Reproducibility of Differential Proteomic Technologies in CPTAC Fractionated Xenografts

Journal of Proteome Research

Volumn 15, Issue 3, 2016, Pages 691-706

  Tabb, David L ^a,j   Wang, Xia ^b   Carr, Steven A ^c   Clauser, Karl R ^c   Mertins, Philipp ^c   Chambers, Matthew C ^a   Holman, Jerry D ^a   Wang, Jing ^a   Zhang, Bing ^a   Zimmerman, Lisa J ^b   Chen, Xian ^d   Gunawardena, Harsha P ^d   Davies, Sherri R ^e   Ellis, Matthew J C ^e   Li, Shunqiang ^e   Townsend, R Reid ^e   Boja, Emily S ^f   Ketchum, Karen A ^g   Kinsinger, Christopher R ^f   Mesri, Mehdi ^f   Rodriguez, Henry ^f   Liu, Tao ^h   Kim, Sangtae ^h   McDermott, Jason E ^h   Payne, Samuel H ^h   Petyuk, Vladislav A ^h   Rodland, Karin D ^h   Smith, Richard D ^h   Yang, Feng ^h   Chan, Daniel W ⁱ   Zhang, Bai ⁱ   Zhang, Hui ⁱ   Zhang, Zhen ⁱ   Zhou, Jian Ying ⁱ   Liebler, Daniel C ^b   more..

^a Department of Biomedical Informatics ^*  (United States)

^b VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^d UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^e WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

^f NATIONAL CANCER INSTITUTE   (United States)

^g Medical Science and Computing   (United States)

^h PACIFIC NORTHWEST NATIONAL LABORATORY   (United States)

ⁱ JOHNS HOPKINS UNIVERSITY   (United States)

^j UNIVERSITY OF CINCINNATI   (United States)

more..

Author keywords

CPTAC; Differential proteomics; iTRAQ; label free; quality control; technology assessment; xenografts

Indexed keywords

ANALYTIC METHOD; ANIMAL EXPERIMENT; ANIMAL MODEL; ARTICLE; EXTRACTED PRECURSOR ION CHROMATOGRAPHY; HUMAN; INFORMATION PROCESSING; ION CHROMATOGRAPHY; ITRAQ LABELING; LIQUID CHROMATOGRAPHY; LUMINAL B BREAST CANCER; MASS SPECTROMETRY; MOUSE; NONHUMAN; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN EXPRESSION; PROTEOMICS; QUALITY CONTROL; QUANTITATIVE ANALYSIS; REPRODUCIBILITY; TUMOR XENOGRAFT; WORKFLOW; BREAST TUMOR; CHEMISTRY; DEVICES; FEMALE; GENE EXPRESSION PROFILING; METABOLISM; OBSERVER VARIATION; PROCEDURES; STANDARDS; STATISTICAL ANALYSIS; TANDEM MASS SPECTROMETRY; XENOGRAFT;

PROTEOME;

BREAST NEOPLASMS; CHROMATOGRAPHY, LIQUID; DATA INTERPRETATION, STATISTICAL; FEMALE; GENE EXPRESSION PROFILING; HETEROGRAFTS; HUMANS; METABOLIC NETWORKS AND PATHWAYS; OBSERVER VARIATION; PROTEOME; PROTEOMICS; QUALITY CONTROL; REPRODUCIBILITY OF RESULTS; TANDEM MASS SPECTROMETRY;

EID: 84960343160     PISSN: 15353893     EISSN: 15353907     Source Type: Journal    
DOI: 10.1021/acs.jproteome.5b00859     Document Type: Article

References (59)

