SDS-PAGE with Taq DNA Polymerase. SDS-PAGE is an useful technique to separate proteins according to their electrophoretic mobility. (Photo credit: Wikipedia)

Proteomics and Biomarker Discovery

Reporter: Larry H. Bernstein, MD, FCAP

Advanced Proteomic Technologies for Cancer Biomarker Discovery

Sze Chuen Cesar Wong; Charles Ming Lok Chan; Brigette Buig Yue Ma; Money Yan Yee Lam; Gigi Ching Gee Choi; Thomas Chi Chuen Au; Andrew Sai Kit Chan; Anthony Tak Cheung Chan

Published: 06/10/2009

This report is extracted from the article above with editing and shortening as much as possible for the reader, and updated from LCGCNA Aug 12,  2012; 8
www.chromatographyonline.com

Part I

Abstract

This review will focus on four state-of-the-art proteomic technologies, namely 2D difference gel electrophoresis, MALDI imaging mass spectrometry, electron transfer dissociation mass spectrometry and reverse-phase protein array. The major advancements these techniques have brought about biomarker discovery will be presented in this review. The wide dynamic range of protein abundance, standardization of protocols and validation of cancer biomarkers, and a 5-year view of potential solutions to such problems is discussed.

English: Public domain image from cancer.gov http://visualsonline.cancer.gov/details.cf?imageid=3483. TECAN Genesis 2000 robot preparing Ciphergen SELDI-TOF protein chips for proteomic  analysis. (Photo credit: Wikipedia)

Introduction

A common method used for isolating and identifying cancer biomarkers involves the use of serum or tissue protein identification. Unfortunately, currently used tumor markers have low sensitivities and specificities.^[2] Therefore, the development of novel tumor markers might be helpful in improving cancer diagnosis, prognosis and treatment.

The rapid development of proteomic technologies during the past 10 years has brought about a massive increase in the discovery of novel cancer biomarkers. Such biomarkers may have broad applications, such as for the detection of the presence of a disease, monitoring of disease clearance and/or progression, monitoring of treatment response and demonstration of drug targeting of a particular pathway and/or target. In general, proteomic approaches begin with the collection of biological specimens representing two different physiological conditions, cancer patients and reference subjects. Proteins or peptides are extracted and separated, and the protein or peptide profiles are compared against each other in order to detect differentially expressed proteins. Commonly, quantitative proteomics is mainly performed by protein separation using either 2DE- or liquid chromatography (LC)-based methods coupled with protein identification using mass spectrometry (MS). Limitations include inability to obtain protein profiles directly from tissue sections for correlation with tissue morphology, limited ability to analyze post-translational modifications (PTMs) and low capacity for high-throughput validation of identified markers. Progress in proteomic technologies has led to the development of 2D DIGE, MALDI imaging MS (IMS), electron transfer dissociation (ETD) MS, and reverse-phase protein array (RPA).

2D Difference Gel Electrophoresis

The 2DE method has been one of the mainstream technologies used for proteomic investigations.^[3,4] In this method, proteins are separated in the first dimension according to charge by isoelectric focusing, followed by separation in the second dimension according to molecular weight, using polyacrylamide gel electrophoresis. The gels are then stained to visualize separated protein spots,^[5] separating up to 1000 protein spots in a single experiment and  protein spots are then excised and identified using mass spectrometry (MS).^[6,7]

We previously used a 2DE approach to compare the proteomic profiles to identify differentially expressed proteins that may be involved in the development of nasopharyngeal cancer, ^[8]   as well as proteins that were responsive to treatment with the chemotherapeutic agent 5-fluorouracil (5FU) in the colorectal cancer SW480 cell line. Briefly, cell lysates from SW480 cells that were either treated with 5FU or were controls were separated using 2DE. After staining and analysis of the gels, differentially expressed protein spots were excised and identified using MS. The upregulation of heat-shock protein (Hsp)-27 and peroxiredoxin 6 and the downregulation of Hsp-70 were successfully validated by immunohistochemical (IHC) staining of SW480 cells.^[9]

