A database of protein expression in lung cancer

Unveiling the potential of proteomic and genetic signatures for precision therapeutics in lung cancer management

Lung cancer's enduring global significance necessitates ongoing advancements in diagnostics and therapeutics. Recent spotlight on proteomic and genetic biomarker research offers a promising avenue for understanding lung cancer biology and guiding treatments. This review elucidates genetic and proteomic lung cancer biomarker progress and their treatment implications. Technological strides in mass spectrometry-based proteomics and next-generation sequencing enable pinpointing of genetic abnormalities and abnormal protein expressions, furnishing vital data for precise diagnosis, patient classification, and customized treatments. Biomarker-driven personalized medicine yields substantial treatment improvements, elevating survival rates and minimizing adverse effects. Integrating omics data (genomics, proteomics, etc.) enhances understanding of lung cancer's intricate biological milieu, identifying novel treatment targets and biomarkers, fostering precision medicine. Liquid biopsies, non-invasive tools for real-time treatment monitoring and early resistance detection, gain popularity, promising enhanced management and personalized therapy. Despite advancements, biomarker repeatability and validation challenges persist, necessitating interdisciplinary efforts and large-scale clinical trials. Integrating artificial intelligence and machine learning aids analyzing vast omics datasets and predicting treatment responses. Single-cell omics reveal cellular connections and intratumoral heterogeneity, valuable for combination treatments. Biomarkers enable accurate diagnosis, tailored medicines, and treatment response tracking, significantly impacting personalized lung cancer care. This approach spurs patient-centered trials, empowering active patient engagement. Lung cancer proteomic and genetic biomarkers illuminate disease biology and treatment prospects. Progressing towards individualized efficient therapies is imminent, alleviating lung cancer's burden through ongoing research, omics integration, and technological strides.

Mass spectrometry-based proteomics: the road to lung cancer biomarker discovery

Lung cancer is the leading cause of cancer death in men and women in Western nations, and is among the deadliest cancers with a 5-year survival rate of 15%. The high mortality caused by lung cancer is attributable to a late-stage diagnosis and the lack of effective treatments. So, it is crucial to identify new biomarkers that could function not only to detect lung cancer at an early stage but also to shed light on the molecular mechanisms that underlie cancer development and serve as the basis for the development of novel therapeutic strategies. Considering that DNA-based biomarkers for lung cancer showed inadequate sensitivity, specificity, and reproducibility, proteomics could represent a better tool for the identification of useful biomarkers and therapeutic targets for this cancer type. Among the proteomics technologies, the most powerful tool is mass spectrometry. In this review, we describe studies that use mass spectrometry-based proteomics technologies to analyze tumor proteins and peptides, which might represent new diagnostic, prognostic, and predictive markers for lung cancer. We focus in particular on those findings that hold promise to impact significantly on the clinical management of this disease.

Identification of new hematopoietic cell subsets with a polyclonal antibody library specific for neglected proteins

The identification of new markers, the expression of which defines new phenotipically and functionally distinct cell subsets, is a main objective in cell biology. We have addressed the issue of identifying new cell specific markers with a reverse proteomic approach whereby approximately 1700 human open reading frames encoding proteins predicted to be transmembrane or secreted have been selected in silico for being poorly known, cloned and expressed in bacteria. These proteins have been purified and used to immunize mice with the aim of obtaining polyclonal antisera mostly specific for linear epitopes. Such a library, made of about 1600 different polyclonal antisera, has been obtained and screened by flow cytometry on cord blood derived CD34+CD45dim cells and on peripheral blood derived mature lymphocytes (PBLs). We identified three new proteins expressed by fractions of CD34+CD45dim cells and eight new proteins expressed by fractions of PBLs. Remarkably, we identified proteins the presence of which had not been demonstrated previously by transcriptomic analysis. From the functional point of view, looking at new proteins expressed on CD34+CD45dim cells, we identified one cell surface protein (MOSC-1) the expression of which on a minority of CD34+ progenitors marks those CD34+CD45dim cells that will go toward monocyte/granulocyte differentiation. In conclusion, we show a new way of looking at the membranome by assessing expression of generally neglected proteins with a library of polyclonal antisera, and in so doing we have identified new potential subsets of hematopoietic progenitors and of mature PBLs.

dbDEPC 2.0: updated database of differentially expressed proteins in human cancers

