Lung cancer causes approximately 1.2 million deaths per year worldwide, and non-small cell lung cancer (NSCLC) represents 85% of all lung cancers. Understanding the molecular events in non-small cell lung cancer (NSCLC) is essential to improve early diagnosis and treatment for this disease.
Methodology and Principal Findings
In an attempt to identify novel NSCLC related genes, we performed a genome-wide screening of chromosomal copy number changes affecting gene expression using microarray based comparative genomic hybridization and gene expression arrays on 32 radically resected tumor samples from stage I and II NSCLC patients. An integrative analysis tool was applied to determine whether chromosomal copy number affects gene expression. We identified a deletion on 14q32.2-33 as a common alteration in NSCLC (44%), which significantly influenced gene expression for HSP90, residing on 14q32. This deletion was correlated with better overall survival (P = 0.008), survival was also longer in patients whose tumors had low expression levels of HSP90. We extended the analysis to three independent validation sets of NSCLC patients, and confirmed low HSP90 expression to be related with longer overall survival (P = 0.003, P = 0.07 and P = 0.04). Furthermore, in vitro treatment with an HSP90 inhibitor had potent antiproliferative activity in NSCLC cell lines.
We suggest that targeting HSP90 will have clinical impact for NSCLC patients.
Citation: Gallegos Ruiz MI, Floor K, Roepman P, Rodriguez JA, Meijer GA, et al. (2008) Integration of Gene Dosage and Gene Expression in Non-Small Cell Lung Cancer, Identification of HSP90 as Potential Target. PLoS ONE 3(3): e0001722. doi:10.1371/journal.pone.0001722
Academic Editor: Christoph Plass, Ohio State University, United States of America
Received: December 28, 2007; Accepted: February 4, 2008; Published: March 5, 2008
Copyright: © 2008 Gallegos Ruiz et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors have no support or funding to report.
Competing interests: Paul Roepman is employed by Agendia B.V.
Lung cancer is the leading cause of cancer deaths worldwide , and non-small cell lung cancer (NSCLC) represents 85% of lung cancers. A better understanding of the molecular events underlying the development and progression of the disease may contribute to improve clinical management of NSCLC patients. A number of genes, e.g. P53, RAS, P16 and EGFR, have been shown to be altered in NSCLC . Given the heterogeneous and complex nature of this tumor type, it is likely that many genes driving NSCLC tumorigenesis have yet to be identified.
Chromosomal aberrations are thought to be critical events in human tumorigenesis, and several genomic regions frequently harboring DNA gains (3q, 5p, 7q, 8q, 11q and 16p) and losses (3p, 4q, 5q, 6q, 8p 9p and 13q, 17q) have been identified in NSCLC patients . Using array based comparative genomic hybridization (aCGH) and gene expression microarrays, DNA copy number changes and gene expression can be measured throughout the whole genome of tumor cells. By combining the data from these analyses, it is possible to obtain an integrated genome wide view of gene dosage aberrations and their effect on gene expression, which might help in identifying genes important in NSCLC .
In the present study, we have performed an integrative analysis of chromosomal copy number and gene expression on radically resected tumor samples from 32 NSCLC patients. Two new algorithms, ‘CGH call’ and ‘ACE-it’, were applied to analyze the data. We identified a deletion on chromosome region 14q32.2-33 in 44% of NSCLC patients. This deletion was related with improved patient survival, and was associated with decreased expression of HSP90, a molecular chaperone for several oncoproteins that is being explored as a novel target in anticancer therapy. Low HSP90 expression was correlated with improved survival in the 32 NSCLC patients analyzed initially. Further analysis of three independent sets of NSCLC patients confirmed a significant association between patient survival and HSP90 expression. In addition, in vitro experiments show NSCLC cell lines to be extremely sensitive to the HSP90 inhibitor 17-AAG. Our data suggest and important role for HSP90 in NSCLC.
Patients and samples
The test set consisted of radically resected tumor specimens of 32 early stage NSCLC patients. Three patients had a survival time of less than 30 days and were considered postoperative deaths. Therefore these three patients are not included in the survival analyses. Patients had a median follow up of 86 months (range 0.4-135.5). Verbal informed consent had been obtained from all patients and handling of samples was in accordance with protocols approved by the ethical board “subcommissie voor de ethiek van het mensgebonden onderzoek” from the VU University Medical Center in Amsterdam.
The first validation set consisted of 140 radically resected NSCLC patients from the European lung cancer consortium. Patients had a median follow up of 35 months. All patients included had had no prior malignancy, pathological tumor stage 1 or 2 (T1-2), node stage 0+1 (N0-1), no distant metastasis (M0) at time of operation, and no residual disease after resection (R0). None of these patients received (neo)adjuvant chemo- or radiotherapy. The second validation consisted of 111 early stage NSCLC patients from Bild et al. . The third validation set consisted of the publicly available “datasets 1 and 2” from Lu et al.  and contained 54 early stage NSCLC patients. A full description of patient characteristics of all four patient sets is provided in Table 1.
Isolation of genomic DNA and array Comparative Genomic Hybridization
Cryo-sections of frozen tissue samples, flanking the sections used for RNA and DNA isolation, were verified by the study pathologist (WM) to contain at least 50% of tumor cells. Genomic DNA was extracted from each sample using Trizol following manufacturer instructions (Life Technologies, Breda, The Netherlands). DNA labeling and hybridization on CGH 30K oligonucleotide microarrays was performed as described by van den IJssel et al .
RNA isolation and gene expression micro arrays
RNA isolation and cDNA labeling followed standard protocols. Hybridization was performed on Agilent platform according to standard procedures described by the manufacturer and elsewhere .
For array CGH, spot analysis and quality control were performed using BlueFuse version 3.2 (BlueGenome, Cambridge, UK). Breakpoints, gains, losses and amplifications were detected using the algorithm CGH call. This algorithm converts raw log2ratios to absolute measures of “loss”, “normal”, “gain” or “amplification” by applying a segmentation algorithm combined with a probability mixture model . In order to statistically test whether gene expression was affected by gene dosage we applied an array CGH expression integration tool, ACE-it, in which the called array CGH and normalized log10 ratios for expression arrays were used as input data. ACE-it uses the one-sided Wilcoxon rank sum statistics to test which chromosomal copy number aberrations recurrently affect RNA expression. Calculated p-values are adjusted for multiple testing using the Benjamini Hochberg method . ACE-it only tests genes that meet the criteria of contamination and balance, which are controlled through a threshold on the number of samples. Here the threshold was set at a fixed default setting of 9 samples, meaning that only those chromosomal positions were taken into account that had at least 9 samples in one CGH calling status and no more than 9 in the other status. The entire array CGH data set of the test series (n = 32) is available at the GEO database (http://www.ncbi.nlm.nih.gov/projects/geo, accession number GSE7878). The gene expression data of both the test set (n = 32) and validation set 1 (n = 140) for the 359 genes identified by ACE-it is available at the Array Express database (www.ebi.ac.uk/aerep/login, accession number Array design: A-MEXP-749, Experiment data: E-TABM-270).
Gene expression data of validation set 2 was obtained using Affymetrix Hu133plus2 chips (GEO accession number GSE3141). The mean value of the MAS5 calculated signal intensities of four probesets detecting HSP90AA1 was used in our calculations.
The third validation set contained gene expression data from Affymetrix Hu95 and Hu133 chips (GEO accession number GSE6253). The Hu95 chip contained one probeset detecting HSP90AA1 and the Hu133 chip contained four probesets, of which the mean value of the RMA calculated signal intensities was used in our calculations.
An univariate cox regression analysis was performed to investigate relation of gene expression values with survival time. Survival curves were constructed using the Kaplan Meier method and differences in overall were evaluated using the log-rank test. In the test set, three patients with less than 30 days survival time were excluded from the survival analysis, as their death was considered surgical mortality. To determine the independent effects of HSP90 expression, histologic subtype, tumor stage, age and gender, a multivariate cox regression analysis was performed. A P value of less than 0.05 was considered statistically significant.
Multiplex Ligation dependent Probe Amplification (MLPA)
For Multiplex Ligation dependent Probe Amplification (MLPA), the subtelomere probe set P070 (MRC Holland, Amsterdam, The Netherlands) containing a probe located within the band 14q32.33 (region 104874216 to 105070384) was used. MLPA was performed according to manufacturer's instructions using 100ng DNA as input. DNA isolated from blood of a pool of healthy donors was used as reference sample. Probe signals were normalized by dividing the peak area of chromosome 14q by the peak area of chromosome 14p. MLPA generated 14q/14p ratios are plotted against the mean normalized log2ratios of the oligos in area 104787271-105071522 from the array (total of 11 oligos). This region covers the region of the MLPA probe.
In order to validate expression values obtained via gene expression arrays, we performed quantitative real-time PCR using Taqman® technology and the ABI PRISM 7500 Sequence Detection System instrument equipped with the SDS version 1.3.0 software (Applied Biosystems, Foster City, CA, USA). Forward and reverse primers and probes were designed and produced by Applied Biosystems for HSP90AA1 (Hs00743767_sH), and for the endogenous control gene GUSB (Hs00939626_mi). PCR was carried out in a 25-µl reaction volume that contained 50 ng of cDNA, 1× TaqMan Universal PCR Master Mix, and the primer and probe sets for HSP90AA1 and GUSB. Each sample was analyzed in duplicate, and the average threshold cycle (Ct) values of each sample for GUSB, were subtracted from the average Ct values for HSP90AA1. Taqman-generated ΔCt values were log transformed to expression values and plotted against the Δlog2ratios between GUSB and HSP90AA1 from the expression array.
Cell growth inhibition studies
Growth inhibition following in vitro exposure to the clinically used Hsp90 inhibitor 17-AAG (InvivoGen, San Diego, CA) was examined in 4 EGFR wild type NSCLC cell lines (H460, H157, H441 and A549, obtained from Drs. P. Dennis and F. Kaye, NCI, Bethesda, MD). Briefly, 105 cells were seeded in 6-well tissue culture plates (Sigma-Aldrich, St. Louis, MO), allowed to adhere, and then continuously exposed to various concentrations of 17-AAG (0, 10, 30, 100, 300, 1000 nM) for 24, 48, or 72 hours. At each time point, cells were detached from the wells, incubated with trypan blue, and viable (trypan blue-excluding) cell number was determined in triplicate using a hemacytometer. The mean cell number with standard error bars is shown at each time point. In addition, the IC50 (drug concentration at which 50% growth inhibition is obtained) of 17-AAG at 72 hours was determined for each cell line.
Gene dosage-related gene expression changes in NSCLC
Chromosomal aberrations were abundant in the 32 NSCLC patients analyzed. In order to identify breakpoints of gains and losses we applied the algorithm CGH call . In Table 2 the chromosomal regions in which gains or losses were present in at least 20% of patients are listed. The statistical tool ACE-it  was used to determine whether gene copy number affected gene expression. A total of 359 transcripts turned out to be significantly affected by copy number. In Figure 1 the areas of affected genes are indicated for 32 NSCLC patients, shown in green (gained regions) or red (lost regions).
Summary plot for called gains and losses in 32 resected NSCLC patients with DNA copy number changes indicated in grey. Positive values indicate the percentage of samples found with a gain. Negative values indicate the percentage of samples harboring a loss at the specified chromosome location. Genes in specified regions affected by copy number gain are indicated in green and genes affected by copy number loss are indicated in red. A selection of affected genes is indicated. The full list of 359 affected transcripts can be found in supplementary table S1.
An univariate cox regression survival analysis was performed for expression of all 359 genes that were identified to be influenced by copy number (see supplementary table S1). After multiple testing correction (using the Benjamini Hochberg method) none of 359 genes remained significant for survival on this small patient set (n = 32). The top list of genes correlated with survival (ranked on raw p-values) contained mainly genes located on chromosomes 3 and 5 gained regions. We also observed one gene in the top-20 list, HSP90AA1, located on chromosome 14. The HSP90AA1 gene (generally referred to as HSP90), located on 14q32.2, was the only gene in this region with significantly reduced expression in patients affected by loss of this region (P = 0.05). These observations prompted us to investigate this locus in more detail.
Genomic aberrations on 14q and HSP90AA1 gene expression correlation with survival
We investigated the correlation of the recurrent tight deletion at region 14q32.2-33, with survival. Interestingly, patients in whom this region was deleted had an improved overall survival (OS) compared to patients harboring a normal gene dosage at this locus (5 year OS 69% vs. 41%, P = 0.004-Figure 2A).
(A) Kaplan-Meier curves for overall survival are shown for 29 patients in relation to gene dosage in chromosome region 14q32.33. (B) Overall survival for 29 patients in relation to HSP90 expression. Low expression was defined as expression lower than the median of the total 32 samples, “normal” expression was defined as higher than the median of 32 samples. Three of 32 patients included in the analysis of gene dosage and expression were excluded from the survival analysis because of a survival time of less than 30 days.
In order to investigate HSP90 expression in relation with survival, we divided the 32 patients into two groups based on their survival status two years after tumor resection. About half of the patients were categorized as “high-risk” and half as “low-risk”. To identify whether both risk groups could be discriminated using gene expression of HSP90, we constructed Kaplan Meier survival estimates for two equally large groups with “low” and “normal”, based on the median HSP90 expression. Low expression of HSP90 was associated with better overall survival (5 year OS 70% vs. 40%, P = 0.13–Figure 2B) although differences between the two groups were initially not significant due probably to a combination of a low magnitude of the difference and a low number of patients analyzed.
Technical validation of 14q deletion and HSP90 expression
To validate the deletion observed in region 14q32.2-33 we performed multiplex ligation dependent probe amplification  (MLPA) analysis using a subtelomere probeset containing a probe in region 14q32.33. The correlation coefficient (R2) between the loss of this area detected by array CGH and by MLPA was 0.439.
To validate the HSP90 expression data obtained with microarrays we performed quantitative RT PCR using the Taqman® technology. A good correlation between the expression of HSP90 measured with the two different techniques was observed (R2 = 0.6431).
Validation of HSP90 expression and relation with survival in independent patient series
To further investigate the association between low HSP90 expression and NSCLC patient prognosis, we used three independent validation sets of NSCLC patients. In all three patient sets, the “low-risk”/“high-risk” patient distribution across patient cohorts was approximately two-thirds vs. one-third. Therefore, we used the 33-percentile of HSP90 expression as cut off for separation of patients with “normal” (i.e. high-risk) and “low” (i.e. low-risk) expression. For all three validation sets, low expression of HSP90 was correlated with improved overall survival. This correlation was significant for the first and third validation sets (P = 0.003 and P = 0.04), and borderline significant for the second set (P = 0.07) (Figure 3). Multivariate analysis revealed that HSP90 prognostic value was independent from the stage, histologic subtype, age and gender of patients (Table 3).
Overall survival for (A) 140 patients with NSCLC, validation set 1 (B) 111 NSCLC patients, validation set 2 and (C) 54 patients with NSCLC, validation set 3. The cut off for distinction between low and “normal” expression was based on the 33-percentile of expression values.
Validation of Hsp90 as a viable molecular target in a panel of NSCLC cell lines
Hsp90 is a molecular chaperone that stabilizes several oncoproteins, including EGFR, and constitutes a novel potential target for anticancer therapy. The data above showed that Hsp90 expression level is a prognostic indicator of long-term survival in a large series of NSCLC patients, and suggest that Hsp90 inhibitors may have broader utility in this disease than previously recognized. To examine this possibility, we tested whether pharmacologic inhibition of Hsp90 function would impact the in vitro cell growth of a panel of NSCLC cell lines. The growth of these cell lines, all bearing wild-type EGFR, was profoundly inhibited, in a dose- and time-dependent manner, by sub-micromolar concentrations of 17-AAG, an Hsp90 inhibitor  currently in phase II clinical trial (Figure 4). At 72 hours, the IC50 was below 50 nM 17-AAG in all cases, and higher drug concentrations consistently resulted in marked cytotoxicity.
(A–D) time- and dose-dependent inhibition of the in vitro growth of H460, H157, H441, and A549 NSCLC cell lines following exposure to 17-AAG. Cells were seeded at 105/well, and viable cell number was determined on subsequent days as described in Methods. 17-AAG concentrations at (H441) or above (A549, H460 & H157) 30 nM uniformly resulted in time-dependent loss of cell viability. The IC50 value of 17-AAG (continuous exposure for 72 h) for each cell line is as follows: H460 = 30 nM, H157 = 15 nM, H441 = 8 nM, and A549 = 20 nM.
Based on array-CGH data in NSCLC, it has been shown that multiple molecular carcinogenesis pathways exist that are most likely related to gender and smoking habits . Furthermore, it was shown that there is a large overlap between aberrations observed in the adenocarcinoma and squamous cell carcinoma subtype, except for 3q gains which seem to be more specific for the squamous cell carcinoma subtype . Various gene expression signatures have been correlated to survival of NSCLC patients –. In addition, molecular studies have allowed the development of personalized treatment approaches in several tumor types –. However, it is likely that many cancer-related target genes have not been identified yet. In this regard, integrated genome wide screening of copy number changes and gene expression using microarrays has been recently carried out in various tumor types to identify genes whose expression is affected by gene dosage , –. These studies aim to identify novel cancer-related genes and to define novel biomarkers for response or prognostic signatures. In both NSCLC and ductal pancreatic cancer, two focal amplifications of 8p12 and 20q11 have been studied in detail leading to two candidate genes (WHSC1L1 and TPX2) important in these diseases 
We here performed an integrated genome wide screening of gene copy number changes and gene expression in 32 radically resected NSCLC patients, in order to identify novel NSCLC-related genes. By using ACE-it, a novel informatics tool for integration of gene dosage and gene expression data, we identified 359 transcripts to be significantly affected by copy number. A cox survival analysis on all 359 genes revealed no significant relation after multiple testing. The top list of genes related to survival mainly included genes residing on gained regions 3q and 5p. These regions cover many genes and to pinpoint the gene of most importance is a challenging task. Further investigations should elucidate the importance of these genes in relation to NSCLC, in particular of those in the top list of correlation with survival such as SLC45A2, WDR70 and NIPBL (see supplementary table S1). In this paper we focused on the recurrent deletion on chromosome 14 and the gene HSP90, which was also in the top list of relation with survival and not previously investigated in detail. Deletion of region 14q32.2-33 was correlated with improved survival, further suggesting that it may contain one or more genes related to NSCLC progression. Deletion of this region has been previously described by one group reporting genomic aberrations in NSCLC, but was not investigated in further detail . Out of the 109 genes mapping to the 14q32.2-33 region, HSP90 was the only gene with significantly lower expression in patients harboring the 14q32.2-33 deletion. In the initial series of 29 patients (3 patients excluded from survival analysis), we observed improved survival in patients with lower levels of HSP90. The association between HSP90 expression levels and NSCLC patient prognosis was confirmed to be significant in three independent validation sets of NSCLC patients. Multivariate analysis including stage, histology, age and gender showed that HSP90 remained independently related to survival.
A critical issue in defining “low” and “normal” expression is the choice of an appropriate cut off value. In the initial analyses we used the median of expression ratios as cut off between “low” and “normal” expression, since the low-risk and high-risk separation of patients was equal. However, in the validation sets the low-risk and high-risk survival groups were not equally balanced (two thirds versus one third). Consequently the cut off values used in these data sets was not the median value, but the 33-percentile.
In this study, using a genome wide integrative analysis of gene copy number and expression we were able to identify expression of HSP90 as an important gene in early stage NSCLC patients. HSP90 is a chaperone protein involved in the stabilization of multiple oncoproteins such as EGFR, Her-2 and Akt . Recent work has shown that HSP90 plays a role in maintaining the active conformation of EGFR and in particular EGFR mutants , . We show here that several NSCLC cell lines bearing wild-type EGFR are sensitive to HSP90 inhibition, indicating that the inhibitory effect of 17-AAG can not be solely attributed to mutant EGFR. HSP90 has been recently recognized as a potential cancer therapeutic target and investigations of HSP90 inhibitors are ongoing , . In this regard, glioblastoma cells overexpressing EGFR, but resistant to inhibition by EGFR kinase inhibitors, were sensitive to HSP90 inhibition . Therefore, while Hsp90 may be required to stabilize over-expressed or mutated EGFR, our data support a more wide-ranging and complex role for Hsp90 in mediating NSCLC growth and survival. The low nanomolar sensitivity observed in the 4 cell lines tested in our experiments, is in agreement with other published reports of highly 17-AAG sensitive tumor cell lines –. These concentrations are readily achievable in patients for prolonged time periods using current scheduling and dosing regimens .
In summary, the observation that HSP90 expression level is a prognostic factor for NSCLC patient survival (independent of EGFR mutational status), coupled with the extreme sensitivity of EGFR wild type NSCLC cells to the Hsp90 inhibitor 17-AAG, suggests that Hsp90 inhibitors may have greater clinical utility in NSCLC than has been previously considered and warrants further investigation of the dependence of other proto-oncogenes on this chaperone protein in NSCLC.
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We thank the European lung cancer consortium for providing microarray gene expression data of the 140 independent NSCLC patients; Netherlands Cancer Institute: Sjaak Burgers, Tony van de Velde; Medical University of Gdansk: Jacek Jassem, Amelia Szymanowska, Marcin Skrzypski, Barbara Szostakiewicz, Witold Rzyman; Medical University of Bialystok: Jerzy Laudanski, Miroslaw Kozlowski, Lech Chyczewski; Thoraxklinik Heidelberg: Michael Meister, Philipp A. Schnabel, Henrik Dienemann and Hans Hoffman. We also give our special thanks to Sjoerd Vosse (Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands) for helpful bioinformatics discussions and to Hans Gille (Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands) for performing MLPA analysis.
Conceived and designed the experiments: BY GM GG JR MG. Performed the experiments: KF KB MG. Analyzed the data: PR WM MG. Contributed reagents/materials/analysis tools: EJ JN TM Nv ES LN. Wrote the paper: BY PR GM GG JR ES LN MG. Other: Designed the study: GG.
- 1. Jemal A, Siegel R, Ward E, Murray T, Xu JQ, et al. (2006) Cancer statistics, 2006. Ca-A Cancer Journal for Clinicians 56: 106–130.
- 2. Sekido Y, Fong KM, Minna JD (2003) Molecular genetics of lung cancer. Annual Review of Medicine-Selected Topics in the Clinical Sciences 54: 73–87.
- 3. Balsara BR, Testa JR (2002) Chromosomal imbalances in human lung cancer. Oncogene 21: 6877–6883.
- 4. Chin K, DeVries S, Fridlyand J, Spellman PT, Roydasgupta R, et al. (2006) Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. Cancer Cell 10: 529–541.
- 5. van de Wiel MA, Kim KI, Vosse SJ, van Wieringen WN, Wilting SM, et al. (2007) CGHcall: calling aberrations for array CGH tumor profiles. Bioinformatics 23: 892–894.
- 6. van Wieringen WN, Belien JAM, Vosse SJ, Achame EM, et al. (2006) ACE-it: a tool for genome-wide integration of gene dosage and RNA expression data. Bioinformatics 22: 1919–1920.
- 7. Bild AH, Yao G, Chang JT, Wang Q, Potti A, et al. (2006) Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 439: 353–357.
- 8. Lu Y, Lemon W, Liu PY, Yi YJ, Morrison C, et al. (2006) A gene expression signature predicts survival of patients with stage I non-small cell lung cancer. Plos Medicine 3: 2229–2243.
- 9. van den IJssel P, Tijssen M, Chin SF, Eijk P, Carvalho B, et al. (2005) Human and mouse oligonucleotide-based array CGH. Nucleic Acids Research 33:
- 10. Glas AM, Floore A, Delahaye LJMJ, Witteveen AT, Pover RCF, et al. (2006) Converting a breast cancer microarray signature into a high-throughput diagnostic test. Bmc Genomics 7:
- 11. van de Wiel MA, Kim KI, Vosse SJ, van Wieringen WN, Wilting SM, et al. (2007) CGHcall: calling aberrations for array CGH tumor profiles. Bioinformatics 23: 892–894.
- 12. Benjamini Y, Hochberg Y (1995) Controlling the False Discovery Rate-A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B-Methodological 57: 289–300.
- 13. Schouten JP, McElgunn CJ, Waaijer R, Zwijnenburg D, Diepvens F, et al. (2002) Relative quantification of 40 nucleic acid sequences by multiplex ligation-dependent probe amplification. Nucleic Acids Research 30: e57.
- 14. Schulte TW, Neckers LM (1998) The benzoquinone ansamycin 17-allylamino-17-demethoxygeldanamycin binds to HSP90 and shares important biologic activities with geldanamycin. Cancer Chemotherapy and Pharmacology 42: 273–279.
- 15. Shibata T, Uryu S, Kokubu A, Hosoda F, Ohki M, et al. (2005) Genetic classification of lung adenocarcinoma based on array-based comparative genomic hybridization analysis: Its association with clinicopathologic features. Clinical Cancer Research 11: 6177–6185.
- 16. Tonon G, Wong KK, Maulik G, Brennan C, Feng B, et al. (2005) High-resolution genomic profiles of human lung cancer. Proceedings of the National Academy of Sciences of the United States of America 102: 9625–9630.
- 17. Beer DG, Kardia SLR, Huang CC, Giordano TJ, Levin AM, et al. (2002) Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nature Medicine 8: 816–824.
- 18. Wigle DA, Jurisica I, Radulovich N, Pintilie M, Rossant J, et al. (2002) Molecular profiling of non-small cell lung cancer and correlation with disease-free survival. Cancer Research 62: 3005–3008.
- 19. Chen HY, Yu SL, Chen CH, Chang GC, Chen CY, et al. (2007) A five-gene signature and clinical outcome in non-small-cell lung cancer. New England Journal of Medicine 356: 11–20.
- 20. Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, et al. (2004) Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. New England Journal of Medicine 350: 2129–2139.
- 21. Pao W, Wang TY, Riely GJ, Miller VA, Pan QL, et al. (2005) KRAS mutations and primary resistance of lung adenocarcinomas to gefitinib or erlotinib. Plos Medicine 2: 57–61.
- 22. Slamon D, Pegram M (2001) Rationale for trastuzumab (Herceptin) in adjuvant breast cancer trials. Seminars in Oncology 28: 13–19.
- 23. Balsara BR, Sonoda G, duManoir S, Siegfried JM, Gabrielson E, et al. (1997) Comparative genomic hybridization analysis detects frequent, often high-level, overrepresentation of DNA sequences at 3q, 5p, 7p, and 8q in human non-small cell lung carcinomas. Cancer Research 57: 2116–2120.
- 24. Pollack JR, Sorlie T, Perou CM, Rees CA, Jeffrey SS, et al. (2002) Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proceedings of the National Academy of Sciences of the United States of America 99: 12963–12968.
- 25. Redon R, Muller D, Caulee K, Wanherdrick K, Abecassis J, et al. (2001) A simple specific pattern of chromosomal aberrations at early stages of head and neck squamous cell carcinomas: PIK3CA but not p63 gene as a likely target of 3q26-qter gains. Cancer Research 61: 4122–4129.
- 26. Tay ST, Leong SH, Yu K, Aggarwal A, Tan SY, et al. (2003) A combined comparative genomic hybridization and expression microarray analysis of gastric cancer reveals novel molecular subtypes. Cancer Research 63: 3309–3316.
- 27. Choi JS, Zheng LT, Ha E, Lim YJ, Kim YH, et al. (2006) Comparative genomic hybridization array analysis and real-time PCR reveals genomic copy number alteration for lung adenocarcinomas. Lung 184: 355–362.
- 28. Burrows F, Zhang H, Kamal A (2004) Hsp90 activation and cell cycle regulation. Cell Cycle 3: 1530–1536.
- 29. Yang SC, Qu S, Perez-Tores M, Sawai A, Rosen N, et al. (2006) Association with HSP90 inhibits Cbl-mediated down-regulation of mutant epidermal growth factor receptors. Cancer Research 66: 6990–6997.
- 30. Shimamura T, Lowell AM, Engelman JA, Shapiro GI (2005) Epidermal growth factor receptors harboring kinase domain mutations associate with the heat shock protein 90 chaperone and are destabilized following exposure to geldanamycins. Cancer Research 65: 6401–6408.
- 31. Cullman SB, Whitesell L (2006) Heat shock protein 90: A unique chemotherapeutic target. Seminars in Oncology 33: 457–465.
- 32. Zhang H, Burrows F (2004) Targeting multiple signal transduction pathways through inhibition of Hsp90. Journal of Molecular Medicine-Jmm 82: 488–499.
- 33. Thomas CY, Stallings-Mann M, Zang H, Yang YC, Burrows F, et al. (2003) Glioblastoma (GBM) cells that overexpress epidermal growth factor receptors (EGFR) are sensitive to HSP90 but not EGFR kinase inhibitors. Clinical Cancer Research 9: 6098S.
- 34. Braga-Basaria M, Hardy E, Gottfried R, Burman KD, Saji M, et al. (2004) 17-allylamino-17-demethoxygeldanamycin activity against thyroid cancer cell lines correlates with heat shock protein 90 levels. Journal of Clinical Endocrinology and Metabolism 89: 2982–2988.
- 35. Yang J, Yang JM, Iannone M, Shih WJ, Lin Y, et al. (2001) Disruption of the EF-2 kinase/Hsp90 protein complex: A possible mechanism to inhibit glioblastoma by geldanamycin. Cancer Research 61: 4010–4016.
- 36. Zsebik B, Citri A, Isola J, Yarden Y, Szollosi J, et al. (2006) Hsp90 inhibitor 17-AAG reduces ErbB2 levels and inhibits proliferation of the trastuzumab resistant breast tumor cell line JIMT-1. Immunology Letters 104: 146–155.
- 37. Xu WP, Neckers L (2007) Targeting the molecular chaperone heat shock protein 90 provides a multifaceted effect on diverse cell signaling pathways of cancer cells. Clinical Cancer Research 13: 1625–1629.