[Frontiers in Bioscience 14, 3292-3303, January 1, 2009]
Proteomics strategies for target identification and biomarker discovery in cancer
Uros Rajcevic1, Simone P. Niclou1 and Connie R. Jimenez2
1
TABLE OF CONTENTS
1. ABSTRACT
The revolution in genomics and proteomics has produced complex technologies enabling an insight into the functional effectors of cellular processes. In oncology these technologies lead to the identification of biological markers which may provide the starting point for the development and identification of diagnostic tests and therapeutic targets. To identify and validate reliable tumor markers within the proteome, it is necessary, prior to tandem mass spectrometry, to reduce sample complexity. This can be done by robust fractionation and separation techniques. This review addresses the discovery stage of onco-proteomics - the strategies for target identification and biomarker discovery in solid tumors and biofluids. The overview includes different proteomic methods, from gel-based to liquid chromatography (LC)-based separations of proteins/peptides, and the corresponding detection by mass spectrometry. The quantitative methods in mass spectrometry include techniques based on stable isotope labeling of proteins/peptides and label-free methods. A particular emphasis is given to proteomics-based biomarker discovery in biofluids (e.g. plasma, urine, secretome, cerebrospinal fluid) and target identification in tissue for anti-angiogenic therapies.
2. INTRODUCTION
Proteomics represents the large-scale analysis of protein expression, post-translational modifications and protein-protein interactions, thereby providing a link between gene and cellular function. Proteomics has been used for the analysis of proteins isolated from cells, tissues and biofluids. Proteomic data are validated and expanded by various technical disciplines including cell imaging by light and electron microscopy, protein, antibody and tissue arrays, DNA microarray/DNA chip and genetic readout experiments. Mass spectrometry (MS), in which biomolecules are ionized and their mass is measured following their specific trajectories in a vacuum system, has become the method of choice for the detection, identification and quantitation of proteins in complex biological samples. Large scale MS-based proteomics has become possible with the development of atmospheric pressure ionization methods in 1988 allowing for controlled ionization, fragmentation and subsequent analysis of large biomolecules (1, 2). The soft ionization techniques, electrospray ionization (ESI) and matrix assisted laser desorption ionization (MALDI) have revolutionized biological MS and introduced the grounds for disease-oriented proteomics. The availability of whole genome sequence databases and advances in bioinformatic tools has further contributed to MS-based proteomics. In recent years, proteomics has increasingly been applied to disease-related questions including 1. discovery of disease signatures and/or identification of disease-specific biomarkers, 2. uncovering molecular mechanisms of disease and 3. discovery of novel protein targets for therapeutic intervention (3). Biomarkers are molecular indicators reflecting disease-related physiological processes. They are of particular importance to indicate cancer risk, early detection, prognosis and to assess therapeutic response. The advantage of protein-based biomarkers include their proximity to (patho-)physiological processes, their structural diversity and a potential coupling to routine, antibody-based diagnostic assays.
The complexity i.e. the dynamic range of the human proteome is too large to be fully resolved with a single platform in a high-throughput manner. A strong experimental design including reduction of sample complexity prior to analysis is essential (Figure 1). Currently most proteomics strategies rely on a 'bottom up' approach meaning that at some point in the workflow, the (sub-) proteome of interest is digested and analyzed at the peptide level.
Excellent reviews on the different separation techniques and MS instruments for peptide analysis are available elsewhere (4). Here we will present the most common MS-based proteomics strategies (Figure 2) and highlight their use for biomarker discovery and target identification in cancer research.
3. GENERAL CONSIDERATIONS IN SAMPLE SELECTION FOR CANCER PROTEOMICS
For the discovery of reliable cancer biomarkers using patient material, standard operating procedures for both clinical specimen handling and reduction of sample complexity is of utmost importance. Clinical tissue samples need to be well-defined in terms of pathology / morphology/ histology and body fluids need to be properly handled in a consistent manner. Factors that need to be considered before embarking on a cancer proteomics discovery experiment include: 1. sample selection (clinical or research model) appropriate for question/purpose of the research, 2. isolation of the relevant sub-proteome from the sample and 3. establishment of the optimal experimental conditions for each sample type. For example, a relevant proximal fluid (e.g. urine for bladder cancer, cerebrospinal fluid for glioma) may provide an enriched source for biomarker discovery as opposed to blood-plasma. Subsequently targeted detection techniques such as multi-reaction-monitoring-MS and antibody-based methods may be used in a second stage of analysis to validate the biomarker candidates in a larger series of samples and if needed in a convenient biofluid such as serum. If tissue is the sample of choice for discovery, tissue micro-dissection may be employed to reduce cellular heterogeneity and thereby enrich for relevant components (e.g. tumor cells). Furthermore, a careful pathological evaluation is important to ensure selection of well-defined clinical samples. With respect to sample processing, suitable buffer conditions, minimal lysis times and high yield protein isolation are recommended. For each sample type these conditions need to be evaluated and optimized and the reproducibility of the whole procedure assessed. Below we describe several commonly used strategies for downstream quantitative protein and peptide profiling. Table 1 provides an overview of these strategies along with a listing of their applications, depth of analysis, sensitivity and throughput.
4. TWO DIMENSIONAL GEL-BASED PROTEOMICS
In the two-dimensional gel electrophoresis (2DE) - based proteomics, proteins in a complex sample are separated on a polyacrylamide gel, first based on their iso-electric point (pI) and in the second dimension based on their molecular weight. Protein samples can be pre-stained with fluorescent dyes (e.g. CyeDyes) which label the amino acid lysine. Quantitation of individual spots is based on staining intensity and is carried out on gel images using dedicated software. Spots of interest are excised, in gel digested with specific proteases (normally trypsin) and analyzed by MS. Matrix assisted laser desorption/ionization time-of-flight MS (MALDI-TOF-MS) and electrospray ionization tandem MS (ESI MS/MS) are efficiently used for the identification of gel separated proteins. In MALDI-TOF MS spectra, the observed mass of a peptide is presented by its mass to charge ratio (m/z), and 'peptide mass fingerprints' are matched to the calculated peptide masses of a particular protein in the database. In ESI MS/MS the ionized peptides are sequenced and the sequences matched to the databases. 2D gel-based proteomics has a limited dynamic range often identifying the same high abundant proteins regardless of the system studied. This is especially apparent when analyzing unfractionated protein mixtures from whole cell/tissue lysates (5). Other analytical limitations of 2DE are the rather poor separation of high molecular weight and hydrophobic, membrane spanning proteins (6) and the co-migration of proteins complicating the quantitation of the spots (5). Several strategies are available to improve this technique including more sensitive staining methods (7, 8), larger gels (9), sample fractionation prior to separation, limited range IEF (gel zooming), 2DE compatible detergents and automation. A major advantage of the technique is its capacity to detect posttranslational changes by differential migration on the gel.