The 2D DIGE method improved the 2DE technique. Figure 1 shows how two different protein samples (e.g., control and disease) and, optionally, one reference sample (e.g., control and disease pooled together) are labeled with one of three spectrally different fluorophores: cyanine (Cy)2, 3 or 5. They have the same charge, similar molecular weight and distinct fluorescent properties, allowing their discrimination during fluorometric scanning.^[10-12]  The minimal dye causes minimal change in the electrophoretic mobility pattern of the protein, whereas the saturation dye labels all available cysteine residues but causes a shift in electrophoretic mobility labeled proteins.^[13]  The same pooled reference sample used for all gels within an experiment is an internal reference for normalization and spot matching.^[12] The gel is scanned at three different wavelengths yielding images for each of the different samples, and variation between gels is minimized and difficulties are reduced in correctly matching of protein spots across different gels.^[10,11]  Significant advantages of the DIGE technology includes a dynamic range of over four orders of magnitude and full compatibility with MS.  However, careful validations of identified markers using alternative techniques are still needed.

In a study that compared three commonly used DIGE analysis software packages, Kang et al. concluded that although the three softwares performed satisfactorily with minimal user intervention, significant improvements in the accuracy of analysis could be achieved .^[14] Moreover, it was suggested that results concerning the magnitude of differential expression between protein spots after statistical analysis by such softwares must be examined with care.^[14]

Figure 1.  Procedures for performing a 2D DIGE experiment. CY: Cyanine; DIGE: Differential in gel electrophoresis.

The choice of appropriate statistical methods for the analysis of DIGE data has to be considered. Statistical methodological error can be addressed by the use of statistical methods that apply a false-discovery rate (FDR) for the determination of significance. In this method, q-values are calculated for all protein spots. The q-value of each spot corresponds to the expected proportion of false-positives incurred by a change in expression level of that protein spot found to be significant.

Despite the ease of use and enabling the researcher to select an appropriate FDR according to study requirements, this approach was found to be only applicable to DIGE experiments using a two-dye labeling scheme, as a three-dye labeling approach violated the assumption of data independence required for statistical analysis.^[16] Other statistical tests that have been applied for the analysis of DIGE results include significance analysis of microarrays,^[7] principal components analysis^[17,18] and partial least squares discriminant analysis.^[18,19] Detailed discussions of the different statistical approaches applicable to proteomic research are beyond the scope of this review and readers may refer to^[18,20] for further reading.

Using 2D DIGE, Yu et al. successfully identified biomarkers that were associated with pancreatic cancer.^[21] In the study, 24 upregulated and 17 downregulated proteins were identified by MS. Among those proteins, upregulation of apolipoprotein E, α-1-antichymotrypsin and inter-α-trypsin inhibitor were confirmed by western blot analysis. Furthermore, the association of those three proteins with pancreatic cancer was successfully validated in another series of 20 serum samples from pancreatic cancer patients. Using a similar approach, Huang et al. identified and confirmed the upregulation of transferrin in the sera of patients with breast cancer.^[22] When Sun et al. compared the proteomic profiles between malignant and adjacent benign tissue samples from patients with hepatocellular carcinoma, they proved 2D DIGE is not limited to serum or plasma samples.^[23] In their study, overexpression of Hsp70/Hsp90-organizing protein and heterogenous nuclear ribonucleoproteins C1 and C2 were identified by 2D DIGE coupled with MS analysis, and the findings were successfully validated by both western blotting and IHC staining. Next, Kondo et al. applied 2D DIGE to laser-microdissected cells from fresh patient tissues.^[13] Using this protocol, a 1-mm area of an 8-12-µm-thick tissue section was shown to be sufficient. These examples demonstrate the high sensitivity and broad applicability of 2D DIGE for proteomic investigations using various types of patient samples and provide evidence that 2D DIGE is a powerful technique for biomarker discovery.