1. Zhang, B.; Wang, J.; Wang, X.; Zhu, J.; Liu, Q.; Shi, Z.; Chambers, M. C.; Zimmerman, L. J.; Shaddox, K. F.; Kim, S. et al. Proteogenomic characterization of human colon and rectal cancer Nature 2014, 513 (7518) 382-387 10.1038/nature13438
2. Picotti, P.; Aebersold, R.; Domon, B. The implications of proteolytic background for shotgun proteomics Mol. Cell. Proteomics 2007, 6 (9) 1589-1598 10.1074/mcp.M700029-MCP200
3. Fang, Y.; Robinson, D. P.; Foster, L. J. Quantitative analysis of proteome coverage and recovery rates for upstream fractionation methods in proteomics J. Proteome Res. 2010, 9 (4) 1902-1912 10.1021/pr901063t
4. Slebos, R. J. C.; Brock, J. W. C.; Winters, N. F.; Stuart, S. R.; Martinez, M. A.; Li, M.; Chambers, M. C.; Zimmerman, L. J.; Ham, A. J.; Tabb, D. L. et al. Evaluation of strong cation exchange versus isoelectric focusing of peptides for multidimensional liquid chromatography-tandem mass spectrometry J. Proteome Res. 2008, 7 (12) 5286-5294 10.1021/pr8004666
5. Prince, J. T.; Marcotte, E. M. Chromatographic alignment of ESI-LC-MS proteomics data sets by ordered bijective interpolated warping Anal. Chem. 2006, 78 (17) 6140-6152 10.1021/ac0605344
6. Tabb, D. L.; Vega-Montoto, L.; Rudnick, P. A.; Variyath, A. M.; Ham, A.-J. L.; Bunk, D. M.; Kilpatrick, L. E.; Billheimer, D. D.; Blackman, R. K.; Cardasis, H. L. et al. Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry J. Proteome Res. 2010, 9 (2) 761-776 10.1021/pr9006365
7. Bell, A. W.; Deutsch, E. W.; Au, C. E.; Kearney, R. E.; Beavis, R.; Sechi, S.; Nilsson, T.; Bergeron, J. J. M. et al. A HUPO test sample study reveals common problems in mass spectrometry-based proteomics Nat. Methods 2009, 6 (6) 423-430 10.1038/nmeth.1333
8. Mertins, P.; Yang, F.; Liu, T.; Mani, D. R.; Petyuk, V. A.; Gillette, M. A.; Clauser, K. R.; Qiao, J. W.; Gritsenko, M. A.; Moore, R. J. et al. Ischemia in tumors induces early and sustained phosphorylation changes in stress kinase pathways but does not affect global protein levels Mol. Cell. Proteomics 2014, 13 (7) 1690-1704 10.1074/mcp.M113.036392
9. Gajadhar, A. S.; Johnson, H.; Slebos, R. J. C.; Shaddox, K.; Wiles, K.; Washington, M. K.; Herline, A. J.; Levine, D. A.; Liebler, D. C.; White, F. M. et al. Phosphotyrosine signaling analysis in human tumors is confounded by systemic ischemia-driven artifacts and intra-specimen heterogeneity Cancer Res. 2015, 75 (7) 1495-1503 10.1158/0008-5472.CAN-14-2309
10. Yang, F.; Shen, Y.; Camp, D. G.; Smith, R. D. High-pH reversed-phase chromatography with fraction concatenation for 2D proteomic analysis Expert Rev. Proteomics 2012, 9 (2) 129-134 10.1586/epr.12.15
11. Boone, J. D.; Dobbin, Z. C.; Straughn, J. M.; Buchsbaum, D. J. Ovarian and cervical cancer patient derived xenografts: The past, present, and future Gynecol. Oncol. 2015, 138 (2) 486-491 10.1016/j.ygyno.2015.05.022
12. Li, S.; Shen, D.; Shao, J.; Crowder, R.; Liu, W.; Prat, A.; He, X.; Liu, S.; Hoog, J.; Lu, C. et al. Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts Cell Rep. 2013, 4 (6) 1116-1130 10.1016/j.celrep.2013.08.022
13. Luo, R.; Zhao, H. Protein quantitation using iTRAQ: Review on the sources of variations and analysis of nonrandom missingness Stat. Interface 2012, 5 (1) 99-107 10.4310/SII.2012.v5.n1.a9
