Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Timeline
  • Published:

The grand challenge to decipher the cancer proteome

Nature Reviews Cancer volume 10, pages 652–660 (2010)Cite this article

Subjects

Abstract

The quest to decipher protein alterations in cancer has spanned well over half a century. The vast dynamic range of protein abundance coupled with a plethora of isoforms and disease heterogeneity have been formidable challenges. Progress in cancer proteomics has substantially paralleled technological developments. Advances in analytical techniques and the implementation of strategies to de-complex the proteome into manageable components have allowed proteins across a wide dynamic range to be explored. The massive amounts of data that can currently be collected through proteomics allow the near-complete definition of cancer subproteomes, which reveals the alterations in signalling and developmental pathways. This allows the discovery of predictive biomarkers and the annotation of the cancer genome based on proteomic findings. There remains a considerable need for infrastructure development and the organized collaborative efforts to efficiently mine the cancer proteome.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Increased resolving power of proteomics technologies as applied to serum and plasma.
Figure 2: Depth of analysis of the plasma proteome.
Figure 3: Quantitative profiling of proteins in tumour tissue using a modified SILAC strategy32.

References

  1. Fine, J. M. & Creyssel, R. Starch gel electrophoresis studies on abnormal proteins in myeloma and macroglobulinaemia. Nature 183, 392 (1959).

    Article  CAS  PubMed  Google Scholar 

  2. Hanash, S. M., Baier, L. J., McCurry, L. & Schwartz, S. Lineage related polypeptide markers in acute lymphoblastic leukemia detected by two-dimensional electrophoresis. Proc. Natl Acad. Sci. USA 83, 807–811 (1986).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Moreira, J. M. et al. Bladder cancer-associated protein, a potential prognostic biomarker in human bladder cancer. Mol. Cell. Proteomics 9, 161–177 (2010).

    Article  CAS  PubMed  Google Scholar 

  4. Kondo, T. Cancer proteome-expression database: Genome Medicine Database of Japan Proteomics. Expert Rev. Proteomics 7, 21–27 (2010).

    Article  CAS  PubMed  Google Scholar 

  5. Strahler, J. R. et al. High resolution two-dimensional polyacrylamide gel electrophoresis of basic polypeptides: use of immobilized pH gradients in the first dimension. Electrophoresis 8, 165–173 (1987).

    Article  CAS  Google Scholar 

  6. Timms, J. F. & Cramer, R. Difference gel electrophoresis. Proteomics 8, 4886–4897 (2008).

    Article  CAS  PubMed  Google Scholar 

  7. Hanash, S., Pitteri, S. & Faca, V. Mining the plasma proteome for cancer biomarkers. Nature 452, 571–579 (2008).

    Article  CAS  PubMed  Google Scholar 

  8. Cravatt, B. F., Simon, G. M. & Yates, J. R. The biological impact of mass-spectrometry-based proteomics. Nature 450, 991–1000 (2007).

    Article  CAS  PubMed  Google Scholar 

  9. Siuti, N. & Kelleher, N. L. Decoding protein modifications using top-down mass spectrometry. Nature Methods 4, 817–821 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Nesvizhskii, A. I., Vitek, O. & Aebersold, R. Analysis and validation of proteomic data generated by tandem mass spectrometry. Nature Methods 4, 787–797 (2007).

    Article  CAS  PubMed  Google Scholar 

  11. Cox, J. & Mann, M. Is proteomics the new genomics? Cell 130, 395–398 (2007).

    Article  CAS  PubMed  Google Scholar 

  12. Petricoin, E. F., Belluco, C., Araujo, R. P. & Liotta, L. A. The blood peptidome: a higher dimension of information content for cancer biomarker discovery. Nature Rev. Cancer 6, 961–967 (2006).

    Article  CAS  Google Scholar 

  13. Hanash, S. Disease proteomics. Nature 422, 226–232 (2003).

    Article  CAS  PubMed  Google Scholar 

  14. Aebersold, R. & Mann, M. Mass spectrometry-based proteomics. Nature 422, 198–207 (2003).

    Article  CAS  PubMed  Google Scholar 

  15. Hanash, S. M. et al. Highly resolving two-dimensional gels for protein sequencing. Proc. Natl Acad. Sci. USA 88, 5709–5713 (1991).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Constans, A. MALDI - Pioneering ionization technique paved the way for proteomics. The Scientist 19, 37 (2005).

