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Proteomics and Protein Markers |
Departments of
1
Biostatistics,
2 Laboratory Medicine,
3 Molecular and Cellular Oncology, and
4 Surgical Oncology, University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd., Box 447, Houston TX 77030.
5 Ciphergen Biosystems, Inc., 6611 Dumbarton Circle, Fremont, CA 94555.
aAuthor for correspondence. E-mail krc{at}odin.mdacc.tmc.edu.
Background: Recently, researchers have been using mass spectroscopy to study cancer. For use of proteomics spectra in a clinical setting, stringent quality-control procedures will be needed.
Methods: We pooled samples of nipple aspirate fluid from healthy breasts and breasts with cancer to prepare a control sample. Aliquots of the control sample were used on two spots on each of three IMAC ProteinChip® arrays (Ciphergen Biosystems, Inc.) on 4 successive days to generate 24 SELDI spectra. In 36 subsequent experiments, the control sample was applied to two spots of each ProteinChip array, and the resulting spectra were analyzed to determine how closely they agreed with the original 24 spectra.
Results: We describe novel algorithms that (a) locate peaks in unprocessed proteomics spectra and (b) iteratively combine peak detection with baseline correction. These algorithms detected 
200 peaks per spectrum, 68 of which are detected in all 24 original spectra. The peaks were highly correlated across samples. Moreover, we could explain 80% of the variance, using only six principal components. Using a criterion that rejects a chip if the Mahalanobis distance from both control spectra to the center of the six-dimensional principal component space exceeds the 95% confidence limit threshold, we rejected 5 of the 36 chips.
Conclusions: Mahalanobis distance in principal component space provides a method for assessing the reproducibility of proteomics spectra that is robust, effective, easily computed, and statistically sound.
The following articles in journals at HighWire Press have cited this article:
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  A. Prakash, B. Piening, J. Whiteaker, H. Zhang, S. A. Shaffer, D. Martin, L. Hohmann, K. Cooke, J. M. Olson, S. Hansen, et al. Assessing Bias in Experiment Design for Large Scale Mass Spectrometry-based Quantitative Proteomics Mol. Cell. Proteomics, October 1, 2007; 6(10): 1741 - 1748. [Abstract] [Full Text] [PDF]
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 Z. Djuric, G. Chen, J. Ren, R. Venkatramanamoorthy, C. Y. Covington, O. Kucuk, and L. K. Heilbrun Effects of High Fruit-Vegetable and/or Low-Fat Intervention on Breast Nipple Aspirate Fluid Micronutrient Levels Cancer Epidemiol. Biomarkers Prev., July 1, 2007; 16(7): 1393 - 1399. [Abstract] [Full Text] [PDF]
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 J. Albrethsen Reproducibility in Protein Profiling by MALDI-TOF Mass Spectrometry Clin. Chem., May 1, 2007; 53(5): 852 - 858. [Abstract] [Full Text] [PDF]
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 T. Nakagawa, S. K. Huang, S. R. Martinez, A. N. Tran, D. Elashoff, X. Ye, R. R. Turner, A. E. Giuliano, and D. S.B. Hoon Proteomic Profiling of Primary Breast Cancer Predicts Axillary Lymph Node Metastasis Cancer Res., December 15, 2006; 66(24): 11825 - 11830. [Abstract] [Full Text] [PDF]
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F. Bertucci, D. Birnbaum, and A. Goncalves Proteomics of Breast Cancer: Principles and Potential Clinical Applications Mol. Cell. Proteomics, October 1, 2006; 5(10): 1772 - 1786. [Abstract] [Full Text] [PDF]
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T. W. Randolph, B. L. Mitchell, D. F. McLerran, P. D. Lampe, and Z. Feng Quantifying Peptide Signal in MALDI-TOF Mass Spectrometry Data Mol. Cell. Proteomics, December 1, 2005; 4(12): 1990 - 1999. [Abstract] [Full Text] [PDF]
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R. E. Banks, A. J. Stanley, D. A. Cairns, J. H. Barrett, P. Clarke, D. Thompson, and P. J. Selby Influences of Blood Sample Processing on Low-Molecular-Weight Proteome Identified by Surface-Enhanced Laser Desorption/Ionization Mass Spectrometry Clin. Chem., September 1, 2005; 51(9): 1637 - 1649. [Abstract] [Full Text] [PDF]
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 N. Jeffries Algorithms for alignment of mass spectrometry proteomic data Bioinformatics, July 15, 2005; 21(14): 3066 - 3073. [Abstract] [Full Text] [PDF]
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J. S. Morris, K. R. Coombes, J. Koomen, K. A. Baggerly, and R. Kobayashi Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum Bioinformatics, May 1, 2005; 21(9): 1764 - 1775. [Abstract] [Full Text] [PDF]
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 J. M. Koomen, L. N. Shih, K. R. Coombes, D. Li, L.-c. Xiao, I. J. Fidler, J. L. Abbruzzese, and R. Kobayashi Plasma Protein Profiling for Diagnosis of Pancreatic Cancer Reveals the Presence of Host Response Proteins Clin. Cancer Res., February 1, 2005; 11(3): 1110 - 1118. [Abstract] [Full Text] [PDF]
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K. R. Coombes Analysis of Mass Spectrometry Profiles of the Serum Proteome Clin. Chem., January 1, 2005; 51(1): 1 - 2. [Full Text] [PDF]
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D. I. Malyarenko, W. E. Cooke, B.-L. Adam, G. Malik, H. Chen, E. R. Tracy, M. W. Trosset, M. Sasinowski, O. J. Semmes, and D. M. Manos Enhancement of Sensitivity and Resolution of Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometric Records for Serum Peptides Using Time-Series Analysis Techniques Clin. Chem., January 1, 2005; 51(1): 65 - 74. [Abstract] [Full Text] [PDF]
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