Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay

Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay

A cancer biomarker refers to a biological molecule found in the body tissues, fluids, and the blood that are an indication of a normal or abnormal process of cancer. Cancer biomarkers are useful in realizing the presence of the condition as well as fighting against it. Some of the known various forms of cancer biomarkers include protein subgroups like enzymes, receptors, and glycoproteins along with hormones. Other types of cancer biomarkers include changes in genetic mutations, changes in generic signatures, and amplification, these changes are aspects in determining the process of cancer. Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay. Cancer biomarkers are also known as tumor markers. Cancer biomarkers are grouped according to their functional purpose as screening (diagnostic) biomarkers, prognostic biomarkers, and stratification biomarkers among others.Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay.  For biomarkers to be useful, they must be linked with tested and approved improvements in the quality of life and aid surviving the ailments as well as good patient outcome (Kristoff 2011.)
Proteomic knowledge refers to knowledge that pertains to the protein content of the cell proteomics allow for splitting of the various mRNAs and expressing how the genes and soft genes of regulation are in contrast with genomes, it is more dynamic than the genome and consists of other forms of gene expression involving. Proteomic technologies are adopted in quite accurate expressions resulted from disease activity. The advantage of proteomic technology is borne in the fact that the protein itself is the highest point of expression of these protein activities. Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay.
Development of biomarkers based on genomic technologies involves measuring full sets of expression of mRNA like differential display, gene expression arrays along with serial analysis of expression of genes. Developing challenges has been challenged by a non-complete utilization of resource for example proteolysis cleavage and modifications like phosphorylation. The most widely used method of separating proteins is two dimensional which is a gel utilizing method of electrophoresis. In this method, proteins are first separated with their isoelectric points than separated with their molecular masses. Using mobilized pH values and gradients for the first step increases the power to resolve. It is useful in detecting proteins of low abundance. The method of two-dimensional gel is useful in analyzing a whole cell or tissue extracts of proteins.  Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay.


Cancer impacts each patient and family differently. Our current understanding of the disease is primarily limited to clinical hallmarks of cancer, but many specific molecular mechanisms remain elusive. Genetic markers can be used to determine predisposition to tumor development, but molecularly targeted treatment strategies that improve patient prognosis are not widely available for most cancers. Individualized care plans, also described as personalized medicine, still must be developed by understanding and implementing basic science research into clinical treatment. Proteomics holds great promise in contributing to the prevention and cure of cancer because it provides unique tools for discovery of biomarkers and therapeutic targets. As such, proteomics can help translate basic science discoveries into the clinical practice of personalized medicine. Here we describe how biological mass spectrometry and proteome analysis interact with other major patient care and research initiatives and present vignettes illustrating efforts in discovery of diagnostic biomarkers for ovarian cancer, development of treatment strategies in lung cancer, and monitoring prognosis and relapse in multiple myeloma patients.

The discovery of the causative genetic underpinnings of cancer has been a focus of biomedical research for decades. The multigenic nature of cancer has hindered progress in understanding the underlying mechanisms that lead to a specific disease phenotype. Recent advances in high throughput technologies, which evaluate tens of thousands of genes or proteins in a single experiment, are providing new methods for identifying biochemical determinants of the disease process. To facilitate these technologies, the correlation of specific phenotypes to individual genotypes is key to leveraging the use of model organisms and patient samples in cancer research. Integration of these data allows cancer researchers to ask complex questions about the mechanism of specific disease manifestations and to retrieve data sets containing disparate data that can be further analyzed using statistical methods to reveal new insights that should be further investigated.

With the comprehensive cataloging of human genes and links between gene function and disease, the future of medicine looks toward mechanistic personalized medicine approaches to cure diseases such as cancer. Using arrays that can profile gene expression, many groups have been able to define gene expression signatures related to diagnosis (cancer versus benign, subtype of leukemia, etc.), prognosis (likelihood of cure), and prediction (probability of response to therapy). Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay. Although most of these approaches remain in the research domain, some have been thrust into the mainstream of standard clinical practice, e.g. Oncotype DX® for prediction of breast cancer recurrence. Proteomics will be next in line to deliver new tools to help patients with cancer live longer and have a better quality of life.