    Google Scholar 

  17. Fujii, K., Kondo, T., Yamada, M., Iwatsuki, K. & Hirohashi, S. Toward a comprehensive quantitative proteome database: protein expression map of lymphoid neoplasms by 2-D DIGE and MS. Proteomics 6, 4856–4876 (2006).

    Article  CAS  PubMed  Google Scholar 

  18. Fenn., J. B., Mann, M., Meng, C. K., Wong, S. F. & Whitehouse, C. M. Electrospray ionization for mass spectrometry of large biomolecules. Science 246, 64–71 (1989).

    Article  CAS  PubMed  Google Scholar 

  19. Olsen, J. V. et al. A dual pressure linear ion trap Orbitrap instrument with very high sequencing speed. Mol. Cell. Proteomics 8, 2759–2769 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Roesli, C. et al. Comparative analysis of the membrane proteome of closely related metastatic and nonmetastatic tumor cells. Cancer Res. 69, 5406–5414 (2009).

    Article  CAS  PubMed  Google Scholar 

  21. Bell, A. W. et al. A HUPO test sample study reveals common problems in mass spectrometry-based proteomics. Nature Methods 6, 423–430 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Addona, T. A. et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nature Biotech. 27, 633–641 (2009).

    Article  CAS  Google Scholar 

  23. Stoevesandt, O., Taussig, M. J. & He, M. Protein microarrays: high-throughput tools for proteomics. Expert Rev. Proteomics 6, 145–157 (2009).

    Article  CAS  PubMed  Google Scholar 

  24. Hu, S. et al. Profiling the human protein-DNA interactome reveals ERK2 as a transcriptional repressor of interferon signaling. Cell 139, 610–622 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Anderson, T., Wulfkuhle, J., Liotta, L., Winslow, R. L. & Petricoin, E. Improved reproducibility of reverse-phase protein microarrays using array microenvironment normalization. Proteomics 9, 5562–5566 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Taylor, A. D., Hancock, W. S., Hincapie, M., Taniguchi, N. & Hanash, S. M. Towards an integrated proteomic and glycomic approach to finding cancer biomarkers. Genome Med. 1, 57 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Heo, S. H., Lee, S. J., Ryoo, H. M., Park, J. Y. & Cho, J. Y. Identification of putative serum glycoprotein biomarkers for human lung adenocarcinoma by multilectin affinity chromatography and LC-MS/MS. Proteomics 7, 4292–4302 (2007).

    Article  CAS  PubMed  Google Scholar 

  28. Vercoutter-Edouart, A. S., Slomianny, M. C., Dekeyzer-Beseme, O., Haeuw, J. F. & Michalski, J. C. Glycoproteomics and glycomics investigation of membrane N-glycosylproteins from human colon carcinoma cells. Proteomics 8, 3236–3256 (2008).

    Article  CAS  PubMed  Google Scholar 

  29. Kim, Y. S. et al. Functional proteomics study reveals that N-Acetylglucosaminyltransferase V reinforces the invasive/metastatic potential of colon cancer through aberrant glycosylation on tissue inhibitor of metalloproteinase-1. Mol. Cell. Proteomics 7, 1–14 (2008).

    Article  CAS  PubMed  Google Scholar 

  30. Choudhary, C. & Mann, M. Decoding signalling networks by mass spectrometry-based proteomics. Nature Rev. Mol. Cell Biol. 11, 427–439 (2010).

    Article  CAS  Google Scholar 

  31. Leroy, C. et al. Quantitative phosphoproteomics reveals a cluster of tyrosine kinases that mediates SRC invasive activity in advanced colon carcinoma cells. Cancer Res. 69, 2279–2286 (2009).

    Article  CAS  PubMed  Google Scholar 

  32. Geiger, T., Cox, J., Ostasiewicz, P., Wisniewski, J. R. & Mann, M. Super-SILAC mix for quantitative proteomics of human tumor tissue. Nature Methods 7, 383–385 (2010).

    Article  CAS  PubMed  Google Scholar 

  33. Guha, U. et al. Comparisons of tyrosine phosphorylated proteins in cells expressing lung cancer-specific alleles of EGFR and KRAS. Proc. Natl Acad. Sci. USA 105, 14112–14117 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. VanMeter, A. J. et al. Laser capture microdissection and protein microarray analysis of human non-small cell lung cancer: differential epidermal growth factor receptor (EGPR) phosphorylation events associated with mutated EGFR compared with wild type. Mol. Cell. Proteomics 7, 1902–1924 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Guo, A. et al. Signaling networks assembled by oncogenic EGFR and c-Met. Proc. Natl Acad. Sci. USA 105, 692–697 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Rikova, K. et al. Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer. Cell 131, 1190–1203 (2007).