A large amount of differentially expressed proteins (DEPs) have been identified in various cancer proteomics experiments, curation and annotation of these proteins are important in deciphering their roles in oncogenesis and tumor progression, and may further help to discover potential protein biomarkers for clinical applications. In 2009, we published the first database of DEPs in human cancers (dbDEPCs). In this updated version of 2011, dbDEPC 2.0 has more than doubly expanded to over 4000 protein entries, curated from 331 experiments across 20 types of human cancers. This resource allows researchers to search whether their interested proteins have been reported changing in certain cancers, to compare their own proteomic discovery with previous studies, to picture selected protein expression heatmap across multiple cancers and to relate protein expression changes with aberrance in other genetic level. New important developments include addition of experiment design information, advanced filter tools for customer-specified analysis and a network analysis tool. We expect dbDEPC 2.0 to be a much more powerful tool than it was in its first release and can serve as reference to both proteomics and cancer researchers. dbDEPC 2.0 is available at http://lifecenter.sgst.cn/dbdepc/index.do.

Protein expression pattern in response to ionizing radiation in MCF-7 human breast cancer cells

Breast cancer is one of the most common types of cancer in women and is highly treatable by radiotherapy. However, repeated exposure to radiation results in tumor cell resistance. Understanding the molecular mechanisms involved in the response of tumors to γ-irradiation is important for improving radiotherapy. For this reason, we aimed to identify radiation-responsive genes at the protein level. In the present study, we observed differentially expressed proteins using 2D-PAGE and MALDI-TOF-MS for the global analysis of protein expression patterns in response to ionizing radiation (IR). When the expression patterns of proteins were compared to a control gel, numerous spots were found that differed greatly. Among them, 11 spots were found to be significantly different. One set of proteins (GH2, RGS17, BAK1, CCNH, TSG6, RAD51B, IGFBP1 and CASP14) was upregulated and another set of proteins (C1QRF, PLSCR2 and p34(SE1-1)) was downregulated after exposure to γ-rays. These proteins are known to be related to cell cycle control, apoptosis, DNA repair, cell proliferation and other signaling pathways.

Protein expression analysis of inflammation-related colon carcinogenesis

Background:

Chronic inflammation is a risk factor for colorectal cancer (CRC) development. The aim of this study was to determine the differences in protein expression between CRC and the surrounding nontumorous colonic tissues in the mice that received azoxymethane (AOM) and dextran sodium sulfate (DSS) using a proteomic analysis.

Materials and Methods:

Male ICR mice were given a single intraperitoneal injection of AOM (10 mg/kg body weight), followed by 2% (w/v) DSS in their drinking water for seven days, starting one week after the AOM injection. Colonic adenocarcinoma developed after 20 weeks and a proteomics analysis based on two-dimensional gel electrophoresis and ultraflex TOF/TOF mass spectrometry was conducted in the cancerous and nontumorous tissue specimens.

Results:

The proteomic analysis revealed 21 differentially expressed proteins in the cancerous tissues in comparison to the nontumorous tissues. There were five markedly increased proteins (beta-tropomyosin, tropomyosin 1 alpha isoform b, S100 calcium binding protein A9, and an unknown protein) and 16 markedly decreased proteins (Car1 proteins, selenium-binding protein 1, HMG-CoA synthase, thioredoxin 1, 1 Cys peroxiredoxin protein 2, Fcgbp protein, Cytochrome c oxidase, subunit Va, ETHE1 protein, and 7 unknown proteins).

Conclusions:

There were 21 differentially expressed proteins in the cancerous tissues of the mice that received AOM and DSS. Their functions include metabolism, the antioxidant system, oxidative stress, mucin production, and inflammation. These findings may provide new insights into the mechanisms of inflammation-related colon carcinogenesis and the establishment of novel therapies and preventative strategies to treat carcinogenesis in the inflamed colon.

An informatics-assisted label-free quantitation strategy that depicts phosphoproteomic profiles in lung cancer cell invasion

