Reproducibility of Differential Proteomic Technologies in CPTAC Fractionated Xenografts

The NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) employed a pair of reference xenograft proteomes for initial platform validation and ongoing quality control of its data collection for The Cancer Genome Atlas (TCGA) tumors. These two xenografts, representing basal and luminal-B human breast cancer, were fractionated and analyzed on six mass spectrometers in a total of 46 replicates divided between iTRAQ and label-free technologies, spanning a total of 1095 LC-MS/MS experiments. These data represent a unique opportunity to evaluate the stability of proteomic differentiation by mass spectrometry over many months of time for individual instruments or across instruments running dissimilar workflows. We evaluated iTRAQ reporter ions, label-free spectral counts, and label-free extracted ion chromatograms as strategies for data interpretation (source code is available from http://homepages.uc.edu/~wang2x7/Research.htm ). From these assessments, we found that differential genes from a single replicate were confirmed by other replicates on the same instrument from 61 to 93% of the time. When comparing across different instruments and quantitative technologies, using multiple replicates, differential genes were reproduced by other data sets from 67 to 99% of the time. Projecting gene differences to biological pathways and networks increased the degree of similarity. These overlaps send an encouraging message about the maturity of technologies for proteomic differentiation.

[1]  William Stafford Noble,et al.  Posterior error probabilities and false discovery rates: two sides of the same coin. , 2008, Journal of proteome research.

[2]  Forest M White,et al.  Phosphotyrosine signaling analysis in human tumors is confounded by systemic ischemia-driven artifacts and intra-specimen heterogeneity. , 2015, Cancer research.

[3]  Hyungwon Choi,et al.  Significance Analysis of Spectral Count Data in Label-free Shotgun Proteomics*S , 2008, Molecular & Cellular Proteomics.

[4]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[5]  Pavel A. Pevzner,et al.  Universal database search tool for proteomics , 2014, Nature Communications.

[6]  Zheng Guo,et al.  Separate enrichment analysis of pathways for up- and downregulated genes , 2014, Journal of The Royal Society Interface.

[7]  D. Tabb,et al.  Proteomic parsimony through bipartite graph analysis improves accuracy and transparency. , 2007, Journal of proteome research.

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

[9]  Li Ding,et al.  Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts. , 2013, Cell reports.

[10]  Christian A. Rees,et al.  Molecular portraits of human breast tumours , 2000, Nature.

[11]  Trong Khoa Pham,et al.  Isobaric tags for relative and absolute quantitation (iTRAQ) reproducibility: Implication of multiple injections. , 2006, Journal of proteome research.

[12]  L. Foster,et al.  Quantitative analysis of proteome coverage and recovery rates for upstream fractionation methods in proteomics. , 2010, Journal of proteome research.

[13]  Jerry D. Holman,et al.  Identifying Proteomic LC‐MS/MS Data Sets with Bumbershoot and IDPicker , 2012, Current protocols in bioinformatics.

[14]  D. Tabb,et al.  MyriMatch: highly accurate tandem mass spectral peptide identification by multivariate hypergeometric analysis. , 2007, Journal of proteome research.

[15]  A. Nobel,et al.  Supervised risk predictor of breast cancer based on intrinsic subtypes. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[16]  Haiyuan Yu,et al.  A Bayesian Mixture Model for Comparative Spectral Count Data in Shotgun Proteomics , 2011, Molecular & Cellular Proteomics.

[17]  Ruedi Aebersold,et al.  The Implications of Proteolytic Background for Shotgun Proteomics*S , 2007, Molecular & Cellular Proteomics.

[18]  Trong Khoa Pham,et al.  Technical, experimental, and biological variations in isobaric tags for relative and absolute quantitation (iTRAQ). , 2007, Journal of proteome research.

[19]  K. Resing,et al.  Comparison of Label-free Methods for Quantifying Human Proteins by Shotgun Proteomics*S , 2005, Molecular & Cellular Proteomics.

[20]  Harald Mischak,et al.  Two-group comparisons of zero-inflated intensity values: the choice of test statistic matters , 2015, Bioinform..

[21]  Robert E. Kearney,et al.  A HUPO test sample study reveals common problems in mass spectrometry-based proteomics , 2009, Nature Methods.

[22]  Konstantinos Thalassinos,et al.  A comparison of labeling and label-free mass spectrometry-based proteomics approaches. , 2009, Journal of proteome research.

[23]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[24]  Linfeng Wu,et al.  Role of spectral counting in quantitative proteomics , 2010, Expert review of proteomics.

[25]  Robert E. Kearney,et al.  Methods for combining peptide intensities to estimate relative protein abundance , 2010, Bioinform..

[26]  Richard D. Smith,et al.  High-pH reversed-phase chromatography with fraction concatenation for 2D proteomic analysis , 2012, Expert review of proteomics.

[27]  Birgit Schilling,et al.  Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry. , 2010, Journal of proteome research.

