustering algorithm for dealing with correlations in protein profiling SELDI-TOF proteomic data, such as those found in SELDI biomarker discovery studies [7]. "
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    ABSTRACT: Biomarker discovery datasets created using mass spectrum protein profiling of complex mixtures of proteins contain many peaks that represent the same protein with different charge states. Correlated variables such as these can confound the statistical analyses of proteomic data. Previously we developed an algorithm that clustered mass spectrum peaks that were biologically or technically correlated. Here we demonstrate an algorithm that clusters correlated technical aliases only. In this paper, we propose a preprocessing algorithm that can be used for grouping technical aliases in mass spectrometry protein profiling data. The stringency of the variance allowed for clustering is customizable, thereby affecting the number of peaks that are clustered. Subsequent analysis of the clusters, instead of individual peaks, help reduce difficulties associated with technically-correlated data, and can aid more efficient biomarker identification. This software can be used to pre-process and thereby decrease the complexity of protein profiling proteomics data, thus simplifying the subsequent analysis of biomarkers by decreasing the number of tests. The software is also a practical tool for identifying which features to investigate further by purification, identification and confirmation.
    BMC Research Notes 09/2013; 6(1):358. DOI:10.1186/1756-0500-6-358
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    • "As a result, the explicit experimental methods were published in inadequate detail for independent validation. Since then, the trend has been toward identifying the analytes (identified, proteotypic peptides diagnostic of defined proteins) that can establish disease association with, for example, MALDI-TOF as an improvement over anonymous mass spectra.94 There has been discussion of the contentious early analytical issues surrounding cancer biomarker discovery,8,37,38,95 and these lessons should inform experimental biomarker qualification/validation strategies in the future in other areas of application, beyond the area of early cancer detection alone. "
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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.
    Biomarker insights 11/2009; 4:165-79.
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    • "The disadvantages of these techniques are the relatively low throughput of samples and possible memory effects of the column which are not easily compatible with clinical requirements. Instead of LC, functionalized pipette tips, pipette tips filled with column material , target surfaces (SELDI) [20] [21] [22] or particles, either magnetic or on sepharose basis [23] [24], have been employed for purification, separation, and concentration of samples. With respect to the clinical routine use, all of the last mentioned separation devices are disposables reducing the risk of cross contamination of samples and allow for more or less high-throughput analyses and automated workflows. "
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    ABSTRACT: The discovery of novel biomarkers by means of advanced detection tools based on proteomic analysis technologies necessitates the development of improved diagnostic methods for application in clinical routine. On the basis of three different application examples, this review presents the limitations of conventional routine diagnostic assays and illustrates the advantages of immunoaffinity enrichment combined with MALDI-TOF MS. Applying this approach increases the specificity of the analysis supporting a better diagnostic recognition, sensitivity, and differentiation of certain diseases. The use of MALDI-TOF MS as detection method facilitates the identification of modified peptides and proteins providing additional information. Further, employing respective internal standard peptides allows for relative and absolute quantitation which is mandatory in the clinical context. Although MALDI-TOF MS is not yet established for clinical routine diagnostics this technology has a high potential for improvement of clinical diagnostics and monitoring therapeutic efficacy.
    Proteomics 03/2009; 9(6):1442-50. DOI:10.1002/pmic.200800616 · 3.81 Impact Factor
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