Proteomics is an emerging field that can make unique contributions to the prevention and cure of cancer. From strength in protein sequence analysis to broad scale cataloging of proteins and post-translational modifications, a wide variety of proteomics tools are available to effect changes in patient care. Proteomics has the advantage over genomics-based assays because of direct examination of the molecular machinery of cell physiology, including protein expression, sequence variations and isoforms, post-translational modification, and protein-protein complexes. However, certain disadvantages also exist, including (i) stringent requirements for sample collection, preparation, and analysis, (ii) lack of amplification procedures similar to PCR that can allow assay development using limited biological starting material, (iii) requirements for purification strategies to enrich samples for intended work (e.g. phosphoprotein analysis), and (iv) costs necessary for staffing and equipping a shared resource or clinical laboratory able to perform the required assays. Nonetheless proteomics techniques should be implemented with basic clinical medicine along with DNA- and/or mRNA-based profiling strategies to enhance cancer screening, diagnosis, treatment, and follow-up.

An overview of the potential of these cutting edge technologies in the development of personalized medicine has recently been presented by Dalton and Friend (1). Here we build on that foundation and illustrate roles for proteomics in the interaction between research and clinical practice with specific vignettes. To visualize how proteomics may contribute to the development of personalized medicine, researchers must have an understanding of the patient’s journey from cancer diagnosis through treatment as shown in Fig. 1A. Cancer may be detected through routine check-ups, self-exams, or following the presentation of specific symptoms. Any or all of these factors may contribute to the diagnosis of the incoming patient. At this point, staging and molecular profiling will also be performed using samples obtained by tumor biopsy as well as blood and/or urine collection. The development of personalized cancer care has several goals that impact current and future patients: (i) identify needs of the individual patient, (ii) identify biomarkers to predict needs and risks, (iii) develop and implement methods for minimally invasive patient sampling, (iv) match the right treatment to each patient, (v) improve the performance of clinical trials through molecular profiling, and (vi) raise the standard of care by partnering with other hospitals and clinical care centers. Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay.

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Fig. 1.

A model for the development of personalized cancer care. Each patient will follow a similar journey through diagnosis, treatment, and ongoing monitoring (A). The current steps in the process (in bold) are shown with their respective improvements expected from ongoing research. Surviving patients, their families, and caregivers often enter screening programs. Patients who suffer relapse or recurrence will begin the process again. The standard of care may change between initial onset and relapse, providing additional tools for personalized medicine. The interface between physicians and research enables the continuous assessment and improvement of clinical practice (B). By understanding and evaluating challenges at each step in treatment, researchers can suggest or provide solutions, developing and implementing molecularly driven patient care. The Roman numerals indicate the vignettes used to illustrate the impact that proteomics research can have on clinical care.

Using the diagnosis, staging, and molecular profiles, a physician can assess the patient’s prognosis and predict potentially effective therapies.Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay.  The patient should be directed to treatment regimens based on drugs with the proper mechanism of action. The outcome of this step is directed treatment, which optimizes the patient’s survival chances and quality of life. Cancer survivors must be monitored for relapse or recurrence as well as the development of new cancers; frequently they will (re-)enter screening programs. As a consequence of a cancer diagnosis, members of the patient’s family and caregivers may choose to enroll in a screening program as well. The discovery, development, and implementation of biomarkers for ongoing monitoring are also critical to clinical practice.

Communication of the challenges in treatment enables researchers to contribute to clinical practice (Fig. 1B). The interaction between clinicians and researchers must be very strong for iterative examination of clinical practice and the development of personalized medicine. Here specific case studies illustrate potential roles for proteomics in improving patient care. Targeted and broad scale proteomics experiments have been implemented for the discovery of diagnostic biomarkers in ovarian cancer (Vignette I). Phosphoproteomics contributes to preclinical models for directed tyrosine kinase inhibitor treatment of lung cancer (Vignette II). Detection of disease progression in multiple myeloma with quantitative mass spectrometry illustrates aspects of ongoing patient assessment (Vignette III). Finally we discuss institutional infrastructure and an example of successful implementation molecular biomarkers into personalized cancer care.