Using fluorescent labels, two-dimensional differential in-gel electrophoresis (2D-DIGE) has been successfully applied to study mechanisms of tumor (10), biomarkers in plasma and serum (11-13) and proteome differences in varying cancer types (14-16). In a search for biomarkers for routine screening in high-risk patient groups, Bengtsson and collaborators (14) screened more than 60 tissue samples from ovarian cancer. They identified statistically valid markers using non-supervised methods that distinguished between normal, benign, borderline, and malignant tissue. Nevertheless 2D-electrophoresis remains a labor-intensive approach that requires considerable skill and the throughput is limited. Therefore, for protein profiling of large series of clinical samples, gel free analysis coupled to liquid chromatography (LC-MS) or one dimensional gels coupled to LC (GeLC-MS) may be the methods of choice (Figure 1).
5. LC-MS/MS-BASED PROTEOMICS
Multi-dimensional separation-based approaches at the protein and peptide level are frequently coupled to liquid chromatography (LC)-MS which allows for automation (Figure 2). For biofluids, important up-front fractionation includes abundant protein depletion (17) and/or enrichment for information rich subproteomes such as the albuminome (18) or glycoproteome (19). For tissues, sub-cellular fractionation into e.g. organelles, (plasma-)membrane or nuclear fractions will enrich for subsets of proteins and increase sensitivity (20).
In a gel-free proteomics pipeline, mixtures of proteins are digested to peptides by specific proteases and separated - depending on the complexity of the sample - by one or more dimensions of LC, and coupled to automated MS/MS (21) (Figure 1). Microcapillary reverse phase (RP) LC (22-24) was initially used to separate the peptides on the basis of hydrophobicity. To be able to cope with the enormous dynamic range of the proteome it soon became clear, that more than one dimension of peptide separation is needed to provide enough separation power for the mass spectrometer to process hundreds of thousands of peptides present in these complex mixtures. Currently, mostly multidimensional peptide separations are used. The most common two-dimensional LC separation applied combines strong cation exchange (SCX) chromatography with RP chromatography coupled with automated MS/MS, first described as multidimensional protein identification technology (MudPit) (25-28). The peptide fractions separated by RP are injected to the mass spectrometer via online ESI or spotted to the MALDI target plates for subsequent offline analysis by MS/MS (29-31).
In an alternative combined gel- and chromatography-based bottom-up approach, proteins in a mixture are digested in-solution and separated on an immobilized pH gradient gel. 'Focused' peptides are extracted from gel pieces and subjected to LC-MS/MS. Using that approach 1820 distinct proteins were identified with capillary IEF from microdissected tissue samples from glioblastoma cancer patients (32-36). Another powerful method combines classical SDS polyacrylamide gel electrophoresis (SDS-PAGE) with LC in a so-called 'GeLC' approach. Complexity in the protein mixture is reduced by size separation of proteins using SDS-PAGE separation, proteins are in-gel digested and the resulting peptides are separated using single or multiple dimensions of LC (29, 37). The above described in-depth profiling methods are also amenable to quantitative MS, as described in the following sections.
6. QUANTITATIVE MS AND LC-MS
6.1. Sample labeling approaches
Quantifying changes in protein abundance between samples is a key requirement for profiling differences in cell state at the molecular level. Several methods for relative quantification have been developed either with stable isotope labels or chemical labels (reviewed in (38)) (Figure 2). In these methods proteins or peptides are differentially labeled by isotopically 'light' and 'heavy' labels. The samples (usually two or more) are pooled, separated, and peptides common to all the samples appear as peak pairs (triplets etc.), differing in mass determined by isotope label used. Molecular ion intensity as determined by peak heights or peak areas is compared to an estimated abundance of the peptides between the compared samples.
6.1.1. Stable isotope labeling of amino acids in cell culture (SILAC)
This metabolic labeling approach allows for multiplexing up to 3 labels. Stable isotope containing amino acids are added to cell culture media and incorporated in the proteins by metabolic incorporation (in vivo). Isotope labeled peptides are identified and quantified in the MS spectra as precursor ion pairs (or triplets) differing in mass by known amount and their relative abundance is measured by comparing peak intensities or areas. This approach has been successfully used to identify protein-protein interactions involved in EGFR signaling (39) and in insulin-dependent GLUT4 interactions (40).
6.1.2. Heavy water labeling
Tryptic proteolysis of proteins in the presence of light (H2O16) or heavy (H2O18) water results in enzymatic labeling of peptides with light (16O) or heavy (18O) oxygen by natural exchange of two oxygen atoms from the carboxy terminus of the proteolyzed peptides with two oxygen atoms from the surrounding water molecules (41). The heavy oxygen isotope labeled peptide differs from light oxygen isotope labeled peptide by 4Da and can be detected by a corresponding mass shift in the mass spectrum.
6.1.3. Isotope-coded affinity tags (ICAT)
The isotope-coded affinity tags (ICAT) (42) are biotin-tagged chemicals that are added onto the cysteine residues in proteins. Protein samples are labeled with isotopically distinct tags, combined and digested. The biotin tag allows isolation of ICAT labeled peptides by affinity chromatography which greatly reduces sample complexity, while keeping high protein coverage. Following enrichment of ICAT-labeled peptides, the sample is separated by (multidimensional) separation techniques and analyzed by either ESI MS/MS or MALDI MS/MS (30). Co-immunoprecipitation of proteins followed by analysis using ICAT LC-MS/MS technology has identified differences in the composition of subunits of 20S proteosomes (43), proteins interacting with transcription factors and large polymerase II pre-initiation complex (44, 45) and the mSin3 co-repressor complex (46). Moreover, organelle purification in conjunction with ICAT labeling and nanoLC-MS/MS provided a first comprehensive description of the protein constituents in the post-synaptic density of rat brain (47).