    Article  CAS  PubMed  Google Scholar 

  37. Wolf-Yadlin, A., Hautaniemi, S., Lauffenburger, D. A. & White, F. M. Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks. Proc. Natl Acad. Sci. USA 104, 5860–5865 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Leth-Larsen, R. et al. Metastasis-related plasma membrane proteins of human breast cancer cells identified by comparative quantitative mass spectrometry. Mol. Cell. Proteomics 8, 1436–1449 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Shen, J. et al. Identification and validation of differences in protein levels in normal, premalignant, and malignant lung cells and tissues using high-throughput Western Array and immunohistochemistry. Cancer Res. 66, 11194–11206 (2006).

    Article  CAS  PubMed  Google Scholar 

  40. Yao, H. et al. Identification of metastasis associated proteins in human lung squamous carcinoma using two-dimensional difference gel electrophoresis and laser capture microdissection. Lung Cancer 65, 41–48 (2009).

    Article  PubMed  Google Scholar 

  41. Li, D. J. et al. Identificating 14-3-3 sigma as a lymph node metastasis-related protein in human lung squamous carcinoma. Cancer Lett. 279, 65–73 (2009).

    Article  CAS  PubMed  Google Scholar 

  42. Chen, G. et al. Protein profiles associated with survival in lung adenocarcinoma. Proc. Natl Acad. Sci. USA 100, 13537–13542 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Yanagisawa, K. et al. A 25-signal proteomic signature and outcome for patients with resected non-small-cell lung cancer. J. Natl. Cancer Inst. 99, 858–867 (2007).

    Article  CAS  PubMed  Google Scholar 

  44. Xue, H. et al. Identification of serum biomarkers for colorectal cancer metastasis using a differential secretome approach. J. Proteome Res. 9, 545–555.

  45. Luque-Garcia, J. L. et al. Differential protein expression on the cell surface of colorectal cancer cells associated to tumor metastasis. Proteomics 10, 940–952 (2010).

    CAS  PubMed  Google Scholar 

  46. Planque, C. et al. Identification of five candidate lung cancer biomarkers by proteomics analysis of conditioned media of four lung cancer cell lines. Mol. Cell. Proteomics 8, 2746–2758 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Faca, V. M. et al. Proteomic analysis of ovarian cancer cells reveals dynamic processes of protein secretion and shedding of extra-cellular domains. PLoS ONE 3, e2425 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Gronborg, M. et al. Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach. Mol. Cell. Proteomics 5, 157–171 (2006).

    Article  CAS  PubMed  Google Scholar 

  49. Lutz, A. M., Willmann, J. K., Cochran, F. V., Ray, P. & Gambhir, S. S. Cancer screening: a mathematical model relating secreted blood biomarker levels to tumor sizes. PLoS Med. 5, e170 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Wang, C. L. et al. Discovery of retinoblastoma-associated binding protein 46 as a novel prognostic marker for distant metastasis in nonsmall cell lung cancer by combined analysis of cancer cell secretome and pleural effusion proteome. J. Proteome Res. 8, 4428–4440 (2009).

    Article  CAS  PubMed  Google Scholar 

  51. Pernemalm, M. et al. Use of narrow-range peptide IEF to improve detection of lung adenocarcinoma markers in plasma and pleural effusion. Proteomics 9, 3414–3424 (2009).

    Article  CAS  PubMed  Google Scholar 

  52. Tian, M. et al. Proteomic analysis identifies MMP-9, DJ-1 and A1BG as overexpressed proteins in pancreatic juice from pancreatic ductal adenocarcinoma patients. BMC Cancer 8, 241 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Chen, R. et al. Elevated level of anterior gradient-2 in pancreatic juice from patients with pre-malignant pancreatic neoplasia. Mol. Cancer 9, 149 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Pepe, M. S., Feng, Z., Janes, H., Bossuyt, P. M. & Potter, J. D. Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: standards for study design. J. Natl Cancer Inst. 100, 1432–1438 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Pan, J., Chen, H. Q., Sun, Y. H., Zhang, J. H. & Luo, X. Y. Comparative proteomic analysis of non-small-cell lung cancer and normal controls using serum label-free quantitative shotgun technology. Lung 186, 255–261 (2008).