MALDI Imaging Mass Spectrometry

A deeper understanding of the complex biochemical processes occurring within tumor cells and tissues requires a knowledge of the spatial and temporal expression of individual proteins. Currently, such information is mainly obtained by IHC staining for specific proteins in patient tissues.^[8,24,25] Nevertheless, IHC has limited use in high-throughput proteomic biomarker discovery because only a few proteins can be immunostained simultaneously. MALDI IMS allows researchers to analyze proteomic expression profiles directly from patient tissue sections.^[26-28] The protocol begins with mounting a tissue section onto a sample plate (Figure 2). MALDI matrix is then applied onto the tissue sample, which is analyzed by MALDI MS in order to obtain mass spectra from predefined locations across the entire patient tissue section. The mass spectrum from each location is a complete proteomic profile for that particular area. All acquired mass spectra from the entire tissue are then compiled to create a 2D map for that tissue sample. This map could then be compared with those from other tissue samples to identify changes in protein or peptide expression or comparisons of the maps from different areas within the same tissue section could be performed. This technology  importantly allows the high-throughput discovery of novel protein markers. In addition, correlations between protein expression and tissue histology can also be studied easily.

Most studies using MALDI IMS have been performed on frozen tissue sections ranging from 5 to 20 µm in thickness.^[26,27,29] After sectioning, a MALDI matrix is applied either by automated spraying or spotting. The matrix of choice is usually α-cyano-4-hydroxy-cinnamic acid for peptides and sinapinic acid (3,5-dimethoxy-4-hydroxycinnamic acid) for proteins.

Figure 2.  Procedures for MALDI imaging. IMS: Imaging mass spectrometry; MS: Mass spectrometry.

Spotting allows the precise application of matrix to areas of interest and minimizes the diffusion of analyte material across the sample, although the imaging resolution achieved by spotting is lower (~150 µm). A laser beam is then fired towards the area of interest on the tissue section to generate protein ions for analysis by a mass analyzer.^[29] Among the different mass analyzers, TOF analyzers are the most commonly used owing to their high sensitivity, broad mass range and suitability for detection of ions generated by MALDI. Use of other mass analyzers such as TOF-TOF, quadrupole TOF (QTOF), ion traps (ITs) and Fourier transform-ion cyclotron resonance (FT-ICR) have also been reported in other studies.^[30-33]

After obtaining the mass spectra, statistical analysis needs to be performed to identify statistically significant features that could have potential use as biomarkers. But before such analyses can be applied, there has to be background-noise subtraction, spectral normalization and spectral alignment.^[34,35,34] Statistical methods used to identify significant differences in peak intensity are symbolic discriminant analysis and principal component analysis. Symbolic discriminant analysis determines discriminatory features and builds functions based on such features for distinguishing samples according to their classification.^[36,37] Using this approach, Lemaire et al. found a putative proteomic biomarker from ovarian cancer tissues by MALDI IMS that was later identified to be the Reg-α protein, a member of the proteasome activator 11S.^[37] This result was later successfully validated by western blot (protein expression found in 88.8% carcinoma cases vs 18.7% benign disease) and IHC (protein expression found in 63.6% carcinoma tissues vs 16.6% benign tissues).^[37] On the other hand, principal component analysis reduces data complexity by transforming data based on peak intensities to information based on data variance, termed ‘principal components’, resulting in a list of significant peaks (principal components) ordered by decreasing variance.^[35,38,39] Neither symbolic discriminant analysis or principal component analysis is capable of performing unsupervised classification. This aim requires the use of other methods such as hierarchical clustering.^[39,40] In this method identified peaks are clustered as nodes in a pair-wise manner according to similarity until a dendogram is obtained, providing information as to the degree of association of all peak masses in a hierarchical fashion. Peaks that are capable of differentiating between different histological/pathological features could then be chosen for further validation of their value as tumor markers.^[39]

In MALDI IMS, protein identification cannot be performed with confidence solely on the molecular weight. However, Groseclose et al. have developed a method using in situ digestion of proteins directly on tissue section.^[41] They first used MALDI IMS to obtain a map of the protein and peptide spectra, then spotted a consecutive section of the same tissue sample with trypsin for protein digestion, and then spotted matrix solution onto the digested spots and the resulting peptides are identified directly from the tissue by MS/MS. This modification increases the confidence in protein identification. The time required for MALDI IMS analysis per tissue section is as follows: tissue sectioning, mounting and matrix application: 4-8 h; MALDI image acquisition: 1-2 days; spectral analysis: 1-2 h.^[33,39]

Recently, in situ enzymatic digestion has been successfully applied for improving the retrieval of peptides directly from formalin-fixed, paraffin-embedded FFPE tissue samples.^[27] Such development has greatly facilitated the application of MALDI IMS in FFPE tissues.^[26,42] In fact, Stauber et al. identified the downregulation of ubiquitin, transelongation factor 1, hexokinase and neurofilament M from FFPE brain tissues of rat models of Parkinson disease using this modified technique.^[42] The success of performing proteomic profiling using MALDI IMS directly on FFPE tissues opens up great possibility for using archival patient materials in high-throughput biomarker discovery. Novel cancer biomarkers identified using MALDI IMS still require validation by other techniques such as IHC.

