Serum proteomics using mass spectrometry.

The identification and eventual application of tumor markers in cancer screening, early detection, diagnosis, and prognosis is a continuing focus of significant translational cancer research. While many new candidate markers have been discovered and at least partly characterized, very few have found widespread clinical application limited presently to the use of CA-125 in ovarian cancer, CEA, primarily in colon cancer, and PSA in prostate cancer screening and patient monitoring. The rapidly emerging field of cancer genomics and proteomics, and their clinical translation as "molecular diagnosis" and "molecular medicine" are already beginning to transform the field, and the accelerating growth of information and technology in this research area will undoubtedly transform the field of tumor markers and their application in the near future leading to improved molecular tools for cancer diagnosis, prognosis, and treatment and ultimately, to the emergence of novel and more effective cancer therapies, including improved approaches for immunotherapy and cancer prevention strategies. Toward this goal, herein are described detailed methods and workflows for mass spectrometry-based biomarker discovery in serum/plasma utilizing two complementary approaches - matrix-assisted laser desorption ionization time of flight (MALDI-TOF) and nanoflow reversed-phase liquid chromatography (RPLC)-tandem mass spectrometry (MS/MS). These discovery workflows incorporate both abundant protein depletion and sample fractionation upstream of analytical mass spectrometry to optimize the identification and quantitation of lower abundant species.

[1]  J. Yates,et al.  Large-scale analysis of the yeast proteome by multidimensional protein identification technology , 2001, Nature Biotechnology.

[2]  T. Veenstra,et al.  Characterization of the Low Molecular Weight Human Serum Proteome*S , 2003, Molecular & Cellular Proteomics.

[3]  Jeffrey S. Morris,et al.  Signal in noise: evaluating reported reproducibility of serum proteomic tests for ovarian cancer. , 2005, Journal of the National Cancer Institute.

[4]  H. Otu,et al.  Optimization and evaluation of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) with reversed-phase protein arrays for protein profiling , 2005, Clinical chemistry and laboratory medicine.

[5]  Hugh M. Cartwright,et al.  SpecAlign - processing and alignment of mass spectra datasets , 2005, Bioinform..

[6]  A. Oratore,et al.  Proteins pattern alteration in AZT-treated K562 cells detected by two-dimensional gel electrophoresis and peptide mass fingerprinting , 2006, Proteome Science.

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

[8]  Milos Hauskrecht,et al.  Feature Selection for Classification of SELDI-TOF-MS Proteomic Profiles , 2005, Applied bioinformatics.

[9]  D. Chan,et al.  Evaluation of serum protein profiling by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry for the detection of prostate cancer: I. Assessment of platform reproducibility. , 2005, Clinical chemistry.

[10]  Jeffrey S. Morris,et al.  Bias, Randomization, and Ovarian Proteomic Data: A Reply to “Producers and Consumers” , 2005, Cancer informatics.

[11]  M. Eberhardson,et al.  The use of proteomics in identifying differentially expressed serum proteins in humans with type 2 diabetes , 2006, Proteome Science.

[12]  T. Libermann,et al.  SELDI‐TOF MS of quadruplicate urine and serum samples to evaluate changes related to storage conditions , 2006, Proteomics.

[13]  P. Tempst,et al.  Automated serum peptide profiling , 2006, Nature Protocols.

[14]  T. Yip,et al.  Deep proteome profiling of sera from never-smoked lung cancer patients. , 2007, Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie.

[15]  S. Vernon,et al.  A method for improving SELDI-TOF mass spectrometry data quality , 2007, Proteome Science.

[16]  Jennifer A. Siepen,et al.  An informatic pipeline for the data capture and submission of quantitative proteomic data using iTRAQTM , 2007, Proteome Science.

[17]  S. Kern,et al.  Quality control of serum albumin depletion for proteomic analysis. , 2007, Clinical chemistry.

[18]  Milos Hauskrecht,et al.  Intersession reproducibility of mass spectrometry profiles and its effect on accuracy of multivariate classification models , 2007, Bioinform..

[19]  Paul Tempst,et al.  Data analysis of assorted serum peptidome profiles , 2007, Nature Protocols.

[20]  A. Smilde,et al.  How to distinguish healthy from diseased? Classification strategy for mass spectrometry‐based clinical proteomics , 2007, Proteomics.

[21]  D. Chan,et al.  Analytical validation of serum proteomic profiling for diagnosis of prostate cancer: sources of sample bias. , 2008, Clinical chemistry.

[22]  D. Chan,et al.  SELDI-TOF MS whole serum proteomic profiling with IMAC surface does not reliably detect prostate cancer. , 2008, Clinical chemistry.