    Article  CAS  PubMed  Google Scholar 

  56. Yee, J. et al. Connective tissue-activating peptide III: a novel blood biomarker for early lung cancer detection. J. Clin. Oncol. 27, 2787–2792 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Patel, N. et al. Rescue of paclitaxel sensitivity by repression of Prohibitin1 in drug-resistant cancer cells. Proc. Natl Acad. Sci. USA 107, 2503–2508.

    Article  Google Scholar 

  58. Qu, Y., Yang, Y., Liu, B. & Xiao, W. Comparative proteomic profiling identified sorcin being associated with gemcitabine resistance in non-small cell lung cancer. Med. Oncol. 10 Dec 2009 (doi:10.1007/s12032-009-9379-5).

  59. Eriksson, H. et al. Quantitative membrane proteomics applying narrow range peptide isoelectric focusing for studies of small cell lung cancer resistance mechanisms. Proteomics 8, 3008–3018 (2008).

    Article  CAS  PubMed  Google Scholar 

  60. Keenan, J., Murphy, L., Henry, M., Meleady, P. & Clynes, M. Proteomic analysis of multidrug-resistance mechanisms in adriamycin-resistant variants of DLKP, a squamous lung cancer cell line. Proteomics 9, 1556–1566 (2009).

    Article  CAS  PubMed  Google Scholar 

  61. Okano, T. et al. Proteomic signature corresponding to the response to gefitinib (Iressa, ZD1839), an epidermal growth factor receptor tyrosine kinase inhibitor in lung adenocarcinoma. Clin. Cancer Res. 13, 799–805 (2007).

    Article  CAS  PubMed  Google Scholar 

  62. Taguchi, F. et al. Mass spectrometry to classify non-small-cell lung cancer patients for clinical outcome after treatment with epidermal growth factor receptor tyrosine kinase inhibitors: a multicohort cross-institutional study. J. Natl Cancer Inst. 99, 838–846 (2007).

    Article  CAS  PubMed  Google Scholar 

  63. Amann, J. M. et al. Genetic and proteomic features associated with survival after treatment with erlotinib in first-line therapy of non-small cell lung cancer in Eastern Cooperative Oncology Group 3503. J. Thorac. Oncol. 5, 169–178 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Salmon, S. et al. Classification by mass spectrometry can accurately and reliably predict outcome in patients with non-small cell lung cancer treated with erlotinib-containing regimen. J. Thorac. Oncol. 4, 689–696 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Desmetz, C., Maudelonde, T., Mange, A. & Solassol, J. Identifying autoantibody signatures in cancer: a promising challenge. Expert Rev. Proteomics 6, 377–386 (2009).

    Article  CAS  PubMed  Google Scholar 

  66. Madoz-Gurpide, J., Kuick, R., Wang, H., Misek, D. E. & Hanash, S. M. Integral protein microarrays for the identification of lung cancer antigens in sera that induce a humoral immune response. Mol. Cell. Proteomics 7, 268–281 (2008).

    Article  CAS  PubMed  Google Scholar 

  67. Pereira-Faca, S. R. et al. Identification of 14-3-3 theta as an antigen that induces a humoral response in lung cancer. Cancer Res. 67, 12000–12006 (2007).

    Article  CAS  PubMed  Google Scholar 

  68. Qiu, J. et al. Occurrence of autoantibodies to annexin I, 14-3-3 theta and LAMR1 in prediagnostic lung cancer sera. J. Clin. Oncol. 26, 5060–5066 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Nagashio, R. et al. Detection of tumor-specific autoantibodies in sera of patients with lung cancer. Lung Cancer 62, 364–373 (2008).

    Article  PubMed  Google Scholar 

  70. Leidinger, P. et al. Identification of lung cancer with high sensitivity and specificity by blood testing. Respir. Res. 11, 18 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Wu, L. L. et al. Development of autoantibody signatures as novel diagnostic biomarkers of non-small cell lung cancer. Clin. Cancer Res. 16, 3760–3768 (2010).

    Article  CAS  PubMed  Google Scholar 

  72. Rom, W. N. et al. Identification of an autoantibody panel to separate lung cancer from smokers and nonsmokers. BMC Cancer 10, 234 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Tomaino, B. et al. Autoantibody signature in human ductal pancreatic adenocarcinoma. J. Proteome Res. 6, 4025–4031 (2007).

