Literature DB >> 16083255

Correcting common errors in identifying cancer-specific serum peptide signatures.

Josep Villanueva1, John Philip, Carlos A Chaparro, Yongbiao Li, Ricardo Toledo-Crow, Lin DeNoyer, Martin Fleisher, Richard J Robbins, Paul Tempst.   

Abstract

"Molecular signatures" are the qualitative and quantitative patterns of groups of biomolecules (e.g., mRNA, proteins, peptides, or metabolites) in a cell, tissue, biological fluid, or an entire organism. To apply this concept to biomarker discovery, the measurements should ideally be noninvasive and performed in a single read-out. We have therefore developed a peptidomics platform that couples magnetics-based, automated solid-phase extraction of small peptides with a high-resolution MALDI-TOF mass spectrometric readout (Villanueva, J.; Philip, J.; Entenberg, D.; Chaparro, C. A.; Tanwar, M. K.; Holland, E. C.; Tempst, P. Anal. Chem. 2004, 76, 1560-1570). Since hundreds of peptides can be detected in microliter volumes of serum, it allows to search for disease signatures, for instance in the presence of cancer. We have now evaluated, optimized, and standardized a number of clinical and analytical chemistry variables that are major sources of bias; ranging from blood collection and clotting, to serum storage and handling, automated peptide extraction, crystallization, spectral acquisition, and signal processing. In addition, proper alignment of spectra and user-friendly visualization tools are essential for meaningful, certifiable data mining. We introduce a minimal entropy algorithm, "Entropycal", that simplifies alignment and subsequent statistical analysis and increases the percentage of the highly distinguishing spectral information being retained after feature selection of the datasets. Using the improved analytical platform and tools, and a commercial statistics program, we found that sera from thyroid cancer patients can be distinguished from healthy controls based on an array of 98 discriminant peptides. With adequate technological and computational methods in place, and using rigorously standardized conditions, potential sources of patient related bias (e.g., gender, age, genetics, environmental, dietary, and other factors) may now be addressed.

Entities:  

Mesh:

Substances:

Year:  2005        PMID: 16083255      PMCID: PMC1852495          DOI: 10.1021/pr050034b

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  19 in total

1.  Support vector machine classification and validation of cancer tissue samples using microarray expression data.

Authors:  T S Furey; N Cristianini; N Duffy; D W Bednarski; M Schummer; D Haussler
Journal:  Bioinformatics       Date:  2000-10       Impact factor: 6.937

Review 2.  Serum proteomics in cancer diagnosis and management.

Authors:  Kevin P Rosenblatt; Peter Bryant-Greenwood; J Keith Killian; Arpita Mehta; David Geho; Virginia Espina; Emanuel F Petricoin; Lance A Liotta
Journal:  Annu Rev Med       Date:  2004       Impact factor: 13.739

3.  Protocols for disease classification from mass spectrometry data.

Authors:  Michael Wagner; Dayanand Naik; Alex Pothen
Journal:  Proteomics       Date:  2003-09       Impact factor: 3.984

4.  Use of proteomic patterns in serum to identify ovarian cancer.

Authors:  Emanuel F Petricoin; Ali M Ardekani; Ben A Hitt; Peter J Levine; Vincent A Fusaro; Seth M Steinberg; Gordon B Mills; Charles Simone; David A Fishman; Elise C Kohn; Lance A Liotta
Journal:  Lancet       Date:  2002-02-16       Impact factor: 79.321

5.  Detection of cancer-specific markers amid massive mass spectral data.

Authors:  Wei Zhu; Xuena Wang; Yeming Ma; Manlong Rao; James Glimm; John S Kovach
Journal:  Proc Natl Acad Sci U S A       Date:  2003-12-01       Impact factor: 11.205

6.  Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer.

Authors:  Jinong Li; Zhen Zhang; Jason Rosenzweig; Young Y Wang; Daniel W Chan
Journal:  Clin Chem       Date:  2002-08       Impact factor: 8.327

7.  Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients.

Authors:  Yinsheng Qu; Bao-Ling Adam; Yutaka Yasui; Michael D Ward; Lisa H Cazares; Paul F Schellhammer; Ziding Feng; O John Semmes; George L Wright
Journal:  Clin Chem       Date:  2002-10       Impact factor: 8.327

8.  Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data.

Authors:  Baolin Wu; Tom Abbott; David Fishman; Walter McMurray; Gil Mor; Kathryn Stone; David Ward; Kenneth Williams; Hongyu Zhao
Journal:  Bioinformatics       Date:  2003-09-01       Impact factor: 6.937

9.  Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men.

