|
Advertisement |
|
|
||
 |
|
|||||
|
|||||
|
 |
||||||
|
||||||
|
||||||
|
Journal of Clinical Oncology, Vol 25, No 10 (April 1), 2007: pp. 1196-1208 © 2007 American Society of Clinical Oncology. DOI: 10.1200/JCO.2006.07.8626 Molecular Profiling Identifies Prognostic Subgroups of Pediatric Glioblastoma and Shows Increased YB-1 Expression in Tumors
From the Division of Hemato-Oncology, Department of Pediatrics; Department of Pathology, Montréal Children's Hospital Research Institute; Division of Neuro-Surgery, Montréal Children's Hospital; Division of Neuro-Surgery and the Brain Tumor Research Center, Montréal Neurological Institute, McGill University Health Center; Biotechnology Research Institute, National Research Council of Canada, Montréal; Laboratory for Oncogenomic Research, Department of Pediatrics, British Columbia Research Institute for Children's and Women's Health; Department of Surgery, Prostate Cancer Center, Jack Bell Research Laboratories, Vancouver; Second Department of Pediatrics, Semmelweis University; Division of Neuro-surgery, Division of Pathology, National Institute of Neurosurgery, Budapest, Hungary; Department of Neuropathology, Medical University of Lodz, Lodz, Poland; Oncology Department, Pediatrics Hospital, Centro Medico Nacional Siglo XXI, Mexico City, Mexico; Department of Pediatrics, University Hospital, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil; Institut of Neuropathology, University of Bonn, Bonn, Germany Address reprint requests to Nada Jabado, MD, PhD, Montreal Children's Hospital Research Institute, 4060 Ste Catherine West, PT-239, Montreal, Quebec, Canada H3Z 2Z3; e-mail: nada.jabado{at}mcgill.ca
Purpose Pediatric glioblastoma (pGBM) is a rare, but devastating brain tumor. In contrast to GBM in adults (aGBM), little is known about the mechanisms underlying its development. Our aim is to gain insight into the molecular pathways of pGBM. Materials and Methods Thirty-two pGBM and seven aGBM samples were investigated using biochemical and transcriptional profiling. Ras and Akt pathway activation was assessed through the phosphorylation of downstream effectors, and gene expression profiles were generated using the University Health Network Human 19K cDNA arrays. Results were validated using real-time polymerase chain reaction and immunohistochemistry and compared with existing data sets on aGBM. Results There are at least two subsets of pGBM. One subset, associated with Ras and Akt pathway activation, has very poor prognosis and exhibits increased expression of genes related to proliferation and to a neural stem-cell phenotype, similar to findings in aggressive aGBM. This subset was still molecularly distinguishable from aGBM after unsupervised and supervised analysis of expression profiles. A second subset, with better prognosis, is not associated with activation of Akt and Ras pathways, may originate from astroglial progenitors, and does not express gene signatures and markers shown to be associated with long-term survival in aGBM. Both subsets of pGBM show overexpression of Y-box-protein-1 that may help drive oncogenesis in this tumor. Conclusion Our work, the first study of gene expression profiles in pGBM, provides valuable insight into active pathways and targets in a cancer with minimal survival, and suggests that these tumors cannot be understood exclusively through studies of aGBM.