Electron Transfer Dissociation MS

Post-translational modifications play important roles in the structure and function of proteins such as protein folding, protein localization, regulation of protein activity and mediation of protein-protein interaction. Two common forms of PTM that have been implicated in cancer development are phosphorylation and glycosylation. Previously, phosphoproteomic studies have led to the identification of novel tyrosine kinase substrates in breast cancer,^[43] discovery of novel therapeutic targets for brain cancer^[44] and increased understanding of signaling pathways involved in lung cancer formation.^[45,46] Conversely, the identification of abnormally glycosylated proteins, such as mucins, has provided novel biomarkers and therapeutic targets for ovarian cancer.^[47]

The study of PTM begins with digesting the target protein using enzymes such as trypsin,   introducing the fragments into MS for determination of the sites and types of modification and, at the same time, identification of the protein. The analysis is conventionally carried out using collision-induced dissociation (CID) MS, where peptides are collided with a neutral gas for cleavage of peptide bonds to produce b- and y-type ions (Figure 3). A complete series of peptides differing in length by one amino acid is produced, leading to identification of the protein by peptide-sequence determination. However, for phosphopeptides, the presence of phosphate groups would compete with the peptide backbone as the preferred cleavage site. The end result is a reduced set of peptide fragments, which hinders protein identification, and the exact location of the phosphate group on the peptide cannot be determined accurately when there are more than one possible phosphorylation sites.^[48,49]

Figure 3.  Peptide bond-cleavage site for a-, b-, c-, x-, y– and z-type ions.

Electron transfer dissociation is a recently developed dissociation technique for the analysis of peptides by MS, utilizing radiofrequency quadrupole ion traps such as 2D linear IT, spherical IT and Orbitrap™ (Thermo Fisher Scientific Inc., MA, USA) mass analyzers.^[48,49] In this technology, peptides are fragmented by transfer of electrons from anions to induce cleavage of Cα-N bonds along the peptide backbone, hence producing c- and z-type ions (Figure 3). In contrast to CID, ETD preserves the localization of labile PTM and also provides peptide-sequence information.^[48] But ETD fails to fragment peptide bonds adjacent to proline, which are readily cleaved by CID.^[50] A study that compared the performance of CID with that of ETD found that only 12% of the identified peptides were commonly detected between the two techniques. A study reported that CID successfully identified more peptides with charge states of +2 and below, whereas ETD was found to be better at identifying peptide ions with charge states of greater than +2.^[51] Therefore, it is suggested that CID and ETD should be used together to complement each other.^[52]  Han et al. successfully differentiated the isobaric amino acids isoleucine and leucine from one another by performing CID on the resulting z-ions after ETD. The presence of isoleucine residue was then confirmed by the detection of a specific 29-Da loss from the peptide.^[53]  A clear advantage of using ETD for the analysis of phosphopeptides is a near complete series of c- and z-ions without loss of phosphoric acid,^[48] greatly facilitating the determination of the phosphorylation sites and the identification of phosphopeptides. Recently, an analysis of yeast phosphoproteome using ETD successfully identified 1252 phosphorylation sites on 629 proteins, whose expression levels ranged from less than 50 to 1,200,000 copies per cell.^[54] In another study using ETD, a total of 1435 phosphorylation sites were identified from human embryonic kidney 293T cells, of which 1141 (80%) were previously unidentified. Finally, a study by Molina et al. successfully identified 80% of the known phosphorylation sites in more than 1000 yeast phosphopeptides in one single study using a combination of ETD and CID.^[55] In addition, ETD could be applied to investigate other forms of PTM, such as N-linked glycosylations.^[56,57] N-linked glycans contain a common core with branched structures. These can be processed by stepwise addition or removal of monosaccharide residues linked by glycosidic bonds, producing highly varied forms of N-linked glycan structures.^[58-60] A weakness of analyzing glycopeptides using CID is that cleavage of glycosidic bonds occurs with little peptide backbone fragmentation, so that only the glycan structure is available.^[61]  Hogan et al. used CID and ETD together to overcome this problem determining the glycan structure and glycosylation site.^[61] ICID was initially used for cleavage of glycosidic bonds that allowed the entire glycan structure to be inferred from the CID spectrum alone. ETD was later performed to dissociate the same peptide that resulted in a contiguous series of fragment ions with no loss of glycan molecules, allowing the identification of both the site of glycosylation and the identity of the glycoprotein.^[61] Readers are strongly encouraged to refer to^[49] and.^[62] In a comprehensive comparison of CID versus ETD for the identification of peptides without PTMs, CID was found to identify 50% more peptides than ETD (3518 by CID vs 2235 by ETD), but ETD provided somewhat better sequence coverage (67% for CID vs 82% for ETD). It turns out that ETD produced more uniformly fragmented ions with intensities that were five- to ten-times lower than those produced by CID.^[55] Finally, the best sequence coverage of up to 92% was achieved when consecutive CID and ETD were performed.^[55]