Aberrant protein phosphorylation plays important roles in cancer-related cell signaling. With the goal of achieving multiplexed, comprehensive, and fully automated relative quantitation of site-specific phosphorylation, we present a simple label-free strategy combining an automated pH/acid-controlled IMAC procedure and informatics-assisted SEMI (sequence, elution time, mass-to-charge, and internal standard) algorithm. The SEMI strategy effectively increased the number of quantifiable peptides more than 4-fold in replicate experiments (from 262 to 1171, p < 0.05, false discovery rate = 0.46%) by using a fragmental regression algorithm for elution time alignment followed by peptide cross-assignment in all LC-MS/MS runs. In addition, the strategy demonstrated good quantitation accuracy (10-12%) for standard phosphoprotein and variation less than 1.9 fold (within 99% confidence range) in proteome scale and reliable linear quantitation correlation (R(2) = 0.99) with 4000-fold dynamic concentrations, which was attributed to our reproducible experimental procedure and informatics-assisted peptide alignment tool to minimize system variations. In an attempt to explore metastasis-associated phosphoproteomic alterations in lung cancer, this approach was used to delineate differential phosphoproteomic profiles of a lung cancer metastasis model. Without sample fractionation, the SEMI algorithm enabled quantification of 1796 unique phosphopeptides (false discovery rate = 0.56%) corresponding to 854 phosphoproteins from a series of non-small cell lung cancer lines with varying degrees of in vivo invasiveness. Nearly 40% of the phosphopeptides showed >2-fold change in highly invasive cells; validation of phosphoprotein subsets by Western blotting not only demonstrated the consistency of data obtained by our SEMI strategy but also revealed that such dramatic changes in the phosphoproteome result mostly from translational or post-translational regulation. Mapping of these differentially expressed phosphoproteins in multiple cellular pathways related to cancer invasion and metastasis suggests that the site and degree of phosphorylation might have distinct patterns or functions in the complex process of cancer progression.

New biomarkers for lung cancer

IMPORTANCE OF THE FIELD

Despite many efforts to improve early detection, lung cancer remains the leading cause of cancer deaths. Stage is the main determinant of prognosis and the basis for deciding treatment options. Screening tests for lung cancer have not been successful so far.

AREAS COVERED IN THE REVIEW

The article reviews the available literature related to biomarkers in use at present and those that could be used for early diagnosis, staging, prognosis, response to therapy and prediction of recurrence. The single biomarkers are analysed, divided according to the technological methods used and the locations of sampling.

WHAT THE READER WILL GAIN

The reader will gain knowledge on biomarkers in use and those now under study. The reader will also gain insights into the difficulties pertaining to the development of biomarkers, results reproducibility and clinical application.

TAKE HOME MESSAGE

Although some markers seem to be promising, at present there is no consensus on the proven value of their clinical use in lung cancer. The future lies probably in a panel of biomarkers instead of individual assays, or in predictive models derived from the integration of clinical variables and gene expression profiles.

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.

Mass spectrometry-based proteomic profiling of lung cancer. Ann Am Thorac Soc. 2009;6:159-170. [PMID: 19349484 DOI: 10.1513/pats.200809-108lc]

In an effort to further our understanding of lung cancer biology and to identify new candidate biomarkers to be used in the management of lung cancer, we need to probe these tissues and biological fluids with tools that address the biology of lung cancer directly at the protein level. Proteins are responsible of the function and phenotype of cells. Cancer cells express proteins that distinguish them from normal cells. Proteomics is defined as the study of the proteome, the complete set of proteins produced by a species, using the technologies of large-scale protein separation and identification. As a result, new technologies are being developed to allow the rapid and systematic analysis of thousands of proteins. The analytical advantages of mass spectrometry (MS), including sensitivity and high-throughput, promise to make it a mainstay of novel biomarker discovery to differentiate cancer from normal cells and to predict individuals likely to develop or recur with lung cancer. In this review, we summarize the progress made in clinical proteomics as it applies to the management of lung cancer. We will focus our discussion on how MS approaches may advance the areas of early detection, response to therapy, and prognostic evaluation.

Aldo-keto reductases from the AKR1B subfamily: retinoid specificity and control of cellular retinoic acid levels

NADP(H)-dependent cytosolic aldo-keto reductases (AKRs) have been added to the group of enzymes which contribute to oxidoreductive conversions of retinoids. Recently, we found that two members from the AKR1B subfamily (AKR1B1 and AKRB10) were active in the reduction of all-trans- and 9-cis-retinaldehyde, with K(m) values in the micromolar range, but with very different k(cat) values. With all-trans-retinaldehyde, AKR1B10 shows a much higher k(cat) value than AKR1B1 (18 min(-1)vs. 0.37 min(-1)) and a catalytic efficiency comparable to that of the best retinaldehyde reductases. Structural, molecular dynamics and site-directed mutagenesis studies on AKR1B1 and AKR1B10 point that subtle differences at the entrance of their retinoid-binding site, especially at position 125, are determinant for the all-trans-retinaldehyde specificity of AKR1B10. Substitutions in the retinoid cyclohexene ring, analyzed here further, also influence such specificity. Overall it is suggested that the rate-limiting step in the reaction mechanism with retinaldehyde differs between AKR1B1 and AKR1B10. In addition, we demonstrate here that enzymatic activity of AKR1B1 and AKR1B10 lowers all-trans- and 9-cis-retinoic acid-dependent trans-activation in living cells, indicating that both enzymes may contribute to pre-receptor regulation of retinoic acid and retinoid X nuclear receptors. This result supports that overexpression of AKR1B10 in cancer (an updated review on this topic is included) may contribute to dedifferentiation and tumor development.