6.1.4. Isobaric tags for relative and absolute quantification of peptides (iTRAQ)
iTRAQ technology is a multiplexing protein quantitation strategy that provides relative and absolute measurements of proteins in complex mixtures (48). In this method, isobaric tags different for each sample are chemically linked to amine residues on peptides (N-terminal amine and lysine side chain amine). The differentially labeled intact peptide masses are indistinguishable, but produce diagnostic fragment peaks in MS/MS mode that provide relative quantitative information on proteins. iTRAQ reagent strategy currently contains up to eight isotopically distinct tags (an advantage over three in SILAC and two in ICAT) enabling time course and disease progression experiments in a multiplexed fashion. Straightforward labeling protocol, efficient and ubiquitous labeling of the peptides has rendered iTRAQ increasingly popular. Spiking the samples with an internal standard allows for absolute quantitation. This method has been applied to differential protein profiling in lung cancer where 51 differentially expressed proteins were found during epithelial-mesenchymal transition (49). In a search for biomarkers in endometrial cancer nine potential candidates have been discovered, using a combination of differentially labeled tags, iTRAQ and cleavable ICAT (cICAT) (50).
6.2. Label-free quantitation in LC MS-based proteomics
Recently, measurements of mass spectral peak intensities and spectral counts have emerged as promising methods for quantifying changes in protein abundance. Several studies have demonstrated a relationship between protein abundance and sampling statistics, such as sequence coverage, peptide count, and spectral count in label-free LC MS/MS shotgun proteomics (51, 52). Of these parameters, spectral counts, the number of MS/MS events observed for a protein in the mass spectrometer, shows the highest technical reproducibility (52). Spectral counting proved to be a more sensitive method for detecting proteins that undergo changes in abundance, as compared to peak area intensity measurements that yield more accurate estimates of protein ratios (53). Quantification of absolute ion abundance may turn out to be a more sensitive method, as it does not rely on shotgun sequencing, so undersampling is not an issue. However, a sophisticated LC-MS data pre-processing pipeline is needed for this type of label-free analysis. Working towards this goal, there has been during the last 2 years an explosion in new bioinformatics tools that aid in automated data normalization, time alignment, peak detection, peak quantification, and peak matching, including MapQuant (54), MZmine (55), MSInspect (56), OpenMs (57) and PEPPeR (58). An inventory of pipe-line requirements and a comparison of open-source/freely available tools have recently been presented (59).
7. BIOFLUID PEPTIDE PROFILING BY MASS SPECTROMETRY AFTER HIGH-THROUGHPUT SINGLE STEP PEPTIDE CAPTURE
Biofluid peptide profiling approaches combining peptide capture with surface enhanced laser desorption ionization (SELDI)-TOF-MS or MALDI-TOF-MS, to measure intact peptides by their accurate molecular weight (mass) have proven to be a powerful tool for surveying endogenous small proteins and peptides comprising the serum peptidome or degradome (reviewed in (60)). These profiling methods are complementary to techniques such as electrophoresis and LC, which have a bias for detecting larger molecules. Using global capture chemistries such as C8 or C18 in serum, sensitivity is limited to high abundant peptides. However, when targeted affinity approaches are used, sensitivity is greatly enhanced while throughput is maintained. MALDI-TOF-MS-based serum profiling approaches are reproducible when careful study design and sample handling is combined with carefully controlled instrument calibration, automated sample preparation, and robust data preprocessing and analysis.
The clinical application of serum peptide profiling combines MS with bioinformatics to discover signatures of subsets of peptides and define a diagnostic classifier. Targeted tandem MS in a second stage of analysis may be optionally employed for identification of peptides underlying disease signatures. Over the last 5 years, many studies have established distinctive serum peptide patterns that correlate with clinically relevant parameters in cancer (see references in (61, 62)) (reviewed by Omenn et al. 2006). In particular, the pattern of processing by cancer-specific exoproteases may provide specific surrogate biomarkers (18, 63). In this study it was shown that a 68 peptide signature accurately distinguished 3 cancer types and controls. Recently, Lopez et al. (64, 65) applied MALDI-TOF-MS-based serum peptide profiling to albumin-bound peptides and proteins in an ovarian cancer study (65). Several biomarker panels enabled differentiation of stage I ovarian cancer from unaffected (age-matched) control women. Interestingly, targeted MS/MS of signature peptides isolated using the carrier protein-based approach identified protein fragments, many from low-abundance proteins or proteins not previously seen in serum. Together, the above findings suggest that serum low molecular weight proteome or peptidome contain fragments derived from 2 sources: (a) high-abundance endogenous circulating proteins and (b) cell and tissue proteins. Obviously, for a comprehensive analysis of cell and tissue-derived proteins in serum, more elaborate fractionation is needed combined with LC-MS/MS (see below).
8. BIOMARKER DISCOVERY IN BIOFLUIDS
Due to the complexity and large dynamic range of protein levels in biofluids, for large scale protein identification, multi-dimensional separation-based approaches at the protein and/or peptide level have emerged, most often coupled to LC-MS/MS. One promising approach to enrich for potential biomarkers while reducing sample complexity is the analysis of the glycoproteome of serum, cerebrospinal fluid and urine (66-68). Disease-specific glycosylation of proteins, secreted or stored, represent a rich source of potential disease markers in body fluids. Proof-of-principle of glycoproteomics was demonstrated in the analysis of plasma from mice with carcinogen-induced skin cancer where it was shown that normal and cancer sera can be unambiguously discriminated using unsupervised clustering of the resulting LC-MS peptide patterns (19). Targeted MS/MS of differential peptides in bladder cancer patients using concanavalin A affinity chromatography coupled to nanoLC-MS/MS revealed 186 proteins (including low abundant and 40 novel proteins) (69). Several proteins were associated with the presence of bladder cancer. These findings warrant further studies in large cohorts of bladder cancer patients.
