A proteomic approach to the identification of lung cancer markers

Quantitative multiplexed simulated-cell identification by SERS in microfluidic devices

A reliable identification of cells on the basis of their surface markers is of great interest for diagnostic and therapeutic applications. We present a multiplexed labeling and detection strategy that is applied to four microparticle populations, each mimicking cellular or bacterial samples with varying surface concentrations of up to four epitopes, using four distinct biotags that are meant to be used in conjunction with surface enhanced Raman spectroscopy (SERS) instead of fluorescence, together with microfluidics. Four populations of 6 μm polystyrene beads were incubated with different mixtures, "cocktails" of four SERS biotags (SBTs), simulating the approach that one would follow when seeking to identify multiple biomarkers encountered in biological applications. Populations were flowed in a microfluidic flow-focusing device and the SERS signal from individual beads was acquired during continuous flow. The spectrally rich SERS spectra enabled us to separate confidently the populations by utilizing principal component analysis (PCA). Also, using classical least squares (CLS), we were able to calculate the contributions of each SBT to the overall signal in each of the populations, and showed that the relative SBT contributions are consistent with the nominal percentage of each marker originally designed into that bead population, by functionalizing it with a given SBT cocktail. Our results demonstrate the multiplexing capability of SBTs in potential applications such as immunophenotyping.

New serum biomarkers for detection of tuberculosis using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

BACKGROUND

New technologies for the early detection of tuberculosis (TB) are urgently needed. Pathological changes within an organ might be reflected in proteomic patterns in serum. The aim of the present study was to screen for the potential protein biomarkers in serum for the diagnosis of TB using proteomic fingerprint technology.

METHODS

Proteomic fingerprint technology combining protein chips with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) was used to profile the serum proteins from 50 patients with TB, 25 patients with lung disease other than TB, and 25 healthy volunteers. The protein fingerprint expression of all the serum samples and the resulting profiles between TB and control groups were analyzed with the Biomarker Wizard system.

RESULTS

A total of 30 discriminating m/z peaks were detected that were related to TB (p<0.01). The model of biomarkers constructed by the Biomarker Patterns Software based on the three biomarkers (2024, 8007, and 8598 Da) generated excellent separation between the TB and control groups. The sensitivity was 84.0% and the specificity was 86.0%. Blind test data indicated a sensitivity of 80.0% and a specificity of 84.2%.

CONCLUSIONS

The data suggested a potential application of SELDI-TOF MS as an effective technology to profile serum proteome, and with pattern analysis, a diagnostic model comprising three potential biomarkers was indicated to differentiate people with TB and healthy controls rapidly and precisely.

Upregulation of plasma C9 protein in gastric cancer patients

Gastric cancer is one of the leading causes of cancer-related deaths worldwide. Current biomarkers used in the clinic do not have sufficient sensitivity for gastric cancer detection. To discover new and better biomarkers, protein profiling on plasma samples from 25 normal, 15 early-stage and 21 late-stage cancer was performed using an iTRAQ-LC-MS/MS approach. The level of C9 protein was found to be significantly higher in gastric cancer compared with normal subjects. Immunoblotting data revealed a congruent trend with iTRAQ results. The discriminatory power of C9 between normal and cancer states was not due to inter-patient variations and was independent from gastritis and Helicobacter pylori status of the patients. C9 overexpression could also be detected in a panel of gastric cancer cell lines and their conditioned media compared with normal cells, implying that higher C9 levels in plasma of cancer patients could be attributed to the presence of gastric tumor. A subsequent blind test study on a total of 119 plasma samples showed that the sensitivity of C9 could be as high as 90% at a specificity of 74%. Hence, C9 is a potentially useful biomarker for gastric cancer detection.

