- Email: [email protected]
Molecular Fingerprinting in Human Lung Cancer
translational medicin e Molecular Fingerprinting in Human Lung Cancer Kiyoshi Yanagisawa,1 Baogang J. Xu,2 David P. Carbone1, Richard M. Caprioli3 Abstract 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. Clinical Lung Cancer, Vol. 5, No. 2, 113-118, 2003
Key words: Mass spectrometry, Microarray, Proliferating cell nuclear antigen, Proteomics, Thyroid transcription factor, Vascular endothelial growth factor
Introduction
quately understood through the analysis of individual genes or small numbers of genes. The complementary DNA (cDNA) microarray analysis is beginning to be employed with some success to simultaneously investigate thousands of RNA expression levels and identify patterns associated with biology.4-6
Lung cancer represents a challenging global clinical problem and is the leading cause of cancer death in the United States for both men and women, with an estimated 171,900 new cases and 157,200 deaths in 2003.1 Lung cancer also displays a tremendous biologic variability in metastatic potential, local invasive phenotype, and response to therapy. Only a small fraction of cases are cured, and median survival is short. Some patients will die of metastatic disease and some will die of only local disease. Some will respond to chemotherapy or molecularly targeted therapy and some will not. Despite complex aggressive approaches to therapy and great strides in understanding its biology and etiology, corresponding improvements in outcome are not yet apparent.2,3 Therefore, improved understanding of the molecular basis of lung cancer behavior is an extremely urgent health problem. After decades of research and thousands of studies, it is now obvious that tumor behavior cannot be ade1Vanderbilt-Ingram
Cancer Center and Department of Medicine Spectrometry Research Center 3Department of Biochemistry Vanderbilt University School of Medicine, Nashville, TN 2Mass
Submitted: Apr 21, 2003; Revised: Jun 2, 2003; Accepted: Jun 17, 2003 Address for correspondence: David P. Carbone, MD, PhD, VanderbiltIngram Cancer Center, 648 MRB II, Vanderbilt University School of Medicine, Nashville, TN 37232 Fax: 615-936-3322; e-mail: [email protected]
Microarray Technology Gene expression profiling using microarrays has great potential as a systematic approach for discovering new classes of tumors or assigning known tumors to classes that predict outcome or response to therapy. A DNA array consists of rows and columns of oligonucleotides or cDNAs on a miniature silicon chip or glass slide. Oligonucleotides can be synthesized in situ on the array, and are comprised of short fragments of DNA complementary to known messenger RNAs (mRNAs) of interest. Alternatively, cDNA arrays can be made containing immobilized sequences that are much longer than the corresponding oligonucleotide. Arrays can consist of 20,000 or more oligonucleotides or cDNAs. In the microarray analysis, RNA is extracted from tumor or normal tissues and labeled with a fluorescence dye with reverse transcriptase. The labeled cDNA is hybridized to the oligonucleotide or cDNA array in the absence or presence of a reference RNA, respectively. Groups of genes can be identified by comparing the expression levels of each gene (intensity of fluorescence), allowing groups of tumors to be identified by comparing similar patterns of gene expression.
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Proteomics and Lung Cancer The first report to show that the expression patterns of diverse classes of genes contained information that could be used to classify tumors was described by Golub et al, who analyzed a group of acute leukemias.7 Based on gene expression patterns, it was possible to differentiate acute myeloid leukemia from acute lymphoblastic leukemia and B-cell/T-cell subtypes without any standard histologic information. Although these distinctions could be made by simpler means, it was a significant observation that clinically useful classifications could be made simply by gene expression patterns. In fact, a case of unclear histology by standard criteria was accurately classified by this gene analysis, demonstrating its potential utility beyond standard pathologic methods. This study set the stage for many studies in diverse tumor types.
Gene Expression Profiling in Lung Cancer Human lung adenocarcinoma, the most common subtype of lung cancer, is clinically heterogeneous as stated before, but despite this, all tumors of this subtype, and in fact all subtypes of non–small-cell lung cancer (NSCLC), are currently staged and treated the same. Garber et al,6 Bhattacharjee et al,5 and Beer et al4 attempted to subclassify lung adenocarcinomas based on gene expression pattern alone and studied the correlation of gene expression patterns with clinicopathologic variables in this disease. Garber and colleagues obtained gene expression profiles from 67 human lung cancers, including 41 adenocarcinomas, 16 squamous cell carcinomas, 5 large-cell carcinomas, and 5 smallcell carcinomas using a spotted cDNA microarray containing 17,108 unique genes.6 They were able to divide patients with adenocarcinoma into 3 subgroups based on gene-expression patterns, and found that the observed clinical outcomes were different for these 3 adenocarcinoma subgroups. Key genes involved in this classification included thyroid transcription factor, hepsin, cathepsin L, vascular endothelial growth factor (VEGF) C, and intracellular adhesion molecule–1. Bhattacharjee and colleagues analyzed gene expression profiles of 139 lung adenocarcinomas using an oligonucleotide array with 12,600 unique sequences.5 They showed that hierarchical and probabilistic clustering defined 4 distinct subclasses of primary lung adenocarcinomas, and those with neuroendocrine features were associated with a less favorable survival outcome than the others. Genes that defined the neuroendocrine-cluster adenocarcinomas included dopa decarboxylase, achaete-scute homologue 1, serine protease, and kallikrein 11. In the third study, Beer and colleagues analyzed gene-expression profiles of 86 adenocarcinomas as well as 10 normal lung samples using a 12,600-gene oligonucleotide array.4 They found that groups of genes could distinguish high-risk stage I adenocarcinomas from those with lower risk. This observation was validated using an independent sample of adenocarcinomas that confirmed these high- and lowrisk groups. More than 100 genes were selected according to their statistical criteria, including c-erbB-2 and those that encode VEGF, calcium-binding protein (S100P), cytokeratin (CK) 7 and CK18, and Fas-associated death domain protein. These data sets examining human lung adenocarcinomas allowed observation of significant molecular heterogeneity in this
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subgroup, but different and overlapping conclusions on important subsets and genes were reached for classification and prognostication. Although these results are promising, further work is needed to determine whether gene-expression profiles of lung adenocarcinomas obtained by analysis of microarrays can predict important biologic and clinical features of these tumors, including patient survival and pathologic subclassification. Use of larger cohorts is clearly needed, and a significant effort gathering hundreds of human lung adenocarcinomas is now under way to validate and test these observations in an independent, prospectively collected cohort of patients.