This increase in sequence coverage using the combined approach is needed for studies requiring de novo peptide identifications. As such, this strategy is particularly suited for studies involved in the discovery, identification and characterization of novel peptides or proteins and their PTMs for biomarker use. A prerequisite of this technique is that the biological samples under investigation must undergo some form of fractionation before they are amenable to analysis by ETD or CID. This is achieved by the use of LC techniques, such as reverse-phase, strong cation exchange or strong anion exchange chromatography, and serves to reduce the complexity and wide dynamic range of protein-expression levels commonly found in biological specimens. Given the important roles of PTM in the function and activity of proteins, this technology paves the way for exploring the intricate cellular activities within a cancer cell.

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Advanced Proteomic Technologies for Cancer Biomarker Discovery

Part II

Reverse-phase Protein Array

One of the goals of proteomics is to identify protein changes associated with the development of diseases such as cancer.  Even with the rapid development of proteomic technologies during the past few years, analysis of patient samples is still a challenge. Difficulties arise from the fact that^[63,64]:

• Proteomic patterns differ among cell types;
• Protein expression changes occur over time;
• Proteins have a broad dynamic range of expression levels spanning several orders of magnitude;
• Proteins can be present in multiple forms, such as polymorphisms and splice variants;
• Traditional proteomic methods require relatively large amounts of protein
• Many proteomic technologies cannot be used to study protein-protein interactions.

The principle of RPA is simple and involves the spotting of patient samples in an array format onto a nitrocellulose support (Figure 4). Hundreds of patient specimens can be spotted onto an array, allowing a comparison of a large number of samples at once.^[65] Each array is incubated with one particular antibody, and signal intensity proportional to the amount of analyte in the sample spot is generated.^[66] Signal detection is commonly performed by fluorescence, chemiluminescence or colorimetric methods. The results are quantified by scanning and analyzed by softwares such as P-SCAN and ProteinScan, which can be downloaded from^[84] for free.^[67,68]

Figure 4.  Principle of reverse-phase protein array.

Main advantages of RPA technology include^[69-71]:

• Various types of biological samples can be used;
• The possibility of investigating PTMs;
• Protein-protein interactions can be studied;
• Labeling of patient samples with fluorescent dyes (e.g., 2D DIGE) or mass tags (e.g., isotope-coded affinity tag [ICAT]) are not required;
• Any samples spotted as a dilution allows quantifying in the linear range of detection;
• Quantitative measurement of any protein is possible compared to reference standards of known amounts on the same array.