Comparative proteomic analysis of cancerous and adjacent normal lung tissues

Lung cancer is the leading cause of cancer-related mortality in industrialized countries. Unfortunately, most lung cancers are found too late for a cure, therefore early detection and treatment is very important. We have applied proteomic analysis by using two-dimensional gel electrophoresis and peptide mass fingerprinting techniques for examination of cancerous and adjacent non-cancerous lung tissues from the same patient. The aim of the study was to find proteins, which could be used as biomarkers for diagnosis and monitoring of this disease. Indeed, we found differences in expression of several proteins, related to various cellular activities, such as, chaperoning (e,g. GRP96, GRP78, HSP27), metabolism and oxidation stress (e.g. L-fucose, GST), cytoskeleton (e.g., tubulin beta 2/3, beta actin), cell adhesion (e.g. annexin A5/3), binding proteins (e.g. 14-3-3 theta) and signal transduction. These changes may be important for progression of carcinogenesis; they may be used as the molecular-support for future diagnostic markers.

gammaH2AX: A potential DNA damage response biomarker for assessing toxicological risk of tobacco products

Differentiation among American cigarettes relies primarily on the use of proprietary tobacco blends, menthol, tobacco substitutes, paper porosity, paper additives, and filter ventilation. These characteristics substantially alter per cigarette yields of tar and nicotine in standardized protocols promulgated by government agencies. However, due to compensatory alterations in smoking behavior to sustain a preferred nicotine dose (e.g., by increasing puff frequency, inhaling more deeply, smoking more cigarettes per day, or blocking filter ventilation holes), smokers actually inhale similar amounts of tar and nicotine regardless of any cigarette variable, supporting epidemiological evidence that all brands have comparable disease risk. Consequently, it would be advantageous to develop assays that realistically compare cigarette smoke (CS)-induced genotoxicity regardless of differences in cigarette construction or smoking behavior. One significant indicator of potentially carcinogenic DNA damage is double strand breaks (DSBs), which can be monitored by measuring Ser 139 phosphorylation on histone H2AX. Previously we showed that phosphorylation of H2AX (defined as gammaH2AX) in exposed lung cells is proportional to CS dose. Thus, we proposed that gammaH2AX may be a viable biomarker for evaluating genotoxic risk of cigarettes in relation to actual nicotine/tar delivery. Here we tested this hypothesis by measuring gammaH2AX levels in A549 human lung cells exposed to CS from a range of commercial cigarettes using various smoking regimens. Results show that gammaH2AX induction, a critical event of the mammalian DNA damage response, provides an assessment of CS-induced DNA damage independent of smoking topography or cigarette type. We conclude that gammaH2AX induction shows promise as a genotoxic bioassay offering specific advantages over the traditional assays for the evaluation of conventional and nonconventional tobacco products.

Serum biomarker profiling in cancer studies: a question of standardisation?

Companion animals are exposed to similar environmental conditions and carcinogens as humans. In some animal cancers, there also appears to be the same genetic changes associated as in humans. However, little work has been carried out in cancer biomarker identification in animals. The recent dramatic advances in molecular medicine, genomics, proteomics and translational research will allow biomarker identification, which may provide the best strategies for veterinarians and clinicians to combat disease by early diagnosis and administration of effective treatments. Proteomics may have important applications in cancer diagnosis, prognosis and predictive clinical outcome that could directly change clinical practice by affecting critical elemen-ts of care and management. This review summarizes the advances in proteomics that has propelled us to this exciting age of clinical proteomics, and highlights the future work that is required for this to become a reality. In this review, we will discuss the available proteomic technologies and their limitations, and highlight the key areas of research and how they have been used to discover cancer biomarkers. The principles described here are equally applicable to human and animal disease, but implementation of 'omic' technologies requires stringent guidelines for collection of clinical material, the application of analytical techniques and interpretation of the data.

Mass spectrometry based targeted protein quantification: methods and applications

The recent advance in technology for mass spectrometry-based targeted protein quantification has opened new avenues for a broad range of proteomic applications in clinical research. The major breakthroughs are highlighted by the capability of using a "universal" approach to perform quantitative assays for a wide spectrum of proteins with minimum restrictions and the ease of assembling multiplex detections in a single measurement. The quantitative approach relies on the use of synthetic stable isotope labeled peptides or proteins, which precisely mimic their endogenous counterparts and act as internal standards to quantify the corresponding candidate proteins. This report reviews recently developed platform technologies for emerging applications of clinical proteomics and biomarker development.

Cancer and the tumor microenvironment: a review of an essential relationship

PURPOSE

The role of the microenvironment during the initiation and progression of carcinogenesis is now realized to be of critical importance, both for enhanced understanding of fundamental cancer biology, as well as exploiting this source of relatively new knowledge for improved molecular diagnostics and therapeutics.

