Differential Display Techniques: Mass Spectrometry Profiling—

MS profiling has been shown to be an effective method for detecting differences in plasma of cancer patients and controls. This method is attractive because the approach can be rapid, and parallel processing enables the high throughput required for clinical sample analysis. Implementation appears deceptively simple. Despite controversy around the initial report using selection by surface retentate chromatography and mass analysis with MALDI MS to fingerprint ovarian cancer patients, controls, and patients with benign disease (18), MS profiling approaches have substantially improved even in light of the limited number of proteins or peptides that can be detected (19). Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay. After initial investigations of chemical fractionation methods using reverse phase, ion exchange, and IMAC, most of the components of the low molecular weight serum/plasma proteome were found to be intact highly abundant proteins or proteolytic fragments of plasma proteins. This method of selection and detection is limited in sensitivity, peak capacity, and dynamic range; therefore, it is unlikely to detect components at levels below 1 μmunless it is combined with immunoprecipitation (20).

Nevertheless MS profiling has detected differences between ovarian cancer patients and controls, including the α subunit of haptoglobin (21) and a panel consisting of apolipoprotein A-I, transthyretin, and inter-α-trypsin inhibitor heavy chain H4 (ITIH4) (22). Subsequent work using capture by immobilized antibodies shows variations in ITIH4 processing in several types of cancer when compared with controls (23). Further investigation of transthyretin has revealed differences in redox modifications including cysteinylation and glutathionylation (24). Clearly the extensive characterization of targeted molecules produces the most value because specific molecular markers can be determined. In addition, MS profiling highlighted the role of protease activity in serum and plasma that may also be used to distinguish cancer patients from controls (25,26).

Differential Display Techniques: 2DE and Multidimensional LC Protein Separations—

Higher molecular weight proteins have been analyzed by 2DE and multidimensional liquid phase separations. These approaches enable the assessment of changes to intact molecules including proteolytic processing and other post-translational modifications like glycosylation or phosphorylation. After protein identification, these modifications can be investigated further in the hope of developing an assay for detecting the specific differentially expressed isoforms.

In fluorescence DIGE, each sample is labeled with a different fluorescent probe, combined, and separated by isoelectric focusing and SDS-PAGE. Differences in protein expression can be distinguished as spots that are either red or green; complete overlap is represented by yellow (Fig. 2A). This approach has been popular because of the ease of interpreting the fluorescence images, the high number of protein spots (typically > 1,000), and the fact that the cancer and control samples are processed together. In addition, post-translational modifications can be targeted directly in 2D gel approaches using antibodies or specific fluorescent stains that recognize phospho- and glycoproteins.

In ovarian cancer biomarker discovery, 2DE techniques have been used to examine the isoforms of abundant serum proteins, indicating differences in phosphorylated fibrinogen α (27) as well as haptoglobin and transferrin (28). Another study presented several potential protein biomarkers, including complement components, serum glycoproteins, serum protease inhibitors, transferrin, and afamin (29). The last of these markers was further verified by ELISAs and compared with C-reactive protein and CA125. DIGE or targeted staining techniques in 2D gel analysis can provide information about post-translational modifications to abundant proteins in the plasma.

Multidimensional liquid phase separations complement 2DE. Proteome fractionation approaches have evolved to include immunodepletion of the most abundant plasma proteins (e.g. top six or top 12 removal) followed by ion exchange, liquid phase isoelectric focusing or chromatofocusing, and reverse phase separations of the lower abundance components. The intensity of the proteins in the final separation, whether reverse phase chromatography or SDS-PAGE, is compared with select targets for protein identification as shown in Fig. 2B. These data were acquired using the commercialized version of 2D protein LC (PF2D, Beckman Coulter). The targeted fractions are recovered, digested with trypsin, and submitted for protein identification using LC-MS/MS peptide sequencing. Current results implicate abundant plasma proteins as candidate markers for ovarian cancer. Even in mouse models with enormous tumor burden, the most prevalent differences correspond to host response or abundant plasma proteins (30). Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay.