14. Liu, H.; Sadygov, R. G.; Yates, J. R. A model for random sampling and estimation of relative protein abundance in shotgun proteomics Anal. Chem. 2004, 76 (14) 4193-4201 10.1021/ac0498563
15. Cox, J.; Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification Nat. Biotechnol. 2008, 26 (12) 1367-1372 10.1038/nbt.1511
16. Anderle, M.; Roy, S.; Lin, H.; Becker, C.; Joho, K. Quantifying reproducibility for differential proteomics: noise analysis for protein liquid chromatography-mass spectrometry of human serum Bioinformatics 2004, 20 (18) 3575-3582 10.1093/bioinformatics/bth446
17. Kim, Y. J.; Zhan, P.; Feild, B.; Ruben, S. M.; He, T. Reproducibility assessment of relative quantitation strategies for LC-MS based proteomics Anal. Chem. 2007, 79 (15) 5651-5658 10.1021/ac070200u
18. Old, W. M.; Meyer-Arendt, K.; Aveline-Wolf, L.; Pierce, K. G.; Mendoza, A.; Sevinsky, J. R.; Resing, K. A.; Ahn, N. G. Comparison of label-free methods for quantifying human proteins by shotgun proteomics Mol. Cell. Proteomics 2005, 4 (10) 1487-1502 10.1074/mcp.M500084-MCP200
19. Zybailov, B.; Coleman, M. K.; Florens, L.; Washburn, M. P. Correlation of relative abundance ratios derived from peptide ion chromatograms and spectrum counting for quantitative proteomic analysis using stable isotope labeling Anal. Chem. 2005, 77 (19) 6218-6224 10.1021/ac050846r
20. Chong, P. K.; Gan, C. S.; Pham, T. K.; Wright, P. C. Isobaric tags for relative and absolute quantitation (iTRAQ) reproducibility: Implication of multiple injections J. Proteome Res. 2006, 5 (5) 1232-1240 10.1021/pr060018u
21. Gan, C. S.; Chong, P. K.; Pham, T. K.; Wright, P. C. Technical, experimental, and biological variations in isobaric tags for relative and absolute quantitation (iTRAQ) J. Proteome Res. 2007, 6 (2) 821-827 10.1021/pr060474i
22. Patel, V. J.; Thalassinos, K.; Slade, S. E.; Connolly, J. B.; Crombie, A.; Murrell, J. C.; Scrivens, J. H. A comparison of labeling and label-free mass spectrometry-based proteomics approaches J. Proteome Res. 2009, 8 (7) 3752-3759 10.1021/pr900080y
23. Wang, H.; Alvarez, S.; Hicks, L. M. Comprehensive comparison of iTRAQ and label-free LC-based quantitative proteomics approaches using two Chlamydomonas reinhardtii strains of interest for biofuels engineering J. Proteome Res. 2012, 11 (1) 487-501 10.1021/pr2008225
24. Trinh, H. V.; Grossmann, J.; Gehrig, P.; Roschitzki, B.; Schlapbach, R.; Greber, U. F.; Hemmi, S. iTRAQ-Based and Label-Free Proteomics Approaches for Studies of Human Adenovirus Infections Int. J. Proteomics 2013, 2013, 581862 10.1155/2013/581862
25. Megger, D. A.; Pott, L. L.; Ahrens, M.; Padden, J.; Bracht, T.; Kuhlmann, K.; Eisenacher, M.; Meyer, H. E.; Sitek, B. Comparison of label-free and label-based strategies for proteome analysis of hepatoma cell lines Biochim. Biophys. Acta, Proteins Proteomics 2014, 1844 (5) 967-976 10.1016/j.bbapap.2013.07.017
26. Schulze, W. X.; Usadel, B. Quantitation in mass-spectrometry-based proteomics Annu. Rev. Plant Biol. 2010, 61, 491-516 10.1146/annurev-arplant-042809-112132
27. Meissner, F.; Mann, M. Quantitative shotgun proteomics: considerations for a high-quality workflow in immunology Nat. Immunol. 2014, 15 (2) 112-117 10.1038/ni.2781