METHODS

This review focuses on: (1) the approaches of preparing and analyzing secreted proteins, (2) the contribution of tumor microenvironment elements in cancer, and (3) the potential molecular targets for cancer therapy.

RESULTS

The microenvironment of a tumor is an integral part of its physiology, structure, and function. It is an essential aspect of the tumor proper, since it supplies a nurturing environment for the malignant process. A fundamental deranged relationship between tumor and stromal cells is essential for tumor cell growth, progression, and development of life threatening metastasis. Improved understanding of this interaction may provide new and valuable clinical targets for cancer management, as well as risk assessment and prevention. Non-malignant cells and secreted proteins from tumor and stromal cells are active participants in cancer progression.

CONCLUSIONS

Monitoring the change in the tumor microenvironment via molecular and cellular profiles as tumor progresses would be vital for identifying cell or protein targets for cancer prevention and therapy.

Cigarette smoke induces endoplasmic reticulum stress and the unfolded protein response in normal and malignant human lung cells

Background

Although lung cancer is among the few malignancies for which we know the primary etiological agent (i.e., cigarette smoke), a precise understanding of the temporal sequence of events that drive tumor progression remains elusive. In addition to finding that cigarette smoke (CS) impacts the functioning of key pathways with significant roles in redox homeostasis, xenobiotic detoxification, cell cycle control, and endoplasmic reticulum (ER) functioning, our data highlighted a defensive role for the unfolded protein response (UPR) program. The UPR promotes cell survival by reducing the accumulation of aberrantly folded proteins through translation arrest, production of chaperone proteins, and increased degradation. Importance of the UPR in maintaining tissue health is evidenced by the fact that a chronic increase in defective protein structures plays a pathogenic role in diabetes, cardiovascular disease, Alzheimer's and Parkinson's syndromes, and cancer.

Methods

Gene and protein expression changes in CS exposed human cell cultures were monitored by high-density microarrays and Western blot analysis. Tissue arrays containing samples from 110 lung cancers were probed with antibodies to proteins of interest using immunohistochemistry.

Results

We show that: 1) CS induces ER stress and activates components of the UPR; 2) reactive species in CS that promote oxidative stress are primarily responsible for UPR activation; 3) CS exposure results in increased expression of several genes with significant roles in attenuating oxidative stress; and 4) several major UPR regulators are increased either in expression (i.e., BiP and eIF2α) or phosphorylation (i.e., phospho-eIF2α) in a majority of human lung cancers.

Conclusion

These data indicate that chronic ER stress and recruitment of one or more UPR effector arms upon exposure to CS may play a pivotal role in the etiology or progression of lung cancers, and that phospho-eIF2α and BiP may have diagnostic and/or therapeutic potential. Furthermore, we speculate that upregulation of UPR regulators (in particular BiP) may provide a pro-survival advantage by increasing resistance to cytotoxic stresses such as hypoxia and chemotherapeutic drugs, and that UPR induction is a potential mechanism that could be attenuated or reversed resulting in a more efficacious treatment strategy for lung cancer.

Serological immunoreactivity against colon cancer proteome varies upon disease progression

Sera from colon carcinoma patients were used to identify tumor-associated antigens (TAAs) by screening tumor proteome resolved by 2D electrophoresis. A panel of six TAAs eliciting a serological immune response in colorectal cancer was identified, showing a modification in antigen recognition by B cells in patients as a function of colon cancer progression. The expression of these proteins was either confined or increased in tumor as compared to normal mucosa.

A 25-signal proteomic signature and outcome for patients with resected non-small-cell lung cancer

BACKGROUND

Among patients with non-small-cell lung cancer (NSCLC), those with poor prognosis cannot be distinguished from those with good prognosis.