Brain tumors are the leading cause of cancer-related mortality in children. Pediatric grade IV astrocytomas (pediatric glioblastoma [pGBM]), non-neuronal tumors originating from the astrocytic lineage,1-3 account for 15% of all pediatric brain tumors and have a 3-year survival of less than 20% and high morbidity.4 Considerable information is available on adult GBM (aGBM), where this tumor is frequent and deadly and thought to arise by at least two molecular pathways. Secondary GBM occur in adults aged younger than 40 years, evolve from low-grade astrocytomas, and have a high frequency of p53 mutations and a low frequency of epidermal growth factor receptor (EGFR) amplification. Primary GBM targets older patients and exhibits gain of function mutations of EGFR. Both forms are indistinguishable to pathologists, and share aberrations of the p53 and retinoblastoma (RB) pathways and similar prognosis.1-3,5 A number of gene expression profiling analyses performed in aGBM helped to identify molecular events leading to oncogenesis and provided more accurate means for classification of subtypes and outcomes.6-13 Fewer molecular data exist on the mechanisms underlying the development and progression of pGBM, mainly because of the relative lack of frozen samples.14,15 pGBMs are histologically indistinguishable from aGBM. Although they occur as de novo tumors, they exhibit p53 mutations but only rarely EGFR amplification.16-18 Moreover, the few cytogenetic studies show that pediatric and adult astrocytomas have different chromosomal imbalances,19,20 and published data study pGBM in conjunction with other grade and lineage gliomas (grade 3, mixed oligoastrocytomas).15,21 A better understanding of the molecular pathogenesis of pGBM is required for the development of more effective therapies, particularly because most current treatments are based on molecular knowledge gained from aGBM. Compelling data from human studies and animal models of GBM indicate a key role for the combined activation of the Ras and Akt pathways that control cell growth, differentiation, and survival.1,3,5,22,23 Activity of Ras is aberrantly increased in most aGBM and aGBM cell lines, and Akt activation is observed in approximately 70% of aGBM.10,24 Activation of these pathways in pGBM has not been investigated. To gain insight into the molecular pathways driving oncogenesis in pGBM, we investigated 32 pGBM and, in parallel, seven aGBM tumors.
Characteristics of Samples and Central Pathologic Review A neuropathologist independently blindly reviewed all samples to ensure consistent classification based on contemporary guidelines from the WHO.21 Only grade 4 astrocytomas (GBM) were used. Clinical findings of all patients with GBM and control brains are shown in Tables 1 and 2 and the Appendix (online only).
Cell Line, Protein Extraction, Sodium Dodecyl Sulfate–Polyacrylamide Gel Electrophoresis, Immunoblot, and Immunohistochemical Analysis U87 was grown as described.25 Extracts were prepared from cell pellets or from human brain tissue and processed as previously described.10 Immunohistochemical analyses for pErk, pAkt, glial fibrillary acidic protein (GFAP), p53, and Y-Box-Protein-1 (YB1) were performed as described in the Appendix (online-only) and in Pollack et al21 and Sutherland et al.26
Laser Capture Microdissection RNA Extraction, and Linear Amplification
Microarray Analyses
Activation of Ras and Akt Pathways Occurs in Only a Subset of pGBM and Is Associated With Poor Outcome We investigated activation of the Ras and Akt pathways in 18 frozen pGBM tumors by Western blot analysis using phosphorylation-specific antibodies against known effectors of these pathways. U87 aGBM cell-line, and a pooled protein lysate from three pediatric control brains (1, 7, and 15 years) were used as positive and negative controls respectively. Slides from the same blocks were stained with hematoxylin and eosin, confirming that only lysates from pGBM tumor populations were being analyzed. Phosphorylation of Raf, Mek1/2, and Erk1/2 was seen in 12 of 18 pGBM samples and in the U87 cell line, indicating activation of the Ras pathway (Fig 1; data not shown). Phosphorylation of these downstream effectors of Ras was not observed in six of 18 pGBM samples. Phosphorylation of Akt and GSK3 was observed in 10 samples (Fig 1; data not shown), all of which were also active for Ras and had lower levels of phosphate and tensin homolog (PTEN), the dual-activity phosphatase known to participate in inactivating the Akt pathway.29
We validated results obtained by Western blot using an immunohistochemical approach on 18 formalin-fixed, paraffin-embedded (FFPE) pGBM samples that included four samples previously tested by Western blot on their frozen counterparts, and another new set of 14 pGBM for which no frozen counterparts were available (Appendix). Sections from samples were tested for pErk (associated with Ras activation), pAkt (associated with Akt activation), and glial fibrillary acidic protein (GFAP; astrocytic marker) immunoreactivity. Five samples showed no staining for pErk, while showing normal staining for GFAP of adjacent sections, excluding problems related to tissue preservation (Fig A1D). The remaining samples showed strong staining for pErk (Fig 2). pErk-positive regions contained spindle-like tumor cells showing atypical elongated nuclei and a fibrillary staining typical of GBM and active Ras.30 pAkt was present in 13 of 18 samples that were also positive for pErk (Fig 2). Where material for both technical approaches was available, the same results were obtained.
