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An Overview of Lung Cancer Genomics and Proteomics

Cancer Therapeutics Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland; and George Washington University Genetics Program, Washington, DC

Correspondence and requests for reprints should be addressed to Phillip A. Dennis, National Cancer Institute, Building 8, Room 5101, 8901 Wisconsin Ave., Bethesda, MD 20889. E-mail: pdennis{at}nih.gov

	Abstract

Top Abstract Introduction THE OMICS OF LUNG... DISCUSSION References

 
Lung cancer is the cause of nearly 170,000 cancer deaths in the United States each year, accounting for nearly 25% of all deaths from cancer. The 5-yr survival rate for lung cancer is < 15% from the time of diagnosis. This is largely due to the late stage of diagnosis and the lack of effective treatments, reflecting the need for a better understanding of the mechanisms that underlie lung carcinogenesis. Unlike the study of a single gene, protein, or pathway, genomic and proteomic technologies enable a systematic overview that provides the potential to improve our understanding of this disease. Ultimately, this could improve the diagnosis, prognosis, and clinical management of patients with lung cancer. Here, we review studies that generated profiles of gene and protein expression in lung cancer specimens and relevant model systems, and make recommendations to facilitate the clinical application of these technologies.

Key Words: lung cancer • genomics • proteomics

	Introduction

Top Abstract Introduction THE OMICS OF LUNG... DISCUSSION References

 
Lung cancer accounts for 30% of cancer deaths in the United States every year, and it is the second most frequently diagnosed cancer in men and women (1). Among men the number of lung cancer deaths nearly triples the number of prostate cancer deaths, and among women, the number of lung cancer deaths is almost double the number of breast cancer deaths. Eighty percent of lung cancer cases are non–small cell lung cancer (NSCLC) and 20% are small cell lung cancer (SCLC). Regardless of subtype, the 5-yr survival rate for lung cancer is among the lowest of all cancers at 10–15% (1, 2). Although more than half of lung cancers are diagnosed at a late stage, when cure is unlikely, the survival of patients diagnosed with Stage I lung cancer is also surprisingly low (2). Thus, there is a great need to understand the molecular alterations that confer a poor prognosis and to use this information to improve diagnosis and patient management.

Genomics and proteomics were conceived to allow for the rapid, complete, and parallel analysis of the genes and proteins that are expressed in a given cell or tissue type. In the context of cancer, differential profiling can be used to determine if the genomic and/or proteomic profile of a set of cancers differs from a set of normal tissues or from one another. In addition, gene and protein expression profiles have potential to improve the clinical management of lung cancer by improving classification either via class prediction or class discovery, or by providing data to develop a diagnostic classifier. Gene and protein expression profiles could identify new molecular targets and improve patient care through the identification of profiles that predict responsiveness to therapy or prognosis.

The past decade has been marked by the rapid development of technologies for the high-throughput analysis of gene and protein expression. Microarray technology was initially described for the analysis of cDNAs by Schena and colleagues in 1995 (3), and was applied to the analysis of oligonucleotides in 1997 (4, 5) (Figure 1). It wasn't until 2001 that microarray technology was first applied to the global analysis of a large number of histologically diverse lung cancer specimens (6). The tools of proteomics, namely two-dimensional gel electrophoresis and mass spectrometry, have been developing over the past three decades, but it was not until 1995 that high-throughput analysis became possible due to the ability to use mass spectrometry for the quantitative analysis of complex mixtures (7, 8). The development of mass spectrometry was paralleled by the establishment of databases for the identification of peptides in such mixtures (9–13) (reviewed in Refs. 14 and 15). The first proteomic study of lung cancer was published in 2002 and has been followed by three additional studies in the past 2 yr. In contrast, several dozen microarray studies of lung cancer have been published since 1999. Although there are many platforms for the parallel analysis of gene expression, DNA microarrays are the most universally applied to the study of clinical lung cancer specimens, and are therefore the only platform included in this review. In addition, there are several approaches to analyzing the volumes of data that a single microarray experiment can generate, including hierarchical clustering, principal component analysis and training-testing, and nearest neighbor analysis. More detailed discussion of these methods are beyond the scope of this review, but the interested reader can refer to other publications on the topic (16–18).

View larger version (49K): [in this window] [in a new window]   Figure 1. The development of genomics and proteomics and their application to the analysis of lung cancer. A timeline of the key advances in the development of microarray-based genomic and mass spectrometry-based proteomic technologies is shown on the left. The application of these technologies to the analysis of lung cancer specimens is shown on the right.

	THE OMICS OF LUNG CANCER

Top Abstract Introduction THE OMICS OF LUNG... DISCUSSION References

Classification of Tumors: Class Prediction and Class Discovery
Studies that used microarray analysis to classify NSCLC specimens based on gene or protein expression profiles are shown in Table 1 (6, 19–32). In many of these studies, a class discovery approach showed that gene expression profiles could not only group tumor samples consistent with classical histology, but also could identify subgroups within histologic subclasses. Moreover, many studies showed that profiles generated from one set of samples could then be applied to a test set of tumors to identify the histologic subclass in a class prediction approach. A recent retrospective analysis of the gene expression profiles generated by the initial studies of Bhattacharjee and coworkers (6), Beer and colleagues (31), and Garber and associates (25) showed that statistical methods could predict a gene-set that defines histology and survival (33). Despite these encouraging results, inconsistency in the number of classes observed has limited the clinical application of these findings.

Genomic analysis has already provided clarification regarding important diagnostic distinctions. These include the identification of the cell of origin for SCLC (34), the distinction of histologically similar tumors such as malignant pleural mesothelioma (MPM) and AC (35), and the discrimination of primary from metastatic lung cancer (6, 27, 28).

The first lung cancer microarray study focused on understanding the cell of origin for SCLC (34). Morphologic characteristics and molecular markers suggest that SCLC originates from a neuroendocrine progenitor. However, the clinical course of SCLC is not like pure neuroendocrine carcinoid tumors, and a microarray analysis of SCLC specimens by Anbazhagan and coworkers provides support for the hypothesis that SCLC arises from lung epithelium (34). The authors assert that reclassification of these tumors as epithelial-derived tumors based on molecular changes rather than classification based on morphologic characteristics would be more accurate (34). Such a reclassification would be consistent with the exposure and transformation of lung epithelial cells by smoking, and the observation that SCLC is almost never observed in non-smokers. In contrast, other groups did identify several markers of neuroendocrine cells that were upregulated in SCLC specimens when the gene expression profiles of SCLC versus NSCLC tumors were compared (6, 25). It is not clear whether the differences between these studies are artifacts due to differences in the method of analysis (i.e., comparison of SCLC to normal bronchial epithelium and pulmonary carcinoids in the study by Anbazhagen and colleagues study versus comparison of SCLC to NSCLC in the other studies), or reflective of the small number of SCLC specimens in the studies by Bhattacharjee and associates (6) and Garber and coworkers (25). Because of the lower prevalence of SCLC, fewer genomic analyses have been performed on this type of lung cancer. Analysis of a larger number of SCLC specimens might better classify these tumors as derived from epithelial or neuroendocrine tissues.

Regarding differentiation between tumors with similar histology, Gordon and colleagues showed that MPM and AC can be accurately distinguished 99% of the time using a set of three gene pairs that were inversely correlated with MPM and AC (35). Using specific gene pair ratios may be more clinically applicable because it could be done with little sample preparation and manipulation, and because it does not require the use of a reference sample.

Since the publication of Gordon and associates (35), two other studies have suggested that the expression profiles of smaller groups of genes could be used as a classifier to discriminate between the histologic types of lung cancer and to distinguish primary from metastatic lung cancer (27, 28). Yamagata and colleagues (27) showed that a classifier based on sets of gene expression values could be used on blinded samples to differentiate NSCLC from normal lung, as well as primary lung tumors from lung metastases from tumors originating at other sites. However, this classifier was less accurate at differentiating between subtypes of NSCLC in a blinded analysis—AC was correctly identified, but 1 of 4 blinded SCC were incorrectly classified and large cell carcinoma (LCC) could not be differentiated from other histologic subtypes of NSCLC (27). These limitations may partially reflect the small size of the study. Proteomic studies have also used a subset of markers to discern pathologic differences. Yanagisawa and coworkers (28) showed that a set of 82 MS peaks could discriminate between tumor and normal tissue in the training set with 100% accuracy. When applied to the blinded test set, 42/43 samples were correctly classified as tumor or normal (28). Collectively, these studies show that a small number of genes or mass spectral peaks combined in a formal classifier have the potential to further define subsets of lung cancer.

Correlation of Gene and Protein Expression Profiles with Prognosis
Microarray analysis has the potential to predict the survival of patients with NSCLC. For example, although cluster analysis by Beer and colleagues did not perfectly segregate stage I from stage III lung AC, the authors point out that the stage I tumors that clustered with stage III samples came from patients that exhibited worse survival (31). This study shows that the expression of genes that confer a poor prognosis is independent of stage of disease at the time of diagnosis. Because microarray analysis can identify tumor subsets that share molecular alterations important for cancer progression, incorporating gene expression profiles might provide added therapeutic and prognostic value when combined with traditional staging and histologic analysis.

Other studies have also correlated gene or protein expression profiles with prognosis (6, 21, 25, 27–32, 36–39). In these studies, subsets of genes or protein peaks differentially expressed in tumors could predict survival differences among patients with AC. In an extension of their study on the use of multiple gene pairs to classify lung cancer based on histology, Gordon and associates found that sets of three gene expression ratios could accurately predict prognosis more than 90% of the time (39). The authors then used the most predictive gene pairs to analyze data from the earlier study of Bhattacharjee and coworkers (6). In this analysis, the set of gene expression ratios could correctly predict survival of patients with stage I AC tumors 64% of the time in low-tumor-content samples, and 74% of the time for high-tumor-content samples (39).

The development of prognostic identifiers could guide clinical decision-making, but such application requires validation in prospective clinical trials. The inclusion of genomic and proteomic analyses into future clinical trials, and the analysis of specimens from lung cancer patients receiving standard of care therapy could provide information that might ultimately lead to more accurate, individualized clinical decisions.

Correlation of Gene Expression Profiles with Smoking Status and Lung Cancer
Smoking accounts for 85–90% of lung cancer cases. However, the remaining cases occur among never-smokers. This raises the hypothesis that lung cancers in smokers and never-smokers might arise through different mechanisms. Support for this hypothesis is manifest by the selective expression of EGFR mutations in NSCLC specimens from never-smokers (40, 41).

Three groups have applied technologies for tumor profiling to dissect the molecular mechanisms underlying smoking-induced and smoking-independent lung cancer (Table 1; Refs. 32, 42, 43). In the study by Miura and colleagues, laser-capture microdissection was used to isolate tumor tissue before analysis (32). The analysis revealed 45 genes that were differentially expressed in the tumors of smokers versus nonsmokers. Powell and associates (42) compared the gene expression profiles of tumor and matched normal tissues for six smokers and six nonsmokers. Hierarchical clustering using all data did not separate tumors from smokers versus nonsmokers, but it did separate tumor and nontumor tissue and grouped the nontumor tissues from nonsmokers together. Otherwise, gene expression profiles did not differentiate between each of the groups of tissues unless the analysis was confined to data from genes with the most divergent expression between the groups. This implies that although the underlying "normal" tissues from smokers versus nonsmokers are quite different, their cancers cannot be distinguished using microarray technology. Although similar mechanisms may mediate the development of lung cancer in smokers and nonsmokers alike, the selective expression of EGFR mutations in nonsmokers raises the possibility that differences between smokers and nonsmokers may be most apparent by identifying mutations in relevant oncogenes and tumor suppressor genes. Alternatively, the similarity of tumors from smokers and nonsmokers may simply reflect the small size of this study and illustrates the need for further investigation of this issue. In the third study correlating changes in gene expression with smoking, Spira and colleagues compared the gene expression profiles of bronchial epithelial cells isolated from current, former, and never-smokers (43). Their analysis showed that several gene expression changes that are observed in current smokers persist in former smokers. This is important because over 50% of lung cancer cases are diagnosed in former smokers, and these persistent changes in gene expression may underlie the increased risk of these patients.

Identification of Novel Molecular Targets by Gene and Protein Expression Profiling
Genomic and proteomic technologies produce a wealth of data that can be mined for specific mechanistic information, in addition to their usefulness in detecting patterns. Many studies have identified and validated specific genes whose expression differs between tumor and normal tissue, or throughout the histologic progression of cancer (23, 37, 44–54). Pending validation, these genes may serve as biomarkers for diagnosis or targets for therapy. For example, two groups found that the insulinoma-associated 1 gene is overexpressed in SCLC (6, 25). In addition, gene expression profiles from the most clinically aggressive AC samples showed increased expression of genes that play a role in angiogenesis such as VEGF and PPAR, as well as degradation of extracellular proteins such as cathepsin L and plasminogen activator of urokinase. The analysis of Yamagata and coworkers identified one gene, ACTN4, whose expression level was correlated with poor survival, and therefore could serve as a marker for survival (27). Other gene expression profiling studies have shown that phosphoglycerate kinase 1 (PGK1) is overexpressed in lung tumors and correlates with poor survival, as well as 14 other genes that are important in the glycolytic pathway (31, 55).

By comparing gene expression profiles in tumors from smokers and never-smokers, Powell and colleagues identified several genes whose expression levels either positively or negatively correlated with the number of pack-years of smoking (42). Although it is tempting to view these genes as possible preventative or therapeutic targets, it is also possible that the data are confounded by the fact that the authors did not collect data on gene expression changes associated with smoking in the absence of cancer (42). Interestingly, the study by Spira and associates (43) showed that the expression of several tumor suppressor genes and oncogenes are permanently altered in bronchial epithelial cells from former and current smokers. In contrast, alterations in genes associated with metabolism and antioxidation pathways were reversible depending on the number of years of abstinence (43). The genes that remain altered in former smokers may be better targets for the prevention and treatment of smoking-induced lung cancer.

Proteomic studies also identified proteins that could serve as therapeutic targets. For example, Chen and coworkers (36) showed that four proteins that regulate gluconeogenesis and glycolysis are associated with survival; phosphoglycerate kinase 1, phosphoglycerate mutase, enolase, and pyruvate kinase M1. Combined with their gene expression analyses described earlier, this study provides further evidence for the idea that patients whose tumors exhibit increased glycolysis have a poorer prognosis (36). In addition to the markers identified by Chen and colleagues, Yanagisawa and associates (28) identified three proteins that could serve as tumor markers; small ubiquitin-related modifier-2 (SUMO-2), thymosin-ß4, and ubiquitin. Thymosin-ß4 has been previously associated with proliferation in cancerous tissue (56), but SUMO represents a potential new protein for analysis of its role in carcinogenesis (28). All of these potential markers require validation in prospective trials.

Analysis of Relevant Murine Models
Although very few mouse models of lung cancer exist and their relevance to human lung cancer is often questioned, they have been used to study lung tumorigenesis in a defined genetic background. Gene expression studies have begun to correlate changes in gene expression with murine lung tumorigenesis. For example, Gariboldi and coworkers (57) used microarray analysis to compare the gene expression profiles of normal mouse lungs from strains that varied in their susceptibility and resistance to lung cancer. They identified 89 genes whose expression profile correlated with each particular strain's tumor susceptibility. Of these 89 genes, 26 were previously identified for their involvement in cancer (57). Consistent with their hypothesis, this study showed that the gene expression profile of normal murine lung could predict tumor susceptibility for each strain. Although differences in gene expression may reflect "unconnected strain differences," the authors suggest that this study provides evidence for the existence of genes whose expression in normal lung predisposes for lung cancer in mice. These studies are consistent with the study by Ramaswamy and colleagues, who showed that the development of metastasis is strain-dependent (58). In another study, Bonner and colleagues (22) compared the gene expression profiles of murine lung adenomas and adenocarcinomas from strain A/J x 129/SvJ F₁ mice and identified genes that could differentiate between the stages, as well as genes whose expression consistently increased or decreased in the human tumors independent of histology. Finally, this group also identified gene expression changes in the tumors that were either consistent with, or opposite to, the expression in the developing lung of this strain (22). The results of these studies have refined our knowledge of murine lung tumorigenesis and could facilitate the creation of novel mouse models that might more closely resemble human lung cancer.

Analysis of Lung Cancer Cells In Vitro
Gene expression studies have also evaluated the effects of reconstituting tumor suppressor genes that are commonly lost in lung cancer. Two noteworthy in vitro studies have been published in lung cancer cellular systems. Hong and colleagues studied the effect of the phosphatase and tensin homology deleted on chromosome 10 (PTEN) tumor suppressor gene on the overall gene expression profile of a human lung cancer cell line CL_1–5 (59), and Russo and coworkers used microarray analysis to look for targets of the retinoblastoma tumor suppressor gene (pRb/p300) in the human lung AC cell line H23 (60). In both studies, wild-type tumor suppressor genes of interest were transfected into the parental cells and the gene expression profiles were compared with the profiles generated from the parental cell transfected with the control vector. Overexpression of PTEN led to decreased expression of 19 genes and 4 ESTs, including 3 that had previously been described to play a role in cell invasiveness, such as laminin ß₃, integrin ₆, and Akt2. PTEN overexpression also increased expression of 17 genes and 2 ESTs, including protein phosphatase 2A1B, an enzyme that was previously shown to maintain integrity of the cytoskeleton (59). In the studies on pRb, Russo and colleagues showed that 40 genes were downregulated in the pRb/p300 overexpressing cells compared with control. Many of these genes are important in cancer, including cyclin D1, Raf-1, and B-Myb (60). Together, these studies have contributed to a better understanding of how known tumor suppressor genes function in lung cancer cells.

	DISCUSSION

Top Abstract Introduction THE OMICS OF LUNG... DISCUSSION References

 
The studies reviewed here reveal that genomics and proteomics are powerful tools for classification of tumor subtypes and identification of genes or proteins that may serve as diagnostic, predictive, or prognostic markers. The fact that molecular fingerprints generated by genomic and proteomic analysis can recapitulate the histologic variation between NSCLC subtypes is good proof of principle, but one has to ask: how does this improve upon what we already knew? Several issues will need to be addressed before the potential of these powerful technologies is realized and integrated into the clinical setting.

In many of these studies, the results of classification using cluster analysis of gene expression profiles did not match the results of classification based on tumor histology (24, 26, 27). In one analysis, immunohistochemistry was used to assess samples whose expression profile did not cluster with histologic subclass (24). Borczuk and associates (21) showed that segregation of these tumors was likely due to the presence of nontumor cells that explain the segregation into groups of cancers with another subtype. Similarly, the reanalysis of "incorrectly classified" samples by Giordano and coworkers and Yamagata and colleagues revealed that their original pathologic classification might have been incorrect (26, 27). Thus, although the molecular profile was an accurate diagnostic method, these studies highlight the fact that array technology relies on the molecular information that is given to it for its power to predict. One possible solution to eliminate "contaminating" nontumor cells from tumor specimens is to employ laser capture microdissection (61). Laser capture dissection can be used to separate tumor from normal tissue and can alleviate problems due to contamination of tumor tissue from normal. However, exclusion of surrounding stroma also eliminates cells that contribute to the tumor microenvironment.

Validation of changes in gene expression at the protein level would improve many of these studies, because changes in protein expression provide a more functional readout. Although evaluating protein expression may not be necessary to develop fingerprints for diagnostic use, it is necessary if markers that are identified by this method are to be used in their own right as diagnostic markers or as targets for therapy. Thus, despite the fact that many genes were identified as potential biomarkers, all of them require further validation. Even though proteomics provides a more functional approach than genomics, proteomics alone might provide an insufficient readout because post-translational modifications such as phosphorylation control the stability and function of many proteins. The development of sophisticated proteomic techniques such as phospho-proteomics will add another level of complexity to these systematic analyses (62).

The genomic and proteomic studies of lung cancer reviewed here have advanced the biologic understanding of lung cancer and have demonstrated the potential of these technologies to improve the classification and diagnosis of lung cancer. Numerous genes and proteins were identified as biomarkers, but the mere identification of markers does not provide any information about whether the changes in expression of genes or proteins are causal for biologic behavior. Although focusing on known genes and proteins has already yielded new information, unknown markers may also lend insight into the biology of lung cancer. These nascent studies using new and rapidly developing technologies have yielded exciting results. Improving sample size, reproducibility, and the ability to discriminate tissue or tumor subtypes will facilitate application of these new technologies to the clinic.

	Footnotes

 
Conflict of Interest Statement: C.A.G. has no declared conflicts of interest; and P.A.D. has no declared conflicts of interest.

(Received in final form December, 16 2004)

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