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.
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Fine, J. M. & Creyssel, R. Starch gel electrophoresis studies on abnormal proteins in myeloma and macroglobulinaemia. Nature 183, 392 (1959).
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).
Moreira, J. M. et al. Bladder cancer-associated protein, a potential prognostic biomarker in human bladder cancer. Mol. Cell. Proteomics 9, 161–177 (2010).
Kondo, T. Cancer proteome-expression database: Genome Medicine Database of Japan Proteomics. Expert Rev. Proteomics 7, 21–27 (2010).
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).
Timms, J. F. & Cramer, R. Difference gel electrophoresis. Proteomics 8, 4886–4897 (2008).
Hanash, S., Pitteri, S. & Faca, V. Mining the plasma proteome for cancer biomarkers. Nature 452, 571–579 (2008).
Cravatt, B. F., Simon, G. M. & Yates, J. R. The biological impact of mass-spectrometry-based proteomics. Nature 450, 991–1000 (2007).
Siuti, N. & Kelleher, N. L. Decoding protein modifications using top-down mass spectrometry. Nature Methods 4, 817–821 (2007).
Nesvizhskii, A. I., Vitek, O. & Aebersold, R. Analysis and validation of proteomic data generated by tandem mass spectrometry. Nature Methods 4, 787–797 (2007).
Cox, J. & Mann, M. Is proteomics the new genomics? Cell 130, 395–398 (2007).
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).
Hanash, S. Disease proteomics. Nature 422, 226–232 (2003).
Aebersold, R. & Mann, M. Mass spectrometry-based proteomics. Nature 422, 198–207 (2003).
Hanash, S. M. et al. Highly resolving two-dimensional gels for protein sequencing. Proc. Natl Acad. Sci. USA 88, 5709–5713 (1991).
Constans, A. MALDI - Pioneering ionization technique paved the way for proteomics. The Scientist 19, 37 (2005).
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).
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).
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).
Roesli, C. et al. Comparative analysis of the membrane proteome of closely related metastatic and nonmetastatic tumor cells. Cancer Res. 69, 5406–5414 (2009).
Bell, A. W. et al. A HUPO test sample study reveals common problems in mass spectrometry-based proteomics. Nature Methods 6, 423–430 (2009).
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).
Stoevesandt, O., Taussig, M. J. & He, M. Protein microarrays: high-throughput tools for proteomics. Expert Rev. Proteomics 6, 145–157 (2009).
Hu, S. et al. Profiling the human protein-DNA interactome reveals ERK2 as a transcriptional repressor of interferon signaling. Cell 139, 610–622 (2009).
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).
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).
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).
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).
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).
Choudhary, C. & Mann, M. Decoding signalling networks by mass spectrometry-based proteomics. Nature Rev. Mol. Cell Biol. 11, 427–439 (2010).
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).
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).
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).
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).
Guo, A. et al. Signaling networks assembled by oncogenic EGFR and c-Met. Proc. Natl Acad. Sci. USA 105, 692–697 (2008).
Rikova, K. et al. Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer. Cell 131, 1190–1203 (2007).
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).
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).
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).
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).
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).
Chen, G. et al. Protein profiles associated with survival in lung adenocarcinoma. Proc. Natl Acad. Sci. USA 100, 13537–13542 (2003).
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).
Xue, H. et al. Identification of serum biomarkers for colorectal cancer metastasis using a differential secretome approach. J. Proteome Res. 9, 545–555.
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).
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).
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).
Gronborg, M. et al. Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach. Mol. Cell. Proteomics 5, 157–171 (2006).
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).
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).
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).
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).
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).
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).
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).
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).
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.
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).
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).
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).
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).
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).
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).
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).
Desmetz, C., Maudelonde, T., Mange, A. & Solassol, J. Identifying autoantibody signatures in cancer: a promising challenge. Expert Rev. Proteomics 6, 377–386 (2009).
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).
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).
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).
Nagashio, R. et al. Detection of tumor-specific autoantibodies in sera of patients with lung cancer. Lung Cancer 62, 364–373 (2008).
Leidinger, P. et al. Identification of lung cancer with high sensitivity and specificity by blood testing. Respir. Res. 11, 18 (2010).
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).
Rom, W. N. et al. Identification of an autoantibody panel to separate lung cancer from smokers and nonsmokers. BMC Cancer 10, 234 (2010).
Tomaino, B. et al. Autoantibody signature in human ductal pancreatic adenocarcinoma. J. Proteome Res. 6, 4025–4031 (2007).
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).
Hong, S. H. et al. An autoantibody-mediated immune response to calreticulin isoforms in pancreatic center. Cancer Res. 64, 5504–5510 (2004).
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).
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).
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).
Berglund, L. et al. A genecentric Human Protein Atlas for expression profiles based on antibodies. Mol. Cell. Proteomics 7, 2019–2027 (2008).
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).
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).
Jones, P. et al. PRIDE: a public repository of protein and peptide identifications for the proteomics community. Nucleic Acids Res. 34, D659–D663 (2006).
Deutsch, E. W. et al. Human Plasma PeptideAtlas. Proteomics 5, 3497–3500 (2005).
Zhang, H. et al. UniPep-a database for human N-linked glycosites: a resource for biomarker discovery. Genome Biol. 7, R73 (2006).
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).
Mathivanan, S. et al. Human Proteinpedia enables sharing of human protein data. Nature Biotech. 26, 164–167 (2008).
Mann, M. Can proteomics retire the western blot? J. Proteome Res. 7, 3065–3065 (2008).
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).
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).
Faca, V. et al. Quantitative analysis of acrylamide labeled serum proteins by LC-MS/MS. J. Proteome Res. 5, 2009–2018 (2006).
Faca, V. M. et al. A mouse to human search for plasma proteome changes associated with pancreatic tumor development. PLoS Med. 5, e123 (2008).
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).
Hanash, S. M. & Strahler, J. R. Advances in two-dimensional electrophoresis. Nature 337, 485–486 (1989).
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).
Mann, M., Hendrickson, R. C. & Pandey, A. Analysis of proteins and proteomes by mass spectrometry. Ann. Rev. Biochem. 70, 437–473 (2001).
Domon, B. & Aebersold, R. Mass spectrometry and protein analysis. Science 312, 212–217 (2006).
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).
Zhu, H., Bilgin, M. & Snyder, M. Proteomics. Ann. Rev. Biochem. 72, 783–812 (2003).
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).
Nimrichter, L. et al. Intact cell adhesion to glycan microarrays. Glycobiology 14, 197–203 (2004).
Tateno, H. et al. A novel strategy for mammalian cell surface glycome profiling using lectin microarray. Glycobiology 17, 1138–1146 (2007).
Haab, B. B. Antibody arrays in cancer research. Mol. Cell. Proteomics 4, 377–383 (2005).
Kirby, R. et al. Aptamer-based sensor arrays for the detection and quantitation of proteins. Anal. Chem. 76, 4066–4075 (2004).
Gaster, R. S. et al. Matrix-insensitive protein assays push the limits of biosensors in medicine. Nature Med. 15, 1327–1332 (2009).
The authors would like to thank colleagues in the Molecular Diagnostics Program at FHCRC for stimulating discussions.
The authors declare no competing financial interests.
- 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.
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.
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.
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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
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