Alternatively to the analysis of natural body fluids, proteins secreted or shed from cancer cells and tumor tissue (secretome) may provide a new avenue for discovery of blood-based biomarkers. During the last 2 years, several studies have shown differential analyses between the secretomes of cancer cell-types or -stages. Moreover, selected secretome candidates have been validated using antibody-based assays in serum or tissue microarrays (reviewed by Jimenez et al. (70)). In a pioneering study for breast cancer biomarkers in tumor derived medium (71), 267 proteins were identified including proteins involved in cell adhesion, motility and invasion. In the medium of prostate cancer cells, prostate-specific antigen (PSA) was detected in response to androgen stimulation (72) indicating the potential to identify low abundant proteins within the secretome. Two recent 2D-LC-MS/MS studies of considerable interest focused on breast cancer cell lines reflecting different stages of progression (73, 74). Analysis of conditioned media produced from 3 different breast cancer cell lines representing semi-normal, non-invasive and metastatic origins revealed about 1100 unique proteins in addition to known blood biomarkers such as several kallikreins, various proteases, receptors, protease inhibitors, cytokines and growth factors. Moreover, of the 15 proteins previously reported to be altered in serum or tumor from women with breast cancer, 10 were found in the secretome of the tumor cell lines. These and other studies indicate that secretome analysis is a promising alternative approach for mining low abundant proteins. Many of the identified proteins may show a potential as circulating serum-based biomarkers.
9. TARGET IDENTIFICATION FOR ANTI-ANGIOGENIC THERAPY
An important turning point in solid tumor growth is the transition from the avascular to the vascular phase (1). Formation of blood vessels in the tumour bed allows unlimited tumor growth and facilitates metastasis, as tumour cells are in close contact with the vascular bed (2). Anti-angiogenesis therapy is a potentially powerful strategy to block and/or reduce tumor growth. The identification of novel angiogenesis-specific proteins is crucial for the development of new anti-angiogenic therapies and for the establishment of new biomarkers to follow treatment response.
Using a 2DE approach a set of proteins was identified that are potentially involved in the synergistic anti-angiogenic effects of vinblastine and rapamycin (75). Changes in the relative abundance of over 1000 proteins were detected in primary human endothelial cells treated with the angiogenesis-promoting steroid sokotrasterol sulfate using quantifiable cleavable ICAT labeling linked to microcapillary reversed-phase (microRP) LC-MS/MS (76). Among the up-regulated proteins, several were identified to be involved in cell communication and morphogenesis that were novel to endothelial cells. In another study aiming at identifying angiogenesis-related proteins in glioma, glioma blood vessels were dissected by laser microdissection and subjected to MALDI Fourier Transform mass spectrometry (MALDI FTMS) and nano-LC MALDI TOFTOF MS (77). 16 peptides were expressed in the glioma vessel group, compared to the normal brain, and four were confirmed by nano-LC MALDI TOFTOF MS. The expression of two proteins, colligin 2 and fibronectin was confirmed by immunohistochemistry.
In our laboratory we applied 4-plex iTRAQ technology linked to LC-MALDI on a human/rat xenograft glioblastoma model to identify differential protein expression in highly infiltrative, non-angiogenic brain tumors compared to fully angiogenic tumors (Figure 3). The animal model is based on the serial xenotransplantation of glioblastoma tumor spheroids in the brain of immunodeficient rodents (Figure 3A) (78). In this model the complex features of invasion and angiogenesis are partially separated into an invasive tumor in early generation rats and highly angiogenic and less invasive tumor in late generation rats. Invasive tumors show stem cell markers, such as nestin, vimentin, musashi (78). Although comparative genomic hybridization (CGH) showed the invasive and angiogenic tumors as well as the corresponding initial human GBM to have almost identical chromosomal profiles, DNA microarray analysis revealed specific gene expression patterns. This suggests that rather than clonal selection it is transcriptional regulation and interaction with the microenvironment that drives the phenotypic shift (78). Profiling the proteome in these two tumor phenotypes in our xenograft model is thus an important step forward in understanding the molecular mechanisms leading to invasion and angiogenesis. By iTRAQ we were able to identify over 7000 proteins (C.I. >95%) in membrane enriched fractions (Figure 3B), of which about 3000 were isoform and species specific (Rajcevic et al., unpublished data). With specially developed software we were able to distinguish between the isoform-specific rat/stromal and human/tumor proteins and thus separate the host-tumor compartment of the model at the bioinformatics level. About 300 proteins (about 10%) were regulated at least 1.5 fold in angiogenic vs. invasive phenotypes (p<0.05). The majority of regulated proteins was of human origin (tumour-derived) and was upregulated in the angiogenic tumors. Based on protein profiling (Figure 3C) we assume that the host may be strongly involved in triggering the angiogenesis in the tumors, whereas the tumors themselves support the vascularisation, when triggered. The differential phenotypes of the xenograft model and our ability to distinguish between tumor and host proteins using advanced bioinformatics tools, provides a unique possibility to study the angiogenic switch in the context of tumor/host interactions.
10. CONCLUDING REMARKS
The quest for reliable, high quality cancer markers and specific therapeutic targets using various proteomic approaches requires ever increasing power of the proteomic analysis to yield proteins with the necessary sensitivity and specificity to discriminate between normal and cancer cells. Since the development of soft ionization techniques for bio-polymers including proteins, in mass spectrometry, huge progress has been made in MS-based proteomics with respect to sample processing, sensitivity, speed of data acquisition and data analysis. The challenge of the complexity of proteomes has been (partially) overcome by the combination of multidimensional fractionation and separation strategies. However, multi-dimensional strategies are typically low throughput. Therefore, the discovery phase of in-depth cancer proteomics studies to date, have generally yielded large numbers of promising biomarker/target candidates in relatively low numbers of biological samples. These candidates need to be followed up and validated using alternative high throughput methods. In addition to classical antibody-based techniques, targeted mass spectrometry employing multi-reaction monitoring is emerging as a promising high throughput, antibody-free validation approach to bridge the gap between discovery and clinical assay development. Only upon validation in independent cohorts, and with the right tools (high affinity reagents, typically antibodies), the translational medicine phase can be initiated and the promise of cancer proteomics exploited.
11. ACKNOWLEDGEMENTS
The authors acknowledge the support from the European Commission FP6 Integrated Project Angiotargeting (Contract No 504743) and from the Ministry of Culture, Higher Education and Research of Luxembourg.