28. Bantscheff, M.; Lemeer, S.; Savitski, M. M.; Kuster, B. Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present Anal. Bioanal. Chem. 2012, 404 (4) 939-965 10.1007/s00216-012-6203-4
29. Vaux, D. L.; Fidler, F.; Cumming, G. Replicates and repeats-what is the difference and is it significant? A brief discussion of statistics and experimental design EMBO Rep. 2012, 13 (4) 291-296 10.1038/embor.2012.36
30. Parker, J. S.; Mullins, M.; Cheang, M. C. U.; Leung, S.; Voduc, D.; Vickery, T.; Davies, S.; Fauron, C.; He, X.; Hu, Z. et al. Supervised risk predictor of breast cancer based on intrinsic subtypes J. Clin. Oncol. 2009, 27 (8) 1160-1167 10.1200/JCO.2008.18.1370
31. Perou, C. M.; Sørlie, T.; Eisen, M. B.; van de Rijn, M.; Jeffrey, S. S.; Rees, C. A.; Pollack, J. R.; Ross, D. T.; Johnsen, H.; Akslen, L. A. et al. Molecular portraits of human breast tumours Nature 2000, 406 (6797) 747-752 10.1038/35021093
32. Ding, L.; Ellis, M. J.; Li, S.; Larson, D. E.; Chen, K.; Wallis, J. W.; Harris, C. C.; McLellan, M. D.; Fulton, R. S.; Fulton, L. L. et al. Genome remodelling in a basal-like breast cancer metastasis and xenograft Nature 2010, 464 (7291) 999-1005 10.1038/nature08989
33. Wang, X.; Chambers, M. C.; Vega-Montoto, L. J.; Bunk, D. M.; Stein, S. E.; Tabb, D. L. QC metrics from CPTAC raw LC-MS/MS data interpreted through multivariate statistics Anal. Chem. 2014, 86 (5) 2497-2509 10.1021/ac4034455
34. Tabb, D. L.; Fernando, C. G.; Chambers, M. C. MyriMatch: highly accurate tandem mass spectral peptide identification by multivariate hypergeometric analysis J. Proteome Res. 2007, 6 (2) 654-661 10.1021/pr0604054
35. Kim, S.; Pevzner, P. A. MS-GF+ makes progress towards a universal database search tool for proteomics Nat. Commun. 2014, 5, 5277 10.1038/ncomms6277
36. Eng, J. K.; Jahan, T. A.; Hoopmann, M. R. Comet: an open-source MS/MS sequence database search tool Proteomics 2013, 13 (1) 22-24 10.1002/pmic.201200439
37. Deutsch, E. W.; Mendoza, L.; Shteynberg, D.; Farrah, T.; Lam, H.; Tasman, N.; Sun, Z.; Nilsson, E.; Pratt, B.; Prazen, B. et al. A guided tour of the Trans-Proteomic Pipeline Proteomics 2010, 10 (6) 1150-1159 10.1002/pmic.200900375
38. Jones, A. R.; Eisenacher, M.; Mayer, G.; Kohlbacher, O.; Siepen, J.; Hubbard, S. J.; Selley, J. N.; Searle, B. C.; Shofstahl, J.; Seymour, S. L. et al. The mzIdentML data standard for mass spectrometry-based proteomics results Mol. Cell. Proteomics 2012, 11 (7) M111.014381 10.1074/mcp.M111.014381
39. Käll, L.; Storey, J. D.; MacCoss, M. J.; Noble, W. S. Posterior error probabilities and false discovery rates: two sides of the same coin J. Proteome Res. 2008, 7 (1) 40-44 10.1021/pr700739d
40. Holman, J. D.; Ma, Z.-Q.; Tabb, D. L. Identifying proteomic LC-MS/MS data sets with Bumbershoot and IDPicker Curr. Protoc. Bioinf. 2012, 10.1002/0471250953.bi1317s37
41. Zhang, B.; Chambers, M. C.; Tabb, D. L. Proteomic parsimony through bipartite graph analysis improves accuracy and transparency J. Proteome Res. 2007, 6 (9) 3549-3557 10.1021/pr070230d
42. Chen, Y.-Y.; Chambers, M. C.; Li, M.; Ham, A.-J. L.; Turner, J. L.; Zhang, B.; Tabb, D. L. IDPQuantify: combining precursor intensity with spectral counts for protein and peptide quantification J. Proteome Res. 2013, 12 (9) 4111-4121 10.1021/pr400438q
