Biomarker discovery by proteomics: challenges not only for the analytical chemist.

This forum article outlines some of the major challenges in present day biomarker discovery research. Notably the dilemma of reaching sufficient concentration sensitivity versus the required analysis time per sample is highlighted using a model calculation. A number of possible developments and recent research findings are considered to show possible ways out of this dilemma. Finally, the challenge of processing large, megavariate datasets prior to statistical analysis is presented.

[1]  Yongyi Mao,et al.  Informatics Platform for Global Proteomic Profiling and Biomarker Discovery Using Liquid Chromatography-Tandem Mass Spectrometry*S , 2004, Molecular & Cellular Proteomics.

[2]  R. Tibshirani,et al.  Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[3]  M. Hilario,et al.  Processing and classification of protein mass spectra. , 2006, Mass spectrometry reviews.

[4]  A. Pothen,et al.  Protocols for disease classification from mass spectrometry data , 2003, Proteomics.

[5]  J. Listgarten,et al.  Statistical and Computational Methods for Comparative Proteomic Profiling Using Liquid Chromatography-Tandem Mass Spectrometry , 2005, Molecular & Cellular Proteomics.

[6]  Richard D. Smith,et al.  Ultrahigh-throughput proteomics using fast RPLC separations with ESI-MS/MS. , 2005, Analytical chemistry.

[7]  A. Olshen,et al.  Differential exoprotease activities confer tumor-specific serum peptidome patterns. , 2005, The Journal of clinical investigation.

[8]  J. Greef,et al.  Rescuing drug discovery: in vivo systems pathology and systems pharmacology , 2005, Nature Reviews Drug Discovery.

[9]  Frans van den Berg,et al.  Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data , 2004 .

[10]  Bruce Randall Donald,et al.  Probabilistic Disease Classification of Expression-Dependent Proteomic Data from Mass Spectrometry of Human Serum , 2003, J. Comput. Biol..

[11]  R. Bischoff,et al.  The use of affinity sorbents in targeted proteomics. , 2006, Drug discovery today. Technologies.

[12]  Elisabeth Verpoorte,et al.  Fast, high-efficiency peptide separations on a 50-microm reversed-phase silica monolith in a nanoLC-MS set-up. , 2006, Journal of chromatography. A.

[13]  P. Mielke,et al.  Permutation Methods: A Distance Function Approach , 2007 .

[14]  P. Tempst,et al.  Correcting common errors in identifying cancer-specific serum peptide signatures. , 2005, Journal of proteome research.

[15]  E. Verpoorte,et al.  Silica monolithic columns: synthesis, characterisation and applications to the analysis of biological molecules. , 2005, Journal of separation science.

[16]  R. Jansen,et al.  Analysis of human serum by liquid chromatography-mass spectrometry: improved sample preparation and data analysis. , 2006, Journal of chromatography. A.

[17]  Hua Lin,et al.  Quantifying reproducibility for differential proteomics: noise analysis for protein liquid chromatography-mass spectrometry of human serum , 2004, Bioinform..

[18]  H. D. de Bruijn,et al.  Sample preparation of human serum for the analysis of tumor markers. Comparison of different approaches for albumin and gamma-globulin depletion. , 2003, Journal of chromatography. A.

[19]  G. Nicol,et al.  Reversed-phase high-performance liquid chromatographic prefractionation of immunodepleted human serum proteins to enhance mass spectrometry identification of lower-abundant proteins. , 2005, Journal of proteome research.

[20]  E. Marchiori,et al.  Sample handling for mass spectrometric proteomic investigations of human sera. , 2005, Analytical chemistry.