Application of highly sensitive fluorescent dyes (CyDye DIGE Fluor saturation dyes) to laser microdissection and two-dimensional difference gel electrophoresis (2D-DIGE) for cancer proteomics

Proteome data combined with histopathological information provides important, novel clues for understanding cancer biology and reveals candidates for tumor markers and therapeutic targets. We have established an application of a highly sensitive fluorescent dye (CyDye DIGE Fluor saturation dye), developed for two-dimensional difference gel electrophoresis (2D-DIGE), to the labeling of proteins extracted from laser microdissected tissues. The use of the dye dramatically decreases the protein amount and, in turn, the number of cells required for 2D-DIGE; the cells obtained from a 1 mm2 area of an 8-12 microm thick tissue section generate up to 5,000 protein spots in a large-format 2D gel. This protocol allows the execution of large-scale proteomics in a more efficient, accurate and reproducible way. The protocol can be used to examine a single sample in 5 d or to examine hundreds of samples in large-scale proteomics.

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.

Biomarker discovery in lung cancer--promises and challenges of clinical proteomics

Lung cancer is a devastating illness with an overall poor prognosis. To effectively address this disease, early detection and novel therapeutics are required. Early detection of lung cancer is challenging, in part because of the lack of adequate tumor biomarkers. The goal of this review is to summarize the knowledge of current biomarkers in lung cancer, with a focus on important serum biomarkers. The current knowledge on the known serum cytokines and tumor biomarkers of lung cancer will be presented. Emerging trends and new findings in the search for novel diagnostic and therapeutic tumor biomarkers using proteomics technologies and platforms are emphasized, including recent advances in mass spectrometry to facilitate tumor biomarker discovery program in lung cancer. It is our hope that validation of these new research platforms and technologies will result in improved early detection, prognostication, and finally the treatment of lung cancer with potential novel molecularly targeted therapeutics.

Proteomics analysis in lung cancer: challenges and opportunities

Recent technological developments in proteomic analysis are bringing us new insights into the molecular classification of tumours. Although proteomic analysis in cancer profiling is still under development both in terms of the instruments used and the data analytical tools, this method has great potential advantages for the analysis of biospecimens of many types. Direct measurement of abnormally expressed or modified proteins in the tumour tissue and/or patient blood may be an effective approach for discovering new biomarkers. Proteomics has the significant advantage of being able to discern not only changes in expression levels but also in post-translational modifications. Thus, the proteomics approach to protein profiling and biomarker discovery uncovers biomarkers from a different viewpoint than microarray analysis. This review summarizes the range of proteomics technologies employed for cancer profiling, and how they have been used to derive new classification models for human lung cancer.

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.

Toward a comprehensive quantitative proteome database: protein expression map of lymphoid neoplasms by 2-D DIGE and MS

Using 2-D DIGE, we constructed a quantitative 2-D database including 309 proteins corresponding to 389 protein spots across 42 lymphoid neoplasm cell lines. The proteins separated by 2-D PAGE were identified by MS and assigned to the expression data obtained by 2-D DIGE. The cell lines were categorized into four groups: those from Hodgkin's lymphoma (HL) (4 cell lines), B cell malignancies (19 cell lines), T cell malignancies (16 cell lines), and natural killer (NK) cell malignancies (3 cell lines). We characterized the proteins in the database by classifying them according to their expression level. We found 28 proteins with more than a 2-fold difference between the cell line groups. We also noted the proteins that allowed multidimensional separation to be achieved (1) between HL cells and other cells, (2) between the cells derived from B cells, T cells and NK cells, and (3) between HL cells and anaplastic large cell lymphoma cells. Decision tree classification identified five proteins that could be used to classify the 42 cell lines according to differentiation. These results suggest that the quantitative 2-D database using 2-D DIGE will be a useful resource for studying the mechanisms underlying the differentiation phenotypes of lymphoid neoplasms.