[28]  S. Ruben,et al.  Reproducibility assessment of relative quantitation strategies for LC-MS based proteomics. , 2007, Analytical chemistry.

[29]  S. Alvarez,et al.  Comprehensive comparison of iTRAQ and label-free LC-based quantitative proteomics approaches using two Chlamydomonas reinhardtii strains of interest for biofuels engineering. , 2012, Journal of proteome research.

[30]  Derek J. Bailey,et al.  Neutron-encoded mass signatures for multi-plexed proteome quantification , 2013, Nature Methods.

[31]  John D. Storey,et al.  Empirical Bayes Analysis of a Microarray Experiment , 2001 .

[32]  Olga Vitek,et al.  Statistical design of quantitative mass spectrometry-based proteomic experiments. , 2009, Journal of proteome research.

[33]  B. Roschitzki,et al.  iTRAQ-Based and Label-Free Proteomics Approaches for Studies of Human Adenovirus Infections , 2013, International journal of proteomics.

[34]  Martin Eisenacher,et al.  The mzIdentML Data Standard for Mass Spectrometry-Based Proteomics Results , 2012, Molecular & Cellular Proteomics.

[35]  Jeffrey R. Whiteaker,et al.  Proteogenomic characterization of human colon and rectal cancer , 2014, Nature.

[36]  Jae K. Lee,et al.  Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays , 2003, Bioinform..

[37]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[38]  Joshua F. McMichael,et al.  Genome Remodeling in a Basal-like Breast Cancer Metastasis and Xenograft , 2010, Nature.

[39]  Zachary C. Dobbin,et al.  Ovarian and cervical cancer patient derived xenografts: The past, present, and future. , 2015, Gynecologic oncology.

[40]  Lloyd M. Smith,et al.  Proteoform: a single term describing protein complexity , 2013, Nature Methods.

[41]  David L. Tabb,et al.  QC Metrics from CPTAC Raw LC-MS/MS Data Interpreted through Multivariate Statistics , 2014, Analytical chemistry.

[42]  D. Vaux,et al.  Replicates and repeats—what is the difference and is it significant? , 2012, EMBO reports.

[43]  David L Tabb,et al.  IDPQuantify: combining precursor intensity with spectral counts for protein and peptide quantification. , 2013, Journal of proteome research.

[44]  P. Gregersen,et al.  Overlapping Probabilities of Top Ranking Gene Lists, Hypergeometric Distribution, and Stringency of Gene Selection Criterion , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[45]  D. Tabb,et al.  Evaluation of strong cation exchange versus isoelectric focusing of peptides for multidimensional liquid chromatography-tandem mass spectrometry. , 2008, Journal of proteome research.

[46]  Ronald J Moore,et al.  Ischemia in Tumors Induces Early and Sustained Phosphorylation Changes in Stress Kinase Pathways but Does Not Affect Global Protein Levels* , 2014, Molecular & Cellular Proteomics.

[47]  Natalie I. Tasman,et al.  A guided tour of the Trans‐Proteomic Pipeline , 2010, Proteomics.

[48]  Michael K. Coleman,et al.  Correlation of relative abundance ratios derived from peptide ion chromatograms and spectrum counting for quantitative proteomic analysis using stable isotope labeling. , 2005, Analytical chemistry.

[49]  Bing Zhang,et al.  NetGestalt: integrating multidimensional omics data over biological networks , 2013, Nature Methods.

[50]  J. Eng,et al.  Comet: An open‐source MS/MS sequence database search tool , 2013, Proteomics.

[51]  Sabry Razick,et al.  Interaction databases on the same page , 2011, Nature Biotechnology.

[52]  M. Mann,et al.  MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification , 2008, Nature Biotechnology.

[53]  David L. Tabb,et al.  proBAMsuite, a Bioinformatics Framework for Genome-Based Representation and Analysis of Proteomics Data* , 2015, Molecular & Cellular Proteomics.

[54]  B. Usadel,et al.  Quantitation in mass-spectrometry-based proteomics. , 2010, Annual review of plant biology.

[55]  B. Sitek,et al.  Comparison of label-free and label-based strategies for proteome analysis of hepatoma cell lines. , 2014, Biochimica et biophysica acta.

[56]  Hongyu Zhao,et al.  Protein quantitation using iTRAQ: Review on the sources of variations and analysis of nonrandom missingness. , 2012, Statistics and its interface.

[57]  Bernhard Kuster,et al.  Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present , 2012, Analytical and Bioanalytical Chemistry.

[58]  J. Yates,et al.  A model for random sampling and estimation of relative protein abundance in shotgun proteomics. , 2004, Analytical chemistry.

[59]  E. Marcotte,et al.  Chromatographic alignment of ESI-LC-MS proteomics data sets by ordered bijective interpolated warping. , 2006, Analytical chemistry.

[60]  Matthias Mann,et al.  Quantitative shotgun proteomics: considerations for a high-quality workflow in immunology , 2014, Nature Immunology.