METHODS

Matrix-assisted laser desorption-ionization mass spectrometry was used to analyze protein profiles of 174 specimens from NSCLC tumors and 27 specimens from normal lung tissue and to derive a prognosis-associated proteomic signature. Frozen resected tissue specimens were randomly divided into a training set (116 NSCLC and 20 normal lung specimens) and an independent, blinded validation set (58 NSCLC and seven normal lung specimens). Mass spectrometry signals from training set specimens that were differentially associated with specimens from patients with a high risk of recurrence (i.e., who died within 5 years of surgical treatment because of relapse) compared with those from patients with a low risk of recurrence (i.e., alive with no symptoms of relapse after a median follow-up of 89 months) were selected by use of the Fisher's exact test, the Kruskal-Wallis test, and the significance analysis of microarray test. These signals were used to build an individualized, weighted voting-based prognostic signature. The signature was then validated in the independent dataset. Survival was assessed by multivariable Cox regression analysis. Proteins corresponding to individual signals were identified by ion-trap mass spectrometry coupled with high-performance liquid chromatography. All statistical tests were two-sided.

RESULTS

From 2630 mass spectrometry signals from specimens in the training cohort, we derived a signature of 25 signals that was associated with both relapse-free survival and overall survival. Among stage I NSCLC patients in the validation set, the signature was statistically significantly associated with both overall survival (hazard ratio [HR] of death for patients in the high-risk group compared with those in the low-risk group = 61.1, 95% confidence interval [CI] = 8.9 to 419.2, P<.001) and relapse-free survival (HR of relapse = 11.7, 95% CI = 3.1 to 44.8, P<.001). Proteins corresponding to signals in the signature were identified that had various cellular functions, including ribosomal protein L26-like 1, acylphosphatase, and phosphoprotein enriched in astrocytes 15.

CONCLUSIONS

We defined a mass spectrometry signature that was associated with survival among NSCLC patients and appeared to distinguish those with poor prognosis from those with good prognosis.

Lung cancer staging: proteomics

The results of ACOSOG Z4031 may provide landmark information for the use of proteomic profiling to diagnose lung cancer noninvasively and to provide more accurate predictions of survival. Although the technological developments allowing generalized use of proteomic and genomic analyses are relatively recent, major progress in understanding the molecular basis of lung cancer has been made. Predicting survival is only the first step in the use of proteomics. If a reliable protein profile can be identified that is associated with poor prognosis, these proteins can then be identified and become therapeutic targets. It is not difficult to envision a day when a simple blood test will diagnose a lung cancer, perhaps even before it is clinically apparent, and, at the same time, identify the chemotherapeutic agents to which the tumor is sensitive, allowing individually directed treatment.

Distorted relation between mRNA copy number and corresponding major histocompatibility complex ligand density on the cell surface

The major histocompatibility complex (MHC) presents peptides derived from degraded cellular proteins to T-cells and is thus crucial for triggering specific immune responses against viral infections or cancer. Up to now, there has been no evidence for a correlation between levels of mRNA (the "transcriptome") and the density of MHC-peptide complexes (the "MHC ligandome") on cells. Because such dependences are of intrinsic importance for the detailed understanding of translation efficiency and protein turnover and thus for systems biology in general and for tumor immunotherapy in practical application, we quantitatively analyzed the levels of mRNA and corresponding MHC ligand densities in samples of renal cell carcinomas and their autologous normal kidney tissues. Relative quantification was carried out by gene chip analysis and by stable isotope peptide labeling, respectively. In comparing more than 270 pairs of gene expression and corresponding peptide presentation ratios, we demonstrate that there is no clear correlation (r = 0.32) between mRNA levels and corresponding MHC peptide levels in renal cell carcinoma. A significant number of peptides presented predominantly on tumor or normal tissue showed no or only minor changes in mRNA expression levels. In several cases, peptides could even be identified despite the virtual absence of the respective mRNA. Thus we conclude that a majority of epitopes from tumor-associated antigens will not be found in approaches based mainly on mRNA expression studies as mRNA expression reflects a distorted picture of the situation on the cell surface as visible for T-cells.

Proteomic analysis of MCF-7 cell lines expressing the zinc-finger or the proline-rich domain of retinoblastoma-interacting-zinc-finger protein

To identify a growth-promoting activity related to retinoblastoma-interacting-zinc-finger (RIZ) protein, differential protein expression of MCF-7 cell lines expressing the zinc-finger or the proline-rich domain of RIZ protein was analyzed by a robust bottom-up mass-spectrometry proteomic approach. Spots corresponding to qualitative and quantitative differences in protein expression have been selected and identified. Some of these proteins have been previously reported as being associated with different types of carcinomas or involved in cell proliferation and differentiation. Knowledge of specific differentially expressed proteins by MCF-7-derived cell lines expressing RIZ different domains will provide the basis for identifying a growth-promoting activity related to RIZ gene products.