Two-dimensional gel electrophoresis as tool for proteomics studies in combination with protein identification by mass spectrometry

The proteome analysis by 2-DE is one of the most potent methods of analyzing the complete proteome of cells, cell lines, organs and tissues in proteomics studies. It allows a fast overview of changes in cell processes by analysis of the entire protein extracts in any biological and medical research projects. New instrumentation and advanced technologies provide proteomics studies in a wide variety of biological and biomedical questions. Proteomics work is being applied to study antibiotics-resistant strains and human tissues of various brain, lung, and heart diseases. It cumulated in the identification of antigens for the design of new vaccines. These advances in proteomics have been possible through the development of advanced high-resolution 2-DE systems allowing resolution of up to 10 000 protein spots of entire cell lysates in combination with protein identification by new highly sensitive mass spectrometric techniques. The present technological achievements are suited for a high throughput screening of different cell situations. Proteomics may be used to investigate the health effects of radiation and electromagnetic field to clarify possible dangerous alterations in human beings.

Proteome analysis of human lung squamous carcinoma

Few lung cancer-specific molecular markers have been established in regard of "early-stage" diagnosis and prognosis. In this study the proteome analysis of human lung squamous carcinoma (hLSC) was carried out using two strategies to explore the carcinogenic mechanisms and identify its molecular markers more directly and comprehensively. Comparative proteome analysis on 20 hLSC tissues and paired normal bronchial epithelial tissues revealed 76 differential proteins, among which 68 proteins were identified by PMF. The identified proteins fell into three categories: oncoproteins, cell cycle regulators and signaling molecules. To validate the identified differential proteins, the expressions levels of three differential proteins mdm2, c-jun and EGFR were determined by immunohistochemical staining and immunoblots. The results verified proteome analysis results. Serological proteome analysis (SERPA) of ten hLSC tissues was performed to identify the tumor-associated antigens. The results revealed 36 +/- 8 differential proteins reactive with patients' autologous sera, of which 14 proteins were identified. Six of the 14 proteins, alpha enolase, pre-B cell-enhancing factor precursor, triosephosphate isomerase, phosphoglycerate mutase 1, fructose-bisphosphate aldolase A, and guanine nucleotide-binding protein beta subunit-like protein, were also up-regulated in hLSCs in the comparative proteomic study, which suggests potential application of these 6 hLSC-associated antigens in diagnosis and therapy of hLSC.

Proteome profile of the MCF7 cancer cell line: a mass spectrometric evaluation

The development of novel proteomic technologies that will enable the discovery of disease specific biomarkers is essential in the clinical setting to facilitate early diagnosis and increase survivability rates. We are reporting a shotgun two-dimensional (2D) strong cationic exchange/reversed-phase liquid chromatography/electrospray ionization tandem mass spectrometry (SCX/RPLC/ESI-MS/MS) protocol for the analysis of proteomic constituents in cancerous cells. The MCF7 breast cancer cell line was chosen as a model system. A series of optimization steps were performed to improve the LC/MS experimental setup, sample preparation, data acquisition and database search protocols, and a data filtering strategy was developed to enable confident identification of a large number of proteins and potential biomarkers. This research has resulted in the identification of >2000 proteins using multiple filtering and p-value sorting. Approximately 1600-1900 proteins had p < 0.001, and, of these, approximately 60% were matched by >or=2 unique peptides. Alternatively, >99% of the proteins identified by >or=2 unique peptides had p < 0.001. When searching the data against a reversed database of proteins, the rate of false positive identifications was 0.1% at the peptide level and 0.4% at the protein level. The typical reproducibility in detecting overlapping proteins across replicate runs exceeded 90% for proteins matched by >or=2 unique peptides. According to their biological function, approximately 200 proteins were involved in cancer-relevant cellular processes, and over 25 proteins were previously described in the literature as putative cancer biomarkers, as they were found to be differentially expressed between normal and cancerous cell states. Among these, biomarkers such PCNA, cathepsin D, E-cadherin, 14-3-3-sigma, antigen Ki-67, TP53RK, and calreticulin were identified. These data were generated by subjecting to MS analysis approximately 42 microg of sample, analyzing 16 SCX peptide fractions, and interpreting approximately 55,000 MS2 spectra. Total MS time required for analysis was 40 h.