It has been shown that RPA is extremely sensitive as it is capable of detecting up to zeptomole (1 x 10^-21 mole) levels of target proteins with less than 10% variance. The analysis of few cell signaling events is known.^[65,70,71] The assay sensitivity depends on antibody affinity, which depends upon antigen-antibody pairs.^[68] Of course, only known proteins with available antibodies can be identified. Therefore, this method is more suitable for biomarker screening or validation than discovery of novel proteins. To assist researchers in selecting suitable antibodies, two open antibody databases show their western blot results using cell lysates.^{[72,73,85,86]}

One application of RPA is to investigate the signaling pathways in human cancers. Zha et al. compared the survival signaling events between Bcl 2-positive and -negative lymphomas and found that survival signals, independent of Bcl 2 expression, were detected in follicular lymphoma and confirmed by validation with IHC.^[71] In another study, patient-specific signaling pathways have been identified in breast cancers using RPA. Bayesian clustering of a set of 54 subjects successfully separated normal subjects from cancer patients based on an epithelial signaling signature. Principal component analysis was capable of distinguishing normal from cancer patient samples by using a signature composed of a panel of kinase substrates.^[69] Differences in cell signaling between patient-matched primary and metastatic lesions have also been found using RPA. In the study, six patient-matched primary ovarian tumors probed with antibodies against signaling proteins, and the signaling profiles differed significantly between primary and metastatic tumors and upregulation of phosphor c-kit was capable of distinguishing five of the six metastatic tumors from the primary lesions.^[70] These findings suggest that treatment strategies may need to target signaling events among disseminated tumor cells.

Reverse-phase protein array has also been used to validate mathematical models of cellular pathways. The p53-Mdm2 feedback loop is one of the most well-studied cellular-feedback mechanisms.^[74] Normally, p53 activates transcription and expression of Mdm2, which, in turn, suppresses p53 activity. This negative-feedback loop ensures the low-level expression of p53 under normal conditions. Mathematical models have previously been used to investigate this negative-feedback loop.^[67] Ramalingam et al. has shown, by using RPA, that part of the mechanism of the p53-Mdm2 feedback loop can be explained by current mathematical models.^[75]

Another important application of RPA is for the identification of cancer specific antigens.  Using this method serum from 14 lung cancer patients, colon cancer patients and normal subjects were incubated and eight fractions of the cell lysate were recognized by the sera from four patients, while none of the sera from normal individuals was positive.^[76] This study demonstrates the diagnostic potential of identifying cancer antigens that induce immune response in cancer patients by using RPA.

Expert Commentary and Five-year View

The development of 2D DIGE in the past few years has provided researchers with a more accurate method for relative quantification of proteins substantially reducing the number of replicates required for 2D gels and increased its applicability for high-throughput biomarker discovery. MALDI MS has immensely facilitated the direct discovery of biomarkers from patient tissue. Even though archival patient tissue samples are a potential source of materials for tumor marker research, high-throughput techniques for biomarker discovery using such samples has been problematic. With the development of MALDI IMS, investigators can now perform studies that aim to discover novel biomarkers directly from tissue sections and are able to correlate their expression with the histopathological changes of tumors. Previously, investigation into the sites of protein PTM has been difficult since MS-dissociation techniques, such as CID, would lead to preferential loss of PTM, but the use of ETD as a complementary peptide ion-dissociation method has allowed researchers to investigate the precise location and structure of the PTM, and to identify peptide sequence with higher confidence.

The rapid technological improvements in proteomic technologies will identify potential biomarkers for clinical use. Independent validation studies using clinical specimens must be performed before such markers can be applied clinically,. In this regard, RPA has added a potential for high-throughput screening or validation of newly found markers. Using this technique, it will be possible for researchers to quantitatively measure and validate novel markers on hundreds of patient samples simultaneously.

A big problem for proteomic researchers iincludes the abundance of proteins in biological samples. This could be partially solved by depletion of abundant proteins or by fractionation of protein samples according to characteristics. It is envisaged that, in the future, proteomic technologies will be developed to a stage that is capable of analyzing complex protein mixtures without preparatory fractionation. Such progress has recently been achieved in LC-MS, where the use of a high-field, asymmetric waveform, ion-mobility spectrometry device as an interface to an IT MS resulted in a more than fivefold increase in dynamic range without increasing the length of the LC-MS analysis.^[77]

Another area that needs improvement is the standardization of protocols for patient-sample collection because results were found to be inconsistent among various studies using MS.^[78] It is also considered that part of the reason for this inconsistency is due to the differences in sample-collection or sample-handling procedures.^[78,79] The Human Proteome Organization previously published its findings on pre-analytical factors that affect plasma proteomic patterns and provides suggestions for sample handling.^[80,81] In addition to the pre-analytical stages, it is imperative to stress that consistent and strict adherence to predefined procedures or standards, from sample collection, sample processing, experimentation, data analysis through to result validation, are of utmost importance to minimize variations and achieve consistent and reproducible results.