Proteomics Direct evaluation of proteins in tumor cells has many theoretical advantages over study of DNA alterations or RNA expression patterns. Most genotypic changes in cancer are thought to manifest through their expression as proteins, and RNA expression levels are often not tightly correlated with protein expression. The mRNA expression may not detect important posttranslational modifications of proteins, such as proteolytic processing, phosphorylation, or glycosylation, clearly very important processes in determining protein function with clearly documented dysregulation in cancer.8-10 Accordingly, comprehensive analysis of protein expression patterns in tissues, serum, and other human materials might improve our ability to unravel and understand the molecular complexities of human tumors.
Two-Dimensional Gel Electrophoresis The technology most commonly used for proteomic analysis is 2-dimensional (2D) gel electrophoresis (GE), which allows the separation and display of hundreds of proteins from a complex mixture. Two-dimensional GE is still the foundation of most proteomic studies and has the advantage of allowing the separation of relatively large proteins from total cell lysates. This technique relies on Figure 1
Outline of a General Strategy to Perform Proteomic Analysis with 2-Dimensional Gel Electrophoresis Solubilize proteins from cells/tissues 2-Dimensional gel electrophoresis Image analysis of gel Isolation of spots of interest
N-terminal sequencing
Trypsin digestion of proteins in gel Mass spectrometry analysis of tryptic digested fragments Database search for identification of proteins
Kiyoshi Yanagisawa et al the use of large thin gels to separate proteins based on charge and size. Expression of proteins can be assessed by a charge-coupled device camera combining silver-based staining or fluorimetry. To compare 2 cell types directly in the same gel, total protein extracts from normal and tumor cells can be labeled with different fluorescent dyes, mixed, and run on the same high-resolution 2D gel. Differentially expressed proteins of interest can be identified by alterations in the ratios of the signals of the 2 fluors and identified by N-terminal sequencing or mass spectrometry (MS). This proteomic approach is therefore a powerful tool for the study of changes in protein expression inherent to the developing pathophysiology of any 2 cell types or tissues, but is more difficult to apply to the understanding of patterns in hundreds of samples.11,12 Figure 1 shows the overview of current strategy of 2D GE. The use of 2D GE to analyze protein expression profiles in patients with cancer was first reported by Wright in 1974.13 However, this first study described only differences in protein expression patterns, and no specific protein was identified other than albumin. During this early proteomic era, proteins could be identified only after separation either by comigration, specific antibody labeling, or chemical microsequencing. All these approaches were time-consuming and difficult. Since these early studies, progress in genomic sequencing and the availability of Internet databases, as well as the development of MS-based strategies for protein identification, have set the stage for the proteomic era of lung cancer analysis. So far, several reports have been published in which protein profiles of lung cancer were analyzed with 2D GE and molecular markers for lung cancer were described.14-23 Table 1 summarizes the results of several lung cancer proteomic studies. Early studies of resected lung cancer compared to normal fibroblasts and peripheral blood lymphocytes identified proliferating cell nuclear antigen and lamin B as markers associated with lung cancer, as well as unknown polypeptides.19,20,22 Using MS analysis of spots on 2D gels, truncated portions of CK6D and CK8, as well as cathepsins, were identified as markers of tumor proliferation.18 These observations were confirmed and extended to demonstrate that 14 of 21 isoforms of CK7, CK8, CK18, and CK19 are overexpressed in tumor tissue, and 2 isoforms of CK7, 1 of CK8, and 1 of CK19 were associated with survival.23 Specific posttranslational modifications were identified, demonstrating the value of proteomic approaches. Autoantibodies from patients with cancer have also been used to identify potentially important proteins separated by 2D gels of lung cancer proteins, and identified protein gene product 9.5 as a neurospecific polypeptide detectable in serum associated with a subset of patients with lung cancer.17 More sophisticated approaches combining cDNA microarray and 2D gels have recently been developed.10,15,16 These data are a convincing proof of principle, and interesting leads have been uncovered for biomarkers, but progress has been slow in making the transition to the clinic. In addition, the fact that a limited number of differences has been identified by proteomic approaches during the past decade between cancer and normal lung epithelial cells emphasizes the need for the use of novel sensitive and reproducible technology for protein profiling in human lung cancer.
Table 1
Various Protein Markers Identified by 2-Dimensional Gel Electrophoresis/Proteomics in Lung Cancer
Name
Swiss-Prot Identification Number
Function
AOE37213
Q13162
Antioxidant
ATP5D13
P30049
ATP synthase
PDA313
P30101
Oxidation
GSTM413
Q03013
Conjugation of glutathione
PDI13
P07237
Binds thyroid hormone
UBL113,16
P09936
Process ubiquitin
CatD17
P07339
Acid protease
ROC17
P07910
Ribonucleosome assembly
PCNA18,21
P12004
DNA replication
CK722
P08729
Cytokeratins
CK822
P05787
Cytokeratins
CK1822
P05783
Cytokeratins
CK1922
P08727
Cytokeratins
Mass spectrometry was used as method of identification. All proteins listed cause upregulation in lung cancer. Abbreviations: AOE = antioxidant enzyme; ATP = adenosine triphosphate; CatD = cathepsin D; CK = cytokeratin; GSTM = glutathione S-transferase, mu variation; PCNA = proliferating cell nuclear antigen; PDA3 = protein disulfide isomerase A3 precursor; PDI = protein disulfide isomerase; ROC = nuclear ribonucleoproteins c1/c2 (huRNPC1/hnRNPC2); UBL = ubiquitin-like protein
Primary Fingerprinting with Mass Spectrometry Analyzing cell proteins with 2D gels is a technically challenging, time-intensive process that is not readily applicable for rapidly assessing changes in protein expression from hundreds of samples. There is presently a need to develop technologies to improve the accuracy and sensitivity of high-throughput protein profiling. Mass spectrometry represents one technology that is proving its utility in recent studies. A mass spectrometer is a highly accurate and sensitive instrument that can separate proteins of very similar molecular weight (concrete observations can be found later in this article). In simplified terms, a typical instrument ionizes a protein mixture and accelerates the resulting ions across a fixed voltage potential in a high vacuum and measures the amount of time, or time of flight (TOF) required for the ion to hit a detector. This TOF is a direct measure of the mass-to-charge ratio of the ionized molecules obtained from a sample. For a single positively charged molecule, produced from the addition of a proton to a molecule during the ionization process, the molecular weight of the molecule can be accurately measured. Typically, this technology allows us to achieve mass accuracy of approximately 1 in 10,000 Da, far better than any gel system. The 2 types of MS to be discussed herein use a laser to desorb and ionize proteins from a surface and are thus called laser desorption techniques. Some promising data have been published on the power of the so-called surface-enhanced laser desorption/ionization
