Mesothelioma

Study objective: We investigated the gene expression profiles of malignant pleural mesothelioma (MPM) specimens to identify novel genes that are potentially involved in the oncogenic transformation of human pleural cells.

Design: Complementary DNA (cDNA) microarray transcriptional profiling studies of 10 MPM cell lines and 4 MPM primary tumor specimens were performed using hierarchic clustering. To confirm microarray data, we used real-time polymerase chain reaction and immunoblotting.

Results: Cluster analysis differentiated among epithelial (E), sarcomatoid, and biphasic MPM variants. Expression profiling identified common overexpressed or underexpressed genes in MPM. Notably, matriptase messenger RNA was found to he overexpressed by 826-fold in E MPM, with protein expression subsequently confirmed by immunoblot analysis. This recently characterized trypsin-like serine protease has been implicated in tumor invasion and metastasis of E-derived cancers, but has not been described until now in MPM. We also identified other novel genes, such as insulin-like growth factor binding protein 5 and a cDNA clone similar to proteolipid MAL2.

Conclusions: Thus, further large-seale profiling of MPM may elucidate previously unrecognized molecular mechanisms by identifying novel genes that are involved in malignant transformation. Our study has now found matriptase to be one of these mesothelioma-associated genes, with potential pathogenic and therapeutic significance.

Key words: expression profiling; matriptase; mesothelioma; microarray; real-time polymerase chain reaction

Abbreviations: B = biphasic; cDNA = complementary, DNA; Ct = threshold cycle; E = epithelial; GUSB = [beta]-glueuronidase; IGF = insulin like growth factor; IGFBP = insulin like growth factor-binding protein; MPM = malignant pleural mesothelioma mRNA = messenger RNA; QRT-PCR = quantitative real time polymerase chain reaction; S = sarcomatoid

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Malignant pleural mesothelioma (MPM) is an aggressive neoplasm of the serosal lining of the pleural cavity arising from mesothelial cells (ie, from undifferentiated cells representing the adult remnants of the surface coelomic mesoderm). Mesothelial cells are biphasic (B) and may give rise both to the lining keratin positive epithelial (E) cells and to the paucicellular submesothelial layer. The biphasic nature of mesothelial cells results in the following three major forms of MPM: E; sarcomatoid (S), or fibrous; and B. MPM currently accounts for about 2,500 to 3,000 deaths per year in the United States. (1) Up to 80% of eases of MPM occur in patients 10 to 20 years after exposure to asbestos. (2) Due to this latency period between asbestos exposure and tumor development, the associated mortality rate in men, but not in women, continues to rise in industrialized countries at the rate of 5 to 10% per year, with a median survival time between 4 and 18 months. (3,4) This rising mortality rate is occurring despite the implementation of legislation limiting asbestos use and exposure in most industrialized countries.

Aside from asbestos exposure, other factors such as ionizing radiation or tumor DNA virus sinvian virus-40 may act synergistically in MPM pathogenesis. (5,6) Also, several well-defined acquired genetic targets have been identified in MPM, including the 9p21 locus (p161NK4a, p14ARF) and the 22q11-q13.1 locus (NF2). (2) However, the molecular mechanisms controlling the transformation of mesothelial cells remain poorly defined. This is underscored by the observation that these well-characterized etiologies incompletely account for the known incidences of MPM. About 10 to 20% of MPM occurrences have been documented in patients without previous exposure to asbestos, (2) and only 60% of MPM tumors are known to contain SV-40 viral DNA. (6) Accordingly, multiple active pathways are thought to be possible.

Only two studies (7,8) to date have used the microarray technique to search for additional genes and pathways that are potentially involved in MPM biology. These investigations were limited by the extremely small number of MPM cell lines examined and by the lack of additional analysis of primary tumor tissues. To identify novel candidate genetic targets, we used hierarchic cluster analysis in our current study to compare the gene profiles of a larger sampling to MPM cell lines and primary tumors vs nonmalignant mesothelium on complementary DNA (cDNA) microarrays.