When investigating putative prognostic factors, striking results were obtained for Ras activation. Children with active Ras (21 of 32) had poor survival with only one survivor, whereas five of 11 with no Ras activation are alive and disease free, with a follow-up of at least 4 years (log-rank P < .009). This result reflects Akt activation because most patients with active Ras also had active Akt (19 of 21). Sex, younger age, treatment, and p53 expression were not associated with better survival with the limitation of sample size.18 Availability of frozen tissue from pGBM is limited. To determine whether the data obtained on Ras and Akt activation in pGBM has potential prognostic value, we performed the same immunohistochemical analysis on an independent data set of 21 FFPE pGBM samples (Table 2; Appendix). pErk and pAkt were highly immunoreactive in 13 of 21 of these samples, whereas both stainings were negative in eight of 21 samples (Fig 2). Survival data further validated our data on the association of Ras and Akt activation with poor survival in pGBM (Table 2). Results from all patients suggest that there are at least two forms of pGBM: one form with poor survival, with only one patient alive, associated with an active Ras/Akt pathways (35 [66%] of 53 samples for Ras and 31[58.5%] of 53 for Akt) and a second form with better prognosis (nine of 18 children alive and disease free), without Ras or Akt activation (18 [34%] of 53; log-rank P < .0001; Fig 3; Tables 1 and 2).
Transcriptional Profiling Distinguishes Two Subsets of pGBM on the Basis of Their Association With Ras/Akt Activity and Reveals a Molecular Signature for pGBM That Is Distinct From aGBM To study gene expression changes, we selected an approach that allows us to compare changes between tumor and nontumor brain as well as changes between pGBM samples. RNA from 14 pGBM frozen samples was isolated and hybridized to Human 19K cDNA spotted arrays together with the same reference pooled RNA extracted from three control pediatric brains. Laser capture microdissection (LCM) was used to selectively capture malignant astrocytes. RNA was subjected to two rounds of T7 linear amplification (aRNA) to circumvent the limited amount of frozen material.31-33 GeneSpring's Filter on Confidence tool identified 2,593 transcripts with statistically significant changes in abundance in the 14 pGBM samples compared with the pooled control (Welsch t test P < .0001; multiple testing correction, Benjamini and Hochsberg; false-discovery rate, 3.4%). Two-dimensional hierarchical clustering organized and visualized the profiles of these transcripts (y-axis) from each of the 14 samples (x-axis; Fig 4) and indicated a high degree of homogeneity in pGBM. Other statistical algorithms (the Wilcoxon-Mann-Whitney test of significance analysis of microarrays) were tested with the same results. We analyzed the data set using a module-level view obtained from a cancer compendium34; and also organized the gene sets using GoMiner, a computer resource that incorporates the hierarchical structure of the Gene Ontology Consortium35 to automate a functional categorization of gene lists based on biologic processes. Both methods aim to distill a higher order for the analysis of complex data sets. They yielded similar results, showing, as expected, that pGBMs are actively proliferating tumors (Fig 5; Appendix Tables A1 to A4, online only).
To visualize similarities within samples, we used PCA, a method of data reduction in which the high dimensionality of the data is reduced to two to three viewable dimensions representing linear combinations of genes that account for most of the variance of the data set. PCA separated samples into two groups, indicating the presence of at least two distinct populations of pGBM (Fig 6A). These populations were associated with Ras/Akt activity in a sample (Ras active color coded in yellow, inactive in red) and not with other known markers for pGBM (necrosis, proliferation index, p53 expression, age).18,36 ANOVA testing identified 1,437 transcripts that could distinguish tumors associated with differing Ras activity (Welch t test P cutoff of .0001; multiple testing correction, Benjamini and Hochsberg). Most transcripts were distinct from those that distinguished pGBM from control brain (Fig 6A; Fig A2A, online only). Gene ontology terms analysis shows that transcripts associated with Ras/Akt active tumors exhibit enhanced rates of nucleic acid, protein synthesis, and metabolism (Tables A1 and A2, online-only). With the limitation of sample size, ANOVA testing of expression profiles did not reveal a difference based on treatment, necrosis, age, or p53 overexpression.15,21
To determine whether pGBM is molecularly distinguishable from aGBM, we performed microarray analysis on seven aGBM samples using the same LCM/RNA amplification approach and the same reference control aRNA. Using scatter plots, several of the changes in transcript abundance that distinguish tumors from control brain are maintained between aGBM and pGBM (Fig A2, online-only). Using PCA, components 2 and 3 showed that aGBM (coded in blue) cluster separately from both types of pGBM (Fig 6A). Furthermore, ANOVA testing using the same stringency as before identified a sharp signature of 1,569 transcripts that distinguished pGBM from aGBM (Figs 6A and A2; Tables A1-A4; Appendix). These transcripts combined with the 1,437 genes that distinguished pGBM on the basis of their association with Ras activity were separated by conditional hierarchical clustering (Fig 7; Table A5, online only). A first tree-branching separated Ras-nonactive pGBM samples from Ras-active samples. A second branching in samples associated with Ras activation separated pGBM from aGBM. One long-term survivor aGBM clustered with Ras-nonactive samples. Sample showed weak pErk staining (Fig A3, online only).
Data from transcriptional profiling mirror data obtained on the protein level indicate that there are at least two subgroups of pGBM and that both differ molecularly from aGBM.
Validation of the Data Set and Identification of Targets Involved in pGBM Gliomagenesis
PGBM Associated With No Ras/Akt Activation Express No Neural Stem-Cell Markers and Upregulate Apoptosis-Associated Transcripts Recent evidence suggests that HGG may arise from stem cell–like cancer cells at multiple stages of differentiation.37 Tumors associated with Ras/Akt activation and poor prognosis overexpressed markers associated with neural stem cells, including CD133, nestin, maternal embryonic leucine zipper kinase (MELK), vimentin, and Dlx2, whereas the subset of pGBM associated with no activation of Ras/Akt and better outcome only showed increased vimentin expression (Fig 10). 13,37,38 Expression levels were validated by quantitative RT-PCR (qRT-PCR) for all of these transcripts (data not shown). Using GoMiner, differentially modulated transcripts associated with Ras-nonactive pGBM showed overexpression of gene sets involved in apoptosis. Conversely, aGBM and pGBM associated with a Ras-active pathway showed overexpression of gene sets involved in protein synthesis, translation, transcription, DNA repair, and synthesis (Tables A1 to A4, online-only).
YB1 Is Overexpressed in pGBM and May Increase EGFR Expression in Akt-Active Samples YB1 showed increased expression (3.8-fold compared with healthy brain) in 12 of 14 pGBM samples, but not in aGBM (Fig 6B). YB1 was further investigated because it is involved in brain embryogenesis and contributes to oncogenesis in a range of epithelial cancers, potentially through Akt mediated phosphorylation.26,39-41 We validated expression of YB1 by qRT-PCR and by immunohistochemistry (Figs 11 and 12; Tables 1 and 2). YB1 was overexpressed in 26 of 32 samples with mostly nuclear localization in samples with active Akt, and was cytoplasmic in samples with no active Akt. These data were corroborated by results obtained on the independent data set of 21 pGBM samples: 12 of 21 positive with nuclear staining for YB1 in Akt-active samples (Table 2). Nuclear YB1 increases expression of several genes including EGFR.42 We found EGFR overexpression by qRT-PCR and immunohistochemistry mainly in Akt-active samples (Tables 1 and 2).
This study is one of the first reports of gene expression profiling in pGBM that focuses exclusively on pGBM. Protein analysis and transcriptional profiling suggest that there are at least two subtypes of pGBM, one associated with Ras/Akt-activation and poor prognosis and the other with no obvious Ras/Akt activity and a better outcome (Fig 1). This is in contrast to aGBM, in which Ras pathway is activated in most tumors.10,24 Even though they share, as expected, common gene sets that are mainly related to the general tumorigenic process, both subtypes of pGBM exhibit distinct profiles from those of aGBM (Figs 1 to 12). Previous studies on pHGG reported that patients older than 3 years had increased p53 expression in tumors and that both parameters correlated with worse outcome.18,21,36 p53 overexpression in this study was not associated with differences in survival or in gene expression patterns, probably because of the sample size and the limited number of infants we tested. Established markers of better outcome in aHGG include younger age, grade 3, absence of necrosis on histology, no Akt activation, and, more recently, molecular signatures associated with NOTCH signaling and proneural markers.7,11-13 In this study on grade 4 tumors, we found Akt activation, expression of markers of neural stem cells (nestin, dlx2, CD133, vimentin, and ELK), and a proliferative and, to a lesser extent, a mesenchymal signature to be common prognostic factors between pGBM associated with Ras/Akt activation and aGBM. However, despite these similarities, we could still distinguish both subsets using unsupervised and supervised analysis (Figs 4 to 9). The subset of pGBM showing a better outcome did not have neuronal lineage markers as were seen in long-term aHGG survivors,13 whereas we observed upregulation of genes associated with apoptosis and a phenotype consistent with immature astroglial cells. YB1 was overexpressed in 38 (72%) of 53 of pGBM (Figs 10 to 12; Tables 1 and 2). This RNA-binding protein/transcription factor is involved in brain development,40 and its nuclear localization is associated with poorer outcome, increased MDR expression, and tumor progression in several cancers that did not previously include CNS tumors.43 YB1 was mainly expressed in the cytoplasm of pGBM samples with no Ras/Akt activation, and may have contributed to general transcriptional repression through its binding to pro-mRNAs (Figs 10 to 12; Table 2).41 In breast cancer cell lines, Akt-mediated YB1 phosphorylation leads to nuclear translocation promoting increased expression of EGFR, a known oncogene in primary aGBM.42,44 In this study, YB1 was mainly nuclear in pGBM associated with Ras/Akt activation (Tables 1 and 2). These samples showed increased EGFR and increased expression of transcripts associated with cell proliferation (Tables 1 and 2). Many of the YB1-associated messages encode stress- and growth-related proteins, raising the possibility that Akt-mediated YB1 phosphorylation increases the production of proteins regulating cell proliferation and oncogenic transformation. Moreover, nuclear YB1 interacts with p53 and inhibits this tumor suppressor's ability to cause cell death and to transactivate cell-death genes.45 We hypothesize that YB1 may be one target of active Akt contributing to gliomagenesis in pGBM by relieving the translational repression of YB126,41 on numerous pro-mRNAs, increasing EGFR levels and Ras activity, and interfering with p53 function. Ras and Akt cooperate in tumorigenesis and increase translation efficiency in tumor cells10,23,24,46,47 as also shown in this study (Figs 4 to 9; Tables A1 to A4, online-only), which may also account for differences in survival we see between both subgroups of pGBM. Our work suggests that pGBM cannot be understood exclusively through studies of aGBM. We have strong leads for mechanistic events, including YB1 expression, that warrant additional work and provide insight into molecular profiles in a pediatric cancer where survival is minimal.
The authors indicated no potential conflicts of interest.
Conception and design: Damien Faury, André Nantel, Steffen Albrecht, Nada Jabado Financial support: Nada Jabado Administrative support: Nada Jabado Provision of study materials or patients: André Nantel, Sandra E. Dunn, Péter Hauser, Miklós Garami, László Bognár, Zoltán Hanzély, Pawel P. Liberski, Enrique Lopez-Aguilar, Elvis Valera, Luis G. Tone, Rolando Del Maestro, Martin Gleave, Jose-Luis Montes, Torsten Pietsch Collection and assembly of data: Damien Faury, André Nantel, Sandra E. Dunn, Marie-Christine Guiot, Takrima Haque, Torsten Pietsch, Steffen Albrecht, Nada Jabado Data analysis and interpretation: Damien Faury, Marie-Christine Guiot, Takrima Haque, Anne-Sophie Carret, Steffen Albrecht, Nada Jabado Manuscript writing: Damien Faury, André Nantel, Sandra E. Dunn, Miklós Garami, Rolando Del Maestro, Jose-Luis Montes, Nada Jabado Final approval of manuscript: Damien Faury, André Nantel, Sandra E. Dunn, Marie-Christine Guiot, Takrima Haque, Péter Hauser, Miklós Garami, László Bognár, Zoltán Hanzély, Pawel P. Liberski, Enrique Lopez-Aguilar, Elvis Valera, Luis G. Tone, Anne-Sophie Carret, Rolando Del Maestro, Martin Gleave, Jose-Luis Montes, Steffen Albrecht, Nada Jabado
Characteristics of GBM Samples and Controls Thirty-nine samples had a positive central review of GBM and included 25 FF and 14 FFPE specimens. In addition, FFPE sections were available from the same surgical procedure for four of the FF specimens. Samples and complete pathology reports were obtained from the Pediatric Cooperative Human Tissue Network, the London/Ontario Tumor Bank, and from collaborators in other centers. Patients' ages ranged from 6 months to 16 years for pediatric samples (n = 32) and from 30 to 72 years for adult samples (n = 7). FF and FFPE healthy brain specimens were obtained from children aged between 1 and 15 years from the same sources after surgery for epilepsy. All samples were obtained with informed consent after approval of the institutional review board of the respective hospitals in which the patients were treated. Only the three adult patients with secondary GBM had previously received radiotherapy. Sections (5 µmol/L) for immunohistochemical analysis from an independent data set of 21 FFPE pGBMs were obtained from European collaborators. Clinical characteristics of these patients are provided in Table 2. Pediatric control brains were obtained from the London Ontario Tumor Bank and from the Montreal Children's Hospital. All control samples were also reviewed by the neuropathologist (S.A.) to ascertain for quality of tissues, astrocytic content, and absence of tumor material. Six control brains from children aged between 1 and 15 years were included in this study because they had both frozen and FFPE blocks available, an astrocytic content above 80% as ascertained by GFAP staining, and absence of reactive gliosis. Three of these samples were pooled and used as controls for Western blot (Fig 1) and microarray analysis (Figs 2 to 4) and individually stained with the various antibodies in the immunohistochemical analysis. The three other samples were used as additional controls in the microarrays analysis.
Cell Lines and Antibodies
Total Protein Extraction, Sodium Dodecyl Sulfate–Polyacrylamide Gel Electrophoresis, and Immunoblot Analysis
Immunohistochemical Analysis
RNA Extraction and Amplification From Samples
qRT-PCR Each reaction was done in triplicate on a Mx4000 Multiplex Quantitative PCR System (Stratagene, Cedar Creek, TX). After 40 cycles of amplification, an additional step allowing dissociation curve analysis was performed. Specificity of the amplification process was evaluated by dissociation curve analysis and agarose gel electrophoresis. Fold changes were calculated using the standard curve method. Total RNA from three aGBM cell lines (U87, SF-126, and SF-539) were pooled. A 10-fold serial dilution of this mixture was used to construct a standard curve for both reference and target genes. Amplification efficiencies (E) were calculated as E = 10 (–1/S), where S represents the slope of the standard curve. Only efficiencies within 90% and 110% were accepted. Quantities of target were calculated by plotting the Ct values to the corresponding standard curve. Tumoral quantities were then normalized to an endogenous control (ß-actin). Fold change is given by dividing the normalized target quantity by the value of the calibrator (nontumoral tissue control). The normalized target amount of the calibrator is set to the value 1.
We thank Rima Rozen, PhD, and Philippe Gros, PhD, for critical reading of the manuscript.
Supported by the Canadian Institute of Health Research and the Penny Cole Foundation (N.J.), an NRC Genome Health Initiative grant (A.N.), the Hungarian Scientific Research Fund (O.T.K.A.) Contract No. T-04639, and the National Research and Development Fund (N.K.F.P.) Contract No. 1A/002/2004 (P.H., M.G., L.B., Z.H.). N.J. is the recipient of a Chercheur Boursier Award from Fonds de la Recherche en Sante du Québec. D.F. and A.N. contributed equally to this article. This is National Research Council publication No. 47482. 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. Maher EA, Furnari FB, Bachoo RM, et al: Malignant glioma: Genetics and biology of a grave matter. Genes Dev 15:1311-1333, 2001 2. Holland EC: Gliomagenesis: Genetic alterations and mouse models. Nat Rev Genet 2:120-129, 2001[CrossRef][Medline] 3. Zhu Y, Parada LF: The molecular and genetic basis of neurological tumours. Nat Rev Cancer 2:616-626, 2002[CrossRef][Medline] 4. Packer RJ: Primary central nervous system tumors in children. Curr Treat Options Neurol 1:395-408, 1999[Medline] 5. Louis DN, Holland EC, Cairncross JG: Glioma classification: A molecular reappraisal. Am J Pathol 159:779-786, 2001 6. Mischel PS, Shai R, Shi T, et al: Identification of molecular subtypes of glioblastoma by gene expression profiling. Oncogene 22:2361-2373, 2003[CrossRef][Medline] 7. Mischel PS, Nelson SF, Cloughesy TF: Molecular analysis of glioblastoma: Pathway profiling and its implications for patient therapy. Cancer Biol Ther 2:242-247, 2003[Medline] 8. Nigro JM, Misra A, Zhang L, et al: Integrated array-comparative genomic hybridization and expression array profiles identify clinically relevant molecular subtypes of glioblastoma. Cancer Res 65:1678-1686, 2005 9. Liang Y, Diehn M, Watson N, et al: Gene expression profiling reveals molecularly and clinically distinct subtypes of glioblastoma multiforme. Proc Natl Acad Sci U S A 102:5814-5819, 2005 10. Rajasekhar VK, Viale A, Socci ND, et al: Oncogenic Ras and Akt signaling contribute to glioblastoma formation by differential recruitment of existing mRNAs to polysomes. Mol Cell 12:889-901, 2003[CrossRef][Medline] 11. Nutt CL, Mani DR, Betensky RA, et al: Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Res 63:1602-1607, 2003 12. Rich JN, Hans C, Jones B, et al: Gene expression profiling and genetic markers in glioblastoma survival. Cancer Res 65:4051-4058, 2005 13. 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] 14. Pollack IF, Hamilton RL, Finkelstein SD, et al: Molecular abnormalities and correlations with tumor response and outcome in glioma patients. Neuroimaging Clin N Am 12:627-639, 2002[CrossRef][Medline] 15. Rood BR, Macdonald TJ: Pediatric high-grade glioma: Molecular genetic clues for innovative therapeutic approaches. J Neurooncol 75:267-72, 2005[CrossRef][Medline] 16. Raffel C, Frederick L, O'Fallon JR, et al: Analysis of oncogene and tumor suppressor gene alterations in pediatric malignant astrocytomas reveals reduced survival for patients with PTEN mutations. Clin Cancer Res 5:4085-4090, 1999 17. Bredel M, Pollack IF, Hamilton RL, et al: Epidermal growth factor receptor expression and gene amplification in high-grade non-brainstem gliomas of childhood. Clin Cancer Res 5:1786-1792, 1999 18. Pollack IF, Finkelstein SD, Burnham J, et al: Age and TP53 mutation frequency in childhood malignant gliomas: Results in a multi-institutional cohort. Cancer Res 61:7404-7407, 2001 19. Orr LC, Fleitz J, McGavran L, et al: Cytogenetics in pediatric low-grade astrocytomas. Med Pediatr Oncol 38:173-177, 2002[CrossRef][Medline] 20. Rickert CH, Strater R, Kaatsch P, et al: Pediatric high-grade astrocytomas show chromosomal imbalances distinct from adult cases. Am J Pathol 158:1525-1532, 2001 21. Pollack IF, Finkelstein SD, Woods J, et al: Expression of p53 and prognosis in children with malignant gliomas. N Engl J Med 346:420-427, 2002 22. Holland EC, Sonenberg N, Pandolfi PP, et al: Signaling control of mRNA translation in cancer pathogenesis. Oncogene 23:3138-3144, 2004[CrossRef][Medline] 23. Holland EC, Celestino J, Dai C, et al: Combined activation of Ras and Akt in neural progenitors induces glioblastoma formation in mice. Nat Genet 25:55-57, 2000[CrossRef][Medline] 24. Guha A, Feldkamp MM, Lau N, et al: Proliferation of human malignant astrocytomas is dependent on Ras activation. Oncogene 15:2755-2765, 1997[CrossRef][Medline] 25. Shi Q, Bao S, Maxwell JA, et al: Secreted protein acidic, rich in cysteine (SPARC), mediates cellular survival of gliomas through AKT activation. J Biol Chem 279:52200-52209, 2004 26. Sutherland BW, Kucab J, Wu J, et al: Akt phosphorylates the Y-box binding protein 1 at Ser102 located in the cold shock domain and affects the anchorage-independent growth of breast cancer cells. Oncogene 24:4281-4292, 2005[CrossRef][Medline] 27. Mariani L, McDonough WS, Hoelzinger DB, et al: Identification and validation of P311 as a glioblastoma invasion gene using laser capture microdissection. Cancer Res 61:4190-4196, 2001 28. Yim SH, Ward JM, Dragan Y, et al: Microarray analysis using amplified mRNA from laser capture microdissection of microscopic hepatocellular precancerous lesions and frozen hepatocellular carcinomas reveals unique and consistent gene expression profiles. Toxicol Pathol 31:295-303, 2003[CrossRef][Medline] 29. Choe G, Horvath S, Cloughesy TF, et al: Analysis of the phosphatidylinositol 3'-kinase signaling pathway in glioblastoma patients in vivo. Cancer Res 63:2742-2746, 2003 30. Hu X, Pandolfi PP, Li Y, et al: MTOR promotes survival and astrocytic characteristics induced by Pten/AKT signaling in glioblastoma. Neoplasia 7:356-368, 2005[CrossRef][Medline] 31. Watanabe H, Tanaka F, Doyu M, et al: Differential somatic CAG repeat instability in variable brain cell lineage in dentatorubral pallidoluysian atrophy (DRPLA): a laser-captured microdissection (LCM)-based analysis. Hum Genet 107:452-457, 2000[CrossRef][Medline] 32. Emmert-Buck MR, Bonner RF, Smith PD, et al: Laser capture microdissection. Science 274:998-1001, 1996 33. Bonaventure P, Guo H, Tian B, et al: Nuclei and subnuclei gene expression profiling in mammalian brain. Brain Res 943:38-47, 2002[CrossRef][Medline] 34. Segal E, Friedman N, Koller D, et al: A module map showing conditional activity of expression modules in cancer. Nat Genet 36:1090-1098, 2004[Medline] 35. Zeeberg BR, Feng W, Wang G, et al: GoMiner: A resource for biological interpretation of genomic and proteomic data. Genome Biol 4:R28, 2003[CrossRef][Medline] 36. Pollack IF, Hamilton RL, Finkelstein SD, et al: The relationship between TP53 mutations and overexpression of p53 and prognosis in malignant gliomas of childhood. Cancer Res 57:304-309, 1997 37. Sanai N, Alvarez-Buylla A, Berger MS: Neural stem cells and the origin of gliomas. N Engl J Med 353:811-822, 2005 38. Singh SK, Clarke ID, Terasaki M, et al: Identification of a cancer stem cell in human brain tumors. Cancer Res 63:5821-5828, 2003 39. Kohno K, Izumi H, Uchiumi T, et al: The pleiotropic functions of the Y-box-binding protein, YB-1. Bioessays 25:691-698, 2003[CrossRef][Medline] 40. Lu ZH, Books JT, Ley TJ: YB-1 is important for late-stage embryonic development, optimal cellular stress responses, and the prevention of premature senescence. Mol Cell Biol 25:4625-4637, 2005 41. Evdokimova V, Ruzanov P, Anglesio MS, et al: Akt-mediated YB-1 phosphorylation activates translation of silent mRNA species. Mol Cell Biol 26:277-292, 2006 42. Wu J, Lee C, Yokom D, et al: Disruption of the Y-box binding protein-1 results in suppression of the epidermal growth factor receptor and HER-2. Cancer Res 66:4872-4879, 2006 43. Kuwano M, Uchiumi T, Hayakawa H, et al: The basic and clinical implications of ABC transporters, Y-box-binding protein-1 (YB-1) and angiogenesis-related factors in human malignancies. Cancer Sci 94:9-14, 2003[CrossRef][Medline] 44. Berquin IM, Pang B, Dziubinski ML, et al: Y-box-binding protein 1 confers EGF independence to human mammary epithelial cells. Oncogene 24:3177-3186, 2005[CrossRef][Medline] 45. Homer C, Knight DA, Hananeia L, et al: Y-box factor YB1 controls p53 apoptotic function. Oncogene 24:8314-8325, 2005[CrossRef][Medline] 46. Bredel M, Pollack IF, Freund JM, et al: Inhibition of Ras and related G-proteins as a therapeutic strategy for blocking malignant glioma growth. Neurosurgery 43:124-132, 1998[Medline] 47. Parsa AT, Holland EC: Cooperative translational control of gene expression by Ras and Akt in cancer. Trends Mol Med 10:607-613, 2004[CrossRef][Medline] 48. Kreisberg JI, Malik SN, Prihoda TJ, et al: Phosphorylation of Akt (Ser473) is an excellent predictor of poor clinical outcome in prostate cancer. Cancer Res 64:5232-5236, 2004 Submitted June 16, 2006; accepted January 2, 2007.
This article has been cited by other articles:
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
|||||||||||
|
|||||||||||
|
|
Copyright © 2007 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
|
TOP
ABSTRACT
INTRODUCTION