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Proteomics and Lung Cancer Figure 2 Representative Matrix-Assisted Laser Desorption/ Ionization Time-of-Flight Mass Spectrometry of Lung Tumor Tissue
Figure 3 Detection of Discriminatory Biomarker Sets in Lung Tumors with Matrix-Assisted Laser Desorption/ Ionization Time-of-Flight Mass Spectrometry
3000
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Relative Intensity
Normal Lung
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Mass-to-Charge Ratio
(SELDI) MS. In this technology, the solubilized sample is allowed to adsorb directly onto a variety of proprietary surfaces with different chemical characteristics such as charge or hydrophobicity which preseparates proteins on this basis before being laser-desorbed for MS. The studies using SELDI MS have been able to directly analyze the protein expression pattern of serum and define protein patterns capable of distinguishing patients with cancer from normal controls.24-29 The studies using SELDINS have been able to directly analyze the protein expression pattern of serum as tumor tissue and define protein patterns capable of distinguishing cancer from noncancer, but there are no studies directly using MS to generate proteomic classifications able to distinguish subsets of tumors or predict patient outcomes. We have been investigating the use of matrix-assisted laser desorption/ionization (MALDI) TOF MS for this application. This technology does not require solubilization of tissue samples or any preseparation but can acquire MS images directly from small numbers of cells in single frozen sections of tumors. MALDI TOF MS can not only directly analyze peptides and proteins in unprocessed human tumor tissue sections, but can also be used for high-resolution imaging of individual biomolecules present in tissue sections.30-33 To perform MALDI MS on a tumor sample, a 12-μm frozen tissue section is freeze-dried onto a conductive MALDI plate and spotted with a matrix, usually a low molecular weight crystalline compound such as a cinnamic acid analogue. When dry, the sample is irradiated with a finely focused laser beam from a 377-nm N2 laser. The beam is approximately circular with a diameter of 25 μm and is directed at regions of interest in the tumor based on examination of an adjacent section processed for conventional light microscopy. We have begun using MALDI TOF MS technology to analyze protein expression profiles in a few hundred cells from single frozen sections of human surgically resected lung tumors. The use of MALDI TOF MS for detection and identification of peptides and proteins from biologic tissues has been previously reported by our group.30 A representative spectrum obtained from a surgically resected human NSCLC sample is shown in Figure 2.
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Squamous Cell Lung Cancer
Adenocarcinoma
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* * 3000
5500
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10,500
13,000
Mass-to-Charge Ratio Representative spectra obtained from both tumor and normal lung tissue samples are shown with the molecular weight calculation (mass-to-charge ratios). The asterisks indicate examples of the mass spectrometry peaks identified as discriminatory pattern between normal tissue and tumor. The arrowheads indicate differentially expressed peaks between adenocarcinoma and squamous cell carcinoma.
MALDI Time-of-Flight Mass Spectrometry for Protein-Expression Profiling in Lung Cancer. We analyzed protein expression profiles of 6 tissue samples. These included 3 human chemotherapy-naive and radiation therapy–naive surgically resected lung tumor specimens, including 1 adenocarcinoma, 2 squamous cell carcinomas, and 1 pulmonary metastasis from lung, as well as 2 normal lung samples. Spectra were obtained from an approximately 1-mm diameter region from a single frozen tumor section selected by a lung pathologist using an adjacent hematoxylin and eosin–stained section as a guide. The resulting spectra were aligned and the peaks were correlated using custom software to bin these peaks across spectra obtained from these specimens. Then, we performed statistical analyses to select specific MS signals that were differentially expressed between tumor and normal lung tissue samples as well as among different histologies or different background. Finally, we sought to obtain the identities of these discriminating MS peaks (described later). Using this approach, we are able to analyze > 1000 distinct protein species from less than a nanogram of each tissue sample. Representative examples of MALDI TOF MS spectra from 3 tissue samples (2 primary NSCLCs, 1 adenocarcinoma and 1 squamous carcinoma, and 1 normal lung sample) are shown in Figure 3. Examples of differentially expressed signals between lung cancer and normal lung can be seen in the view of the spectra, and signals are higher in the tumor samples than in the normal lung sample (Figure 3). These peaks are clearly distinguishable from the background noise. We also compared protein expression profiles between an adenocarcinoma and a squa-
Kiyoshi Yanagisawa et al
Identification of Protein Markers in Non–Small-Cell Lung Cancer One disadvantage of most proteomic technologies (eg, 2D GE, SELDI MS, and MALDI MS) is that proteins are detected on the basis of physical characteristics (ie, pI, molecular weight, and massto-charge ratio) and their actual identity is usually initially unknown. Whereas anonymous profiles of proteins might be useful for classification, clues to the biology underlying these processes can be derived from their identification and functional investigation. To begin to identify the proteins that make up these profiles, we prepared an extract of a human surgically resected NSCLC and then fractionated the proteins in the appropriate size range by high-pressure liquid chromatography. Mass spectrometric analysis performed using MALDI TOF MS easily permitted localization of the fraction containing the proteins of interest. For example, we identified very similar molecular weights; however, distinct proteins 4939 ± 1 Da and 4964 ± 1 Da in molecular weight, which were highly expressed in primary NSCLC and not in normal lung, eluted at 30 and 28 minutes in the chromatograph, respectively. Sequence analysis of 4 tryptic peptides using an electrospray quadrupole TOF MS confirmed the identification of the protein as a thymosin-β10 and thymosin-β4, respectively (data not shown).
Discussion During the late 1990s, microarray technology was employed to investigate gene-expression profiles in many different types of human cancer and defined candidate subclasses and prognostic subsets of human lung adenocarcinomas based on gene expression patterns.4-6 However, mRNA expression cannot always indicate which proteins are expressed or how their activity might be modulated after translation.8-10 Accordingly, there is increasing interest in the analysis of the proteome in tumor tissues and other clinical materials to complement genome-based approaches and provide additional information. Parallel development of computational and statistical methods for the analysis of proteomic data is also essential. The available proteomic approaches are yielding new information, but also present new technical challenges as well. For example, there is no protein equivalent of polymerase chain reaction for amplification of low-abundance proteins, so highly sensitive and quantitative techniques capable of detection from one to several million molecules per cell are needed. Currently, a typical 2D GE can reliably separate and detect approximately 600-
Figure 4 Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry Analyses in Human Lung Metastasis
Primary Tumor
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Relative Intensity
mous cell carcinoma. We selected differentially expressed MS signals, and examples of these peaks are also shown in Figure 3. The ability to assess changes in protein expression occurring during tumor progression will also aid in the elucidation of the fundamental mechanisms underlying metastasis in patients. To investigate the potential of MALDI TOF MS analysis to study this process, we analyzed protein profiles from patient-matched primary lung tumor and pulmonary metastasis. Spectra obtained are shown in Figure 4 and reveal that primary tumor–specific and metastasis-specific fingerprints can be identified. Thus, in this individual patient, the protein-expression pattern was distinct between primary tumor and pulmonary metastasis.
3000
* Pulmonary Metastasis
5600
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12,000
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Mass-to-Charge Ratio Representative spectra obtained from primary lung tumor and lung metastasis from 1 patient. Asterisks indicate peaks expressed differently between primary tumor and pulmonary metastasis.
1000 proteins. However, the human genome may contain as many as 40,000 genes, and as a result of mRNA splicing, posttranslational modifications, and proteolysis, the number of distinct protein species is clearly higher than the number of spots detectable on a 2D GE or by any current proteomic technology. It is unlikely that improvements in resolution and capacity of gels will significantly improve on the available technology in this respect, but it appears that significant patterns of even the most abundant proteins could yield useful information. Technology has improved significantly in the identification and biochemical characterization of proteins of interest in 2D GE analysis. Protein identification is now performed by cutting spots from gels, proteolysis treatment, and peptide sequencing by tandem MS, followed by searching of genomic and proteomic databases. Mass spectrometry and advanced bioinformatics have made possible the sensitive identification of spots in 2D GE gels, and have been central to the development of the field of proteomics. Mass spectrometry thus allows us to address multiple challenges in 2D gel analysis: we are now able (1) to lower the protein quantities needed for identification; (2) to characterize posttranslational modifications directly from spots in a gel; and (3) to study noncovalent protein interactions. However, certain technological processes in 2D GE analysis, particularly protein separation and analysis of images, are extremely skill-based and time-consuming and remain difficult to automate. To solve these problems, many complementary technologies are being developed and, either alone or in combination, will undoubtedly play important roles in the proteomics and functional or structural genomics-based approaches in expression profiling or molecular interaction screening. These include pro-
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Proteomics and Lung Cancer tein arrays,34 the yeast 2-hybrid system,35 phage display antibody libraries,36 MALDI TOF MS,30 and biologic activity profiling of families of proteins such as proteases.37 In this review, we described some of our preliminary observations of direct protein expression profiling using MALDI TOF MS. From less than a nanogram of a single section of fresh tumor tissue dried onto a metal plate, a protein fingerprint can be obtained in just a few minutes. The major drawbacks of this approach are its limitation to smaller proteins, generally < 100 kDa, requirement of further identification of proteins of interest, and variability in laser desorption efficiency. Using this technology, we have been able to obtain proteomic patterns that were differentially expressed between lung tumor and normal lung and between primary lung tumor and metastasis. The proteins identified so far, and those that will be identified in the near future, using MS technologies may be useful as novel biomarkers for detection and classification of human lung cancers and lead us to a better understanding of lung cancer biology than might be achieved by RNA analysis alone. It is of note that Garber and colleagues reported that primary tumor and intrapulmonary metastasis from the same patient showed the similar gene expression patterns in unsupervised clustering analysis.6 However, significant differences between primary tumor and intrapulmonary metastasis do exist in the analyses of gene and protein expression profiles. Because such small tissue samples can be used, it would be of great interest to apply this technology to the analyses of protein expression profiles in the tissue samples from needle aspirates or from the different cell subtypes within the lung. Proteomic patterns associated with features accurately obtained by light microscopy, while potentially useful to identify novel biologic targets, may have little clinical utility by themselves. It would be also of great interest to attempt to derive patterns associated with response to specific therapies, smoking exposure, or preneoplasia, and the risk of progression to cancer. Moreover, because drugs, drug products, and drug targets can be directly analyzed in tissue sections, expression profiling directly from fresh tissue may become an essential component of pharmacoproteomics in the future. Thus, the ability to rapidly monitor specific protein expression patterns in tiny amounts of target tissues could be useful for the rapid translation of prognostic and diagnostic finding in clinical practice. If these proteomic patterns are confirmed using newer technology and larger numbers of patients, these findings could have significant implications for the clinical management of patients with NSCLC.
Conclusion Each of these technologies described in this article gives different snapshots of information regarding the complex working of cancer cells and host/tumor interactions, and each may yield complementary information that may someday converge to be of clinical utility. Future developments in the field are likely to be assisted by further technological innovations in MS, robotics, and informatics, as well as software pattern recognition and the ability to identify and characterize proteins of interest. Mass spectrometry–based proteomics promises to enable new insights for
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classifying lung cancers, predicting tumor behavior, studying protein modifications in signaling networks, and identifying targets for the design of new anticancer drugs.
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Relationship between TA01 and TA02 polypeptides associated with lung adenocarcinoma and histocytological features. Br J Cancer 1997; 75:978-985. 21. Schmid HR, Schmitter D, Blum P, et al. Lung tumor cells: a multivariate approach to cell classification using two-dimensional protein pattern. Electrophoresis 1995; 16:1961-1968. 22. Okuzawa K, Franzen B, Lindholm J, et al. Characterization of gene expression in clinical lung cancer materials by two-dimensional polyacrylamide gel electrophoresis. Electrophoresis 1994; 15:382-390. 23. Gharib TG, Chen G, Wang H, et al. Proteomic analysis of cytokeratin isoforms uncovers association with survival in lung adenocarcinoma. Neoplasia 2002; 4:440-448. 24. Petricoin EF III, Ornstein DK, Paweletz CP, et al. Serum proteomic patterns for detection of prostate cancer. J Natl Cancer Inst 2002; 94:1576-1578. 25. Ardekani AM, Liotta LA, Petricoin EF III. Clinical potential of proteomics in the diagnosis of ovarian cancer. 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Profiling proteins from azoxymethaneinduced colon tumors at the molecular level by matrix-assisted laser desorption/ionization mass spectrometry. Proteomics 2001; 1:1320-1326. 32. Chaurand P, Schwartz SA, Caprioli RM. Imaging mass spectrometry: a new tool to investigate the spatial organization of peptides and proteins in mammalian tissue sections. Curr Opin Chem Biol 2002; 6:676-681. 33. Stoeckli M, Chaurand P, Hallahan DE, et al. Imaging mass spectrometry: a new technology for the analysis of protein expression in mammalian tissues. Nat Med 2001; 7:493-496. 34. Walter G, Bussow K, Cahill D, et al. Protein arrays for gene expression and molecular interaction screening. Curr Opin Microbiol 2000; 3:298-302. 35. Uetz P, Giot L, Cagney G, et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 2000; 403:623-627. 36. Griffiths AD, Duncan AR. Strategies for selection of antibodies by phage display. Curr Opin Biotechnol 1998; 9:102-108. 37. 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Key words: Mass spectrometry, Microarray, Proliferating cell nuclear antigen, Proteomics, Thyroid transcription factor, Vascular endothelial growth factor
Introduction
quately understood through the analysis of individual genes or small numbers of genes. The complementary DNA (cDNA) microarray analysis is beginning to be employed with some success to simultaneously investigate thousands of RNA expression levels and identify patterns associated with biology.4-6
Lung cancer represents a challenging global clinical problem and is the leading cause of cancer death in the United States for both men and women, with an estimated 171,900 new cases and 157,200 deaths in 2003.1 Lung cancer also displays a tremendous biologic variability in metastatic potential, local invasive phenotype, and response to therapy. Only a small fraction of cases are cured, and median survival is short. Some patients will die of metastatic disease and some will die of only local disease. Some will respond to chemotherapy or molecularly targeted therapy and some will not. Despite complex aggressive approaches to therapy and great strides in understanding its biology and etiology, corresponding improvements in outcome are not yet apparent.2,3 Therefore, improved understanding of the molecular basis of lung cancer behavior is an extremely urgent health problem. After decades of research and thousands of studies, it is now obvious that tumor behavior cannot be ade1Vanderbilt-Ingram
Cancer Center and Department of Medicine Spectrometry Research Center 3Department of Biochemistry Vanderbilt University School of Medicine, Nashville, TN 2Mass
Submitted: Apr 21, 2003; Revised: Jun 2, 2003; Accepted: Jun 17, 2003 Address for correspondence: David P. Carbone, MD, PhD, VanderbiltIngram Cancer Center, 648 MRB II, Vanderbilt University School of Medicine, Nashville, TN 37232 Fax: 615-936-3322; e-mail: [email protected]
Microarray Technology Gene expression profiling using microarrays has great potential as a systematic approach for discovering new classes of tumors or assigning known tumors to classes that predict outcome or response to therapy. A DNA array consists of rows and columns of oligonucleotides or cDNAs on a miniature silicon chip or glass slide. Oligonucleotides can be synthesized in situ on the array, and are comprised of short fragments of DNA complementary to known messenger RNAs (mRNAs) of interest. Alternatively, cDNA arrays can be made containing immobilized sequences that are much longer than the corresponding oligonucleotide. Arrays can consist of 20,000 or more oligonucleotides or cDNAs. In the microarray analysis, RNA is extracted from tumor or normal tissues and labeled with a fluorescence dye with reverse transcriptase. The labeled cDNA is hybridized to the oligonucleotide or cDNA array in the absence or presence of a reference RNA, respectively. Groups of genes can be identified by comparing the expression levels of each gene (intensity of fluorescence), allowing groups of tumors to be identified by comparing similar patterns of gene expression.
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Proteomics and Lung Cancer The first report to show that the expression patterns of diverse classes of genes contained information that could be used to classify tumors was described by Golub et al, who analyzed a group of acute leukemias.7 Based on gene expression patterns, it was possible to differentiate acute myeloid leukemia from acute lymphoblastic leukemia and B-cell/T-cell subtypes without any standard histologic information. Although these distinctions could be made by simpler means, it was a significant observation that clinically useful classifications could be made simply by gene expression patterns. In fact, a case of unclear histology by standard criteria was accurately classified by this gene analysis, demonstrating its potential utility beyond standard pathologic methods. This study set the stage for many studies in diverse tumor types.
Gene Expression Profiling in Lung Cancer Human lung adenocarcinoma, the most common subtype of lung cancer, is clinically heterogeneous as stated before, but despite this, all tumors of this subtype, and in fact all subtypes of non–small-cell lung cancer (NSCLC), are currently staged and treated the same. Garber et al,6 Bhattacharjee et al,5 and Beer et al4 attempted to subclassify lung adenocarcinomas based on gene expression pattern alone and studied the correlation of gene expression patterns with clinicopathologic variables in this disease. Garber and colleagues obtained gene expression profiles from 67 human lung cancers, including 41 adenocarcinomas, 16 squamous cell carcinomas, 5 large-cell carcinomas, and 5 smallcell carcinomas using a spotted cDNA microarray containing 17,108 unique genes.6 They were able to divide patients with adenocarcinoma into 3 subgroups based on gene-expression patterns, and found that the observed clinical outcomes were different for these 3 adenocarcinoma subgroups. Key genes involved in this classification included thyroid transcription factor, hepsin, cathepsin L, vascular endothelial growth factor (VEGF) C, and intracellular adhesion molecule–1. Bhattacharjee and colleagues analyzed gene expression profiles of 139 lung adenocarcinomas using an oligonucleotide array with 12,600 unique sequences.5 They showed that hierarchical and probabilistic clustering defined 4 distinct subclasses of primary lung adenocarcinomas, and those with neuroendocrine features were associated with a less favorable survival outcome than the others. Genes that defined the neuroendocrine-cluster adenocarcinomas included dopa decarboxylase, achaete-scute homologue 1, serine protease, and kallikrein 11. In the third study, Beer and colleagues analyzed gene-expression profiles of 86 adenocarcinomas as well as 10 normal lung samples using a 12,600-gene oligonucleotide array.4 They found that groups of genes could distinguish high-risk stage I adenocarcinomas from those with lower risk. This observation was validated using an independent sample of adenocarcinomas that confirmed these high- and lowrisk groups. More than 100 genes were selected according to their statistical criteria, including c-erbB-2 and those that encode VEGF, calcium-binding protein (S100P), cytokeratin (CK) 7 and CK18, and Fas-associated death domain protein. These data sets examining human lung adenocarcinomas allowed observation of significant molecular heterogeneity in this
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subgroup, but different and overlapping conclusions on important subsets and genes were reached for classification and prognostication. Although these results are promising, further work is needed to determine whether gene-expression profiles of lung adenocarcinomas obtained by analysis of microarrays can predict important biologic and clinical features of these tumors, including patient survival and pathologic subclassification. Use of larger cohorts is clearly needed, and a significant effort gathering hundreds of human lung adenocarcinomas is now under way to validate and test these observations in an independent, prospectively collected cohort of patients.
Proteomics Direct evaluation of proteins in tumor cells has many theoretical advantages over study of DNA alterations or RNA expression patterns. Most genotypic changes in cancer are thought to manifest through their expression as proteins, and RNA expression levels are often not tightly correlated with protein expression. The mRNA expression may not detect important posttranslational modifications of proteins, such as proteolytic processing, phosphorylation, or glycosylation, clearly very important processes in determining protein function with clearly documented dysregulation in cancer.8-10 Accordingly, comprehensive analysis of protein expression patterns in tissues, serum, and other human materials might improve our ability to unravel and understand the molecular complexities of human tumors.
Two-Dimensional Gel Electrophoresis The technology most commonly used for proteomic analysis is 2-dimensional (2D) gel electrophoresis (GE), which allows the separation and display of hundreds of proteins from a complex mixture. Two-dimensional GE is still the foundation of most proteomic studies and has the advantage of allowing the separation of relatively large proteins from total cell lysates. This technique relies on Figure 1
Outline of a General Strategy to Perform Proteomic Analysis with 2-Dimensional Gel Electrophoresis Solubilize proteins from cells/tissues 2-Dimensional gel electrophoresis Image analysis of gel Isolation of spots of interest
N-terminal sequencing
Trypsin digestion of proteins in gel Mass spectrometry analysis of tryptic digested fragments Database search for identification of proteins
Kiyoshi Yanagisawa et al the use of large thin gels to separate proteins based on charge and size. Expression of proteins can be assessed by a charge-coupled device camera combining silver-based staining or fluorimetry. To compare 2 cell types directly in the same gel, total protein extracts from normal and tumor cells can be labeled with different fluorescent dyes, mixed, and run on the same high-resolution 2D gel. Differentially expressed proteins of interest can be identified by alterations in the ratios of the signals of the 2 fluors and identified by N-terminal sequencing or mass spectrometry (MS). This proteomic approach is therefore a powerful tool for the study of changes in protein expression inherent to the developing pathophysiology of any 2 cell types or tissues, but is more difficult to apply to the understanding of patterns in hundreds of samples.11,12 Figure 1 shows the overview of current strategy of 2D GE. The use of 2D GE to analyze protein expression profiles in patients with cancer was first reported by Wright in 1974.13 However, this first study described only differences in protein expression patterns, and no specific protein was identified other than albumin. During this early proteomic era, proteins could be identified only after separation either by comigration, specific antibody labeling, or chemical microsequencing. All these approaches were time-consuming and difficult. Since these early studies, progress in genomic sequencing and the availability of Internet databases, as well as the development of MS-based strategies for protein identification, have set the stage for the proteomic era of lung cancer analysis. So far, several reports have been published in which protein profiles of lung cancer were analyzed with 2D GE and molecular markers for lung cancer were described.14-23 Table 1 summarizes the results of several lung cancer proteomic studies. Early studies of resected lung cancer compared to normal fibroblasts and peripheral blood lymphocytes identified proliferating cell nuclear antigen and lamin B as markers associated with lung cancer, as well as unknown polypeptides.19,20,22 Using MS analysis of spots on 2D gels, truncated portions of CK6D and CK8, as well as cathepsins, were identified as markers of tumor proliferation.18 These observations were confirmed and extended to demonstrate that 14 of 21 isoforms of CK7, CK8, CK18, and CK19 are overexpressed in tumor tissue, and 2 isoforms of CK7, 1 of CK8, and 1 of CK19 were associated with survival.23 Specific posttranslational modifications were identified, demonstrating the value of proteomic approaches. Autoantibodies from patients with cancer have also been used to identify potentially important proteins separated by 2D gels of lung cancer proteins, and identified protein gene product 9.5 as a neurospecific polypeptide detectable in serum associated with a subset of patients with lung cancer.17 More sophisticated approaches combining cDNA microarray and 2D gels have recently been developed.10,15,16 These data are a convincing proof of principle, and interesting leads have been uncovered for biomarkers, but progress has been slow in making the transition to the clinic. In addition, the fact that a limited number of differences has been identified by proteomic approaches during the past decade between cancer and normal lung epithelial cells emphasizes the need for the use of novel sensitive and reproducible technology for protein profiling in human lung cancer.
Table 1
Various Protein Markers Identified by 2-Dimensional Gel Electrophoresis/Proteomics in Lung Cancer
Name
Swiss-Prot Identification Number
Function
AOE37213
Q13162
Antioxidant
ATP5D13
P30049
ATP synthase
PDA313
P30101
Oxidation
GSTM413
Q03013
Conjugation of glutathione
PDI13
P07237
Binds thyroid hormone
UBL113,16
P09936
Process ubiquitin
CatD17
P07339
Acid protease
ROC17
P07910
Ribonucleosome assembly
PCNA18,21
P12004
DNA replication
CK722
P08729
Cytokeratins
CK822
P05787
Cytokeratins
CK1822
P05783
Cytokeratins
CK1922
P08727
Cytokeratins
Mass spectrometry was used as method of identification. All proteins listed cause upregulation in lung cancer. Abbreviations: AOE = antioxidant enzyme; ATP = adenosine triphosphate; CatD = cathepsin D; CK = cytokeratin; GSTM = glutathione S-transferase, mu variation; PCNA = proliferating cell nuclear antigen; PDA3 = protein disulfide isomerase A3 precursor; PDI = protein disulfide isomerase; ROC = nuclear ribonucleoproteins c1/c2 (huRNPC1/hnRNPC2); UBL = ubiquitin-like protein
Primary Fingerprinting with Mass Spectrometry Analyzing cell proteins with 2D gels is a technically challenging, time-intensive process that is not readily applicable for rapidly assessing changes in protein expression from hundreds of samples. There is presently a need to develop technologies to improve the accuracy and sensitivity of high-throughput protein profiling. Mass spectrometry represents one technology that is proving its utility in recent studies. A mass spectrometer is a highly accurate and sensitive instrument that can separate proteins of very similar molecular weight (concrete observations can be found later in this article). In simplified terms, a typical instrument ionizes a protein mixture and accelerates the resulting ions across a fixed voltage potential in a high vacuum and measures the amount of time, or time of flight (TOF) required for the ion to hit a detector. This TOF is a direct measure of the mass-to-charge ratio of the ionized molecules obtained from a sample. For a single positively charged molecule, produced from the addition of a proton to a molecule during the ionization process, the molecular weight of the molecule can be accurately measured. Typically, this technology allows us to achieve mass accuracy of approximately 1 in 10,000 Da, far better than any gel system. The 2 types of MS to be discussed herein use a laser to desorb and ionize proteins from a surface and are thus called laser desorption techniques. Some promising data have been published on the power of the so-called surface-enhanced laser desorption/ionization
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Proteomics and Lung Cancer Figure 2 Representative Matrix-Assisted Laser Desorption/ Ionization Time-of-Flight Mass Spectrometry of Lung Tumor Tissue
Figure 3 Detection of Discriminatory Biomarker Sets in Lung Tumors with Matrix-Assisted Laser Desorption/ Ionization Time-of-Flight Mass Spectrometry
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Relative Intensity
Normal Lung
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(SELDI) MS. In this technology, the solubilized sample is allowed to adsorb directly onto a variety of proprietary surfaces with different chemical characteristics such as charge or hydrophobicity which preseparates proteins on this basis before being laser-desorbed for MS. The studies using SELDI MS have been able to directly analyze the protein expression pattern of serum and define protein patterns capable of distinguishing patients with cancer from normal controls.24-29 The studies using SELDINS have been able to directly analyze the protein expression pattern of serum as tumor tissue and define protein patterns capable of distinguishing cancer from noncancer, but there are no studies directly using MS to generate proteomic classifications able to distinguish subsets of tumors or predict patient outcomes. We have been investigating the use of matrix-assisted laser desorption/ionization (MALDI) TOF MS for this application. This technology does not require solubilization of tissue samples or any preseparation but can acquire MS images directly from small numbers of cells in single frozen sections of tumors. MALDI TOF MS can not only directly analyze peptides and proteins in unprocessed human tumor tissue sections, but can also be used for high-resolution imaging of individual biomolecules present in tissue sections.30-33 To perform MALDI MS on a tumor sample, a 12-μm frozen tissue section is freeze-dried onto a conductive MALDI plate and spotted with a matrix, usually a low molecular weight crystalline compound such as a cinnamic acid analogue. When dry, the sample is irradiated with a finely focused laser beam from a 377-nm N2 laser. The beam is approximately circular with a diameter of 25 μm and is directed at regions of interest in the tumor based on examination of an adjacent section processed for conventional light microscopy. We have begun using MALDI TOF MS technology to analyze protein expression profiles in a few hundred cells from single frozen sections of human surgically resected lung tumors. The use of MALDI TOF MS for detection and identification of peptides and proteins from biologic tissues has been previously reported by our group.30 A representative spectrum obtained from a surgically resected human NSCLC sample is shown in Figure 2.
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Squamous Cell Lung Cancer
Adenocarcinoma
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Mass-to-Charge Ratio Representative spectra obtained from both tumor and normal lung tissue samples are shown with the molecular weight calculation (mass-to-charge ratios). The asterisks indicate examples of the mass spectrometry peaks identified as discriminatory pattern between normal tissue and tumor. The arrowheads indicate differentially expressed peaks between adenocarcinoma and squamous cell carcinoma.
MALDI Time-of-Flight Mass Spectrometry for Protein-Expression Profiling in Lung Cancer. We analyzed protein expression profiles of 6 tissue samples. These included 3 human chemotherapy-naive and radiation therapy–naive surgically resected lung tumor specimens, including 1 adenocarcinoma, 2 squamous cell carcinomas, and 1 pulmonary metastasis from lung, as well as 2 normal lung samples. Spectra were obtained from an approximately 1-mm diameter region from a single frozen tumor section selected by a lung pathologist using an adjacent hematoxylin and eosin–stained section as a guide. The resulting spectra were aligned and the peaks were correlated using custom software to bin these peaks across spectra obtained from these specimens. Then, we performed statistical analyses to select specific MS signals that were differentially expressed between tumor and normal lung tissue samples as well as among different histologies or different background. Finally, we sought to obtain the identities of these discriminating MS peaks (described later). Using this approach, we are able to analyze > 1000 distinct protein species from less than a nanogram of each tissue sample. Representative examples of MALDI TOF MS spectra from 3 tissue samples (2 primary NSCLCs, 1 adenocarcinoma and 1 squamous carcinoma, and 1 normal lung sample) are shown in Figure 3. Examples of differentially expressed signals between lung cancer and normal lung can be seen in the view of the spectra, and signals are higher in the tumor samples than in the normal lung sample (Figure 3). These peaks are clearly distinguishable from the background noise. We also compared protein expression profiles between an adenocarcinoma and a squa-
Kiyoshi Yanagisawa et al
Identification of Protein Markers in Non–Small-Cell Lung Cancer One disadvantage of most proteomic technologies (eg, 2D GE, SELDI MS, and MALDI MS) is that proteins are detected on the basis of physical characteristics (ie, pI, molecular weight, and massto-charge ratio) and their actual identity is usually initially unknown. Whereas anonymous profiles of proteins might be useful for classification, clues to the biology underlying these processes can be derived from their identification and functional investigation. To begin to identify the proteins that make up these profiles, we prepared an extract of a human surgically resected NSCLC and then fractionated the proteins in the appropriate size range by high-pressure liquid chromatography. Mass spectrometric analysis performed using MALDI TOF MS easily permitted localization of the fraction containing the proteins of interest. For example, we identified very similar molecular weights; however, distinct proteins 4939 ± 1 Da and 4964 ± 1 Da in molecular weight, which were highly expressed in primary NSCLC and not in normal lung, eluted at 30 and 28 minutes in the chromatograph, respectively. Sequence analysis of 4 tryptic peptides using an electrospray quadrupole TOF MS confirmed the identification of the protein as a thymosin-β10 and thymosin-β4, respectively (data not shown).
Discussion During the late 1990s, microarray technology was employed to investigate gene-expression profiles in many different types of human cancer and defined candidate subclasses and prognostic subsets of human lung adenocarcinomas based on gene expression patterns.4-6 However, mRNA expression cannot always indicate which proteins are expressed or how their activity might be modulated after translation.8-10 Accordingly, there is increasing interest in the analysis of the proteome in tumor tissues and other clinical materials to complement genome-based approaches and provide additional information. Parallel development of computational and statistical methods for the analysis of proteomic data is also essential. The available proteomic approaches are yielding new information, but also present new technical challenges as well. For example, there is no protein equivalent of polymerase chain reaction for amplification of low-abundance proteins, so highly sensitive and quantitative techniques capable of detection from one to several million molecules per cell are needed. Currently, a typical 2D GE can reliably separate and detect approximately 600-
Figure 4 Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry Analyses in Human Lung Metastasis
Primary Tumor
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mous cell carcinoma. We selected differentially expressed MS signals, and examples of these peaks are also shown in Figure 3. The ability to assess changes in protein expression occurring during tumor progression will also aid in the elucidation of the fundamental mechanisms underlying metastasis in patients. To investigate the potential of MALDI TOF MS analysis to study this process, we analyzed protein profiles from patient-matched primary lung tumor and pulmonary metastasis. Spectra obtained are shown in Figure 4 and reveal that primary tumor–specific and metastasis-specific fingerprints can be identified. Thus, in this individual patient, the protein-expression pattern was distinct between primary tumor and pulmonary metastasis.
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Mass-to-Charge Ratio Representative spectra obtained from primary lung tumor and lung metastasis from 1 patient. Asterisks indicate peaks expressed differently between primary tumor and pulmonary metastasis.
1000 proteins. However, the human genome may contain as many as 40,000 genes, and as a result of mRNA splicing, posttranslational modifications, and proteolysis, the number of distinct protein species is clearly higher than the number of spots detectable on a 2D GE or by any current proteomic technology. It is unlikely that improvements in resolution and capacity of gels will significantly improve on the available technology in this respect, but it appears that significant patterns of even the most abundant proteins could yield useful information. Technology has improved significantly in the identification and biochemical characterization of proteins of interest in 2D GE analysis. Protein identification is now performed by cutting spots from gels, proteolysis treatment, and peptide sequencing by tandem MS, followed by searching of genomic and proteomic databases. Mass spectrometry and advanced bioinformatics have made possible the sensitive identification of spots in 2D GE gels, and have been central to the development of the field of proteomics. Mass spectrometry thus allows us to address multiple challenges in 2D gel analysis: we are now able (1) to lower the protein quantities needed for identification; (2) to characterize posttranslational modifications directly from spots in a gel; and (3) to study noncovalent protein interactions. However, certain technological processes in 2D GE analysis, particularly protein separation and analysis of images, are extremely skill-based and time-consuming and remain difficult to automate. To solve these problems, many complementary technologies are being developed and, either alone or in combination, will undoubtedly play important roles in the proteomics and functional or structural genomics-based approaches in expression profiling or molecular interaction screening. These include pro-
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Proteomics and Lung Cancer tein arrays,34 the yeast 2-hybrid system,35 phage display antibody libraries,36 MALDI TOF MS,30 and biologic activity profiling of families of proteins such as proteases.37 In this review, we described some of our preliminary observations of direct protein expression profiling using MALDI TOF MS. From less than a nanogram of a single section of fresh tumor tissue dried onto a metal plate, a protein fingerprint can be obtained in just a few minutes. The major drawbacks of this approach are its limitation to smaller proteins, generally < 100 kDa, requirement of further identification of proteins of interest, and variability in laser desorption efficiency. Using this technology, we have been able to obtain proteomic patterns that were differentially expressed between lung tumor and normal lung and between primary lung tumor and metastasis. The proteins identified so far, and those that will be identified in the near future, using MS technologies may be useful as novel biomarkers for detection and classification of human lung cancers and lead us to a better understanding of lung cancer biology than might be achieved by RNA analysis alone. It is of note that Garber and colleagues reported that primary tumor and intrapulmonary metastasis from the same patient showed the similar gene expression patterns in unsupervised clustering analysis.6 However, significant differences between primary tumor and intrapulmonary metastasis do exist in the analyses of gene and protein expression profiles. Because such small tissue samples can be used, it would be of great interest to apply this technology to the analyses of protein expression profiles in the tissue samples from needle aspirates or from the different cell subtypes within the lung. Proteomic patterns associated with features accurately obtained by light microscopy, while potentially useful to identify novel biologic targets, may have little clinical utility by themselves. It would be also of great interest to attempt to derive patterns associated with response to specific therapies, smoking exposure, or preneoplasia, and the risk of progression to cancer. Moreover, because drugs, drug products, and drug targets can be directly analyzed in tissue sections, expression profiling directly from fresh tissue may become an essential component of pharmacoproteomics in the future. Thus, the ability to rapidly monitor specific protein expression patterns in tiny amounts of target tissues could be useful for the rapid translation of prognostic and diagnostic finding in clinical practice. If these proteomic patterns are confirmed using newer technology and larger numbers of patients, these findings could have significant implications for the clinical management of patients with NSCLC.
Conclusion Each of these technologies described in this article gives different snapshots of information regarding the complex working of cancer cells and host/tumor interactions, and each may yield complementary information that may someday converge to be of clinical utility. Future developments in the field are likely to be assisted by further technological innovations in MS, robotics, and informatics, as well as software pattern recognition and the ability to identify and characterize proteins of interest. Mass spectrometry–based proteomics promises to enable new insights for
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classifying lung cancers, predicting tumor behavior, studying protein modifications in signaling networks, and identifying targets for the design of new anticancer drugs.
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