    Article  CAS  PubMed  Google Scholar 

  74. Tomaino, B. et al. Circulating autoantibodies to phosphorylated α-enolase are a hallmark of pancreatic cancer. J. Proteome Res. 10 Jun 2010 (doi:10.1021/pr100213b).

  75. Hong, S. H. et al. An autoantibody-mediated immune response to calreticulin isoforms in pancreatic center. Cancer Res. 64, 5504–5510 (2004).

    Article  CAS  PubMed  Google Scholar 

  76. Desmetz, C. et al. Identification of a new panel of serum autoantibodies associated with the presence of in situ carcinoma of the breast in younger women. Clin. Cancer Res. 15, 4733–4741 (2009).

    Article  CAS  PubMed  Google Scholar 

  77. Omenn, G. S. et al. Overview of the HUPO Plasma Proteome Project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database. Proteomics 5, 3226–3245 (2005).

    Article  CAS  PubMed  Google Scholar 

  78. States, D. J. et al. Challenges in deriving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study. Nature Biotech. 24, 333–338 (2006).

    Article  CAS  Google Scholar 

  79. Berglund, L. et al. A genecentric Human Protein Atlas for expression profiles based on antibodies. Mol. Cell. Proteomics 7, 2019–2027 (2008).

    Article  CAS  PubMed  Google Scholar 

  80. Bjorling, E. et al. A web-based tool for in silico biomarker discovery based on tissue-specific protein profiles in normal and cancer tissues. Mol. Cell. Proteomics 7, 825–844 (2008).

    Article  PubMed  CAS  Google Scholar 

  81. Yang, X. & Lazar, I. M. MRM screening/biomarker discovery with linear ion trap MS: a library of human cancer-specific peptides. BMC Cancer 9, 96 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Jones, P. et al. PRIDE: a public repository of protein and peptide identifications for the proteomics community. Nucleic Acids Res. 34, D659–D663 (2006).

    Article  CAS  PubMed  Google Scholar 

  83. Deutsch, E. W. et al. Human Plasma PeptideAtlas. Proteomics 5, 3497–3500 (2005).

    Article  CAS  PubMed  Google Scholar 

  84. Zhang, H. et al. UniPep-a database for human N-linked glycosites: a resource for biomarker discovery. Genome Biol. 7, R73 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Craig, R., Cortens, J. P. & Beavis, R. C. Open source system for analyzing, validating, and storing protein identification data. J. Proteome Res. 3, 1234–1242 (2004).

    Article  CAS  PubMed  Google Scholar 

  86. Mathivanan, S. et al. Human Proteinpedia enables sharing of human protein data. Nature Biotech. 26, 164–167 (2008).

    Article  CAS  Google Scholar 

  87. Mann, M. Can proteomics retire the western blot? J. Proteome Res. 7, 3065–3065 (2008).

    Article  CAS  PubMed  Google Scholar 

  88. Rosenblum, B. B., Neel, J. V. & Hanash, S. M. Two-dimensional electrophoresis of plasma polypeptides reveals “high” heterozygosity indices. Proc.Natl Acad. Sci. USA 80, 5002–5006 (1983).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Faca, V. et al. Contribution of protein fractionation to depth of analysis of the serum and plasma proteomes. J. Proteome Res. 6, 3558–3565 (2007).

    Article  CAS  PubMed  Google Scholar 

  90. Faca, V. et al. Quantitative analysis of acrylamide labeled serum proteins by LC-MS/MS. J. Proteome Res. 5, 2009–2018 (2006).

    Article  CAS  PubMed  Google Scholar 

  91. Faca, V. M. et al. A mouse to human search for plasma proteome changes associated with pancreatic tumor development. PLoS Med. 5, e123 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. Longsworth, L. G., Shedlovsky, T. & Macinnes, D. A. Electrophoretic patterns of normal and pathological human blood serum and plasma. J. Exp. Med. 70, 399–413 (1939).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Hanash, S. M. & Strahler, J. R. Advances in two-dimensional electrophoresis. Nature 337, 485–486 (1989).

    Article  CAS  PubMed  Google Scholar 

  94. Rasmussen, H. H., Mortz, E., Mann, M., Roepstorff, P. & Celis, J. E. Identification of transformation sensitive proteins recorded in human two-dimensional gel protein databases by mass spectrometric peptide mapping alone and in combination with microsequencing. Electrophoresis 15, 406–416 (1994).