Any newly identified potential biomarker must also be validated using an independent cohort of patients in order to establish its clinical value, but the translation of results from the laboratory to the clinic has been slow. Consequently, it has been suggested that quantitative MS could be used for the detection of proteins.^[82] The increasing availability of MS facilities to researchers worldwide will facilitate the detection, measurement and validation of protein biomarkers using quantitative MS techniques. Even after validation of such results in the laboratory, diagnostic tests will need to be developed for the marker and large-scale clinical trials would also have to be performed to confirm the results.  All these efforts require cooperation of personnel from various disciplines, such as scientists, medical professionals, pharmaceutical companies and governments. Finally, it is hoped that, through improved understanding of the protein expression as cancer progresses will lead to the discovery and development of useful cancer biomarkers for patient diagnosis, prognosis, monitoring and treatment.

Key Issues

• 2DE coupled with mass spectrometry has been the main workhorse for the proteomic discovery of novel biomarkers in the past 10 years, and the development of 2D difference gel electrophoresis has substantially improved the quantification accuracy of 2DE.
• MALDI imaging mass spectrometry has allowed the identification of novel proteomic features directly from patient tissue section for correlation with histopathological changes.
• Electron transfer dissociation mass spectrometry has opened up the possibility of identifying the structure and localization of the post-translational modification and the peptide/protein.
• Reverse-phase protein array is a powerful tool for the high-throughput validation of novel biomarkers across hundreds of patient samples simultaneously.

References

63.  States DJ, Omenn GS, Blackwell TW et al. Challenges in deriving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study. Nat. Biotechnol. 24(3),333-338 (2006).

64. Wulfkuhle JD, Edmiston KH, Liotta LA, Petricoin EF 3rd. Technology insight: pharmacoproteomics for cancer – promises of patient-tailored medicine using protein microarrays. Nat. Clin. Pract. Oncol. 3(5),256-268 (2006).

•• Excellent review on the clinical application of reverse-phase protein array.

65. Tibes R, Qiu Y, Lu Y et al. Reverse phase protein array: validation of a novel proteomic technology and utility for analysis of primary leukemia specimens and hematopoietic stem cells. Mol. Cancer Ther. 5(10),2512-2521 (2006).

66. LaBaer J, Ramachandran N. Protein microarrays as tools for functional proteomics. Curr. Opin. Chem. Biol. 9(1),14-19 (2005).

67. Ramalingam S, Honkanen P, Young L et al. Quantitative assessment of the p53-Mdm2 feedback loop using protein lysate microarrays. Cancer Res. 67(13),6247-6252 (2007).

68. Nishizuka S, Ramalingam S, Spurrier B et al. Quantitative protein network monitoring in response to DNA damage. J. Proteome Res. 7(2),803-808 (2008).

69. Petricoin EF 3rd, Bichsel VE, Calvert VS et al. Mapping molecular networks using proteomics: a vision for patient-tailored combination therapy. J. Clin. Oncol. 23(15),3614-3621 (2005).

70. Sheehan KM, Calvert VS, Kay EW et al. Use of reverse-phase protein microarrays and reference standard development for molecular network analysis of metastatic ovarian carcinoma. Mol. Cell Proteomics 4(4),346-355 (2005).

71. Zha H, Raffled M, Charboneau L et al. Similarities of prosurvival signals in Bcl 2-positive and Bcl 2-negative follicular lymphomas identified by reverse phase protein microarray. Lab. Invest. 84(2),235-244 (2004).

72. Major SM, Nishizuka S, Morita D et al. AbMiner: a bioinformatic resource on available monoclonal antibodies and corresponding gene identifiers for genomic, proteomic, and immunologic studies. BMC Bioinformatics 7,192 (2006).