Analysis of human serum by liquid chromatography–mass spectrometry: Improved sample preparation and data analysis

Discovery of biomarkers is a fast developing field in proteomics research. Liquid chromatography coupled on line to mass spectrometry (LC-MS) has become a powerful method for the sensitive detection, quantification and identification of proteins and peptides in biological fluids like serum. However, the presence of highly abundant proteins often masks those of lower abundance and thus generally prevents their detection and identification in proteomics studies. To perform future comparative analyses of samples from a serum bank of cervical cancer patients in a longitudinal and cross-sectional manner, methodology based on the depletion of high-abundance proteins followed by tryptic digestion and LC-MS has been developed. Two sample preparation methods were tested in terms of their efficiency to deplete high-abundance serum proteins and how they affect the repeatability of the LC-MS data sets. The first method comprised depletion of human serum albumin (HSA) on a dye ligand chromatographic and immunoglobulin G (IgG) on an immobilized Protein A support followed by tryptic digestion, fractionation by cation-exchange chromatography, trapping on a C18 column and reversed-phase LC-MS. The second method included depletion of the six most abundant serum proteins based on multiple immunoaffinity chromatography followed by tryptic digestion, trapping on a C18 column and reversed-phase LC-MS. Repeatability of the overall procedures was evaluated in terms of retention time and peak area for a selected number of endogenous peptides showing that the second method, besides being less time consuming, gave more repeatable results (retention time: <0.1% RSD; peak area: <30% RSD). Application of an LC-MS component detection algorithm followed by principal component analysis (PCA) enabled discrimination of serum samples that were spiked with horse heart cytochrome C from non-spiked serum and the detection of a concentration trend, which correlated to the amount of spiked horse heart cytochrome C to a level of 5 pmol cytochrome C in 2 microl original serum.

Antibodies as oncogenes: a hypothesis

We would like to put forward the hypothesis that cancer patients may produce autoantibodies that promote uncontrolled cell growth and thereby function as oncogenes. Dying cells release proteins that stimulate the production of autoantibodies, an event also known to occur during the earliest stages of tumor growth. If some of these autoantibodies are directed against cell surface hormone receptors, they could oligomerize the receptors and inadvertently transmit growth signals. The abnormal signals could result in uncontrolled cell proliferation and, eventually, oncogenesis. Thus, some specific autoantibodies from among the large repertoires of autoantibodies present in the sera of cancer patients are, in reality, oncogenes. If such oncogenic autoantibodies are indeed present and can be identified, inhibition of their molecular action may be an effective therapeutic modality.

Proteomic maps of the cancer-associated infectious agents

The number of infectious agents associated with cancer is increasing. There is a need to develop approaches for the early detection of the infected host which might lead to tumor development. Recent advances in proteomic approaches provide that opportunity, and it is now possible to generate proteomic maps of cancer-associated infectious agents. Protein arrays, interaction maps, data archives, and biological assays are being developed to enable efficient and reliable protein identification and functional analysis. Herein, we discuss the current technologies and challenges in the field, and application of protein signatures in cancer detection and prevention.

Comparison of different depletion strategies for improved resolution in proteomic analysis of human serum samples

Serum proteins may often serve as indicators of disease and is a rich source for biomarker discovery. However, the large dynamic range of proteins in serum makes the analysis very challenging because high-abundant proteins tend to mask those of lower abundance. A prefractionation step, such as depletion of a few high-abundant proteins before protein profiling, can assist in the discovery and detection of less abundant proteins that may prove to be informative biomarkers. In the present study, five different depletion columns were investigated considering efficiency, specificity, and reproducibility. Our research included quantitative determination of total protein, albumin, and immunoglobulin G (IgG) concentrations, one- and two-dimensional gels and mass spectrometric analysis of the serum samples before and after the depletion step. Our results showed that all five depletion columns tested removed albumin and IgG with high efficiency. We found that based on reproducibility and binding specificity, the Multiple Affinity Removal Column that removed a total of six high-abundant proteins (albumin, IgG, antitrypsin, IgA, transferring, and haptoglobin) offered the most promising depletion approach. Among the disposable (single-use) products, the ProteoExtract Albumin/IgG Removal kit displayed the best results. Depleted serum from the Multiple Affinity Removal column was further evaluated by 2-D gel electrophoresis (2-DE) analysis, and the results indicated increased resolution and improved intensity of low-abundant proteins in a reproducible fashion. Our study provides a comprehensive investigation of commercially available depletion columns and will be of high importance for future proteomic studies on serum samples.