43. Choi, H.; Fermin, D.; Nesvizhskii, A. I. Significance analysis of spectral count data in label-free shotgun proteomics Mol. Cell. Proteomics 2008, 7 (12) 2373-2385 10.1074/mcp.M800203-MCP200
44. Lundgren, D. H.; Hwang, S.-I.; Wu, L.; Han, D. K. Role of spectral counting in quantitative proteomics Expert Rev. Proteomics 2010, 7 (1) 39-53 10.1586/epr.09.69
45. Booth, J. G.; Eilertson, K. E.; Olinares, P. D. B.; Yu, H. A bayesian mixture model for comparative spectral count data in shotgun proteomics Mol. Cell. Proteomics 2011, 10 (8) M110.007203 10.1074/mcp.M110.007203
46. Gleiss, A.; Dakna, M.; Mischak, H.; Heinze, G. Two-group comparisons of zero-inflated intensity values: the choice of test statistic matters Bioinformatics 2015, 31 (14) 2310-2317 10.1093/bioinformatics/btv154
47. Ogata, H.; Goto, S.; Sato, K.; Fujibuchi, W.; Bono, H.; Kanehisa, M. KEGG: Kyoto Encyclopedia of Genes and Genomes Nucleic Acids Res. 1999, 27 (1) 29-34 10.1093/nar/27.1.29
48. Fury, W.; Batliwalla, F.; Gregersen, P. K.; Li, W. Overlapping probabilities of top ranking gene lists, hypergeometric distribution, and stringency of gene selection criterion Conf. Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Conf. 2006, 1, 5531-5534 10.1109/IEMBS.2006.260828
49. Hong, G.; Zhang, W.; Li, H.; Shen, X.; Guo, Z. Separate enrichment analysis of pathways for up- and downregulated genes J. R. Soc., Interface 2014, 11 (92) 20130950 10.1098/rsif.2013.0950
50. Benjamini, Y.; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing J. R. Stat. Soc. Ser. B Methodol. 1995, 57 (1) 289-300
51. Shi, Z.; Wang, J.; Zhang, B. NetGestalt: integrating multidimensional omics data over biological networks Nat. Methods 2013, 10 (7) 597-598 10.1038/nmeth.2517
52. Turinsky, A. L.; Razick, S.; Turner, B.; Donaldson, I. M.; Wodak, S. J. Interaction databases on the same page Nat. Biotechnol. 2011, 29 (5) 391-393 10.1038/nbt.1867
53. Wang, X.; Slebos, R. J. C.; Chambers, M. C.; Tabb, D. L.; Liebler, D. C.; Zhang, B. proBAMsuite, a bioinformatics framework for genome-based representation and analysis of proteomics data Mol. Cell. Proteomics 2015, 14, M115.052860 10.1074/mcp.M115.052860
54. Jain, N.; Thatte, J.; Braciale, T.; Ley, K.; O'Connell, M.; Lee, J. K. Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays Bioinformatics 2003, 19 (15) 1945-1951 10.1093/bioinformatics/btg264
55. Efron, B.; Tibshirani, R.; Storey, J. D.; Tusher, V. Empirical Bayes Analysis of a Microarray Experiment J. Am. Stat. Assoc. 2001, 96 (456) 1151-1160 10.1198/016214501753382129
56. Smith, L. M.; Kelleher, N. L. Consortium for Top Down Proteomics. Proteoform: a single term describing protein complexity Nat. Methods 2013, 10 (3) 186-187 10.1038/nmeth.2369
57. Carrillo, B.; Yanofsky, C.; Laboissiere, S.; Nadon, R.; Kearney, R. E. Methods for combining peptide intensities to estimate relative protein abundance Bioinformatics 2010, 26 (1) 98-103 10.1093/bioinformatics/btp610
58. Hebert, A. S.; Merrill, A. E.; Bailey, D. J.; Still, A. J.; Westphall, M. S.; Strieter, E. R.; Pagliarini, D. J.; Coon, J. J. Neutron-encoded mass signatures for multiplexed proteome quantification Nat. Methods 2013, 10 (4) 332-334 10.1038/nmeth.2378
59. Oberg, A. L.; Vitek, O. Statistical design of quantitative mass spectrometry-based proteomic experiments J. Proteome Res. 2009, 8 (5) 2144-2156 10.1021/pr8010099