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Most patients with ovarian cancer have widespread metastatic disease at initial diagnosis largely because of the inability to detect ovarian cancer at an early stage (2–5). There is currently no proven, effective method for early detection of ovarian cancer through biomarkers, imaging, or other means (6–11). The most commonly used biomarker for ovarian cancer, CA125 (12), is elevated in only about 50% of stage I ovarian cancer cases (8). Beyond the lack of effective detection, there is no accurate method for diagnosis of ovarian cancer short of surgery; even among symptomatic women, tissue evaluation by a pathologist is the only reliable way to distinguish between women with benign and malignant disease. Because of these limitations, ovarian cancer is detected at later stages where patients have very poor prognosis and few treatment options. Early detection significantly improves patient outcomes.

The need for determining additional biomarkers for early detection of ovarian cancer that complement CA125 has been reviewed in great detail from many perspectives, recently including proteomics. Discovery of appropriate biomarkers would enable population screening and personalized care. An extensive list of candidates has been prepared by Williams et al. (13) that includes proteins, glycans, lipids, and metabolites.Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay.  However, Jacobs and Menon (14) describe the difficulties inherent in screening for ovarian cancer; in particular, the required specificity would need to be essentially 100% to produce a biomarker with sufficient positive predictive value. None of the existing candidates have been implemented in patient care strategies, so discovery efforts continue. Although these reviews shed light on the challenges and prior candidate biomarkers, here we illustrate how early detection of disease has been a proving ground for proteomics strategies and discuss the successes of these cancer biomarker discovery efforts.

Because of the difficult nature of this task and the desire to have unbiased approaches, researchers have applied the entire menu of proteomics tools to finding novel candidate biomarkers. Often these experiments are used as a proving ground for analytical technology, and the rigorous requirements for sample collection, processing, and analysis are determined retrospectively for improvement in the next round of sample analysis. The ongoing development and complexity of proteomics as well as calls to standardize experiments across institutions have raised many dilemmas for investigators. The challenge in broad scale plasma proteome analysis is reflected in the fact that targeting methods rarely have the depth to detect clinically relevant molecules released from a tumor, and proteome cataloging experiments are not practical for case-control studies of sufficient population to detect statistically significant differences. Furthermore each proteomics experiment will have specific strengths and weaknesses because of the method of protein or peptide selection and the type of visualization or detection chosen (15–17).

Mass spectrometry profiling and intact protein separations, including two-dimensional gel electrophoresis (2DE)1 and multidimensional liquid chromatography, have been used to target differences between cancer patients and controls; proteomes from healthy controls and cancer patients have been cataloged using LC-MS/MS shotgun sequencing. Each technique will be reviewed to illustrate its strengths and the data resources that have been produced. Although pattern analysis is integral to the detection and targeting of candidate biomarkers, approaches that rely on fingerprinting alone, without identified target molecules, will be omitted because the addition of novel diagnostic molecules will bring the most value to patient care.

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.

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Fig. 2.

Strategies for ovarian cancer (OvCa) diagnostic biomarker discovery and verification. Differential display techniques visualizing intact proteins are attractive because candidate biomarkers can be manually selected and statistically verified; examples include 2D gel (A), 2D LC (B), and single sample mass spectrometry profiling. Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay. However, greater numbers of candidate biomarkers can be identified by LC-MS/MS shotgun sequencing experiments. In addition to peptide catalogs, the spectral counts (C) and the average peak areas and standard deviations from extracted ion chromatograms (D) can be used to qualitatively and quantitatively compare cancer patients with controls as shown here for haptoglobin. Regardless of the targeting strategy, quantitative mass spectrometry can be used to narrow down the list of candidates for validation (E).

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).