MATERIALS AND METHODS

Cell Lines and Tumor Tissues

A total of 11 cell lines and 4 primary tumor specimens were studied (Table 1). The CRL-2081 MPM cell line, the SV40 virus-transformed mesothelial cell line CRL-9444, and the breast cancer cell line MCF-7 (a positive control (9) for the matriptase messenger RNA [mRNA] and protein expression studies) were used (American Tilde Culture Collection; Manassas, VA). An additional nine MPM cell lines developed and previously characterized by Harvey Pass were obtained from the National Cancer Institute. (10) The mesothelial cell line was grown and propagated in M199 medium (BioSource International; Camarillo. CA), and was supplemented with 10% fetal calf serum according to instructions. All remaining cell lines were maintained in Roswell Park Memorial Institute-1640 mechum (Invitrogen Life Technologies; Carlsbad, CA) supplemented with 10% fetal calf serum and antibiotics. Separate paraffin cell blocks were prepared corresponding to each cell line, and our surgical pathologist confirmed the correct subtype classification.

All available primary tumor specimens (E = 3; B = 1) were obtained from the Tissue Procurement Facility at the University of Minnesota Cancer Center, in accordance with the policies of our Institutional Review Board. These surgical specimens were colleted flesh, were immediately snap-frozen in liquid nitrogen. and were stored at -80[degrees]C until use. All tissue specimens were verified by histopathologic studies and immunohistochemical studies (cytokeratin 5/6, 7. and 20: calretinin: E-cadherin; Ber-EP4; CD15; carcinoembryonic antigen; TTF-1; and B72.3) as containing relatively pure tumor.

RNA Isolation

Cells growing asynchronously were lysed in reagent (Trizol; Invitrogen Life Technologies) when they reached about 80% confluence and then were processed as described previously, (11) with modifications. Before RNA precipitation by isopropyl alcohol, the RNA aqueous phase was mixed with an equal volume of 70% ethanol, and total RNA was subsequently extracted on a silica gel-based membrane spin column (Qiagen; Valencia, CA) per the manufacturer's instructions. On-column deoxyribonuclease digestion was performed with all samples to eliminate potential genomic DNA. RNA yield and purity, were determined by, spectrophotometry. Integrit was verified on 1.5% agarose-formaldehyde gels stained with ethidium bromide. Tissue specimens were processed in a similar fashion, each starting from 100 tug frozen tumor.

Microarrays

Microarray experiments were performed (MieroMax Human cDNA Microarray System II TSA; Perkin Elmer LifeSciences; Boston, MA) according to the manufacturer's instructions. Briefly, 1 [micro]g total RNA of each MPM sample was reverse transcribed and simultaneously labeled with fluorescein-deoxyuridinetriphosphate to produce target cDNA. CRL-9444 target cDNA was labeled with biotin-deoxyuridinetriphosphate and served as the reference "normal" sample in each microarray experiment, as validated by others. (7) Target cDNA was hybridized on 4.800 gene MicroMax microarrays. The washing and detection steps were based on a sequential fluorescence detection process with horseradish peroxidase-conjugated antibodies and tyramide-linked cyanine-3 (Cy3) or cyanine-5 (Cy5) dyes. (12,13) Microarray slides were scanned at 532 nm (Cy3, MPM samples) and 635 nm (Cy5, reference) [ScanArray Express confocal laser scanner; Perkin Elmer. Some microarray experiments were selected for duplicate processing to assure reproducibility

Microarray Analysis

Each gene was represented on a microarray in duplicate, so that a corresponding raw expression ratio was defined as the mean signal intensity of the Cy3/Cy5 replicates. Expression ratios then were filtered per microarray, according to the following algorithm, (14) with modifications. Briefly, the 50th percentile of all measured expression ratios was used as a positive control for each gene. Every gene measurement was divided by this synthetic positive control, assuming that it was at least 0.01. The bottom 10th percentile of expression ratios was used as a test for correct background subtraction. This was never less than the negative of the synthetic positive control. Across all 14 MPM specimens, the threshold values used to define significant relative expression changes were set at 3.0 for overexpression and 0.30 for underexpression.

In a separate but complementary approach, the raw expression ratios for each gene again were subjected to median-centered normalization, followed by K groups analysis (separate groups test), which is a combination of gene rank-ordering tests followed by a Pearson correlation analysis (Expressionist Suite, version 3.1; GeneData; San Francisco, CA). Microarray experiments were divided into three separate groups, according to histologic type. An iterative approach selected a K groups threshold value of 0.9, which best identified a subset of significant, differentially expressed, simultaneously divergent genes expressed between MPM histologic types. This gene subset then was used in agglomerative two-dimensional hierarchic cluster analysis. (15,16)

Real-Time Polymerase Chain Reaction

To examine the reliability of our microarray data, we identified an overexpressed gene (matriptase) with a potential role in MPM pathogenesis and determined its relative expression in identical samples (n = 14) used for microarray analysis by quantitative real time polymerase chain reaction (QRT-PCR). We designed a probe (TaqMan probe; Applied Biosystems; Foster City, CA) and primer set from the matriptase open reading frame (Primer Express software, version 2.0; Applied Biosystems). The probe and primer set for the endogenous control gene [beta]-glucuronidase (GUSB) was identical to that of a published sequence. (17) The sequences of matriptase primers and probe are as follows: forward, 5'-GCGCTCCCTGAAGTCCTTT-3'; reverse, 5'-GTCCTGGGTCCTCTGTACTGTTTT-3'; and probe, 5'-TCACCTCAGTGCTGCCTTTCCCCA-3'. The gene specificity, of sequences derived for primers and probes was confirmed by BLASTn (National Center for Biotechnology Information; Bethesda, MD) searches against the dbEST database (National Center for Biotechnology Information) and the nonredundant set of GenBank.

cDNA was generated from 1 [micro]g total RNA (Gene Amp system; Perkin Elmer) primed with random hexamers. In a 25-[micro]L polymerase chain reaction (TaqMan; final volume after cDNA added), components included the following: 1 x TaqMan buffer; 200 mmol/L each of four deoxynucleotidetriphosphates; 5 mmol/L magnesium chloride, 1.25 U AmpliTaq Cold, 0.5 U AmpErase uracil N-glycosylase (all from Applied Biosystems); 200 nmol/L each of forward and reverse primers; and 100 nmol/L probe (TaqMan). Then, 100 ng cDNA was added, and samples were processed in triplicate under the following cycling parameters (ABI PRISM 7900HT Sequence Detection System; Applied Biosystems): 50[degrees]C for 2 min; 95[degrees]C for 10 min; and 40 cycles at 95[degrees]C for 15 s and at 60[degrees]C for 1 min. The coefficients of variation for triplicate reactions and between assays were < 10%. The expected 79-base pair amplicon product was eluted from 2% agarose gel after electrophoresis and ethidium bromide staining. and its identity was confirmed by sequence analysis.

The relative amount of mRNA for a particular sample was represented by the threshold cycle (Ct) of amplification. The relative quantitation of matriptase transcripts was calculated by the comparative Ct method. (18,19) GUSB was used as all endogenous control for the normalization of sample loading, because levels of GUSB are consistent across diseased and nondiseased lung tissues. (17) The GUSB Ct value was subtracted from the matriptase CT value to obtain a [DELTA]Ct. Likewise, we determined the difference between the [DELTA]Ct values of MPM samples for the target gent and the [DELTA]Ct value of the calibrator (CRL-9444). The calibrator was chosen to be the same reference sample used in microarray hybridizations to facilitate comparison between the different techniques, The relative, normalized quantitative matriptase expression level ([2.sup.-][DELTA][DELTA]Ct) was calculated for all samples.

Immunoblotting

Tumor tissues were kept frozen while being ground to a fine powder, and cell lines were washed in phosphate-buffered saline solution before the addition of cell lysis buffer. (9) Protein concentration was determined by the Bradford dye-binding protein assay (Bio-Rad Laboratories: Hercules, CA). Proteins (50 [micro]g) were resolved by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis, were transferred to a nitrocellulose membrane (Amersham Biosciences: Piscataway. NJ), and then were probed with a matriptase-specific antibody, using previously described protocols. (20) The antimatriptase murine monoclonal antibody M32 (21) was obtained as a generous gift from Dr. Chen-Yung Lin (Lombardi Cancer Center, Georgetown University Medical Center; Washington, DC) and was used at a dilution of 1:2,000. Detection and visualization employed a horseradish peroxidase-labeled secondary antibody coupled to the detection system (ECL Detection System; Amersham Biosciences), followed by exposure to autoradiography film (Amersham Biosciences).

Statistical Analysis

A correlation analysis was performed to determine differences in gene expression results between microarray and QRT-PCR techniques. Matriptase expression ratios (ie, tumor specimen/ reference sample ratio) calculated for samples from each technique were log-transformed and compared, The Pearson correlation coefficient (r value) was calculated for all samples. We also calculated a linear regression line. Statistical analysis was perfurmed using a statistical software package (S-Plus 6; Insightful; Seattle, WA).

RESULTS

Hierarchic Cluster Analysis

Analysis was initiated on the 12 samples (E = 7: S = 2; and B = 3) with known histologic subtype. On average, 2,300 genes were excluded from downstream analysis, based on the inclusion criteria (previously discussed) after data normalization across these microarrays. This process was qualitatively confirmed by visual inspection of each microarray hybridization. The remaining average of 2,500 genes from each microarray underwent a K groups analysis, with three groups defined corresponding to E, S, and B mesothelioma tissue types. This analysis identified a discriminatory subset of 180 genes with significant differential expression among the three separate groups. The members of this subset were diverse and encoded for molecules with various functions, such as signal transduction, cellular structural components, transcription factors, cellular growth, adhesion, and trafficking (Fig 1, Table 2). We also noted numerous cDNA clones in this 180 gene subset representing potential uncharacterized genes. A BLASTn search conducted for the clone DKFZp564B1264 revealed a high degree of homology with proteolipid MAL2 (Table 2), a gene product that was not previously associated with MPM. This clone was significantly overexpressed in E types (mean, 23 fold) vs the other types combined (mean, 2.5-fold).

[FIGURE 1 OMITTED]

Additionally, using this 180 gene subset, we performed two-dimensional hierarchic clustering. The two unclassified MPM cell lines NCI-H2618 and NCI-H2691 were added to this analysis. Overall, duster analysis discriminated among MPM histologic: types, where three corresponding clusters were apparent (Fig 2). Cluster A included all seven E MPM specimens and the 2 cell lines of unknown histology, cluster C contained the S MPM and one B specimen, and cluster B contained the remaining two B MPM specimens. Duplicate populations of NCI-H2618 and NCI H2691 were isolated, embedded in paraffin blocks, and then analyzed by a board-certified pathologist who was blinded to the microarray analysis. Hematoxylin and eosin staining as well as cytokeratin and vimentin immunohistochemical analyses were performed. Histopathologic features of both cell lines were consistent with an E type (data not shown), as previously predicted from the hierarchic clustering.

[FIGURE 2 OMITTED]

Identification of Differentially Expressed Mesothelioma Genes

We identified, by systematic gene sorting, genes with significant overexpression and underexpression that were in common with specimens within a histologic type (Table 2). We identified three additional genes not commonly associated with MPM that may be implicated in mesothelioma tumorigenesis. Matriptase mRNA had a mean overexpression of 826-fold in E MPM, with the next highest matriptase-expressing group being 40 times lower. Insulin-like growth factor binding protein (IGFBP)-5 was significantly underexpressed in all MPM types. Concurrently, the gene encoding for the ligand in this insulin-like growth factor (IGF) system, IGF exon IA, was overexpressed in MPM.

Verification of Microarray Data

We validated the microarray expression data for the matriptase gene at the level of transcription. The matriptase-specific QRT-PCR primer-probe set quantitatively estimated relative amounts of matriptase mRNA transcripts. Overall, results were very similar between the two different techniques, with a correlation coefficient of 0.710 (Fig 3). This data confirmed the reliability of our microarray strategy to identify differentially expressed genes in MPM specimens. We then extended this validation by estimating MPM matriptase protein content in seven of the MPM cell lines and in the four tissue specimens to determine whether the mRNA overexpression was reflected by a corresponding increase in proteins (Fig 4). The M32 monoclonal antibody detected, as expected, both the single-chain and two-chain forms of matriptase (70-kd) in 8 of 11 tested samples. Thus, these results collectively revealed a positive qualitative correlation among the three different analyses of matriptase expression in our MPM specimens.

[FIGURES 3-4 OMITTED]

DISCUSSION

Hierarchic clustering algorithms have revealed subtle, specific patterns of gene expression that are useful for molecular classification of various cancers. (22,23) In our current analysis, agglomerative hierarchic clustering segregated MPM specimens according to histologic type based on a 180-gene subset. Each gene member from this subset was selected based on a significant differential, simultaneously divergent expression pattern between histologic types. The fidelity of this gene subset was confirmed when it was used to correctly classify two unknown MPM cell lines into the E gene cluster.

To the best of our knowledge, this study represents the largest of its kind to specifically aim at finding novel mesothelioma-associated genes, which may provide for insights into mechanisms of tumorigenesis. We analyzed a total of 14 specimens of MPM cell lines and fresh-frozen tissues on a 4,800-gene cDNA microarray. Rihn and colleagues (7) were the first group to employ microarrays to identify potential genes involved in pleural cell malignant transformation, but the major limitation of their study was the use of a single MPM cell line. From subsequent microarray studies, it has been acknowledged that gene-profiling precision and reliability increase with the number of samples grouped for analysis. (24) Another study by Kettunen and colleagues (8) had similar limitations. It investigated the expression patterns of four MPM cell lines (E, one cell line; B, three cell lines) derived from primary tumors on a 588-gene, cancer-specific array. In contrast, Gordon and colleagues (25,26) studied larger numbers of MPM tissue samples with oligonucleotide microarrays, lint their focus was on identifying a minimal number of genes for use as potential clinical diagnostic tests and prognostic tests.

Among genes identified as significantly overexpressed across specimens on microarrays, we quantitatively validated matriptase overexpression by QRT-PCR and further confirmed our results with immunoblotting. Matriptase overexpression may be important in the pathogenesis of MPM. Matriptase has been identified as a type II transmembrane serine protease expressed in a wide variety of epithelial human cancers, including prostate cancer, (27) breast cancer, (28) and ovarian cancer, (29) but not in mesenchymal tissues. (9) It has been proposed to have multiple functions, acting as a potential activator of critical molecules associated with tumor invasion and metastasis. One study (30) demonstrated that matriptase could activate hepatocyte growth factor/scatter factor, a mesenchymall cell-derived cancer cell growth and motility, protein. Takeuchi and colleagues (31) characterized matriptase to be an in vitro activator of prourokinase plasminogen activator, linking it to the activation of additional protease systems implicated in cancer cell invasion and metastasis. Our validated microarray expression data for matriptase are consistent with those in the literature, indicating the highest levels in E MPM, moderate levels in B MPM, mid (relatively) very low levels in S MPM. B MPM shares qualities of the other two histologic types and could be expected to have intermediate matriptase expression, as found in our microarray data. Matriptase overexpression in MPM is, thus, a novel finding that we validated at both the mRNA and protein level. However, future investigations will need to better define the functional role of matriptase in the molecular pathogenesis of MPM.

We also identified additional novel genes with potential importance in molecular mechanisms relating to MPM. For example, IGFBP5 was underexpressed in MPM, but IGF-I was overexpressed across all MPM specimens. The IGF axis was previously characterized to be a complex system composed of the IGF ligands, a superfamily of IGFBPs, and IGF transmembrane receptors. (32,33) Dysregulation at each level in this system has been implicated in cancer growth and in the progression of multiple cell types, including prostate, (34) lug, (35,36) and colon, (37) The precise functional role of IGFBP5 in cancer remains poorly defined and is likely to be cell type-specific. (38) Some studies have documented IGFBP5-induced growth stimulation of prostate cancer cells (39) and breast cancer cells, 4[degrees] while other studies have found that IGFBP5 inhibited cervical carcinoma (41) and osteosarcoma (42) cell proliferation. Thus, we speculate that IGFBP5 acts as an inhibitor of IGF-I in MPM and that underexpression of IGFBP5 may contribute to uncontrolled cellular expansion via an IGF-mediated autocrine growth loop. In addition, we identified from the discriminatory 180-gene subset a cDNA clone similar to the transport and signal transduction proteolipid MAL2 (member of the MAL protein family) that may be important, since MAL has been implicated as a molecular marker in B-cell lymphoma, (43) and recently was identified in E-derived renal and thyroid carcinomas. (44) Furthermore, MAL2 gene expression and protein function have been studied in hepatocellular and colorectal carcinoma cell lines. (45)

A potential limitation of our study is the use of a single type of reference for normal (CRL-9444), which may have introduced experimental bias. Currently, there is no consensus on what constitutes an ideal reference for any particular type of microarray experiment. (46) Multiple strategies have been used successfully by others. Ross and colleagues (47) used a "universal" reference RNA that was derived from an equal mix of 11 diverse human tumor cell lines. Alternatively, Holloway et al (46) and Kikuchi and colleagues (48) used commercially available RNA references derived from pooled, tissue-specific nonmalignant samples. A third, equally effective strategy, described, for example, by Miura and colleagues, (49) used immortalized, nonmalignant cells (bronchial epithelial) that are known to transform into the cancer of interest (lung adenocarcinoma). Thus, without an absolute standard, we chose the mesothelial cell line CRL-9444 as our normal control for the following reasons: (1) any human mesothelial cell is a rational baseline since it represents the noncancerous cell of origin for malignant mesotheliomas; (2) a cell line allowed for adequate and consistent amounts of RNA to be isolated for multiple cDNA microarray experiments; (3) Rihn and colleagues (7) validated its use as a normal reference in their mesothelioma cDNA microarray study; and (4) continued and widened usage of this reference mesothelial cell line in subsequent cDNA microarray studies may facilitate the comparison of data from different research groups.

In summary, transcriptional profiling of MPM cell lines and primary tumor tissues has identified novel genes like matriptase and IGFBP5 with significant differential expression. These genes may have a role in the pathogenesis of MPM, a malignancy known to be heterogeneous and composed of complex phenotypes. Furthermore, these genes may represent novel therapeutic targets. In our study, hierarchic cluster analysis yielded a set of genes that was able to discriminate among histologic MPM types. Our findings suggest that an alternative method of MPM classification may be based on distinct gene expression profiles. Clearly, additional validated, large-scale, microarray-based studies of MPM are needed to confirm these results. Functional gene studies are further required to establish any potential clinical significance.

ACKNOWLEDGEMENT: We gratefully acknowledge Dr. Gloria Niehans (Minneapolis Veterans Affairs Medical Center), for performing pathologic studies of the unclassified mesothelioma cell lines, Dr. Shawn Groth, for performing preliminary matriptase reverse transcription PCR experiments, Suzanne Grindle (University of Minnesota Cancer Center Bioinformatics Core), for providing technical assistance with microarray analysis software, and Robin Bliss (University of Minnesota Cancer Center Biostatistics Core) for assistance with statistical analyses. We also thank Dr. Mary Knatterud for expert editorial assistance.

* From the Department of Surgery (Drs. Hoang, D'Cunha, and Maddaus, and Ms. Casmey), Division of Cardiovascular and Thoracic Surgery, and the Thoracic Oncology Laboratory (Drs. M. Kratzke, Frizelle, and R. Kratzke), Division of Hematology, Oncology, and Transplant, Department of Medicine, University of Minnesota Medical School, Minneapolis, MN.

REFERENCES

(1) Britton M. The epidemiology of mesothelioma. Semin Oncol 2002; 29:18-25

(2) Carbone M, Kratzke RA, Testa JR. The pathogenesis of mesothelioma. Semin Oncol 2002; 29:2-17

(3) Steele JP. Prognostic factors in mesothelioma. Semin Oncol 2002; 29:36-40

(4) Steele JP, Shamash J, Evans MT, et al. Phase II study of vinorelbine in patients with malignant pleural mesothelioma. J Clin Oncol 2000; 18:3912-3917

(5) Mossman BT, Churg A. Mechanisms in the pathogenesis of asbestosis and silicosis. Am J Respir Crit Care Med 1998; 157:1666-1680

(6) Testa JR, Pass HI, Carbone M. Molecular biology of mesothelioma. In: DeVita V, Hellman S, Rosenberg S, eds. Principles and practice of oncology. Philadelphia, PA: Lippincott, 2001; 1937-1943

(7) Rihn BH, Mohr S, McDowell SA, et al. Differential gene expression in mesothelioma. FEBS Lett 2000; 480:95-100

(8) Kettunen E, Nissen AM, Ollikainen T, et al. Gene expression profiling of malignant mesothelioma cell lines: cDNA array study. Int J Cancer 2001; 91:492-496

(9) Oberst M, Anders J, Xie B, et al. Matriptase and HAI-1 are expressed by normal and malignant epithelial cells in vitro and in vivo. Am J Pathol 2001; 158:1301-1311

(10) Pass HI, Stevens EJ, Oie H, et al. Characteristics of nine newly derived mesothelioma cell lines. Ann Thorac Surg 1995; 59:835-844

(11) D'Cunha J, Corfits AL, Herndon JE II, et al. Molecular staging of lung cancer: real-time polymerase chain reaction estimation of lymph node micrometastatic tumor cell burden in stage I non-small cell lung cancer; preliminary results of Cancer and Leukemia Group B Trial 9761. J Thorac Cardiovase Surg 2002; 123:484-491

(12) Kricka LJ. Ultrasensitive immunoassay techniques. Clin Bio chem 1993; 26:325-331

(13) Litt GJ, Bobrow MN. Tyramide signal amplification: applications in the detection of infectious agents. In: Specter S, Bendinelli M, Friedman H, eds. Rapid detection of infectious agents. New York, NY: Plenum Press, 1998; 159-173

(14) Sugita M, Geraci M, Gao B, et al. Combined use of oligonucleotide and tissue microarrays identifies cancer/testis antigens as biomarkers in lung carcinoma. Cancer Res 2002; 62:3971-3979

(15) Eisen MB, Spellman PT, Brown PO, et al. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 1998; 95:14863-14868

(16) Weinstein JN, Myers TG, O'Connor PM, et al. An information intensive approach to the molecular pharmacology of cancer. Science 1997; 275:343-349

(17) Gerard CJ, Andrejka LM, Macina RA. Mitochondrial ATP synthase 6 as an endogenous control in the quantitative RT-PCB analysis of clinical cancer samples. Mol Diagn 2000; 5:39-46

(18) Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real time quantitative PCR and the 2(-Delta Delta C(t)) method. Methods 2001; 25:402-408

(19) Heid CA, Stevens J, Livak KJ, et al. Real time quantitative PCR. Genome Res 1996; 6:986-994

(20) Frizelle SP, Grim J, Zhou J, et al. Re-expression of p16INK4a in mesothelioma cells results in cell cycle arrest, cell death, tumor suppression and tumor regression, Oncogene 1998; 16:3087-3095

(21) Benaud CM, Dickson RB, Lin CY. Regulation of the activity of matriptase on epithelial cell surfaces by a blood derived factor. Eur J Biochem 2001; 268:1439-1447

(22) Bhattacharjee A, Richards WG, Staunton J, et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci U S A 2001; 98:13790-13795

(23) Sorlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 2001; 98:10869-10874

(24) Quackenbush J. Computational analysis of microarray data. Nat Rev Genet 2001; 2:418-427

(25) Gordon GJ, Jensen BY, Hsiao LL, et al. Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Res 2002; 62:4963-4967

(26) Gordon GJ, Jensen BV, Hsiao LL, et al. Using gene expression ratios to predict outcome among patients with mesothelioma. J Natl Cancer Inst 2003; 95:598-605

(27) Takeuchi T, Shuman MA, Craik CS. Reverse biochemistry: use of macromolecular protease inhibitors to dissect complex biological processes and identify, a membrane-type serine protease in epithelial cancer and normal tissue. Proc Natl Acad Sci U S A 1999; 96:11054-11061

(28) Benaud CM, Oberst M, Dickson RB, et al. Deregulated activation of matriptase in breast cancer cells. Clin Exp Metastasis 2002; 19:639-649

(29) Oberst MD, Johnson MD, Dickson RB, et al. Expression of the serine protease matriptase and its inhibitor HAI-1 in epithelial ovarian cancer: correlation with clinical outcome and tumor clinicopathological parameters. Clin Cancer Res 2002; 8:1101-1197

(30) Lee SL, Dickson RB, Lin CY. Activation of hepatocyte growth factor and urokinase/plasminogen activator by matriptase, an epithelial membrane serine protease. J Biol Chem 2000; 275:36720-36725

(31) Takeuchi T, Harris JL, Huang W, et al. Cellular localization of membrane-type serine protease 1 and identification of protease-activated receptor-2 and sing]e chain urokinase-type plasminogen activator as substrates. J Biol Chem 2000; 275:26333-26342

(32) Grimberg A, Cohen P. Role of insulin-like growth factors and their binding proteins in growth control and carcinogenesis. J Cell Physiol 2000; 183:1-9

(33) Firth SM, Baxter RC. Cellular actions of the insulin-like growth factor binding proteins. Endocr Rev 2002; 23:824-854

(34) Grimberg A, Cohen P. Growth hormone and prostate cancer: guilty by association? J Endocrinol Invest 1999; 22:64-73

(35) Allen JT, Bloor CA, Kedia RK, et al. Expression of growth hormone-releasing factor, growth hormone, insulin like growth factor-1 and its binding proteins in human lung. Neuropeptides 2000; 34:98-197

(36) Quinn KA, Treston AM, Unsworth EJ, et al. Insulin like growth factor expression in human cancer cell lines. J Biol Chem 1996; 271:11477-1148:3

(37) Reinmuth N, Fan F, Liu W, et al. Impact of insulin-like growth factor receptor-1 function on angiogenesis, growth, and metastasis of colon cancer. Lab Invest 2002; 82:1377-1389

(38) Schneider MR, Wolf E, Hoeflich A, et al. IGF-binding protein-5: flexible player in time IGF system and effector on its own. J Endocrinol 2002; 172:423-440

(39) Miyake II, Nelson C, Rennie PS, et al. Overexpression of insulin-like growth factor binding protein-5 helps accelerate progression to androgen-independence in the human prostate LNCaP tumor model through activation of phosphatidylinositol 3'-kinase pathway. Endocrinology 2000; 141:2257-2265

(40) Perks CM, McCaig C, Holly JM. Differential insulin-like growth factor (IGF)-independent interactions of IGF binding protein-3 and IGF binding protein-5 on apoptosis in human breast cancer cells: involvement of the mitochondria. J Cell Biochem 2000; 80:248-258

(41) Higo H, Duan C, Clemmons DR, et al. Retinoic acid inhibits cell growth in HPV negative cervical carcinoma cells by induction of insulin-like growth factor binding protein-5 (ICFBP-5) secretion. Biochem Biophys Res Commun 1997; 239:706-709

(42) Conover CA, Kiefer MC. Regulation and biological effect of endogenous insulin-like growth factor binding protein-5 in human osteoblastic cells. J Clin Endocrinol Metab 1993; 76:1153-1159

(43) Copie-Bergman C, Plonquet A, Alonso MA, et al. MAL expression in lymphoid cells: further evidence for MAL as a distinct molecular marker of primary mediastinal large B-cell lymphomas. Mod Pathol 2002; 15:1172-1180

(44) Marazuela M, Acevedo A, Adrados M, et al. Expression of MAL, an integral protein component of time machinery for raft mediated pical transport, in human epithelia. J Histochem Cytochem 2003; 51:665-674

(45) de Marco MC, Martin-Belmonte F, Kremer L, et al. MAL2, a novel raft protein of the MAL family, is an essential component of the machinery for transcytosis in hepatoma HepG2 cells. J Cell Biol 2002; 159:37-44

(46) Holloway AJ, van Laar RK, Tothill RW, et al. Options available from start to finish-for obtaining data from DNA microarrays II. Nat Genet 2002; 32(suppl):481-489

(47) Ross DT, Scherf U, Eisen MB, et al. Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet 2000; 24:227-235

(48) Kikuchi T, Daigo Y, Katagiri T, et al. Expression profiles of non-small cell lung cancers an cDNA microarrays: identification of genes for prediction of lymph-node metastasis and sensitivity to anti-cancer drugs. Oncogene 2003; 22:2192-2205

(49) Miura K, Bowman ED, Simon R, et al. Laser capture microdissection and microarray expression analysis of lung adenocarcinoma reveals tobacco smoking- and prognosis-related molecular profiles. Cancer Res 2002; 62:3244-3250

Dr. Hoang was supported by grants from the Lillehei Heart Institute, the Veterans of Foreign Wars/Ladies Auxiliary Cancer Research Center Endowment Fund, and a by training grant from the National Institutes of Health (grant No. T32HL07062) that was awarded to the Division of Hematology, Oncology, and Transplant, University of Minnesota. Dr. R. Kratzke was supported, in part, by grants from the National Institutes of Health (grant No. R21CA83689) and from the Veterans Affairs Research Service.

Manuscript Received June 3, 2003; revision accepted October 6, 2003.

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (e-mail: permissions@chestnet.org).

Correspondence to. Robert A. Kratzke, MD, Division Hematology, Ontology, and Transplant, 111E MVAMC, 1 Veterans Dr, Minneapolis. MN 55417: e-mail: kratz003@tc.umn.edu

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