12. REFERENCES
1. Fenn, J. B., M. Mann, C. K. Meng, S. F. Wong & C. M. Whitehouse: Electrospray ionization for mass spectrometry of large biomolecules. Science, 246, 64-71 (1989)
doi:10.1126/science.2675315
2. Karas, M. & F. Hillenkamp: Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons. Anal Chem, 60, 2299-301 (1988)
doi:10.1021/ac00171a028
3. Hanash, S.: Disease proteomics. Nature, 422, 226-32 (2003)
doi:10.1038/nature01514
4. Domon, B. & R. Aebersold: Mass spectrometry and protein analysis. Science, 312, 212-7 (2006)
doi:10.1126/science.1124619
5. Gygi, S. P., G. L. Corthals, Y. Zhang, Y. Rochon & R. Aebersold: Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proc Natl Acad Sci U S A, 97, 9390-5 (2000)
doi:10.1073/pnas.160270797
6. Gorg, A., W. Weiss & M. J. Dunn: Current two-dimensional electrophoresis technology for proteomics. Proteomics, 4, 3665-85 (2004)
doi:10.1002/pmic.200401031
7. Rabilloud, T.: Two-dimensional gel electrophoresis in proteomics: old, old fashioned, but it still climbs up the mountains. Proteomics, 2, 3-10 (2002)
doi:10.1002/1615-9861(200201)2:1<3::AID-PROT3>3.0.CO;2-R
8. Unlu, M., M. E. Morgan & J. S. Minden: Difference gel electrophoresis: a single gel method for detecting changes in protein extracts. Electrophoresis, 18, 2071-7 (1997)
doi:10.1002/elps.1150181133
9. Gauss, C., M. Kalkum, M. Lowe, H. Lehrach & J. Klose: Analysis of the mouse proteome. (I) Brain proteins: separation by two-dimensional electrophoresis and identification by mass spectrometry and genetic variation. Electrophoresis, 20, 575-600 (1999)
doi:10.1002/(SICI)1522-2683(19990301)20:3<575::AID-ELPS575>3.0.CO;2-3
10. Gonzalez-Santiago, L., P. Alfonso, Y. Suarez, A. Nunez, L. F. Garcia-Fernandez, E. Alvarez, A. Munoz & J. I. Casal: Proteomic analysis of the resistance to aplidin in human cancer cells. J Proteome Res, 6, 1286-94 (2007)
doi:10.1021/pr060430+
11. Hoffman, S. A., W. A. Joo, L. A. Echan & D. W. Speicher: Higher dimensional (Hi-D) separation strategies dramatically improve the potential for cancer biomarker detection in serum and plasma. J Chromatogr B Analyt Technol Biomed Life Sci, 849, 43-52 (2007)
doi:10.1016/j.jchromb.2006.10.069
12. Kakisaka, T., T. Kondo, T. Okano, K. Fujii, K. Honda, M. Endo, A. Tsuchida, T. Aoki, T. Itoi, F. Moriyasu, T. Yamada, H. Kato, T. Nishimura, S. Todo & S. Hirohashi: Plasma proteomics of pancreatic cancer patients by multi-dimensional liquid chromatography and two-dimensional difference gel electrophoresis (2D-DIGE): up-regulation of leucine-rich alpha-2-glycoprotein in pancreatic cancer. J Chromatogr B Analyt Technol Biomed Life Sci, 852, 257-67 (2007)
doi:10.1016/j.jchromb.2007.01.029
13. Zhou, G., H. Li, D. DeCamp, S. Chen, H. Shu, Y. Gong, M. Flaig, J. W. Gillespie, N. Hu, P. R. Taylor, M. R. Emmert-Buck, L. A. Liotta, E. F. Petricoin, 3rd & Y. Zhao: 2D differential in-gel electrophoresis for the identification of esophageal scans cell cancer-specific protein markers. Mol Cell Proteomics, 1, 117-24 (2002)
doi:10.1074/mcp.M100015-MCP200
14. Bengtsson, S., M. Krogh, C. A. Szigyarto, M. Uhlen, K. Schedvins, C. Silfversward, S. Linder, G. Auer, A. Alaiya & P. James: Large-scale proteomics analysis of human ovarian cancer for biomarkers. J Proteome Res, 6, 1440-50 (2007)
doi:10.1021/pr060593y
15. Lee, I. N., C. H. Chen, J. C. Sheu, H. S. Lee, G. T. Huang, C. Y. Yu, F. J. Lu & L. P. Chow: Identification of human hepatocellular carcinoma-related biomarkers by two-dimensional difference gel electrophoresis and mass spectrometry. J Proteome Res, 4, 2062-9 (2005)
doi:10.1021/pr0502018
16. Morita, A., E. Miyagi, H. Yasumitsu, H. Kawasaki, H. Hirano & F. Hirahara: Proteomic search for potential diagnostic markers and therapeutic targets for ovarian clear cell adenocarcinoma. Proteomics, 6, 5880-90 (2006)
doi:10.1002/pmic.200500708
17. Huang, C. M., H. N. Ananthaswamy, S. Barnes, Y. Ma, M. Kawai & C. A. Elmets: Mass spectrometric proteomics profiles of in vivo tumor secretomes: capillary ultrafiltration sampling of regressive tumor masses. Proteomics, 6, 6107-16 (2006)
doi:10.1002/pmic.200600287
18. Liotta, L. A. & E. F. Petricoin: Serum peptidome for cancer detection: spinning biologic trash into diagnostic gold. J Clin Invest, 116, 26-30 (2006)
doi:10.1172/JCI27467
19. Zhang, H., E. C. Yi, X. J. Li, P. Mallick, K. S. Kelly-Spratt, C. D. Masselon, D. G. Camp, 2nd, R. D. Smith, C. J. Kemp & R. Aebersold: High throughput quantitative analysis of serum proteins using glycopeptide capture and liquid chromatography mass spectrometry. Mol Cell Proteomics, 4, 144-55 (2005)
doi:10.1074/mcp.M400090-MCP200
20. Guimaraes de Araujo, M. E. & L. A. Huber: Subcellular fractionation. Methods Mol Biol, 357, 73-85 (2007)
21. Hunt, D. F., J. R. Yates, 3rd, J. Shabanowitz, S. Winston & C. R. Hauer: Protein sequencing by tandem mass spectrometry. Proc Natl Acad Sci U S A, 83, 6233-7 (1986)
doi:10.1073/pnas.83.17.6233
22. Davis, M. T. & T. D. Lee: Analysis of peptide mixtures by capillary high performance liquid chromatography: a practical guide to small-scale separations. Protein Sci, 1, 935-44 (1992)