Protein Catalogs Created by Shotgun Sequencing as Resources for Biomarker Discovery—

The role of LC-MS/MS shotgun sequencing in the proteome analysis of biofluids has created resources that can be exploited for biomarker discovery. Through a series of analytical improvements, the human plasma proteome has been extensively cataloged by Smith and co-workers (31–33). In addition, the Human Proteome Organisation (HUPO) plasma proteome project has created a reference for more than 3,000 proteins identified in plasma (34–36), including the corresponding gene ontology terms (37). Extensive sequencing efforts have now identified more than 1,500 proteins from the human urinary proteome (38) using healthy samples. In addition, ascites fluid from ovarian cancer patients has been extensively analyzed in a recent publication by Gortzak-Uzan et al.(39). Each of these protein catalogs could be scanned to reveal candidate biomarkers based on disease etiology or organ site.

In addition to creating protein catalogs, (semi-)quantitative measurements can be derived from LC-MS/MS analysis of groups of patients and controls. The peptide counting statistics, which describe the number of peptides or tandem mass spectra assigned to sequences from a given protein, can be used to estimate the relative amount of a protein in a complex mixture as shown for haptoglobin in Fig. 2C. The intensity of the intact peptide in the mass spectra can also be used to quantify the relative amount of protein in each sample; the peak areas are calculated for each individual peptide using extracted ion chromatograms (EICs) as shown for two peptides from haptoglobin in Fig. 2D. Although the primary goal of LC-MS/MS is the catalog of proteins generated by peptide sequence assignments, EIC analysis can provide excellent data on the relative expression level of proteins in cancer patients’ and controls’ plasma. Furthermore the protein and its representative peptides need only be identified with high confidence in one of the samples. With reproducible chromatography and accurate mass measurement, the peaks in the other samples can be correlated prior to EIC analysis. This approach has been termed “label-free” proteomics, and it has been widely and effectively used to characterize biological and clinical samples (40).

Targeting Oncogenic Kinase Signaling Pathways—

Normal cell physiology is controlled by proteins within the cell that act together similar to an electric circuit to ensure normal cell behavior. Cancer is a disease where these circuits are dysregulated or rearranged in such a way that the output drives the cell toward excessive growth and spread to unintended areas of the body. Knowledge of these individual molecules and cohesive signaling modules can help identify proteins involved in driving a particular cancer cell and suggest combinatorial therapeutic strategies. Critical components of these circuits are signaling proteins called kinases that act as relays and regulate the activity of other important genes and proteins.Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay.  Protein kinase signaling pathways regulate the “hallmarks of cancer” including cell growth, survival, invasion/metastasis, and angiogenesis (49). Not surprisingly, it has been known for quite some time that aberrant kinase signaling can lead to tumorigenesis. Notable examples of tyrosine kinases driving cancer are viral SRC and the breakpoint cluster region protein and Abelson murine leukemia viral oncogene homolog 1 (BCR-ABL) fusion protein that displays constitutive kinase activity in chronic myelogenous leukemia (50).

