Literature DB >> 14751995

Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments.

Keith A Baggerly1, Jeffrey S Morris, Kevin R Coombes.   

Abstract

MOTIVATION: There has been much interest in using patterns derived from surface-enhanced laser desorption and ionization (SELDI) protein mass spectra from serum to differentiate samples from patients both with and without disease. Such patterns have been used without identification of the underlying proteins responsible. However, there are questions as to the stability of this procedure over multiple experiments.
RESULTS: We compared SELDI proteomic spectra from serum from three experiments by the same group on separating ovarian cancer from normal tissue. These spectra are available on the web at http://clinicalproteomics.steem.com. In general, the results were not reproducible across experiments. Baseline correction prevents reproduction of the results for two of the experiments. In one experiment, there is evidence of a major shift in protocol mid-experiment which could bias the results. In another, structure in the noise regions of the spectra allows us to distinguish normal from cancer, suggesting that the normals and cancers were processed differently. Sets of features found to discriminate well in one experiment do not generalize to other experiments. Finally, the mass calibration in all three experiments appears suspect. Taken together, these and other concerns suggest that much of the structure uncovered in these experiments could be due to artifacts of sample processing, not to the underlying biology of cancer. We provide some guidelines for design and analysis in experiments like these to ensure better reproducible, biologically meaningfully results. AVAILABILITY: The MATLAB and Perl code used in our analyses is available at http://bioinformatics.mdanderson.org

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Year:  2004        PMID: 14751995     DOI: 10.1093/bioinformatics/btg484

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  134 in total

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Review 2.  Cancer biomarkers.

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5.  Covariance adjustment for batch effect in gene expression data.

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Review 7.  Current status and prospects of clinical proteomics studies on detection of colorectal cancer: hopes and fears.

Authors:  M E de Noo; R A E M Tollenaar; A M Deelder; L H Bouwman
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8.  Analytical validation of serum proteomic profiling for diagnosis of prostate cancer: sources of sample bias.

Authors:  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 Thornquist; Dean Troyer; Yutaka Yasui; Zhen Zhang; Liu Zhu; O John Semmes
Journal:  Clin Chem       Date:  2007-11-02       Impact factor: 8.327

9.  Serum protein profiling of smear-positive and smear-negative pulmonary tuberculosis using SELDI-TOF mass spectrometry.

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10.  The knowledge-integrated network biomarkers discovery for major adverse cardiac events.

Authors:  Guangxu Jin; Xiaobo Zhou; Honghui Wang; Hong Zhao; Kemi Cui; Xiang-Sun Zhang; Luonan Chen; Stanley L Hazen; King Li; Stephen T C Wong
Journal:  J Proteome Res       Date:  2008-07-30       Impact factor: 4.466

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