Peroxiredoxin-I is an autoimmunogenic tumor antigen in non-small cell lung cancer

In eukaryotic cells, peroxiredoxins are both antioxidants and regulators of H(2)O(2)-mediated signaling. We previously found that peroxiredoxin-I (Prx-I) was overexpressed in non-small cell lung cancer (NSCLC) tissue. Since overexpressed protein can induce a humoral immune response, we examined whether serum from NSCLC patients exhibited immunoreactivity against Prx-I using Western blotting. We found that 25 (47%) of 53 NSCLC patients tested had autoantibodies against Prx-I in their sera, whereas such activity was detected in 4 (8%) sera from 50 healthy subjects. Prx-I itself was detected in the sera from 18 (34%) of 53 NSCLC patients but in only 1 (2%) serum from 50 controls. Moreover, 17% of NSCLC sera were positive to both Prx-I antibody and antigen but none in control sera. The data indicate both Prx-I autoantibody and circulating antigen are potential biomarkers for use in serological diagnosis of NSCLC. Interestingly enough, we found that Prx-I was secreted by lung adenocarcinoma cells (A549) but not by non-cancer lung cells (BEAS 2B) or breast cancer cells (MCF7). This cell culture study suggests the possibility of Prx-I secretion from NSCLC tumor tissues.

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.

Integrated selective enrichment target--a microtechnology platform for matrix-assisted laser desorption/ionization-mass spectrometry applied on protein biomarkers in prostate diseases

The performance of a miniaturized sample processing platform for matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS), manufactured by silicon microfabrication, called integrated selective enrichment target (ISET) technology was evaluated in a biological context. The ISET serves as both sample treatment device and MALDI-MS target, and contains an array of 96 perforated nanovials, which each can be filled with 40 nL of reversed-phase beads. This methodology minimizes the number of sample transfers and the total surface area available for undesired adsorption of the analytes in order to provide high-sensitivity analysis. ISET technology was successfully applied for characterization of proteins coisolated by affinity chromatography of prostate-specific antigen (PSA) from human seminal fluid. The application of ISET sample preparation enabled multiple analyses to be performed on a limited sample volume, which resulted in the discovery that prolactin inducible protein (PIP) was coisolated from the samples.

Proteomic analysis distinguishes basaloid carcinoma as a distinct subtype of nonsmall cell lung carcinoma

The histopathologic type of lung cancer is known to be correlated with tumor behavior and prognosis. However, this classification is subjective and no specific molecular markers have been identified. The aim of this study was to identify protein markers in different types of nonsmall cell lung cancers. Two-dimensional polyacrylamide gel electrophoresis analysis was performed with paired samples of three squamous cell carcinomas, three adenocarcinomas, four large cell carcinomas, and four basaloid carcinomas. We found that 25 proteins in 14 cases of lung cancer were differentially expressed compared to matched nontumorous lung tissues. Among these 25 proteins, 11 proteins were down-regulated and 14 were up-regulated in these four types of lung cancer. Alloalbumin venezia, selenium-binding protein 1, carbonic dehydratase, heat shock 20KD-like protein, and SM22 alpha protein were down-regulated in all 14 cases of lung cancer examined, whereas alpha enolase was consistently up-regulated. Supervised hierarchical cluster analysis based on the 25 differentially expressed proteins showed that basaloid carcinoma formed one independent group, whereas the other three cancer types were not uniquely classifiable. Our findings suggest that basaloid carcinoma is a unique subtype of nonsmall cell lung carcinoma.

Proteomic comparison of two-dimensional gel electrophoresis profiles from human lung squamous carcinoma and normal bronchial epithelial tissues

Differential proteome profiles of human lung squamous carcinoma tissue compared to paired tumor-adjacent normal bronchial epithelial tissue were established and analyzed by means of immobilized pH gradient-based two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) and matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS). The results showed that well-resolved, reproducible 2-DE patterns of human lung squamous carcinoma and adjacent normal bronchial epithelial tissues were obtained under the condition of 0.75-mg protein-load. The average deviation of spot position was 0.733±0.101 mm in IEF direction, and 0.925±0.207 mm in SDS-PAGE direction. For tumor tissue, a total of 1241±88 spots were detected, 987±65 spots were matched with an average matching rate of 79.5%. For control, a total of 1190±72 spots were detected, and 875±48 spots were matched with an average matching rate of 73.5%. A total of 864±34 spots were matched between tumors and controls. Forty-three differential proteins were characterized: some proteins were related to oncogenes, and others involved in the regulation of cell cycle and signal transduction. It is suggested that the differential proteomic approach is valuable for mass identification of differentially expressed proteins involved in lung carcinogenesis. These data will be used to establish human lung cancer proteome database to further study human lung squamous carcinoma.