Narrowing the Field of Candidate Biomarkers with Quantitative Mass Spectrometry—

A strong case can be made that extensive verification efforts using quantitative mass spectrometry should be the next step for biomarker development for ovarian cancer. Using molecules described above in curated reviews (13) or from tissue proteome analyses (41–44) investigators could apply absolute quantification to evaluate many candidates in the same sample in a single analytical experiment. Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay. Peptides detected during protein identification experiments can be immediately used for quantitative mass spectrometry analysis. Multiple reaction monitoring is typically used to specifically quantify individual peptides, which represent their proteins of origin (an additional description is included in Vignette III). Even in complex matrices like plasma, individual peptides can be monitored effectively (45–47). Multiplexing strategies have also proven to be effective; Anderson and Hunter (48) used LC-multiple reaction monitoring (MRM) to develop a quantitative assay for 53 plasma proteins, illustrating the breadth of targets that could be accessed in a single analysis. The quantities of the proteins can be plotted by sample group, illustrating the potential utility in separating cancer patients from controls, as shown in Fig. 2E. Overlapping distributions (left) will not make effective candidates; proteins expressed at higher levels in most cancer patients (right) can be further validated by quantitative mass spectrometry or immunoassays using larger sample groups. Many of the current candidate biomarkers for detection of ovarian cancer could be better defined or ruled out using quantitative assays, including MRM or other high resolution LC-MS techniques.

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Following diagnosis, each patient is placed on a particular treatment regimen. At present, few if any of the broadly deployed strategies are molecularly driven. Proteomics can be used for preclinical modeling and probing archived tissue sections for biomarkers of response or resistance. These investigations begin by matching the appropriate proteomics tools to the clinical problem and relevant biological pathways. The application of phosphoproteomics holds great promise for understanding oncogenic signaling pathways and developing biomarkers that could be predictive for patient outcome on specific drug regimens. The focus here is to describe how proteomics technology can be applied to the study of tyrosine kinase signaling pathways and tyrosine kinase inhibitors (TKIs) in cancer. The important points are: (i) signaling pathways are assembled in distinct modules, (ii) many of the targets within these modules have inhibitors moving toward clinical use, (iii) assays that predict function/activity/dependence of these modules may allow for personalized therapy, (iv) the existence of interchangeable modules produces complexity in signaling pathways, and (v) existence of redundant modules and/or complex networks suggests the need for combinatorial strategies for future clinical trials. Proteomics can have a major impact on identifying these modules, developing pharmacodynamic assays, and unraveling the complexity of signaling networks.

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.

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Fig. 3.

Development of treatment strategies using tyrosine kinase inhibitors. Selected TKIs identified in chemical screens enter early phase clinical trials that evaluate safety, tolerability, and pharmacokinetics. Parallel preclinical testing can use chemical proteomics approaches to discern putative binding partners. Identification of non-target partners may be correlated with adverse effects identified in human trials. Proteomic Approaches and Reviews on Cancer Biomarker Researches Essay. Putative binding targets for TKIs can be examined for expression in the active (functional) and phosphorylated state using shotgun phosphoproteomics. Next the effect of TKI on the function of specific targets can be evaluated using quantitative strategies after purifying Tyr(P) (pY) peptides or specific proteins. Knowledge from early phase clinical trials and the achievable concentrations of the TKI in humans can be used to determine dose-response effects on target modulation. Effects on individual targets can be evaluated within the broader effects on signaling networks again using quantitative proteomics or in silico methods. Finally clinical assays can be developed that monitor pharmacodynamic markers in either tumor cells or in blood.

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.


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.

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Fig. 4.

Phosphotyrosine proteomics detects modulation of signaling after TKI treatment.Samples are processed in parallel through lysis, protein denaturation, proteolysis, and phosphotyrosine (pY)-containing peptide capture. Each sample is analyzed with LC-MS/MS on a hybrid linear ion trap-Orbitrap mass spectrometer. After database searches assign sequences of interest with high confidence, average peak areas and standard deviations from extracted ion chromatograms are used to examine the changes in ion signal after TKI treatment.

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.