23. Hunt, D. F., R. A. Henderson, J. Shabanowitz, K. Sakaguchi, H. Michel, N. Sevilir, A. L. Cox, E. Appella & V. H. Engelhard: Characterization of peptides bound to the class I MHC molecule HLA-A2.1 by mass spectrometry. Science, 255, 1261-3 (1992)
doi:10.1126/science.1546328
24. Smith, R. D., J. A. Loo, C. G. Edmonds, C. J. Barinaga & H. R. Udseth: New developments in biochemical mass spectrometry: electrospray ionization. Anal Chem, 62, 882-99 (1990)
doi:10.1021/ac00208a002
25. Link, A. J., J. Eng, D. M. Schieltz, E. Carmack, G. J. Mize, D. R. Morris, B. M. Garvik & J. R. Yates, 3rd: Direct analysis of protein complexes using mass spectrometry. Nat Biotechnol, 17, 676-82 (1999)
doi:10.1038/10890
26. Peng, J., J. E. Elias, C. C. Thoreen, L. J. Licklider & S. P. Gygi: Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome. J Proteome Res, 2, 43-50 (2003)
doi:10.1021/pr025556v
27. Shen, Y., J. M. Jacobs, D. G. Camp, 2nd, R. Fang, R. J. Moore, R. D. Smith, W. Xiao, R. W. Davis & R. G. Tompkins: Ultra-high-efficiency strong cation exchange LC/RPLC/MS/MS for high dynamic range characterization of the human plasma proteome. Anal Chem, 76, 1134-44 (2004)
doi:10.1021/ac034869m
28. Washburn, M. P., D. Wolters & J. R. Yates, 3rd: Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat Biotechnol, 19, 242-7 (2001)
doi:10.1038/85686
29. Chen, Y., S. C. Kim & Y. Zhao: High-throughput identification of in-gel digested proteins by rapid, isocratic HPLC/MS/MS. Anal Chem, 77, 8179-84 (2005)
doi:10.1021/ac051468t
30. Griffin, T. J., S. P. Gygi, B. Rist, R. Aebersold, A. Loboda, A. Jilkine, W. Ens & K. G. Standing: Quantitative proteomic analysis using a MALDI quadrupole time-of-flight mass spectrometer. Anal Chem, 73, 978-86 (2001)
doi:10.1021/ac001169y
31. Hsieh, S., K. Dreisewerd, R. C. van der Schors, C. R. Jimenez, J. Stahl-Zeng, F. Hillenkamp, J. W. Jorgenson, W. P. Geraerts & K. W. Li: Separation and identification of peptides in single neurons by microcolumn liquid chromatography-matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and postsource decay analysis. Anal Chem, 70, 1847-52 (1998)
doi:10.1021/ac9708295
32. Cargile, B. J., J. L. Bundy, T. W. Freeman & J. L. Stephenson, Jr.: Gel based isoelectric focusing of peptides and the utility of isoelectric point in protein identification. J Proteome Res, 3, 112-9 (2004)
doi:10.1021/pr0340431
33. Cargile, B. J., J. L. Bundy & J. L. Stephenson, Jr.: Potential for false positive identifications from large databases through tandem mass spectrometry. J Proteome Res, 3, 1082-5 (2004)
doi:10.1021/pr049946o
34. Chen, J., B. M. Balgley, D. L. DeVoe & C. S. Lee: Capillary isoelectric focusing-based multidimensional concentration/separation platform for proteome analysis. Anal Chem, 75, 3145-52 (2003)
doi:10.1021/ac034014+
35. Krijgsveld, J., S. Gauci, W. Dormeyer & A. J. Heck: In-gel isoelectric focusing of peptides as a tool for improved protein identification. J Proteome Res, 5, 1721-30 (2006)
doi:10.1021/pr0601180
36. Wang, Y., P. A. Rudnick, E. L. Evans, J. Li, Z. Zhuang, D. L. Devoe, C. S. Lee & B. M. Balgley: Proteome analysis of microdissected tumor tissue using a capillary isoelectric focusing-based multidimensional separation platform coupled with ESI-tandem MS. Anal Chem, 77, 6549-56 (2005)
doi:10.1021/ac050491b
37. Breci, L., E. Hattrup, M. Keeler, J. Letarte, R. Johnson & P. A. Haynes: Comprehensive proteomics in yeast using chromatographic fractionation, gas phase fractionation, protein gel electrophoresis, and isoelectric focusing. Proteomics, 5, 2018-28 (2005)
doi:10.1002/pmic.200401103
38. Bantscheff, M., M. Schirle, G. Sweetman, J. Rick & B. Kuster: Quantitative mass spectrometry in proteomics: a critical review. Anal Bioanal Chem, 389, 1017-31 (2007)
doi:10.1007/s00216-007-1486-6
39. Blagoev, B., I. Kratchmarova, S. E. Ong, M. Nielsen, L. J. Foster & M. Mann: A proteomics strategy to elucidate functional protein-protein interactions applied to EGF signaling. Nat Biotechnol, 21, 315-8 (2003)
doi:10.1038/nbt790
40. Foster, L. J., A. Rudich, I. Talior, N. Patel, X. Huang, L. M. Furtado, P. J. Bilan, M. Mann & A. Klip: Insulin-dependent interactions of proteins with GLUT4 revealed through stable isotope labeling by amino acids in cell culture (SILAC). J Proteome Res, 5, 64-75 (2006)
doi:10.1021/pr0502626
41. Yao, X., A. Freas, J. Ramirez, P. A. Demirev & C. Fenselau: Proteolytic 18O labeling for comparative proteomics: model studies with two serotypes of adenovirus. Anal Chem, 73, 2836-42 (2001)
doi:10.1021/ac001404c
42. Gygi, S. P., B. Rist, S. A. Gerber, F. Turecek, M. H. Gelb & R. Aebersold: Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol, 17, 994-9 (1999)
doi:10.1038/13690
43. Froment, C., S. Uttenweiler-Joseph, M. P. Bousquet-Dubouch, M. Matondo, J. P. Borges, C. Esmenjaud, C. Lacroix, B. Monsarrat & O. Burlet-Schiltz: A quantitative proteomic approach using two-dimensional gel electrophoresis and isotope-coded affinity tag labeling for studying human 20S proteasome heterogeneity. Proteomics, 5, 2351-63 (2005)
doi:10.1002/pmic.200401281
44. Brand, M., J. A. Ranish, N. T. Kummer, J. Hamilton, K. Igarashi, C. Francastel, T. H. Chi, G. R. Crabtree, R. Aebersold & M. Groudine: Dynamic changes in transcription factor complexes during erythroid differentiation revealed by quantitative proteomics. Nat Struct Mol Biol, 11, 73-80 (2004)
doi:10.1038/nsmb713
45. Ranish, J. A., E. C. Yi, D. M. Leslie, S. O. Purvine, D. R. Goodlett, J. Eng & R. Aebersold: The study of macromolecular complexes by quantitative proteomics. Nat Genet, 33, 349-55 (2003)
doi:10.1038/ng1101
46. Shiio, Y., D. W. Rose, R. Aur, S. Donohoe, R. Aebersold & R. N. Eisenman: Identification and characterization of SAP25, a novel component of the mSin3 corepressor complex. Mol Cell Biol, 26, 1386-97 (2006)
doi:10.1128/MCB.26.4.1386-1397.2006
47. Li, K. W., M. P. Hornshaw, R. C. Van Der Schors, R. Watson, S. Tate, B. Casetta, C. R. Jimenez, Y. Gouwenberg, E. D. Gundelfinger, K. H. Smalla & A. B. Smit: Proteomics analysis of rat brain postsynaptic density. Implications of the diverse protein functional groups for the integration of synaptic physiology. J Biol Chem, 279, 987-1002 (2004)
doi:10.1074/jbc.M303116200
48. Ross, P. L., Y. N. Huang, J. N. Marchese, B. Williamson, K. Parker, S. Hattan, N. Khainovski, S. Pillai, S. Dey, S. Daniels, S. Purkayastha, P. Juhasz, S. Martin, M. Bartlet-Jones, F. He, A. Jacobson & D. J. Pappin: Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics, 3, 1154-69 (2004)
doi:10.1074/mcp.M400129-MCP200
49. Keshamouni, V. G., G. Michailidis, C. S. Grasso, S. Anthwal, J. R. Strahler, A. Walker, D. A. Arenberg, R. C. Reddy, S. Akulapalli, V. J. Thannickal, T. J. Standiford, P. C. Andrews & G. S. Omenn: Differential protein expression profiling by iTRAQ-2DLC-MS/MS of lung cancer cells undergoing epithelial-mesenchymal transition reveals a migratory/invasive phenotype. J Proteome Res, 5, 1143-54 (2006)
doi:10.1021/pr050455t
50. DeSouza, L., G. Diehl, M. J. Rodrigues, J. Guo, A. D. Romaschin, T. J. Colgan & K. W. Siu: Search for cancer markers from endometrial tissues using differentially labeled tags iTRAQ and cICAT with multidimensional liquid chromatography and tandem mass spectrometry. J Proteome Res, 4, 377-86 (2005)
doi:10.1021/pr049821j
51. Washburn, M. P., R. Ulaszek, C. Deciu, D. M. Schieltz & J. R. Yates, 3rd: Analysis of quantitative proteomic data generated via multidimensional protein identification technology. Anal Chem, 74, 1650-7 (2002)
doi:10.1021/ac015704l
52. Zhang, B., N. C. VerBerkmoes, M. A. Langston, E. Uberbacher, R. L. Hettich & N. F. Samatova: Detecting differential and correlated protein expression in label-free shotgun proteomics. J Proteome Res, 5, 2909-18 (2006)
doi:10.1021/pr0600273
53. Old, W. M., K. Meyer-Arendt, L. Aveline-Wolf, K. G. Pierce, A. Mendoza, J. R. Sevinsky, K. A. Resing & N. G. Ahn: Comparison of label-free methods for quantifying human proteins by shotgun proteomics. Mol Cell Proteomics, 4, 1487-502 (2005)
doi:10.1074/mcp.M500084-MCP200
54. Leptos, K. C., D. A. Sarracino, J. D. Jaffe, B. Krastins & G. M. Church: MapQuant: open-source software for large-scale protein quantification. Proteomics, 6, 1770-82 (2006)
doi:10.1002/pmic.200500201
55. Katajamaa, M., J. Miettinen & M. Oresic: MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics, 22, 634-6 (2006)
doi:10.1093/bioinformatics/btk039
56. Bellew, M., M. Coram, M. Fitzgibbon, M. Igra, T. Randolph, P. Wang, D. May, J. Eng, R. Fang, C. Lin, J. Chen, D. Goodlett, J. Whiteaker, A. Paulovich & M. McIntosh: A suite of algorithms for the comprehensive analysis of complex protein mixtures using high-resolution LC-MS. Bioinformatics, 22, 1902-9 (2006)
doi:10.1093/bioinformatics/btl276
57. Kohlbacher, O., K. Reinert, C. Gropl, E. Lange, N. Pfeifer, O. Schulz-Trieglaff & M. Sturm: TOPP--the OpenMS proteomics pipeline. Bioinformatics, 23, e191-7 (2007)
doi:10.1093/bioinformatics/btl299
58. Jaffe, J. D., D. R. Mani, K. C. Leptos, G. M. Church, M. A. Gillette & S. A. Carr: PEPPeR, a platform for experimental proteomic pattern recognition. Mol Cell Proteomics, 5, 1927-41 (2006)
doi:10.1074/mcp.M600222-MCP200
59. Codrea, M. C., C. R. Jimenez, J. Heringa & E. Marchiori: Tools for computational processing of LC-MS datasets: a user's perspective. Comput Methods Programs Biomed, 86, 281-90 (2007)
doi:10.1016/j.cmpb.2007.03.001
60. Albrethsen, J.: Reproducibility in protein profiling by MALDI-TOF mass spectrometry. Clin Chem, 53, 852-8 (2007)
doi:10.1373/clinchem.2006.082644
61. Hu, S., J. A. Loo & D. T. Wong: Human body fluid proteome analysis. Proteomics, 6, 6326-53 (2006)
doi:10.1002/pmic.200600284
62. Omenn, G. S.: Strategies for plasma proteomic profiling of cancers. Proteomics, 6, 5662-73 (2006)
doi:10.1002/pmic.200600331
63. Villanueva, J., D. R. Shaffer, J. Philip, C. A. Chaparro, H. Erdjument-Bromage, A. B. Olshen, M. Fleisher, H. Lilja, E. Brogi, J. Boyd, M. Sanchez-Carbayo, E. C. Holland, C. Cordon-Cardo, H. I. Scher & P. Tempst: Differential exoprotease activities confer tumor-specific serum peptidome patterns. J Clin Invest, 116, 271-84 (2006)
doi:10.1172/JCI26022
64. Lopez, M. F., A. Mikulskis, S. Kuzdzal, D. A. Bennett, J. Kelly, E. Golenko, J. DiCesare, E. Denoyer, W. F. Patton, R. Ediger, L. Sapp, T. Ziegert, C. Lynch, S. Kramer, G. R. Whiteley, M. R. Wall, D. P. Mannion, G. Della Cioppa, J. S. Rakitan & G. M. Wolfe: High-resolution serum proteomic profiling of Alzheimer disease samples reveals disease-specific, carrier-protein-bound mass signatures. Clin Chem, 51, 1946-54 (2005)
doi:10.1373/clinchem.2005.053090
65. Lopez, M. F., A. Mikulskis, S. Kuzdzal, E. Golenko, E. F. Petricoin, 3rd, L. A. Liotta, W. F. Patton, G. R. Whiteley, K. Rosenblatt, P. Gurnani, A. Nandi, S. Neill, S. Cullen, M. O'Gorman, D. Sarracino, C. Lynch, A. Johnson, W. McKenzie & D. Fishman: A novel, high-throughput workflow for discovery and identification of serum carrier protein-bound peptide biomarker candidates in ovarian cancer samples. Clin Chem, 53, 1067-74 (2007)
doi:10.1373/clinchem.2006.080721
66. Pan, S., Y. Wang, J. F. Quinn, E. R. Peskind, D. Waichunas, J. T. Wimberger, J. Jin, J. G. Li, D. Zhu, C. Pan & J. Zhang: Identification of glycoproteins in human cerebrospinal fluid with a complementary proteomic approach. J Proteome Res, 5, 2769-79 (2006)
doi:10.1021/pr060251s
67. Zhang, H., X. J. Li, D. B. Martin & R. Aebersold: Identification and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. Nat Biotechnol, 21, 660-6 (2003)
doi:10.1038/nbt827
68. Zhang, H., A. Y. Liu, P. Loriaux, B. Wollscheid, Y. Zhou, J. D. Watts & R. Aebersold: Mass spectrometric detection of tissue proteins in plasma. Mol Cell Proteomics, 6, 64-71 (2007)
doi:10.1074/mcp.M600160-MCP200
69. Kreunin, P., J. Zhao, C. Rosser, V. Urquidi, D. M. Lubman & S. Goodison: Bladder cancer associated glycoprotein signatures revealed by urinary proteomic profiling. J Proteome Res, 6, 2631-9 (2007)
doi:10.1021/pr0700807
70. Jimenez, C. R., S. Piersma & T. Pham: High-throughput and targeted in-depth mass spectrometry-based approaches for biofluid profiling and biomarker discovery. Biomarkers in Medicine, 1, 541-565 (2007)
doi:10.2217/17520363.1.4.541
71. Celis, J. E., P. Gromov, I. Gromova, J. M. Moreira, T. Cabezon, N. Ambartsumian, M. Grigorian, E. Lukanidin, P. Thor Straten, P. Guldberg, J. Bartkova, J. Bartek, J. Lukas, C. Lukas, A. Lykkesfeldt, M. Jaattela, P. Roepstorff, L. Bolund, T. Orntoft, N. Brunner, J. Overgaard, K. Sandelin, M. Blichert-Toft, H. Mouridsen & F. E. Rank: Integrating proteomic and functional genomic technologies in discovery-driven translational breast cancer research. Mol Cell Proteomics, 2, 369-77 (2003)
72. Martin, D. B., D. R. Gifford, M. E. Wright, A. Keller, E. Yi, D. R. Goodlett, R. Aebersold & P. S. Nelson: Quantitative proteomic analysis of proteins released by neoplastic prostate epithelium. Cancer Res, 64, 347-55 (2004)
doi:10.1158/0008-5472.CAN-03-2062
73. Kulasingam, V. & E. P. Diamandis: Proteomic analysis of conditioned media from three breast cancer cell lines: A mine for biomarkers and therapeutic targets. Mol Cell Proteomics (2007)
74. Mbeunkui, F., B. J. Metge, L. A. Shevde & L. K. Pannell: Identification of differentially secreted biomarkers using LC-MS/MS in isogenic cell lines representing a progression of breast cancer. J Proteome Res, 6, 2993-3002 (2007)
doi:10.1021/pr060629m
75. Campostrini, N., D. Marimpietri, A. Totolo, C. Mancone, G. M. Fimia, M. Ponzoni & P. G. Righetti: Proteomic analysis of anti-angiogenic effects by a combined treatment with vinblastine and rapamycin in an endothelial cell line. Proteomics, 6, 4420-31 (2006)
doi:10.1002/pmic.200600119
76. Karsan, A., I. Pollet, L. R. Yu, K. C. Chan, T. P. Conrads, D. A. Lucas, R. Andersen & T. Veenstra: Quantitative proteomic analysis of sokotrasterol sulfate-stimulated primary human endothelial cells. Mol Cell Proteomics, 4, 191-204 (2005)
doi:10.1074/mcp.M400152-MCP200
77. Mustafa, D. A., P. C. Burgers, L. J. Dekker, H. Charif, M. K. Titulaer, P. A. Smitt, T. M. Luider & J. M. Kros: Identification of glioma neovascularization-related proteins by using MALDI-FTMS and nano-LC fractionation to microdissected tumor vessels. Mol Cell Proteomics, 6, 1147-57 (2007)
doi:10.1074/mcp.M600295-MCP200
78. Sakariassen, P. O., L. Prestegarden, J. Wang, K. O. Skaftnesmo, R. Mahesparan, C. Molthoff, P. Sminia, E. Sundlisaeter, A. Misra, B. B. Tysnes, M. Chekenya, H. Peters, G. Lende, K. H. Kalland, A. M. Oyan, K. Petersen, I. Jonassen, A. van der Kogel, B. G. Feuerstein, A. J. Terzis, R. Bjerkvig & P. O. Enger: Angiogenesis-independent tumor growth mediated by stem-like cancer cells. Proc Natl Acad Sci U S A, 103, 16466-71 (2006)
doi:10.1073/pnas.0607668103
Key Words: Cancer Proteomics, Mass Spectrometry, Label-Free Proteomics, Quantitative Proteomics, Angiogenesis, Biomarkers, Biofluids, Glioma, Review
Send correspondence to: Uros Rajcevic, NorLux Neuro-Oncology Laboratory, CRP-Sante, 84, Val Fleuri, L-1526 Luxembourg, Tel: 352-26970274 Fax: 352-26970390, E-mail:uros.rajcevic@crp-sante.lu, www.norlux.lu