Proteomics-based identification of human acute leukemia antigens that induce humoral immune response

The identification of panels of tumor antigens that elicit an antibody response may have utility in cancer screening, diagnosis, and establishing prognosis. Until now, autoimmunity in cancer has been mainly revealed in solid tumors. The aim of this study was to apply the proteomic approach to the identification of proteins that commonly elicit a humoral response in acute leukemia (AL). Sera from 21 newly diagnosed patients with AL, 20 patients with solid tumors, and 22 noncancer controls were analyzed for antibody-based reactivity against AL proteins resolved by two-dimensional electrophoresis. As a result, autoantibody against a protein identified by mass spectrometry as Rho GDP dissociation inhibitor 2 was detected in sera from 15 of 21 patients with AL (71%). By contrast, such antibody was detected in sera from one of 20 patients with solid tumors (5%) and one of 22 noncancer controls (4.5%). Five other protein autoantibodies were also found in AL patients with a high frequency and constituted the major target antigens of the AL autoimmune response. The findings of autoantibodies against Rho GDP dissociation inhibitor 2 and other proteins in sera of patients with AL suggest that the proteomic approach we have implemented may have utility for the development of a serum-based assay for AL screening and diagnosis.

Proteomic signatures for histological types of lung cancer

We performed proteomic studies on lung cancer cells to elucidate the mechanisms that determine histological phenotype. Thirty lung cancer cell lines with three different histological backgrounds (squamous cell carcinoma, small cell lung carcinoma and adenocarcinoma) were subjected to two-dimensional difference gel electrophoresis (2-D DIGE) and grouped by multivariate analyses on the basis of their protein expression profiles. 2-D DIGE achieves more accurate quantification of protein expression by using highly sensitive fluorescence dyes to label the cysteine residues of proteins prior to two-dimensional polyacrylamide gel electrophoresis. We found that hierarchical clustering analysis and principal component analysis divided the cell lines according to their original histology. Spot ranking analysis using a support vector machine algorithm and unsupervised classification methods identified 32 protein spots essential for the classification. The proteins corresponding to the spots were identified by mass spectrometry. Next, lung cancer cells isolated from tumor tissue by laser microdissection were classified on the basis of the expression pattern of these 32 protein spots. Based on the expression profile of the 32 spots, the isolated cancer cells were categorized into three histological groups: the squamous cell carcinoma group, the adenocarcinoma group, and a group of carcinomas with other histological types. In conclusion, our results demonstrate the utility of quantitative proteomic analysis for molecular diagnosis and classification of lung cancer cells.

Genomic and Proteomic Profiling of Lung Cancers: Lung Cancer Classification in the Age of Targeted Therapy

Both proteomic and genomic methods offer promise for the classification of human lung carcinomas. This review summarizes the range of proteomic methods in development for lung cancer classification, and describes a number of recent analyses of messenger RNA expression in lung cancer. Multiple independent studies of mRNA expression profiles in lung adenocarcinoma have proven highly reproducible. Analyses of the relationship between expression profiles and tumor development and differentiation, the presence or absence of specific pathogenic mutations, patient prognosis and survival after surgical treatment, and specific histopathology all appear to be promising.

Sample preparation of human serum for the analysis of tumor markers. Comparison of different approaches for albumin and gamma-globulin depletion

LC-MS is a powerful method for the sensitive detection of proteins and peptides in biological fluids. However, the presence of highly abundant proteins often masks those of lower abundance and thus generally prevents their detection and identification in proteomic studies. In human serum the most abundant proteins are albumin and gamma-globulins. We tested several approaches to specifically reduce the level of these proteins based on either specific antibodies, dye ligands (for albumin) and protein A or G (for gamma-globulins). The resulting, depleted serum was analyzed by sodium dodecylsulfate-polyacrylamide gel electrophoresis and LC-MS for the residual presence of these abundant proteins as well as for other serum proteins that should remain after depletion. To test the applicability of this method to real-life samples, depleted serum of a cervical cancer patient was analyzed for the presence of a specific tumor marker protein SCCA1 (squamous cell carcinoma antigen 1; P29508), which is present at ng/ml concentrations. The results demonstrate that SCCA1 can be detected by LC-MS in patient serum following depletion of albumin and gamma-globulins thus opening the possibility of screening patient sera for other, so far unknown, tumor markers.