Considerable enthusiasm continues to focus on targeting aberrant kinase pathways in lung cancer. Both tyrosine and serine/threonine kinases are under investigation as are the pathways they regulate. Sequencing of the human genome identified nearly 100 tyrosine kinase proteins, some of which are known to be involved in the pathogenesis of cancer as well as other tyrosine kinases with potential (as yet undefined) roles. Tyrosine kinases can either span the cellular membrane and become activated by extracellular ligands (receptor tyrosine kinase) or exist as intracellular proteins activated by intracellular events (non-receptor tyrosine kinases). The catalytic core subunit of tyrosine kinases recruits ATP and phosphorylates a tyrosine residue on substrate (downstream) proteins. In some cases, the tyrosine kinase autophosphorylates itself on specific sites, leading to enhanced function as well as producing a potential biomarker for activated kinase. For example, SRC family members can both phosphorylate downstream substrates as well as autophosphorylate itself on tyrosine 419. Because this event leads to enhanced catalytic activity, the degree of autophosphorylation serves as a biomarker of SRC activity in tumor cells. Phosphorylated tyrosine residues on substrate proteins change cellular physiology by modifying protein functions, including enzyme activity, subcellular localization, and/or protein-protein interactions. Important for relaying signaling, phosphorylated tyrosine sites can act as docking sites for proteins containing SRC homology 2 (SH2) domains (51). The human genome encodes ∼110 distinct SH2 domain-containing proteins, and although these domains are generally conserved they still retain enough variability to lead to specificity in signaling (52). SH2-containing proteins have diverse functions including adaptors (Grb2), scaffolds (Shc), kinases (SRC), phosphatases (Shp2), Ras signaling (RasGAP), transcription (STAT), ubiquitination (Cbl), cytoskeletal function (Tensin), and phospholipid second messenger signaling (Polo-like kinase). Signaling originating from tyrosine kinases pass through individual substrate proteins and interactions with SH2 proteins, which are ultimately linked to downstream effectors. Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay. These common sets of effector pathways include Ras/Raf/mitogen-activated protein kinase/extracellular signal-regulated kinase kinase (MEK)/ERK signaling modules, STAT signaling modules, phosphatidylinositol 3-kinase/Akt/mammalian target of rapamycin (mTOR) signaling molecules, protein kinase C modules, and others. These effector cascades regulate downstream proteins, some of which include transcription factors (DNA-binding proteins) that modulate gene expression. As a whole, dysregulation of these pathways alters cellular physiology and produces malignant behavior in cells.

The expression of individual tyrosine kinases, substrates of individual tyrosine kinases, SH2 domain proteins, components of effector cascades, and genomic alterations in lung cancer cells allow for modular signaling networks to be created that can be unique for each particular cell or tumor. Thus, tumor cells produce complex signal transduction networks that can be attacked at different points in an attempt to either kill tumor cells or revert the cells to benign function. The major hurdle is to determine which set of signaling proteins is active and relevant for an individual’s tumor. Thus, although we have a deeper understanding of how signaling networks are created, assays to determine the active signaling proteins in a patient’s tumor require further development.

The future of cancer treatment will be based on personalized approaches that identify the critical molecules necessary for tumor growth and survival and match patients to appropriate molecularly directed therapy. If practicing clinicians can match a kinase inhibitor with an individual patient survival would dramatically improve, and toxicity could be reduced. Perhaps the best example is the use of imatinib for BCR-ABL-dependent chronic myelogenous leukemia (53). The success of imatinib was heavily influenced by the knowledge of BCR-ABL signaling and its critical importance to leukemia cells. Similar stories include the use of imatinib for gastrointestinal stromal tumors (50), Herceptin for HER2-overexpressing breast cancer, and gefitinib/erlotinib for EGFR mutant-driven lung cancers. However, given the apparent low rates of kinase mutations in the human genome from cancers, obvious mutations in key kinases may be rare, and a large group of patients may have tumors driven by kinases that are not mutated (54). In addition, patients without activating mutations may benefit from kinase inhibitors, such as the case of lung cancer patients treated with erlotinib (55–57). Improved outcomes may also appear with logical combinations of inhibitors that target important network “hubs” that regulate tumor survival. Complex proteomics approaches enable discovery of biomarker panels for tumors without obvious genomic mutations in critical tyrosine kinases. Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay.

Identifying Biomarkers That Predict Clinical Outcome following TKI Treatment—

The following sections describe tools and techniques as well as current results that define tyrosine kinase signaling networks before and after drug treatment and assist in development of personalized therapy. A flowchart that pairs proteomics experiments with biochemistry/molecular biology, animal models, and early phase clinical trials is shown in Fig. 3. Chemical proteomics can be used to identify drug targets by affinity chromatography and subsequent protein identification. Modulation of kinase activity can be measured by the amount of autophosphorylation and modification of specific downstream substrates using phosphotyrosine selection and LC-MS/MS; quantitative mass spectrometry measurements can also be incorporated to evaluate the magnitude of the changes.