Authors:  Bao-Ling Adam; Yinsheng Qu; John W Davis; Michael D Ward; Mary Ann Clements; Lisa H Cazares; O John Semmes; Paul F Schellhammer; Yutaka Yasui; Ziding Feng; George L Wright
Journal:  Cancer Res       Date:  2002-07-01       Impact factor: 12.701

10.  A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection.

Authors:  Yutaka Yasui; Margaret Pepe; Mary Lou Thompson; Bao-Ling Adam; George L Wright; Yinsheng Qu; John D Potter; Marcy Winget; Mark Thornquist; Ziding Feng
Journal:  Biostatistics       Date:  2003-07       Impact factor: 5.899

View more
  53 in total

Review 1.  Biomarkers discovery by peptide and protein profiling in biological fluids based on functionalized magnetic beads purification and mass spectrometry.

Authors:  Fulvio Magni; Yuri E M Van Der Burgt; Clizia Chinello; Veronica Mainini; Erica Gianazza; Valeria Squeo; André M Deelder; Marzia Galli Kienle
Journal:  Blood Transfus       Date:  2010-06       Impact factor: 3.443

2.  Differential exoprotease activities confer tumor-specific serum peptidome patterns.

Authors:  Josep Villanueva; David R Shaffer; John Philip; Carlos A Chaparro; Hediye Erdjument-Bromage; Adam B Olshen; Martin Fleisher; Hans Lilja; Edi Brogi; Jeff Boyd; Marta Sanchez-Carbayo; Eric C Holland; Carlos Cordon-Cardo; Howard I Scher; Paul Tempst
Journal:  J Clin Invest       Date:  2006-01       Impact factor: 14.808

3.  Processing MALDI Mass Spectra to Improve Mass Spectral Direct Tissue Analysis.

Authors:  Jeremy L Norris; Dale S Cornett; James A Mobley; Malin Andersson; Erin H Seeley; Pierre Chaurand; Richard M Caprioli
Journal:  Int J Mass Spectrom       Date:  2007-02-01       Impact factor: 1.986

4.  Bayesian analysis of mass spectrometry proteomic data using wavelet-based functional mixed models.

Authors:  Jeffrey S Morris; Philip J Brown; Richard C Herrick; Keith A Baggerly; Kevin R Coombes
Journal:  Biometrics       Date:  2007-09-20       Impact factor: 2.571

5.  Serum biomarker profiling by solid-phase extraction with particle-embedded micro tips and matrix-assisted laser desorption/ionization mass spectrometry.

Authors:  Arti Navare; Manshui Zhou; John McDonald; Fernando G Noriega; M Cameron Sullards; Facundo M Fernandez
Journal:  Rapid Commun Mass Spectrom       Date:  2008-04       Impact factor: 2.419

6.  Detecting glycan cancer biomarkers in serum samples using MALDI FT-ICR mass spectrometry data.

Authors:  Donald A Barkauskas; Hyun Joo An; Scott R Kronewitter; Maria Lorna de Leoz; Helen K Chew; Ralph W de Vere White; Gary S Leiserowitz; Suzanne Miyamoto; Carlito B Lebrilla; David M Rocke
Journal:  Bioinformatics       Date:  2008-12-09       Impact factor: 6.937

7.  Evaluating the effects of preanalytical variables on the stability of the human plasma proteome.

Authors:  Maria E Hassis; Richard K Niles; Miles N Braten; Matthew E Albertolle; H Ewa Witkowska; Carl A Hubel; Susan J Fisher; Katherine E Williams
Journal:  Anal Biochem       Date:  2015-03-10       Impact factor: 3.365

8.  Mesoporous silica chips for selective enrichment and stabilization of low molecular weight proteome.

Authors:  Ali Bouamrani; Ye Hu; Ennio Tasciotti; Li Li; Ciro Chiappini; Xuewu Liu; Mauro Ferrari
Journal:  Proteomics       Date:  2010-02       Impact factor: 3.984

9.  Tailoring of the nanotexture of mesoporous silica films and their functionalized derivatives for selectively harvesting low molecular weight protein.

Authors:  Ye Hu; Ali Bouamrani; Ennio Tasciotti; Li Li; Xuewu Liu; Mauro Ferrari
Journal:  ACS Nano       Date:  2010-01-26       Impact factor: 15.881

10.  Evaluation of the variation in sample preparation for comparative proteomics using stable isotope labeling by amino acids in cell culture.

Authors:  Guoan Zhang; David Fenyö; Thomas A Neubert
Journal:  J Proteome Res       Date:  2009-03       Impact factor: 4.466

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.