Molecular Fingerprinting in Human Lung Cancer

The behavior and outcome of lung cancers are highly variable, and not only is the molecular basis of this variability unknown, but neither standard histopathology nor currently available molecular markers can predict these characteristics. Accordingly, the identification of novel biomarkers to differentiate tumor from normal cells and predict tumor behavior such as pathologic stage, response to chemotherapy, and site of relapse, is of great importance in clinical practice. None of the hundreds of single markers evaluated to date have demonstrated significant clinical utility, but by surveying thousands of genes at once with use of microarrays or proteomic technologies, it is now possible to read the molecular signature of an individual patient's tumor. When the signature is mathematically analyzed, new classes of cancer can be observed and insight can be gained into prediction, prognosis, and mechanism. Although some success has been achieved with genomic approaches, proteomics-based approaches allow examination of expressed proteins of a tissue or cell type, complement the genome initiatives, and are increasingly being used to address biomedical questions. This review aims to summarize the state of the art of gene and protein expression profiling for non-small-cell lung cancer

Proteomics in biomarker discovery and drug development

Proteomics is a research field aiming to characterize molecular and cellular dynamics in protein expression and function on a global level. The introduction of proteomics has been greatly broadening our view and accelerating our path in various medical researches. The most significant advantage of proteomics is its ability to examine a whole proteome or sub-proteome in a single experiment so that the protein alterations corresponding to a pathological or biochemical condition at a given time can be considered in an integrated way. Proteomic technology has been extensively used to tackle a wide variety of medical subjects including biomarker discovery and drug development. By complement with other new technique advances in genomics and bioinformatics, proteomics has a great potential to make considerable contribution to biomarker identification and to revolutionize drug development process. This article provides a brief overview of the proteomic technologies and their application in biomarker discovery and drug development.

Discovery of distinct protein profiles specific for lung tumors and pre-malignant lung lesions by SELDI mass spectrometry

OBJECTIVES

Early lung cancer detection and treatment remain a challenge. The efficacy of surface-enhanced laser desorption/ionization (SELDI) technology in lung cancer detection, has not been defined. This study identifies specific protein peak patterns in malignant lung tumors, and in pre-malignant airways epithelium showing neoplastic transformation.

METHODS

Lung tumor specimens taken from patients participating in a lung cancer screening study (H. Lee Moffitt Cancer Center, Tampa, FL) were laser capture microdissected to obtain pure cell populations from frozen sections of normal lung, atypical adenomatous hyperplasia (AAH) and malignant tumors. SELDI mass spectrometry was used to generate protein profiles in each epithelial cell type.

RESULTS

SELDI mass spectroscopy is highly reproducible in detecting lung tumor-specific protein profiles. Three peaks at 17-23 kDa mass range from tumor cells showed markedly increased compared with normal cells. The peak at 17250 Da was not detected in any of the normal cells. This peak appeared to be present at low levels in the atypical cell samples.

CONCLUSIONS

This study demonstrates the feasibility of detecting "malignant" protein signatures from lung tumor and pre-malignant pulmonary epithelium using SELDI mass spectrometry. Although additional study is necessary to validate these patterns as unique diagnostic tools, these "malignant" protein signatures lend themselves to identification of populations at high-risk for lung cancer and for monitoring response to lung cancer chemopreventive agents.

New cancer biomarkers deriving from NCI early detection research

Cancer is not a single disease but an accumulation of several events, genetic and epigenetic, arising in a single cell over a long time interval. A high priority in the cancer field is to identify these events. This can be achieved by characterizing cancer-associated genes and their protein products. Identifying the molecular alterations that distinguish any particular cancer cell from a normal cell will ultimately help to define the nature and predict the pathologic behavior of that cancer cell. It will also indicate the responsiveness to treatment of that particular tumor. Understanding the profile of molecular changes in any particular cancer will be extremely useful as it will become possible to correlate the resulting phenotype of that cancer with molecular events. Achieving these goals and knowledge will provide an opportunity for discovering new biomarkers for early cancer detection and developing prevention approaches. This will also help us identify new targets for therapeutic development. Advancement in technology includes methods and tools that enable research including, but not limited to, instrumentation, techniques, devices, and analysis tools (e.g., computer software). Resources such as databases, reagents, and tissue repositories are different than technologies. The identification and definition of the molecular profiles of cancer will require the development and dissemination of high-throughput molecular analysis technologies, as well as elucidation of all of the molecular species embedded in the genome of cancer and normal cells. The main challenge in cancer control and prevention is to detect the cancer early. This could then enable effective interventions and therapies contributing to reduction in mortality and morbidity. At a specific time, biomarkers serve as molecular signposts of the physiologic state of a cell. These signposts are the result of genes, their products (proteins) and other organic chemicals made by the cell. Biomarkers could prove to be vital for the identification of early cancer and subjects at risk of developing cancer as a normal cell progresses through the complex process of transformation to a cancerous state. This chapter discusses ongoing research in genetic and proteomic approaches to identify molecular signatures such as protein profiles, microsatellite instability, hypermethylation, and single nucleotide polymorphisms. Other topics covered here include the use of genomics and proteomics as high-throughput technology platforms to facilitate biomarker-aided detection of early cancer. Other areas covered include issues surrounding the analysis, validation, and predictive value of biomarkers using such technologies. Recent advances in noninvasive techniques, such as buccal cell isolates serving as viable sources of biomarkers, complementary to traditional sources such as serum or plasma, are also presented. The review also brings attention to the efforts of the Early Detection Research Network (EDRN) at the National Cancer Institute (NCI), in bringing together scientific expertise from leading national and international institutions, to identify and validate biomarkers for the detection of precancerous and cancerous cells in determining risk for developing cancer. The network's serious determined efforts in linking discovery to process development, resulting in early detection tests and clinical assessment, are also discussed.

Proteomics in translational cancer research: toward an integrated approach

Proteomics provides powerful tools for the study of clinically relevant samples in the context of translational cancer research. Here we briefly review applications of gel-based proteomics for the study of bladder and lung cancer using fresh tissue biopsies. In general, these studies have emphasized the potential of the technology for biomarker discovery, as well as for addressing the issue of cancer heterogeneity.

Proteomics in cancer research

With the human genome sequence now determined, the field of molecular medicine is moving beyond genomics to proteomics. In the field of cancer research, the key question is: how can oncologists best use techniques of proteomics in basic research and clinical application? In the postgenomic era, proteomics promises the discovery of biomarkers and tumor markers for early detection and diagnosis, novel protein-based drug targets for anticancer therapy, and new endpoints for the assessment of therapeutic efficacy and toxicity. This review paper will explore key themes in proteomics and their application in clinical cancer research.

Understanding cancer through proteomics

Proteomics is a rapidly expanding discipline that aims to gain a comprehensive understanding of the expressions, modification, interactions, and regulation of proteins in cells. New high-throughput technologies, such as protein chips and isotope-coded affinity tag peptide labeling, coupled with classic technologies such as two-dimensional gel electrophoresis and mass spectrometry, complement genomic technologies, providing cancer researchers with powerful tools for cancer diagnosis and prognosis and for the identification of targets for therapy.

Identification of tumor-associated antigens using proteomics

In the post-genomic era, the identification of tumor-associated antigens that elicit a humoral response is allowed at the protein level using proteomics. Indeed, the screening of autoantibodies using 2-D Western blot experiments with sera from cancer patients, followed by the subsequent identification of the target protein by mass spectrometry and database search has permitted the exploitation of the B-cell repertoire of patients with cancer. Applied to several types of cancer, a proteomic-based approach has revealed a high frequency of autoantibodies in sera from patients. Several of the antigenic proteins identified may constitute novel cancer markers and may have clinical utility in diagnosis or in establishing prognosis. Furthermore, the approach has allowed to distinguish isoforms that may help to define epitopes. On the other hand, the analysis of the expression levels of some of the antigenic proteins has revealed differential expression in tumors as compared with healthy tissues that might explain antigenicity.