73. Spurrier B, Washburn FL, Asin S, Ramalingam S, Nishizuka S. Antibody screening database for protein kinetic modeling. Proteomics 7(18),3259-3263 (2007).

74. Ciliberto A, Novak B, Tyson JJ. Steady states and oscillations in the p53/Mdm2 network. Cell Cycle 4(3),488-493 (2005).

75. Ma L, Wagner J, Rice JJ, Hu W, Levine AJ, Stolovitzky GA. A plausible model for the digital response of p53 to DNA damage. Proc. Natl Acad. Sci. USA 102(40),14266-14271 (2005).

76. Madoz-Gurpide J, Kuick R, Wang H, Misek DE, Hanash SM. Integral protein microarrays for the identification of lung cancer antigens in sera that induce a humoral immune response. Mol. Cell. Proteomics 7(2),268-281 (2007).

77. Canterbury JD, Yi X, Hoopmann MR, MacCoss MJ. Assessing the dynamic range and peak capacity of nanoflow LC-FAIMS-MS on an ion trap mass spectrometer for proteomics. Anal. Chem. 80(18),6888-6897 (2008).

78. Coombes KR, Morris JS, Hu J, Edmonson SR, Baggerly KA. Serum proteomics – a young technology begins to mature. Nat. Biotechnol. 23(3),291-292 (2005).

78. Hortin GL. Can mass spectrometric protein profiling meet desired standards of clinical laboratory practice? Clin. Chem. 51(1),3-5 (2005).

79. Omenn GS, States DJ, Adamski M 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(13),3226-3245 (2005).

80. Rai AJ, Gelfrand CA, Haywood BC et al. HUPO plasma proteome project specimen collection and handling: towards the standardization of parameters for plasma proteome samples. Proteomics 5(13),3262-3277 (2005).

• Concise report on several pre-analytical factors that impact the results of plasma proteomic profiling.

81. Mann M. Can proteomics retire the western blot? J. Proteome Res. 7(8),3065 (2008).

Update from LC/GC North America.

Solutions for Separation Scientists. Aug 2012; 30(8).

30 years of LCGC

www.chromatographyonline.com

The key advances in separation science is covered in five areas of the discipline:

1. sample preparation
2. gas chromatography(GC) columns
3. GC instrumentation
4. liquid cheomatography (LC) columns
5. LC instrumentation

In the first, there is automated sample preparation in kit form (QuEChERS). A short list of automated sample preparation techniques includes: supercritical fluid extraction (SFE), microwave extraction, automated solvent extraction (ASE), and solid phase extraction (SPE). A panel of experts views the bast basic method of extraction is SPE, and one uses solid phase microextraction with direct immersion and static headspace extraction, along with liquid-liquid extraction.^[2] In GC incremental improvements have been made with ionic liquids, multidimentional GC, and fast GC. LC has advanced dramatically with ultra-high pressure LC and superficially porous particles. LC-MS has become standard equipment routinely used in many labs.^[1]

Biomarkers have to be detected in a background of 10⁴-10⁶ other components of comparable concentration that also partition with the stationary phase. The partition coefficients of many species are similar, or identical to the biomarker target. The issue is how to select and resolve fewer than 100 biomarkers from a milieu of 1 million components in a complex mixture. The novel idea is to target structure instead of general properties of molecules.^[3] How might this work?  A single substrate, metabolite, hormone, or toxin is identified in milliseconds by specific protein receptors. The combinatorial chemistry community has shown that synthetic polynucleotides (aptamers) can be found and amplified that have selectivities approaching antibodies.This is a method well know for years as affinity chromatography. A distinct problem has been the natural process of post translational modification (PTMs), which may create isoforms by addition of a single phosphate ester to be found in the proverbial soup.

1. Bush L. Separation Science: Past, Present and Future. LCGC NA 2012; 30(8):620.

2.McNally ME. Analysis of the State of the Art: Sample Preparation. LCGC NA 2012; 30(8):648-651.

2. Regnier FE. Plates vs Selectivity: An Emerging Issue with Complex Samples.  LCGC NA 2012; 30(8):622.

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