Identifying Novel Drug-Kinase Interactions through Chemical Proteomics—

Because of the conserved nature of tyrosine kinase domains in tyrosine kinases, small molecule inhibitors designed to inhibit one tyrosine kinase protein can often be “dirty” molecules and have effects on other tyrosine kinase proteins. For example, imatinib was found to have inhibitory effects on c-Kit, and this finding was exploited for the successful treatment of gastrointestinal stromal tumors (58). Studies examining the binding of compounds to individual tyrosine kinases reveal a spectrum of specificity ranging from compounds that bind to few tyrosine kinases to compounds that bind to numerous (>20) tyrosine kinases (59). In addition to binding partner identification, these studies also have the ability to derive quantitative information regarding inhibitor binding and selectivity (60). More recent studies have examined entire libraries of tyrosine kinase inhibitors to produce novel drug-protein interactions that can be exploited for future therapeutic benefit. Similar studies have also highlighted the ability of chemical proteomics approaches, derivatizing the drugs to a stationary phase for affinity chromatography, to identify serine/threonine kinases bound to TKIs as well as non-kinase substrates of TKIs (61,62). Thus, in the future as mapping of an individual’s tumor tyrosine kinase profile becomes available, it may be possible to match tumor tyrosine kinase dependence with compounds or mixtures of compounds that bind and inhibit the driver kinases. It is likely that existing compounds have inhibitory actions beyond that of their original design, and these could be used for individual patients. Adverse effects of compounds could also be related to off-target inhibition identified through such screens. Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay.

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Use of Emerging Technologies and Biomarkers to Identify Aberrant Kinase Signaling and Predict Response to Targeted Therapies—

Identifying patients that will benefit from kinase inhibitors remains a critical problem. Some examples of possible assays to predict sensitivity to kinase inhibitors include mutation analysis on genomic DNA, evaluation of gene amplification (fluorescence in situ hybridization), immunohistochemistry, and gene expression analysis (63,64). Emerging technologies that produce robust proteomics analysis will further characterize signaling pathways that can be exploited for therapeutic purposes and may provide additional information relevant for patient selection and/or monitoring. MS-based proteomics may be helpful to identifying tumor cells dependent on kinases for growth and/or survival (65). However, successful implementation requires enrichment because phosphorylated tyrosine residues (Tyr(P)) represent only 0.5% of the total phosphorylated amino acids within a cell (66). Proteomics techniques have been coupled with anti-Tyr(P) antibodies to purify Tyr(P) proteins or proteolytic Tyr(P) peptides for LC-MS/MS analysis (Fig. 4). Phosphotyrosine proteomics has been used to characterize protein networks and pathways downstream of oncogenic HER2 and BCR-ABL (67–69). These methods can also be used to identify novel tyrosine phosphorylation sites and identify oncogenic proteins resulting from activating mutations in protein tyrosine kinases (68–71). The data can then be used in either expert literature curation or machine learning techniques to synthesize network models that can be further evaluated (67). These methodologies can be coupled with TKIs or other compounds to further understand their effect on protein networks. Identification of critical tyrosine kinase proteins in an important oncogenic network may also suggest “druggable” targets that can be entered into therapeutic discovery research.

To illustrate the utility of such an approach, a global survey of phosphotyrosine signaling was performed in both lung cancer cell lines and primary tumors (72). This analysis identified a number of previously identified tyrosine kinases important in lung cancer including HER family proteins, hepatocyte growth factor receptor, vascular endothelial growth factor receptor, IGF-1R, and SRC as well as tyrosine kinases not recognized to be important in lung cancer pathogenesis such as human homolog of avian virus, anaplastic lymphoma kinase, adhesion-related kinase and platelet-derived growth factor receptor. From these experiments, novel causative agents, such as fusion proteins incorporating ALK and ROS and aberrant platelet-derived growth factor receptor α activation, were identified; furthermore sensitivity to imatinib was shown in a small subset of cell lines and tumors. Finally clustering analysis suggests distinct groups of tumors expressing active tyrosine kinases and substrate proteins thus offering the possibility of identifying tumor subsets driven by groups of tyrosine kinases and subsets of patients appropriate for combinations of tyrosine kinase inhibitors in clinical trials. This approach suggests that proteomics analysis can discern individual tumor wiring circuitry that can be exploited for therapeutic benefit. Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay.