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Clinical Investigations |
Departments of Pathology [M. S., R. L. P., W. A. F.], Medicine [M. G., B. G., F. R. H., R. B., P. A. B.], and Pharmacology[G. J., R. L.], University of Colorado Health Sciences Center, Denver, Colorado 80262, and Department of Pathology, F. S. Key Medical Center, Baltimore, Maryland 21224 [E. G.]
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ABSTRACT |
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 Top ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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INTRODUCTION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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It might be expected that, because of their stark morphological distinction from normal lung cells and their aggressive biological behavior, lung cancer cells may exhibit many molecular differences from non-neoplastic lung cells. To date there have been numerous attempts to identify such molecules with limited success. Lung cancer biomarkers measurable in the peripheral blood have included carbohydrate-rich cell matrix molecules such as carcinoembryonic antigen (6) , cytokeratin-derived intermediate filament molecules such as CYFRA-21.1 (7 , 8) , tissue polypeptide antigen (TPA) (9) , and tissue polypeptide specific antigen (TPS) (10) , peptides such as proGRP (11) , neural markers such as neuron specific enolase (12 , 13) and chromogranin A (14 , 15) , and antibodies to immunogenic molecules such as Hu (16) , calcium channel proteins (17) , and p53 (18 , 19) . Thus far tests for these molecules have had limited clinical impact because of low specificity or low frequency of positive results in early stage patients. However, it is likely that the list of biomarkers already tested represents only a fraction of the molecular changes that occur in tumor cells, and that more sensitive and specific biomarkers remain to be discovered.
Recently, high-density OMAs3 have been introduced that permit rapid analysis of expression levels simultaneously for large numbers of genes (20) . This approach overcomes limitations inherent in expression analysis of single genes. With completion of human genome (21) sequencing, comprehensive OMA expression profiles can be created for individual tumors as well as for large classes of tumors. Early OMA analyses of lung cancers have centered on phenotypic classification of specific tumor type (22) and have not specifically focused on biomarker discovery.
Our objective in the present study was to discover potentially useful biomarkers for lung cancer by first identifying large gene expression differences between tumor cell lines and normal lung using high density OMAs. The microarray used (Affymetrix HG-U95Av2) incorporates 12,600 probes accounting for a large fraction of the expressed human genome. We searched for biomarkers that were overexpressed in relation to normal tissue, because they are more likely to be useful for detection and screening of accessible specimens such as sputum, peripheral blood, or urine than biomarkers that are underexpressed. We confirmed the expression levels of the gene group (CTAG) most frequently represented on a list of highly expressed genes by testing a broader series of cell lines using relatively inexpensive RT-PCR methodology. Finally, results of this preliminary testing were confirmed at the protein level by IHC using a TMA containing 187 early stage NSCLCs. This algorithm for biomarker development takes advantage of two high throughput microarray technologies to rapidly identify potentially important biomarkers linked to clinical outcomes and prognostic importance.
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MATERIALS AND METHODS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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1 million cells/ml after 1 week in culture after thawing.
Two controls were used for comparison to expression profiles of tumor cell lines. For one control, bronchial epithelial primary cell cultures were obtained from a bronchoscopic biopsy of a healthy 48-year-old female who had never smoked and who had volunteered under a Colorado Combined Institutional Review Board-approved protocol. The biopsy was explanted onto a T25 culture flask containing bronchial epithelial cell growth medium (Clonetics, Inc., Walkersville, MD) and epithelial cells were allowed to grow from the explant to a diameter of 1 cm (10 days). Cells were then passaged into a second T25 flask and grown to 90% confluence (4 days). The culture cells were again split onto glass coverslips to perform spectral karyotyping on metaphase cells according to the manufacturer’s protocol (Applied Spectral Imaging, Inc., Carlsbad, CA). A second aliquot was split into two T75 flasks and again grown to 90% confluence (4 days). One flask was additionally split into three T75 flasks and expanded for an additional 3 days. Finally, 90% confluent cells were harvested by removal of culture medium followed by immediate addition of RNeasy extraction medium as described above. The total time from biopsy date to RNA harvest was 21 days. Primary cultures processed in this way grow as substrate-adherent monolayers, which are 100% cytokeratin positive on immunohistochemical staining (23)
. Spectral imaging karyotype was diploid with no detectable subchromosomal abnormalities.
A second set of controls consisted of archival data obtained from experiments in which RNA was extracted from benign lung tissue obtained at the time of surgical resection for carcinoma elsewhere in the lung. For these experiments, duplicate tissue samples from 10 lung specimens were snap frozen and stored in liquid nitrogen until use. For RNA extraction, frozen tissue fragments were placed in RNeasy extraction medium and homogenized with a Tissue Tearor homogenizer (Biospec Products, Bartlesville, OK) followed by filtration through a QIAshredder column. The filtrate was used for RNA extraction using the Qiagen RNeasy Mini protocol.
Total RNA extracted from each sample described above was tested for degradation and applied to a separate HG-U95Av2 microarray. Each control RNA from cultured normal bronchial cells or whole lung homogenate was used as a separate normalization control in the Genespring filtering algorithms described below.
Preparation of Labeled cRNA and Hybridization to OMAs.
Before application to test chips the quality of RNA was tested using the one step duplex RT-PCR assay (24)
. In this assay, the ratio of short to long segment ß-actin PCR product is used to quantify the extent of RNA degradation. All of the samples in this study had ratios of <2.6 indicating a low level of degradation.
Double-stranded cDNA was synthesized from 16 to 20 µg total RNA using an oligodeoxythymidylic acid 24 primer with a T7 RNA polymerase promoter site added to the 3' end (Superscript cDNA Synthesis System; Life Technologies, Inc., Rockville, MD). After second-strand synthesis, in vitro transcription was performed using a T7 Megascript kit (Ambion, Austin, TX) in the presence of biotin-11-CTP and biotin-16-UTP (Enzo Diagnostics, Farmingdale, NY) to produce biotin labeled cRNA. Twenty µg of the cRNA product was fragmented at 94°C for 35 min into 35–200 bases in length. The sample was then added to a hybridization solution containing 100 mmol/liter 4-morpholinepropanesulfonic acid, 1 mol/liter Na+, and 20 mmol/liter of EDTA in the presence of 0.01% Tween 20 to a final cRNA concentration of 0.05 mg/ml. Hybridization was performed for 18–20 h by incubating 200 µl of the sample to HG-U95Av2 microarrays, and each microarray was stained with streptavidin-phycoerythrin and scanned at 6-µm resolution by Gene Array scanner G2500A (Hewlett Packard, Boise, ID) according to procedures developed by Affymetrix.
Statistical Analysis.
Detailed protocols for data analysis of Affymetrix microarrays, and extensive documentation of the sensitivity and quantitative aspects of the method have been described (20
, 25)
. Briefly, mismatch probes act as specificity controls that allow the direct subtraction of both background and cross-hybridization signals. To determine the quantitative RNA abundance, the average of the difference representing perfect match - mismatch for each gene-specific probe family is calculated. This data were transferred to GeneSpring software (Silicon Genetics, Redwood City, CA) for additional analysis.
Using the GeneSpring software package, a two step filtering algorithm was implemented to select genes highly expressed by tumor cells in comparison with non-neoplastic lung cells and tissue. In the first step, cultured normal epithelial cells were compared with tumor cell lines using the following settings: the 80th percentile of all measurements was used as a positive control for each sample, and each measurement was divided by this control. The 0.1% measurement was used as a control for background correction. The measurement for each gene was then divided by the corresponding value for the sample of normal bronchial epithelium. A list was compiled of all of the genes expressed by at least two tumor cell lines at >100x over the normal control. This filtering step resulted in the identification of 42 genes. In the second filtering step, the 50th percentile of all measurements was used as a positive control for each sample, and each measurement was divided by this control. The measurement for each gene was then divided by the corresponding value for 19 samples from 10 non-neoplastic lung specimens. A list was then compiled of 107 genes that were expressed in the tumor cell lines at >20x over the non-neoplastic tissue. The contents of the two lists was then compared using the GeneSpring Venn diagram feature and a list of 20 highly overexpressed genes common to the two lists was compiled.
The selected genes were annotated using the GeneOntology database4 within the NetAffx5 analysis system offered by Affymetrix. GeneOntology stores a dynamic controlled vocabulary organized on molecular function, cellular component, and biological process that can be applied to all organisms. The cellular component attributes were used to search for genes that were either extracellular (secreted) or transmembrane molecules as potential biomarkers.
The secretory attributes of the selected genes were further investigated by looking at the leader sequence signal. Briefly the master protein model sequences were obtained from the LocusLink database and analyzed using the program SignalP6 (SignalP version2.0) that detects secretory signal peptides in amino acid sequences. The program splices the first 70 amino acids and runs two different types of detection algorithms: one based on neural network prediction and the other based on Hidden Markov Models. Both are trained against a library of known signal peptides and calculate a final score, which will assign the protein to one of three classes: (a) nonsecretory; (b) signal anchor (NH2 terminus of type II membrane proteins, uncleaved signal peptides); and (c) signal peptide (secretory signal).
Spearman correlation was used for clustering of all of the hybridization experiments. To evaluate the expression profile for melanoma-associated antigens, a list of 64 melanoma-associated genes was compiled using the GeneSpring search feature for melanoma, and Pearson correlation was used for clustering of this list (see Fig. 1
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2 analysis was performed using Microsoft Excel.
Confirmatory RT-PCR Assay.
Gene expression was confirmed by RT-PCR in 25 cell lines with SCLC, NSCLC, and mesothelioma histologies (representative gel shown in Fig. 3
). The RT-PCR assay was performed using One-Step RT-PCR system (Life Technologies, Inc.) with MAGE A-1, 3, 4, 6, 10, 12, ASH1, PGP 9.5, and NY-ESO-1 primers (Table 1)
. Reagents were mixed in a single tube for reverse transcription and amplification for 22 to 30 cycles including denaturation at 94°C for 1 min, annealing at 55°C for 1 min, and extension at 72°C for 2 min. The RT-PCR products were separated on 1.5% agarose gels and visualized by UV transillumination of the gels stained with ethidium bromide.
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TMA IHC.
Paraffin blocks of tumor tissue from 187 patients with NSCLC (stages I-III) were obtained from the University of Colorado Cancer Center and Johns Hopkins Medical Institutions according to IRB-approved protocols. Follow-up of patients represented on the TMA ranged from 18 to 100 months. The distribution of tumor histologies and clinical stage in this group of patients is shown in Table 2
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The sections were then deparaffinized with standard xylene and hydrated through graded alcohols into water. Antigen retrieval was performed using the DAKO Target Retrieval system in a Biocare Medical decloaking chamber. Peroxide blocking was performed with 3% hydrogen peroxide in water. After incubation of the mouse monoclonal anti-MAGE-A antibody, 6C1 (Novacastra) for 1 h at room temperature, the DAKO Envision Plus detection was applied for 30 min also at room temperature. This was followed by application of diaminobenzidine chromogen. The slides were then counterstained in hematoxylin and coverslipped.
Outcome data on cases used for microarray construction was obtained from the University of Colorado tumor registry. Patients were followed for a median of 51 months (range, 18–100).
Scoring of IHC Results.
Each core on the TMA was examined by conventional white light microscopy and the observed staining pattern graded for each core. Percentage of tumor cells positive and intensity of staining was recorded for both tumor cytoplasm and nucleus. A grading score was obtained by multiplying the intensity of staining on an arbitrary 0–4+ scale by the percentage of cells stained separately for nuclear and cytoplasmic staining. The same grading system was used for both TMA samples and lung cancer cell lines.
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RESULTS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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. Each sample of non-neoplastic lung tissue invariably clustered with its corresponding duplicate sample. Complete hybridization data for cell lines, the primary culture, and the tissue homogenates are available.7
The dual normalization and filtration process yielded 20 separate genes (Table 3)
represented by 21 probe sets that were highly overexpressed by at least 2 of the cell lines. Expression levels were related to cell type with 14 genes overexpressed only in SCLC, 4 in NSCLC, and 2 in both SCLC and NSCLC. With the exception of the CTAG gene group, the chromosomal distribution of the overexpressed genes appeared to be random. A wide diversity of gene functions and subcellular localizations were represented among the gene products, ranging from a membrane-associated ion transport protein to nuclear transcription factors (Table 4)
. Four of the genes, ASH1, claudin 10, and the secretogranins I and II, contained signal peptide sequences suggesting the possibility that the gene products are secreted. Only 1 gene, ABCC2, contained a signal anchor peptide sequence.
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. Here again levels of expression were related to tumor type with SCLC lines more highly expressive of MAGE genes than NSCLC. High level overexpression was restricted to a subset of MAGE-A subfamily genes (Fig. 2)
. When the database normalized to cultured bronchial epithelial cells was queried regarding levels for all of the MAGE-A genes on the HG-U95Av2 array, MAGE-A2, 3, 6, 10, and 12 were found to be overexpressed at >100-fold, whereas MAGE-A 1, 4b, and 5a were overexpressed at 40–100-fold. The remaining MAGE family members (8, 9, and 11) were not overexpressed or were overexpressed in only a single tumor. When the query was expanded to include all 64 of the melanoma-associated probes on the array, including 12 for MAGE-A, 4 for MAGE-B, 9 for GAGE, 1 for BAGE, and 4 NY-ESO-1 (LAGE-1), we found only the MAGE-A and NY-ESO genes were overexpressed at high levels (Fig. 1)
, again suggesting that MAGE profiles are tissue-specific, with lung cancer expressing only a fraction of the genes that have been associated with melanocytic differentiation.
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). As graphically depicted in Fig. 4
, most of the high expression markers were detected in similar percentages of cell lines, but NY-ESO-1, MAGE-A10, and ASH-1 were expressed frequently by SCLC but infrequently or not at all by NSCLC. By 2 analysis, differences in expression frequencies were significant at Ps of <0.02, <0.03, and <0.03 for MAGE-10, NY-ESO-1, and ASH, respectively.
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2, P < 0.0003).
TMA IHC.
Nuclear and cytoplasmic staining were analyzed both separately and combined for prognostic significance. Staining was of variable intensity (Fig. 5)
with labeling scores (described on page 13) ranging from 0 to 387; scores were interpreted as positive if they were 1 or higher. MAGE-A was interpreted as highly overexpressed if labeling score was >100. Of the 187 arrayed tumor samples, 44% exhibited some level of nuclear or cytoplasmic staining. There was a strong concordance (2, P < 0.00001) between nuclear and cytoplasmic staining, and in those cases where there was discordance the positive staining was weak. There was also strong correlation between tumor histology and MAGE-A expression status. Squamous carcinomas were more frequently positive than adenocarcinomas, large cell carcinomas, or bronchioloalveolar carcinomas (Table 5
; 2, P < 0.00002).
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DISCUSSION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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A surprising feature of the list of biomarkers that emerged from this schema was strong representation of CTAG genes. This gene group comprised 30% of the 20 biomarker genes identified. Expression of CTAG genes is restricted to normal testicular (and ovarian) germ cells and tumors of a number of cell types (26 , 27) . The first of these genes to be identified were the MAGE-A genes, which were originally named MAGE 1 through 3. They were discovered because they elicit an HLA I-dependent cytotoxic response in sensitized lymphocytes against the melanoma cell line MZ2-MEL (28) . It is of interest that this first report indicated that MAGE expression could be demonstrated not only in melanoma cell lines but also in SCLC and NSCLC as well. The original 3 MAGE genes were soon supplemented by 9 additional MAGE genes discovered by screening cosmid libraries temporarily bringing the total number of MAGE genes to 12 (29) , all encoded at chromosome Xq28 (29 , 30) . Sequencing of chromosomal region Xp21.3 led to the identification of a second subfamily of MAGE genes named MAGE-B (31, 32, 33) . In recent years, the list of MAGE family genes has continued to increase, and the MAGE family now is thought to contain 55 homologous members divided into 9 subfamilies (34) . Although structurally homologous, some recently described MAGE subfamilies are ubiquitously expressed (35, 36, 37) and are not members of the CTAG gene group. Early reports indicating that MAGE genes may be expressed by tumors of many types (28) have been confirmed in many different laboratories for many different types of tumors including brain (38 , 39) , skeletal muscle (40) , esophagus and stomach (41) , Reed-Sternberg cells (42) , bladder (43) , biliary tract (44) , and breast (45) .
A second CTAG gene family, NY-ESO-1, was identified by autologous screening of a cDNA expression library constructed from a case of esophageal carcinoma (26) . A similar if not identical gene was reported a short time later as LAGE-1 (46) . Like the MAGE-A gene family, NY-ESO-1/LAGE-1 maps to chromosome Xq28 (46) .
Expression of CTAG genes by lung tumors has been documented by IHC and RT-PCR in a limited number of studies. In two separate studies using monoclonal antibodies 57B and MA454 that react with MAGE-A protein, Jungbluth et al. (47) have found heterogeneous expression in 32% and 56% (48) of NSCLC, respectively. IHC studies are complicated by the high degree of homology among different MAGE-A proteins so that many anti-MAGE antibodies cross-react with several different MAGE-A subfamily members (49, 50, 51) . The monoclonal antibody used in the present study, 6C1, reacts with an epitope in the COOH-terminal regions of MAGE-A1, -A2, -A3, -A4, -A6, -A10, -A11, and -A12 (51) and may thus be considered an anti pan-MAGE-A reagent. The use of antibody in a sensitive immunoperoxidase procedure on 187 tumors in a TMA linked to clinical and histological data allowed us to determine that 44% NSCLC express MAGE-A protein, that expression varied according to tumor histology, and that expression is unrelated to prognosis.
By RT-PCR, MAGE-A1, -A3, and B2 RNA sequences have been found recently in 70%, 85%, and 85% of a small series of NSCLC and is often accompanied by promoter hypomethylation (52) . Also of interest, this report indicated that bronchial epithelium from a large proportion of 20 former smokers without lung carcinoma also frequently expressed MAGE genes and suggested that MAGE gene expression may occur early in lung carcinogenesis and may be a suitable target for lung cancer prevention.
Whether MAGE-A protein can be found in the blood, urine, or sputum of patients with lung cancer is not known at present. The absence of signal sequences in the MAGE-A genes suggests that MAGE-A proteins are not actively secreted. However, it may not be essential that a protein be actively secreted to be useful as a biomarker because protein may be released from dying tumor cells, which are frequent in lung carcinomas. Also, tumors that occur frequently in central airways (SCLC and squamous carcinomas) are most often MAGE-A/NY-ESO-1-positive, suggesting that these biomarkers may be particularly useful for sputum testing.
An advantage of OMA analysis for potential biomarkers is the ability to interrogate microarray data for expression patterns of all or many members of entire functional pathways. In this context it is of interest that melanoma genes other than the CTAG genes were found not to be overexpressed in lung cancer lines indicating that there are significant differences in activation of functional pathways between these two tumor types. The function of CTAG genes in general and MAGE-A genes in particular is not known. Necdin, a 325 amino acid protein with 30% homology to MAGE proteins (reviewed in Ref. 53 ) has been shown recently to interact with p53, inhibiting p53-induced apoptosis. Whether or not the MAGE proteins function in a similar way is unknown at present but such a function would be consistent with the frequent expression of MAGE proteins in aggressive malignancies.
Several of the remaining highly overexpressed genes have properties that suggest they may be useful biomarkers including signal peptide coding sequences. One of four protein products of genes containing signal peptides has been tested as a lung cancer biomarker. Chromogranin A has been found in the serum of 50% (14) of all of the neuroendocrine tumors and 61–70% (15 , 54) of SCLC, and is generally regarded as a promising marker for the diagnosis of neuroendocrine neoplasia. A second putatively secreted protein, ASH1, has been associated previously with neuroendocrine neoplasia (55) but has not been tested as a serum or urinary biomarker. That this protein is in fact secreted is doubtful, given its role as a transcription factor and its nuclear localization. Other overexpressed genes associated with neuroendocrine differentiation in pulmonary neoplasia include IA-1 (56) and DOPA decarboxylase (57 , 58) .
Many of the listed genes may be useful for detection and monitoring of NSCLC, or both SCLC and NSCLC. These include aldo-keto reductase family 1, ABCC2 (59) , basic hair keratin 1, prostaglandin E synthase (60) , PGP 9.5 (61 , 62) , Na+, and K(+)-ATPase (63, 64, 65) . Additional evaluation of these candidate biomarkers either alone or in combination will be required to establish the utility of these overexpressed genes as useful biomarkers for early detection and monitoring, and to better define their biological role. Better understanding of expression profiles for these genes may also suggest novel approaches to therapeutic intervention lung carcinoma.
Several other large-scale gene expression analyses of lung cancer have been reported recently. An OMA analysis of 186 pulmonary tumors also using the HG-U95A microarray has been performed recently on tumor homogenates (22) . Cluster analysis of the resulting data indicated that gene expression profiles corresponded to histological type for SCLC, squamous carcinoma, and carcinoid tumor, but adenocarcinomas were heterogeneous and could be subdivided into five categories including one for metastasis from colon. Three of the genes identified in the prior study also appear on the present list of overexpressed genes, ASH-1, IA-1, and DOPA decarboxylase. Another analysis of tumor homogenates using 24,000 element cDNA microarrays has also been published recently (66) . From among the several hundred genes with expression patterns that discriminate among differing histological types, only four genes were also present in the current list of overexpressed genes, PGE synthase, cMOAT, ASH-1, and IA-1. Finally, in a recent SAGE analysis (67) , 115 highly differentially expressed genes were reported and among these was the aldoketo reductase, member B10 gene. CTAG genes were not listed in any of these large-scale gene expression studies.
This small number of overlapping genes between the current and other recent studies has several possible explanations. First, the number of specimens examined is smaller in the present analysis than in the other analyses. Second, we attached only limited importance to intertumor heterogeneity of gene expression in this current study. We assumed that lung cancers, because they are heterogeneous in almost every respect, are likely to also exhibit a high degree of heterogeneity in gene expression profiles. We expected that there would be a great imbalance of many cellular pathways engendered by chromosomal and genetic instability, potentially resulting in high levels of overexpression of specific genes. Our objective was to identify these genes and to estimate the likelihood that their products could serve as tumor biomarkers. Finally, we made no distinction among tumors of various histological origins in screening for highest level overexpression. This allowed us to focus on genes that are massively overexpressed in cancer cells in comparison to normal lung regardless of the cell type, an approach specifically tailored for biomarker discovery.
We conclude that the detailed gene expression data can now be readily obtained using OMAs. Testing of even a few suitable specimens can identify potential biomarkers for lung and other cancers that can be rapidly validated by high throughput testing of TMA linked to clinical outcome. This model should be a rich source of promising new biomarkers. To exploit these new analytical tools it will be imperative that correlative biological materials be collected during large-scale screening and treatment trials that are currently being designed.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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1 Supported by NIH Grants U01 CA85070, Early Detection Research Network, and P50-CA85070. 
2 To whom requests for reprints should be addressed, at Departments of Pathology, Box B216, University of Colorado Health Sciences Center, 4200 E. 9th Avenue, Denver, CO 80262. Courier Address: Wilbur A. Franklin, MD, EDRN Biomarker Development Laboratory, Administrative Office Building, Room 065, 4210 E. 11th Avenue, Denver, CO 80262.
3 The abbreviations used are: OMA, oligonucleotide microarray; RT-PCR, reverse transcription-PCR; IHC, immunohistochemistry; NSCLC, non-small cell lung carcinoma; SCLC, small cell lung carcinoma; TMA, tissue microarray; CT, cancer/testis; ASH, Achete-Scute Homologue.
4 Internet address: http://www.geneontology.org.
5 Internet address: http://www.netaffx.com.
6 Internet address: http://www.cbs.dtu.dk/services/SignalP-2.0.
7 Internet address: http://uch.uchsc.edu/uccc/research/GeneExpression/index.html.
Received 2/20/02. Accepted 5/17/02.
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 C. D. Hoang, J. D'Cunha, S. H. Tawfic, A. C. Gruessner, R. A. Kratzke, and M. A. Maddaus Expression profiling of non-small cell lung carcinoma identifies metastatic genotypes based on lymph node tumor burden J. Thorac. Cardiovasc. Surg., May 1, 2004; 127(5): 1332 - 1342. [Abstract] [Full Text] [PDF]
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 C. D. Hoang, J. D'Cunha, M. G. Kratzke, C. E. Casmey, S. P. Frizelle, M. A. Maddaus, and R. A. Kratzke Gene Expression Profiling Identifies Matriptase Overexpression in Malignant Mesothelioma Chest, May 1, 2004; 125(5): 1843 - 1852. [Abstract] [Full Text] [PDF]
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W. A. Franklin Premalignant Evolution of Lung Cancer: Gilles F. Filley Lecture Chest, May 1, 2004; 125(5_suppl): 90S - 94S. [Full Text] [PDF]
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N. Kaminski and M. Krupsky Gene Expression Patterns, Prognostic and Diagnostic Markers, and Lung Cancer Biology Chest, May 1, 2004; 125(5_suppl): 111S - 115S. [Full Text] [PDF]
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J. Zhou, Y. You, J. Zabner, A. J. Ryan, and R. K. Mallampalli The CCT Promoter Directs High-Level Transgene Expression in Distal Lung Epithelial Cell Lines Am. J. Respir. Cell Mol. Biol., January 1, 2004; 30(1): 61 - 68. [Abstract] [Full Text] [PDF]
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I. B. Copland, B. P. Kavanagh, D. Engelberts, C. McKerlie, J. Belik, and M. Post Early Changes in Lung Gene Expression due to High Tidal Volume Am. J. Respir. Crit. Care Med., November 1, 2003; 168(9): 1051 - 1059. [Abstract] [Full Text] [PDF]
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 J. C. Sok, M. A. Kuriakose, V. B. Mahajan, A. N. Pearlman, M. D. DeLacure, and F.-A. Chen Tissue-Specific Gene Expression of Head and Neck Squamous Cell Carcinoma In Vivo by Complementary DNA Microarray Analysis Arch Otolaryngol Head Neck Surg, July 1, 2003; 129(7): 760 - 770. [Abstract] [Full Text] [PDF]
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 Y. Hoshikawa, P. Nana-Sinkam, M. D. Moore, S. Sotto-Santiago, T. Phang, R. L. Keith, K. G. Morris, T. Kondo, R. M. Tuder, N. F. Voelkel, et al. Hypoxia induces different genes in the lungs of rats compared with mice Physiol Genomics, February 6, 2003; 12(3): 209 - 219. [Abstract] [Full Text] [PDF]
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