Differential analysis of two-dimension gel electrophoresis profiles from the normal-metaplasia-dysplasia-carcinoma tissue of human bronchial epithelium

Processes involved in malignant transformation of the lung from preneoplasia are poorly understood. To better understand this process, two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) profiles of proteins from the normal, metaplasia, dysplasia and carcinoma tissues of human bronchial epithelia were examined by differential proteomic analysis. The selected differential protein-spots were identified by peptide mass fingerprint based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and database searching. The average spots for normal epithelium, metaplasia, dysplasia and invasive carcinoma were 1189.50 +/- 39.89, 1227.00 +/- 37.90, 1273.00 +/- 43.31 and 1326.00 +/- 66.63, respectively. Well-resolved, reproducible 2-D PAGE patterns of the normal-metaplasia-dysplasia-carcinoma tissues of bronchial epithelia were obtained. After matching, the number of spots of differential proteins between normal tissue and metaplasia, metaplasia and dysplasia, and dysplasia and invasive cancer tissues were 31.50 +/- 7.67, 41.00 +/- 9.07 and 56.00 +/- 8.96, respectively. In total, 35 differential proteins, expressed only at the later stage of a two-stage comparison, were identified, some of which are known to be involved in regulating the processes of proliferation, differentiation and signal transduction. Current data in this study, for the first time, provide the basis for identification of potential tumor markers of human lung squamous carcinoma and their involvement in the progression of malignant transformation of bronchial epithelium.

Application of proteomic technologies to tumor analysis

The sequencing of the human genome has had an enormous impact on the proteomic analysis of cancer by providing a sequence-based framework for understanding the human proteome of tumor cells, tissues, and biological fluids. There is intense interest in applying proteomic technologies to uncover, at the protein level, processes involved in neoplastic transformation and new biomarkers that correlate with early diagnosis, as well as to accelerate the development of new therapeutic targets. To that effect, new technologies are being developed in order to meet the needs for the high throughput and high sensitivity that is required for cancer-related applications of proteomics. These innovative technologies have greatly enhanced our ability to separate and characterize complex protein mixtures, and have aided our ability to identify proteins with greater sensitivity, thereby providing the groundwork for future scientific breakthroughs and possibly providing impetus for the development of personalized cancer therapy.

Impact of proteomics on bladder cancer research

Detecting bladder cancer at an early stage and predicting how a tumor will behave and act in response to therapy, as well as the identification of new targets for therapeutic intervention, are among the main areas of research that will benefit from the current explosion in the number of powerful technologies emerging within proteomics. The purpose of this article is to briefly review what has been achieved to date using proteomic technologies and to bring forward novel strategies – based on the analysis of clinically relevant samples – that promise to accelerate the translation of basic discoveries into the daily clinical practice.

Bcl-2 as a target for overcoming chemoresistance in small-cell lung cancer

Small-cell lung cancer (SCLC) is an aggressive malignancy that is frequently metastatic at presentation and has a poor prognosis. Although initially sensitive to primary therapy, acquisition of apoptosis resistance is typical, resulting in failure of secondary chemotherapy following relapse. Expression of the antiapoptosis protein Bcl-2 is prevalent in SCLC. The understanding of this oncoprotein's function has increased dramatically over the past decade. In vitro and in vivo evidence supports a role for overexpression of Bcl-2 in SCLC and supports the notion that it is a major factor contributing to apoptosis resistance. Targeting Bcl-2 may provide a novel therapeutic approach to overcoming chemoresistance in SCLC. This article discusses the relevance of Bcl-2 to apoptosis susceptibility in SCLC and its exploitation using gene silencing to improve the clinical outcome in this disease.

Comparative proteomics analysis of human lung squamous carcinoma

Two-dimensional polyacrylamide gel electrophoresis (2-DE) profiles of human lung squamous carcinoma tissue and paired surrounding normal bronchial epithelial tissue were compared. Selected differential protein-spots were identified with peptide mass fingerprinting based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and database searching. Well-resolved and reproducible 2-DE patterns of both the tumor and the normal tissues were acquired. The average deviations of spot position were 0.873+/-0.125mm in IEF direction and 1.025+/-0.213mm in SDS-PAGE direction, respectively. For the tumor tissues, a total of 1349+/-67 spots were detected and 1235+/-48 spots were matched with an average matching rate of 91.5%. For the corresponding normal tissues, a total of 1297+/-73 spots were detected and 1183+/-56 spots were matched with an average matching rate of 91.2%. A total of 1069+/-45 spots were matched between the tumor and the normal tissues. Forty differential proteins between tumor and normal tissues were characterized. Some proteins were the products of oncogenes and others were involved in the regulation of cell cycle and signal transduction. These data are valuable for mass identification of differentially expressed proteins involved in lung carcinogenesis, establishing human lung cancer proteome database and screening molecular marker to further study human lung squamous carcinoma.