    Article  CAS  PubMed  Google Scholar 

  95. Mann, M., Hendrickson, R. C. & Pandey, A. Analysis of proteins and proteomes by mass spectrometry. Ann. Rev. Biochem. 70, 437–473 (2001).

    Article  CAS  PubMed  Google Scholar 

  96. Domon, B. & Aebersold, R. Mass spectrometry and protein analysis. Science 312, 212–217 (2006).

    Article  CAS  PubMed  Google Scholar 

  97. Cravatt, B. F., Wright, A. T. & Kozarich, J. W. Activity-based protein profiling: from enzyme chemistry to proteomic chemistry. Ann. Rev. Biochem. 77, 383–414 (2008).

    Article  CAS  PubMed  Google Scholar 

  98. Zhu, H., Bilgin, M. & Snyder, M. Proteomics. Ann. Rev. Biochem. 72, 783–812 (2003).

    Article  CAS  PubMed  Google Scholar 

  99. Paweletz, C. P. et al. Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front. Oncogene 20, 1981–1989 (2001).

    Article  CAS  PubMed  Google Scholar 

  100. Nimrichter, L. et al. Intact cell adhesion to glycan microarrays. Glycobiology 14, 197–203 (2004).

    Article  CAS  PubMed  Google Scholar 

  101. Tateno, H. et al. A novel strategy for mammalian cell surface glycome profiling using lectin microarray. Glycobiology 17, 1138–1146 (2007).

    Article  CAS  PubMed  Google Scholar 

  102. Haab, B. B. Antibody arrays in cancer research. Mol. Cell. Proteomics 4, 377–383 (2005).

    Article  CAS  PubMed  Google Scholar 

  103. Kirby, R. et al. Aptamer-based sensor arrays for the detection and quantitation of proteins. Anal. Chem. 76, 4066–4075 (2004).

    Article  CAS  PubMed  Google Scholar 

  104. Gaster, R. S. et al. Matrix-insensitive protein assays push the limits of biosensors in medicine. Nature Med. 15, 1327–1332 (2009).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank colleagues in the Molecular Diagnostics Program at FHCRC for stimulating discussions.

Author information

Authors and Affiliations

  1. Samir Hanash and Ayumu Taguchi are at the Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M5-C800, PO BOX 19024, Seattle, Washington 98109, USA,

    Samir Hanash & Ayumu Taguchi

Authors
  1. Samir Hanash
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ayumu Taguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Samir Hanash.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

Related links

DATABASES

National Cancer Institute Drug Dictionary

erlotinib

gefitinib

Pathway Interaction Database

EGFR

FURTHER INFORMATION

Samir Hanash's homepage

Global Proteome Machine

Human Protein Atlas

HUPO

Peptide atlas

PRIDE

Proteome Commons

Proteopedia

RFA-CA-10-016

The Genome Medicine Database of Japan (GemDBJ)

Unipep

Glossary

2D PAGE

A process of separating proteins in gels based on their charge and molecular mass.

Electrospray ionization

ESI. A mass spectrometry method to ionize macromolecules or peptides by electrospray leading to their identification.

Fluorescence difference gel electrophoresis

DIGE. A method that labels protein samples with fluorescent dyes before electrophoresis.

Immobilized pH gradients

IPG. A process of generating a pH gradient by immobilizing gradient chemicals (immobilines) in the acrylamide matrix.

Lectin

A sugar-binding protein that is specific for the sugar moieties it binds.

Matrix-assisted laser desorption ionization

MALDI. A mass spectrometry method to ionize proteins and peptides deposited in a matrix leading to their identification.

Multiple reaction monitoring

MRM. A technique that targets multiple specific peptides for their quantification by mass spectrometry.

Secretome

The ensemble of proteins released by cells into the extracellular environment.

Stable isotope labelling by amino acids in cell culture

SILAC. A method for non-radioactive labelling of proteins in culture based on the uptake of labelled amino acids.

Tandem mass spectrometry

MS/MS. A two-stage separation process in mass spectrometry with fragmentation in-between allowing identification of the precursor.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hanash, S., Taguchi, A. The grand challenge to decipher the cancer proteome. Nat Rev Cancer 10, 652–660 (2010). https://doi.org/10.1038/nrc2918

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrc2918

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer