|
Advertisement |
|
|
||
 |
|
|||||
|
|||||
|
 |
||||||
|
||||||
|
||||||
|
Journal of Clinical Oncology, Vol 26, No 18 (June 20), 2008: pp. 3015-3024 © 2008 American Society of Clinical Oncology. DOI: 10.1200/JCO.2007.15.7164 Stem Cell–Related "Self-Renewal" Signature and High Epidermal Growth Factor Receptor Expression Associated With Resistance to Concomitant Chemoradiotherapy in Glioblastoma
From the Laboratory of Tumor Biology and Genetics, Centre Universitaire Romand de Neurochirurgie; Division of Neuropathology; Multidisciplinary Oncology Center, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne; the National Center of Competence in Research Molecular Oncology at the Swiss Institute of Experimental Cancer Research; the Swiss Institute of Bioinformatics, Epalinges; the Centre Universitaire Romand de Neurochirurgie, the Service of Oncology, University Hospital Geneva, Geneva; the Department of Clinical Research and Radiation Oncology, Inselspital and University of Berne, Berne; the Institute of Neuropathology, University Hospital Zurich, Zurich, Switzerland; Data Center, European Organisation for Research and Treatment of Cancer (EORTC), Brussels, Belgium; Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel; the Department of General Neurology and Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen; Department of Neurooncology, University Clinic Heidelberg, Heidelberg; Institute of Neuropathology, Charité –Universitätsmedizin Berlin, Berlin, Germany; Department of Neurology, Erasmus, Rotterdam, the Netherlands; Institute of Neurology, Medical University of Vienna, Vienna, Austria; and the Department of Clinical Neurosciences, University of Calgary, Calgary, Canada Corresponding author: Monika E. Hegi, PhD, Laboratory of Tumor Biology and Genetics, Centre Universitaire Romand de Neurochirurgie, Centre Hospitalier Universitaire Vaudois and University of Lausanne, 46, rue du Bugnon, Lausanne 1011/Switzerland; e-mail: Monika.Hegi{at}chuv.ch
Purpose Glioblastomas are notorious for resistance to therapy, which has been attributed to DNA-repair proficiency, a multitude of deregulated molecular pathways, and, more recently, to the particular biologic behavior of tumor stem-like cells. Here, we aimed to identify molecular profiles specific for treatment resistance to the current standard of care of concomitant chemoradiotherapy with the alkylating agent temozolomide. Patients and Methods Gene expression profiles of 80 glioblastomas were interrogated for associations with resistance to therapy. Patients were treated within clinical trials testing the addition of concomitant and adjuvant temozolomide to radiotherapy. Results An expression signature dominated by HOX genes, which comprises Prominin-1 (CD133), emerged as a predictor for poor survival in patients treated with concomitant chemoradiotherapy (n = 42; hazard ratio = 2.69; 95% CI, 1.38 to 5.26; P = .004). This association could be validated in an independent data set. Provocatively, the HOX cluster was reminiscent of a "self-renewal" signature (P = .008; Gene Set Enrichment Analysis) recently characterized in a mouse leukemia model. The HOX signature and EGFR expression were independent prognostic factors in multivariate analysis, adjusted for the O-6-methylguanine-DNA methyltransferase (MGMT) methylation status, a known predictive factor for benefit from temozolomide, and age. Better outcome was associated with gene clusters characterizing features of tumor-host interaction including tumor vascularization and cell adhesion, and innate immune response. Conclusion This study provides first clinical evidence for the implication of a "glioma stem cell" or "self-renewal" phenotype in treatment resistance of glioblastoma. Biologic mechanisms identified here to be relevant for resistance will guide future targeted therapies and respective marker development for individualized treatment and patient selection.
In glioblastoma, introduction of combined chemoradiotherapy of concomitant and adjuvant temozolomide (TMZ) and radiotherapy (TMZ/RT  TMZ) has allowed significant prolongation of survival,1,2 in particular in patients with an epigenetically silenced O-6-methylguanine-DNA methyltransferase (MGMT) DNA repair gene.3,4 However, outcome remains unsatisfactory, and ongoing clinical trials explore modulation of MGMT or the addition of targeted agents.1,5 Recognizing molecular tumor signatures of underlying biologic processes associated with resistance in patients treated with this new standard therapy will allow the identification of potential targets for improvement of therapy. Recent concepts for cancer development suggest that a minority population of cancer stem-like cells may determine the biologic behavior of tumors, including response to therapy. Failure to cure cancer has been attributed to the fact that therapies are aimed at the tumor bulk without significantly harming tumor stem-like cells,6 supported by experimental evidence in a respective mouse model showing that this glioblastoma subpopulation of cells is more resistant to radiotherapy.7 Facilitated by markers differentiating stem-cells and progenitors of the different lineages, the origin of leukemic stem cells has been traced back to hematopoietic stem cells as well as progenitor populations that have acquired self-renewal properties.8,9 In contrast, the origin and concept of glioma stem-like cells remains to be fully elucidated. CD133 positivity has been postulated to be a glioma stem-cell marker, considering that this subpopulation of glioma-derived cells seems to have a higher potential to generate and maintain tumors in vivo.7,10 With the goal of identifying molecular mechanisms of treatment resistance, we report here on the analysis of glioblastoma-derived gene expression profiles from patients treated within two prospective clinical trials.1,2 We have identified several biologic processes associated with resistance or responsiveness to combined chemoradiotherapy that provide important information guiding novel treatment strategies and aiming at individualized therapy. Intriguingly, an expression signature associated with resistance shows high similarity with a stem cell–related self-renewal signature.9 Our data provide the first clinical evidence to our knowledge for the involvement of a tumor stem-cell phenotype in the escape of glioblastoma from chemoradiotherapy.
Tumor Samples and Patient Characteristics Gene expression profiles were established from 80 frozen glioblastoma samples obtained from 76 patients, comprising 70 tumors from initial surgery and 10 samples resected at recurrence, and from four non-neoplastic brain tissue samples. All patients were treated within a phase II or a randomized phase III trial1,2 and provided written informed consent for molecular studies of their tumor. The protocol was approved by the ethics committee at each center. Sixty-eight patients with complete molecular and clinical information were included in survival analysis, with a median age of 51 years (range, 26 to 70 years) at enrollment. Thereof, 42 received TMZ/RT TMZ, and 26 were randomly assigned to RT only. Second-line therapy frequently involved alkylating agents including TMZ. Patient characteristics are summarized in Table A1(online only). The validation set comprised 76 independent patients of the European Organisation for Research and Treatment of Cancer (EORTC)/National Cancer Institute of Canada (NCIC) study,1 39 randomly assigned to TMZ/RTTMZ, and 37 to RT (median age, 54 years; range, 25-69 years), whose glioblastomas were available on a tissue microarray (TMA). There was no difference in survival compared with the general trial population, neither in the test population nor the validation set (P > .2).
Gene Expression Profiling
Data Analysis and Statistical Methods The expression matrix of 84 samples and 3,860 most variable probe sets (standard deviation, > 0.75) was input into CTWC using default parameters and two levels of clustering. CTWC analysis can be viewed at: http://bcf.isb-sib.ch/projects/cancer/glio/. Probe sets comprising stable gene clusters emerging from CTWC served as input for supervised analyses. Details on the Cox and partial least square models, and published data sets used9,14-16 are described in the Appendix and Table A3 (online only). The Benjamini-Hochberg procedure was applied for multiple testing correction (false-discovery rates).17
TMA and Immunohistochemistry
Glioblastoma-Derived Neurospheres
Gene Expression Signatures Associated With Tumor Resistance There was no obvious association of patient characteristics or survival with genetic subtypes evident from the sample dendrogram S1(G1), clustering all samples (S1) and all genes (G1) passing a variation filter (Fig 1). The methylation status of the MGMT gene promoter appears to be randomly distributed. All stable gene clusters emerging from this analysis are listed in Table 1, named by the predominant biologic function suggested by the genes they comprise, whereas their inter-relationship is visualized in Fig 2A. Similar gene clusters are obtained when using only the subset of glioblastoma clustered in S4 [G1(S4); Appendix Table A4, online only]. Cluster S4 comprises most glioblastoma (69 of 80), but not the nontumoral tissues that form their own stable cluster, S3 (Fig 1). Similar gene clusters are present in other glioblastoma data sets as visualized in Fig 2B for data sets published by the groups of Nelson and Aldape, respectively.14,15All 18 nonoverlapping, stable gene clusters [G1(S1)] were interrogated for association with survival using Cox proportional hazards, adjusted for age (> 50 years) and MGMT methylation status.3,4 Seven gene clusters were most influential for explaining survival in patients assigned to TMZ/RT TMZ (Table 1; gene lists, Appendix Tables A5-A10, online only). A respective partial least square model yielded comparable results (Appendix Fig A1, online only). However, the MGMT methylation status was yet the most influential predictor of survival (Table 2; Appendix Fig A1, online only). For this study, we focused on the two most significant clusters characterized by HOX (homeobox) gene (G28/G98) and EGFR (epidermal growth factor receptor) gene expression (G25), respectively, and here we briefly comment on the biologic and clinical implications denoted by the other relevant clusters.
Self-Renewal Signature Associated With Resistance to Chemoradiotherapy in Glioblastoma Increased expression of cluster G28, dominated by HOX genes and comprising the cell-cycle checkpoint gene GADD45G,19 was found to be associated with worse outcome (P = .004; hazard ratio [HR] = 2.69; 95% CI, 1.38 to 5.26; Table 1). HOX genes are essential in axis determination during embryonic development and are known to be involved in cancer including glioblastomas.20,21 The interaction term between this HOX cluster and chemoradiotherapy was significant (P = .001) in the Cox model when evaluating all 68 patients from the two treatment arms, implying that high expression of HOX genes may be predictive for resistance to TMZ/RT TMZ therapy. The HOX gene clusters G28 and G98 emerging from clustering all genes (G1) either with all samples (S1) or only with glioblastoma clustered in S4 are almost identical (Pearson correlation, 0.98; 19 of 21 probe-sets; Appendix Table A11, online only). Intriguingly, G98 in addition comprises Prominin 1, encoding the putative glioma stem–cell marker CD133 (Fig 3A), suggesting that in a subpopulation of glioblastoma, concerted upregulation of HOX genes might be associated with a tumor stem-like cell phenotype. In accordance, we find high HOXA10 protein expression in glioblastoma-derived neurospheres, cultured under stem-cell conditions, as displayed in Fig 3B together with CD133 expression. To include information on Prominin 1, we show results on G98 for all following analyses. Fig 3C visualizes the association of short survival with enhanced expression of G98.
Validation in Independent Data Sets The association of the HOX signature with resistance to treatment was subsequently validated in a sample set of the trial not available for initial discovery, arrayed on TMA. HOXA10 was evaluated by immunohistochemistry (Fig 4) as a representative of the correlated set of HOX genes. Strong nuclear expression was often observed in patches of tumor cells situated in the vicinity of blood vessels. High HOXA10 expression was associated with worse outcome in patients randomly assigned to TMZ/RT TMZ therapy (n = 39; HR = 2.57; 95% CI, 1.21 to 5.47; P = .014; Fig 5). The patients randomly assigned to RT only did not show such a relationship, suggesting that high HOXA10 expression may be predictive for resistance to a synergistic effect of concomitant chemoradiotherapy, in concordance with the significant interaction term between treatment and expression of G28 or G98 (Appendix Table A12, online only). Similar HOX clusters can be identified in the Nelson and Aldape glioblastoma data sets.14,15 Correlating G98 gene expression with outcome in these data sets totaling 102 glioblastoma revealed a trend for worse outcome (P = .09; HR = 1.29; 95% CI, 0.97 to 1.72; Appendix Fig A2, online only). Of note, in contrast to our data, these patients were treated before the TMZ/RTTMZ regimen was established. In accordance with better survival, anaplastic glioma (WHO grade 3) profiled in these publications revealed significantly lower expression of G98 genes compared with glioblastoma (WHO grade 4, P < .001 for the Aldape data set14; P = .002 for the Nelson data set15; Wilcoxon rank sum test with continuity correction; Appendix Fig A3, online only). However, within grade 3 glioma of the two data sets increased expression of G98 genes was associated with worse outcome (n = 44; P = .007; HR = 3.35; 95% CI, 1.39 to 8.08; Appendix Fig A2).
HOX Signature Reminiscent of Self-Renewal The core of the HOX cluster was found to be part of the top 20 genes of a self-renewal signature identified by Krivtsov et al9 in murine MLL-AF9-induced leukemic stem cells derived from committed progenitors. Our G98-derived signature was significantly enriched in genes discriminating self-renewal versus non–self-renewal in this expression data set according to Gene Set Enrichment Analysis (GSEA; P = .008; Appendix Fig A4 and Table A13, online only) and the Wilcoxon two-sample test (G98; P < .001).22 The relevance of our signature was further demonstrated in a human data set16 in which G98, similar to the original murine self-renewal signature,9 was able to significantly differentiate MLL-rearranged–acute myeloid leukemia from acute myeloid leukemia (GSEA, P < .001; Wilcoxon two-sample test P = .01; Appendix Table A14, online only). Interestingly, a significant, although low, correlation between the mean DNA copy number of the two BAC (bacterial artificial chromosome) clones (GS1-213H12 and CTB-23D20) bordering the HOXA gene locus on chromosome 7 and the mean expression of the HOXA genes was observed in a set of 60 glioblastoma (Pearson correlation coefficient r = 0.27; P = .03; manuscript in preparation). These flanking BACs were more amplified than their neighbors (Appendix Fig A4, online only). Hence, the herein-proposed HOX-dominated self-renewal signature for glioblastoma may in part be acquired by increased gene dosage.
High EGFR Expression Is Associated With Tumor Resistance
Cluster G18, associated with tumor resistance (P = .03; HR = 1.94; 95% CI, 1.07 to 3.51), displayed some correlation with EGFR expression (r = 0.57; Fig 2A) in particular with Aquaporin 4 (AQP4). AQP4 has been associated with brain tumor related edema.23 Aquaporins require activation of mitogen-activated protein kinase signaling that may be mediated by EGFR activation.24 Another family member, AQP1, has been linked with tumor angiogenesis and cell migration25 and was associated with worse outcome in this study (P = .003; HR = 2.44, 95% CI, 1.36 to 4.04) and in the two external data sets (combined P = .009; HR = 1.51; 95% CI, 1.11 to 2.06).
Blood Vessels Markers Associated With Better Outcome
Innate Immune Response Associated With Better Survival
HOX Signature and EGFR Expression Are Independent Prognostic Factors
Gene expression signatures relevant for treatment resistance to TMZ/RT TMZ have been identified in a prospectively treated population of glioblastoma patients. Although epigenetic inactivation of the MGMT gene promoter remained the most prominent predictive factor, expression signatures allowed identification of patient subgroups that may benefit from specific additional therapies targeting particular mechanisms of resistance. As an independent predictive factor of resistance, we have identified a HOX-dominated gene cluster, evocative of a self-renewal signature recently described for leukemia.9 Strong HOXA10 expression of glioblastoma-derived neurospheres is in line with a role of HOX genes in the glioma stem-like cell compartment. These findings provide the first clinical evidence to our knowledge for the relevance of a stem-like cell phenotype in treatment resistance of glioblastoma. In leukemia, expression of translocation-related fusion proteins led to MLL-mediated chromatin remodeling associated with re-expression of HOX genes.9,34,35 In glioblastoma, however, such fusion proteins have not been described, and no indications from the present data set link MLL expression with the self-renewal signature. We provide evidence that the HOX-dominated gene signature emerges with malignant progression to glioblastoma, and may be acquired in some glioblastomas by low-level amplification, the latter supporting the notion that gliomas may also arise from progenitors, in agreement with mouse models.36 The identification of GADD45G as part of the HOX signature may provide further evidence for an enhanced DNA repair potential that has recently been associated with radiation resistance of glioma stem cells.7 These molecular and clinical data underscore the importance of the self-renewal phenotype that could be explored as a potential treatment target.37 First efforts blunting glioma stem cell–related self-renewal properties of tumors suggest that strategies forcing differentiation (eg, mediated by cytokines such as BMP4) may be promising.29
Targeting resistance to TMZ/RT
Surprisingly, good prognosis was associated with increased expression of a signature for tumor endothelium markers. This signature may predict improved cytotoxic activity by means of better perfusion of the tumor with the chemotherapy agent TMZ. This is in accordance with the concept suggesting that antiangiogenic agents may temporarily lead to "normalization" of aberrant tumor vasculature, resulting in more efficient delivery of drugs and oxygen to the tumor.39,40 In a recent trial, addition of the antiangiogenic integrin-inhibitor cilengitide appears to confer increased antitumor activity in conjunction with TMZ/RT Another interesting insight of our study suggests infiltration of M2-polarized macrophages into the tumors. The altered capacity of these glioma-infiltrating macrophages to induce effective antitumor T-cell response may obstruct therapeutic strategies aimed at boosting adaptive immunity against the tumor. M2 polarization is driven by tumor- and T-cell–derived cytokines,31 consistent with the well-known expression of the immunosuppressive cytokines transforming growth factor-β and interleukin-10 in malignant glioma.41 Thus, for effective immunotherapy/vaccination, full resection of the tumor may be required to remove the microenvironment conferring immunosuppression and tolerance. The molecular signatures identified in this study associated with outcome underline the need for development of multimodality treatments targeting not only the tumor cells, but including strategies aimed at the glioma stem-like cell compartment, and interfering with tumor-host interaction that provides the specialized microenvironment relevant for the maintenance of tumor stem-like cells (the stem-cell niche), angiogenesis, and immune response. The hypotheses generated in this study need to be tested in prospective trials. Our findings may guide a rational choice of agents, targets, trial design, and appropriate patient selection, incorporating biomarkers defining mechanisms of response and resistance.
Although all authors completed the disclosure declaration, the following author(s) indicated a financial or other interest that is relevant to the subject matter under consideration in this article. Certain relationships marked with a "Urdquo; are those for which no compensation was received; those relationships marked with a "C" were compensated. For a detailed description of the disclosure categories, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors. Employment or Leadership Position: None Consultant or Advisory Role: Roger Stupp, Schering-Plough (C), Merck Serono (C); Monika E. Hegi, Oncomethylome Sciences (C) Stock Ownership: None Honoraria: Wolfgang Wick, Schering-Plough, Essex-Pharma; J. Gregory Cairncross, Schering-Plough; Roger Stupp, Schering-Plough, Merck Serono; Monika E. Hegi, Schering-Plough Research Funding: Monika E. Hegi, Schering-Plough, Oncomethylome Sciences Expert Testimony: None Other Remuneration: None
Conception and design: Anastasia Murat, Eugenia Migliavacca, Monika E. Hegi Financial support: Nicolas de Tribolet, Monika E. Hegi Administrative support: Roger Stupp, Monika E. Hegi Provision of study materials or patients: Nicolas de Tribolet, Luca Regli, Wolfgang Wick, Mathilde C.M. Kouwenhoven, Johannes A. Hainfellner, Frank L. Heppner, Pierre-Yves Dietrich, Yitzhak Zimmer, Gregory Cairncross, Roger Stupp Collection and assembly of data: Anastasia Murat, Eugenia Migliavacca, Marie-France Hamou, Robert-Charles Janzer, Roger Stupp, Monika E. Hegi Data analysis and interpretation: Anastasia Murat, Eugenia Migliavacca, Thierry Gorlia, Wanyu L. Lambiv, Tal Shay, Marie-France Hamou, Eytan Domany, Mauro Delorenzi, Monika E. Hegi Manuscript writing: Anastasia Murat, Eugenia Migliavacca, Thierry Gorlia, Eytan Domany, Mauro Delorenzi, Roger Stupp, Monika E. Hegi Final approval of manuscript: Anastasia Murat, Eugenia Migliavacca, Thierry Gorlia, Wanyu L. Lambiv, Tal Shay, Marie-France Hamou, Nicolas de Tribolet, Luca Regli, Wolfgang Wick, Mathilde C.M. Kouwenhoven, Johannes A. Hainfellner, Frank L. Heppner, Yitzhak Zimmer, Gregory Cairncross, Robert-Charles Janzer, Eytan Domany, Mauro Delorenzi, Roger Stupp, Monika E. Hegi
Partial Least Square Regression Partial least square (PLS) regression is a technique that combines features from principal component analysis and multiple linear regression (Wold H: Partial Least Squares. New York, NY Wiley, 1985). It is particularly useful to predict an outcome from a large set of highly correlated predictors. In this study, the PLS procedure in SAS was used to define combinations of the genes expressions (ie, factors) that attempt to explain the genes expression variability and the survival outcome at the same time. For each patient, the martingale residuals obtained from a Cox regression with a constant as the only predictor were used as outcome variable to be explained (Wold H: Partial Least Squares. New York, NY Wiley, 1985). This approach was derived from the works of Therneau et al (Biometrika 77:147-160, 1990) and Leblanc et al (Biometrics 48:411-425, 1992) with the classification and regression tree algorithm and applied to PLS regression. This allowed us to account for the effect of censoring on survival estimates. The optimal number of PLS factors is generally obtained by cross validation, but in this small sample (n = 42 or 68), this method was not conclusive (ie, no optimal number of factors could be obtained). Therefore, we empirically chose to fit a PLS model with two factors to minimize overfitting and obtain interpretable results. The two factors explained 66% of the survival outcome variations. The contribution of each gene was assessed by their coefficients in the linear regression and their X weights plotted in a factorial plane. Arbitrarily, clusters were given X weights by averaging the X weights of their constituent genes.
Description of External Data Sets In addition, we used a mouse expression data set published by Krivtsov et al (Krivtsov AV, Twomey D, Feng Z, et al: Nature 442:818-822, 2006) and a human leukemia data set by Ross et al (Ross ME, Mahfouz R, Onciu M, et al: Blood 104:3679-3687, 2004). For both human malignant glioma validation sets, only the chips that passed quality criteria based on the BioConductor library affyPLM (Gentleman R, Carey V, Huber W, et al: Bioinformatics and Computational Biology Solutions Using R and Bioconductor, Springer, 2005) were included. The external data sets were each separately normalized using robust multiarray average, when Affymetrix CEL files were available (Aldape, Nelson, and Ross). For the Krivtsov mouse expression data set (Krivtsov AV, Twomey D, Feng Z, et al: Nature 442:818-822, 2006), the Affymetrix Microarray Suite version 5.0 processed data, as submitted in the Gene Expression Omnibus database, were used. For the human-murine comparison, we mapped the human gene sets to homolog murine gene sets using the annotation provided by Affymetrix on http//www.affymetrix.com (accessed November 14, 2006; Table A13). For GSEA analysis, the data sets were filtered to retain only the most variant probe sets (standard deviation, > 0.5).
Supervised Analysis
We thank A.-C. Diserens, P. Descombes, and D. Chollet for excellent technical support; D.R. Macdonald, A. Guha, and A. Merlo for providing frozen tumor tissues; and the clinical colleagues and nurses involved in the studies at the participating hospitals.
Supported by the Swiss National Science Foundation (Grant No. 3100AO-108266/1; M.E.H.); Jacqueline Seroussi Memorial Foundation for Cancer Research (M.E.H.), Translational Research Fund of the EORTC (M.E.H.); the Nélia and Amadeo Barletta Foundation (M.E.H., R.S.); National Center of Competence in Research (NCCR) Molecular Oncology at the Swiss Institute for Experimental Cancer Research (ISREC) (M.E.H., M.D.); Ridgefield Foundation (E.D.); EC FP6 (E.D.); and the VITAL-IT project of the Swiss Institute of Bioinformatics (E.M., M.D.). A.M. and E.M. contributed equally to this article. Terms in blue are defined in the glossary, found at the end of this article and online at www.jco.org. Authors’ disclosures of potential conflicts of interest and author contributions are found at the end of this article.
1. Stupp R, Mason WP, van den Bent MJ, et al: Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352:987-996, 2005 2. Stupp R, Dietrich P, Ostermann Kraljevic S, et al: Promising survival for patients with newly diagnosed glioblastoma multiforme treated with concomitant radiation plus temozolomide followed by adjuvant temozolomide. J Clin Oncol 20:1375-1382, 2002 3. Hegi ME, Diserens AC, Godard S, et al: Clinical trial substantiates the predictive value of O-6-methylguanine-DNA methyltransferase promoter methylation in glioblastoma patients treated with temozolomide. Clin Cancer Res 10:1871-1874, 2004 4. Hegi ME, Diserens AC, Gorlia T, et al: MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med 352:997-1003, 2005 5. Stupp R, Hegi ME, Gilbert MR, et al: Chemoradiotherapy in malignant glioma: Standard of care and future directions. J Clin Oncol 25:4127-4136, 2007 6. Reya T, Morrison SJ, Clarke MF, et al: Stem cells, cancer, and cancer stem cells. Nature 414:105-111, 2001[CrossRef][Medline] 7. Bao S, Wu Q, McLendon RE, et al: Glioma stem cells promote radioresistance by preferential activation of the DNA damage response. Nature 444:756-760, 2006[CrossRef][Medline] 8. Bonnet D, Dick JE: Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat Med 3:730-737, 1997[CrossRef][Medline] 9. Krivtsov AV, Twomey D, Feng Z, et al: Transformation from committed progenitor to leukaemia stem cell initiated by MLL-AF9. Nature 442:818-822, 2006[CrossRef][Medline] 10. Singh SK, Hawkins C, Clarke ID, et al: Identification of human brain tumour initiating cells. Nature 432:396-401, 2004[CrossRef][Medline] 11. Getz G, Domany E: Coupled two-way clustering server. Bioinformatics 19:1153-1154, 2003 12. Getz G, Levine E, Domany E: Coupled two-way clustering analysis of gene microarray data. Proc Natl Acad Sci U S A 97:12079-12084, 2000 13. Blatt M, Wiseman S, Domany E: Superparamagnetic clustering of data. Phys Rev Lett 76:3251-3254, 1996[CrossRef][Medline] 14. Freije WA, Castro-Vargas FE, Fang Z, et al: Gene expression profiling of gliomas strongly predicts survival. Cancer Res 64:6503-6510, 2004 15. Phillips HS, Kharbanda S, Chen R, et al: Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 9:157-173, 2006[CrossRef][Medline] 16. Ross ME, Mahfouz R, Onciu M, et al: Gene expression profiling of pediatric acute myelogenous leukemia. Blood 104:3679-3687, 2004 17. Benjamini Y, Hochberg Y: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc B 57:289-300, 1995 18. Clement V, Sanchez P, de Tribolet N, et al: HEDGEHOG-GLI1 signaling regulates human glioma growth, cancer stem cell self-renewal, and tumorigenicity. Curr Biol 17:165-172, 2007[CrossRef][Medline] 19. Vairapandi M, Balliet AG, Hoffman B, et al: GADD45b and GADD45g are cdc2/cyclinB1 kinase inhibitors with a role in S and G2/M cell cycle checkpoints induced by genotoxic stress. J Cell Physiol 192:327-338, 2002[CrossRef][Medline] 20. Abate-Shen C: Deregulated homeobox gene expression in cancer: Cause or consequence? Nat Rev Cancer 2:777-785, 2002[CrossRef][Medline] 21. Abdel-Fattah R, Xiao A, Bomgardner D, et al: Differential expression of HOX genes in neoplastic and non-neoplastic human astrocytes. J Pathol 209:15-24, 2006[CrossRef][Medline] 22. Subramanian A, Tamayo P, Mootha VK, et al: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545-15550, 2005 23. Manley GT, Fujimura M, Ma T, et al: Aquaporin-4 deletion in mice reduces brain edema after acute water intoxication and ischemic stroke. Nat Med 6:159-163, 2000[CrossRef][Medline] 24. Herrlich A, Leitch V, King LS: Role of proneuregulin 1 cleavage and human epidermal growth factor receptor activation in hypertonic aquaporin induction. Proc Natl Acad Sci U S A 101:15799-15804, 2004 25. Saadoun S, Papadopoulos MC, Hara-Chikuma M, et al: Impairment of angiogenesis and cell migration by targeted aquaporin-1 gene disruption. Nature 434:786-792, 2005[CrossRef][Medline] 26. St Croix B, Rago C, Velculescu V, et al: Genes expressed in human tumor endothelium. Science 289:1197-1202, 2000 27. Madden SL, Cook BP, Nacht M, et al: Vascular gene expression in nonneoplastic and malignant brain. Am J Pathol 165:601-608, 2004 28. Chute JP, Muramoto GG, Whitesides J, et al: Inhibition of aldehyde dehydrogenase and retinoid signaling induces the expansion of human hematopoietic stem cells. Proc Natl Acad Sci U S A 103:11707-11712, 2006 29. Piccirillo SG, Reynolds BA, Zanetti N, et al: Bone morphogenetic proteins inhibit the tumorigenic potential of human brain tumour-initiating cells. Nature 444:761-765, 2006[CrossRef][Medline] 30. Calabrese C, Poppleton H, Kocak M, et al: A perivascular niche for brain tumor stem cells. Cancer Cell 11:69-82, 2007[CrossRef][Medline] 31. Mantovani A, Sozzani S, Locati M, et al: Macrophage polarization: Tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes. Trends Immunol 23:549-555, 2002[CrossRef][Medline] 32. Van Ginderachter JA, Movahedi K, Hassanzadeh Ghassabeh G, et al: Classical and alternative activation of mononuclear phagocytes: Picking the best of both worlds for tumor promotion. Immunobiology 211:487-501, 2006[CrossRef][Medline] 33. Hussain SF, Yang D, Suki D, et al: The role of human glioma-infiltrating microglia/macrophages in mediating antitumor immune responses. Neuro-oncol 8:261-279, 2006 34. Dorrance AM, Liu S, Yuan W, et al: Mll partial tandem duplication induces aberrant Hox expression in vivo via specific epigenetic alterations. J Clin Invest 116:2707-2716, 2006[CrossRef][Medline] 35. Krivtsov AV, Armstrong SA: MLL translocations, histone modifications and leukaemia stem-cell development. Nat Rev Cancer 7:823-833, 2007[CrossRef][Medline] 36. Bachoo RM, Maher EA, Ligon KL, et al: Epidermal growth factor receptor and Ink4a/Arf: Convergent mechanisms governing terminal differentiation and transformation along the neural stem cell to astrocyte axis. Cancer Cell 1:269-277, 2002[CrossRef][Medline] 37. Stupp R, Hegi ME: Targeting brain-tumor stem cells. Nat Biotechnol 25:193-194, 2007[CrossRef][Medline] 38. Sathornsumetee S, Rich JN: New treatment strategies for malignant gliomas. Expert Rev Anticancer Ther 6:1087-1104, 2006[CrossRef][Medline] 39. Jain RK: Normalization of tumor vasculature: An emerging concept in antiangiogenic therapy. Science 307:58-62, 2005 40. Batchelor TT, Sorensen AG, di Tomaso E, et al: AZD2171, a pan-VEGF receptor tyrosine kinase inhibitor, normalizes tumor vasculature and alleviates edema in glioblastoma patients. Cancer Cell 11:83-95, 2007[CrossRef][Medline] 41. Kjellman C, Olofsson SP, Hansson O, et al: Expression of TGF-beta isoforms, TGF-beta receptors, and SMAD molecules at different stages of human glioma. Int J Cancer 89:251-258, 2000[CrossRef][Medline] Submitted December 11, 2007; accepted March 11, 2008.
This article has been cited by other articles:
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
|||||||||||
|
|||||||||||
|
|
Copyright © 2008 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
|
TOP
ABSTRACT
INTRODUCTION
, and other related proteins, leading to the generation of proliferative and survival signals within the cell. It also belongs to the larger family of tyrosine kinase receptors and is generally overexpressed in several solid tumors of epithelial origin. 