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.

Current perspectives in cancer proteomics

Proteome technology has been used widely in cancer research and is a useful tool for the identification of new cancer markers and treatment-related changes in cancer. This article details the use of proteome technology in cancer research, and laboratory-based and clinical cancer research studies are described. New developments in proteome technology that enable higher sample-throughput are evaluated and methods for enhancing conventional proteome analysis (based on two-dimensional electrophoresis) discussed. The need to couple laboratory-based proteomics research with clinically relevant models of the disease is also considered, as this remains the next main challenge of cancer-related proteome research.

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.

New approaches for biomarker discovery in lung cancer

Lung cancer remains the most common cause of cancer death in the US and worldwide. Currently, there is no implemented population-based screening for lung cancer. Of all the markers identified, none have achieved sufficient diagnostic significance to reach clinical application. Here we discuss the status of lung cancer early diagnostics, and the genomic and proteomic approaches currently undertaken for biomarker discovery. We then introduce the ANTIBIOMIX approach that enables high-throughput target discovery by interrogating biological samples using a collection of thousands of polyclonal antibodies. The development of specific and sensitive diagnostic assays using patient's biological fluids, such as sputum and serum, will improve screening, monitoring of disease progression and treatment response, and surveillance for recurrence.

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.

Genetic advances will influence the practice of medicine: examples from cancer research and care of cancer patients

Exciting new techniques of genomics, proteomics, and bioinformatics are beginning to influence the practice of medicine, most notably in diagnosis and drug development for patients with various cancers. Examples are drawn from B-cell lymphomas, melanomas, and prostate, lung, and breast cancers. As in all evidence-based clinical practice, physicians will be better prepared if they understand the nature of the tests and the kinds of information from which they and their consultants will make clinical inferences and assist patients in making clinical decisions. Physicians also can help put new technologies in cultural and ethical context.

Recent advances in the molecular diagnosis of lung cancer

Despite extensive effort in improvement of diagnosis and treatment of patients with lung cancer in past three decades, the overall survival of patients with the disease remains dismal. Because the development of lung cancer takes a few decades, early diagnosis of the disease or identification of truly high-risk populations may provide us opportunity to successfully cure or prevent the disease. Recent advances in understanding biological basis of lung tumorigenesis provide new tools for detecting malignant cells or the process of malignant transformation and progression. Along with identification of molecular abnormalities in the early lung tumorigenesis, advanced molecular analytic technologies have been emerged, which may facilitate development of rapid and effective methods for early diagnosis and risk assessment. Here, I discuss recent progresses in understanding of early molecular abnormalities in lung cancer, efforts of translating laboratory findings to clinical tests, and prospective of biomarkers in lung cancer diagnosis and risk assessment.

Proteomic analysis of cytokeratin isoforms uncovers association with survival in lung adenocarcinoma

Cytokeratins (CK) are intermediate filaments whose expression is often altered in epithelial cancer. Systematic identification of lung adenocarcinoma proteins using two-dimensional polyacrylamide gel electrophoresis and mass spectrometry has uncovered numerous CK isoforms. In this study, 93 lung adenocarcinomas (64 stage I and 29 stage III) and 10 uninvolved lung samples were quantitatively examined for protein expression. Fourteen of 21 isoforms of CK 7, 8, 18, and 19 occurred at significantly higher levels (P < .05) in tumors compared to uninvolved adjacent tissue. Specific isoforms of the four types of CK identified correlated with either clinical outcome or individual clinical-pathological parameters. All five of the CK7 isoforms associated with patient survival represented cleavage products. Two of five CK7 isoforms (nos. 2165 and 2091), one of eight CK8 isoforms (no. 439), and one of three CK19 isoforms (no. 1955) were associated with survival and significantly correlated to their mRNA levels, suggesting that transcription underlies overexpression of these CK isoforms. Our data indicate substantial heterogeneity among CK in lung adenocarcinomas resulting from posttranslational modifications, some of which correlated with patient survival and other clinical parameters. Therefore, specific isoforms of individual CK may have utility as diagnostic or predictive markers in lung adenocarcinomas.

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.