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[Cancer Research 65, 76-84, January 1, 2005]
© 2005 American Association for Cancer Research

Molecular Biology, Pathobiology and Genetics

Expression Analysis of Juvenile Pilocytic Astrocytomas by Oligonucleotide Microarray Reveals Two Potential Subgroups

Kwong-Kwok Wong1,2, Yi-Mieng Chang1,2, Yvonne T.M. Tsang1,2, Laszlo Perlaky1,2, Jack Su1,2, Adekunle Adesina4, Dawna L. Armstrong4, Meenakshi Bhattacharjee4, Robert Dauser3, Susan M. Blaney1,2, Murali Chintagumpala1,2 and Ching C. Lau1,2

1 Texas Children's Cancer Center, 2 Departments of Pediatrics, 3 Neurosurgery, and 4 Pathology, Baylor College of Medicine, Houston, Texas

Requests for reprints: K-K. Wong, Department of Pediatrics, Hematology-Oncology, Baylor College of Medicine, Room 1030.09, Feigin Center, 1102 Bates Street, Houston, TX 77030. Phone: 832-824-4373; Fax: 832-825-4038. E-mail: kkwong{at}bcm.tmc.edu


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Juvenile pilocytic astrocytoma (JPA) is one of the most common brain tumors in children. The expression profiles of 21 JPAs, determined using Affymetrix GeneChip U133A, were compared with subjects with normal cerebella. The genes involved in neurogenesis, cell adhesion, synaptic transmission, central nervous system development, potassium ion transport, protein dephosphorylation, and cell differentiation were found to be significantly deregulated in JPA. These 21 JPAs were further clustered into two major groups by unsupervised hierarchical clustering using a set of 848 genes with high covariance (0.5-10). Supervised analysis with Significance Analysis of Microarrays software between these two potential subgroups identified a list of significant differentially expressed genes involved in cell adhesion, regulation of cell growth, cell motility, nerve ensheathment, and angiogenesis. Immunostaining of myelin basic protein on paraffin sections derived from 18 incompletely resected JPAs suggests that JPA without myelin basic protein–positively stained tumor cells may have a higher tendency to progress.

Key Words: juvenile pilocytic astrocytoma • oligonucleotide array • neurogenesis • axonal guidance • 00-00-02 Brain/central nervous system cancers • 04-08-00 Molecular Oncology • 00-00-23 Pediatric cancers • 02-12-00 Gene Expression, Chromatin Regulation, and Oncogenomics • 02-07-01 Gene expression profiling


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Pilocytic astrocytomas, which are classified as grade 1 tumors by the WHO, account for approximately 15% of all childhood brain tumors, and 6% of all brain tumors. Most pilocytic astrocytomas can be completely surgically resected if regional anatomy permits (1). Cure rates following a gross total resection, without any other adjuvant therapy, approach 90% (2). Unfortunately, some juvenile pilocytic astrocytomas (JPA) still recur despite complete surgical removal and more than one third of these recurrent tumors are not amenable to a complete resection (3). The long-term survival for patients following a subtotal resection without adjuvant therapy is poor (4). Reliable markers that could predict the risk of recurrent or progressive JPA would be of value because currently available clinical criteria for predicting recurrence after complete surgical resection are not adequate.

In contrast to the increasing knowledge about the genetic aberrations in high-grade glioma, relatively little is known about the molecular changes in JPA. Thus, we used oligonucleotide microarrays to study the gene expression profiles of JPA (n = 21) versus subjects with normal cerebella (n = 3). The resultant studies, which are summarized in this report, suggest that there may be two subgroups of JPA with differential biological and clinical behavior.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Tissue Samples and RNA Preparation. Under an Institutional Review Board–approved protocol, brain tumor tissues were obtained, after informed consent, from patients undergoing tumor resection or other tumor-related neurosurgical procedures at the Texas Children's Hospital, Baylor College of Medicine. Samples of normal cerebellar tissue (n = 2) were isolated from surgically removed tissue adjacent to resected tumor tissue. Portions of the tumors were fixed in 10% formaldehyde and embedded in paraffin for sectioning and pathologic reviews, and the residual tissue samples were snap-frozen in liquid nitrogen and stored at –80°C for RNA extraction. All samples were obtained at initial diagnosis with no prior exposure to chemotherapy or radiation therapy (Table 1). Normal fetal brain RNA was obtained from Stratagene (La Jolla, CA) and a normal cerebellum RNA was from Ambion, Inc. (Austin, TX). Total RNA was isolated using Trizol reagent (Invitrogen, Carlsbad, CA) followed by DNase I treatment and clean-up on aRNeasy spin column (Qiagen, Valencia, CA). RNA quality and purity were assessed by agarose gel electrophoresis and absorbance measurement at A260/A280.


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  		Table 1. Clinical characteristics of samples used in this study

 
Gene Profiling and Quality Analysis. We used commercially available high-density oligonucleotide microarrays (HG_U133A; Affymetrix, Santa Clara, CA) for this study. Preparation of cRNA, hybridization, scanning, and image analysis of the arrays were done according to manufacturer's protocols. Briefly, 5 µg of total RNA was used to generate cRNA probes and combined with a mixture of control cRNAs (made from bacterial genes BioB, BioC, BioDN, and CreX) before hybridization. All GeneChip images were visually inspected for irregularities. The raw median signal for the 25 arrays (202 ± 34) and the median percentage of genes present (64.9 ± 2.7) indicated the high overall quality of the assays. The ratios of GAPDH 3' to 5' (1.14 ± 0.4) and ratio of actin 3' to 5' (1.85 ± 0.85) indicated the high overall quality of the samples. Quality control data of individual arrays is available in supplemental Table 1.

Real-time Quantitative Reverse Transcription-PCR. One microgram of RNA from each sample was used in a 100 µ1 reaction to generate cDNA using High-Capacity cDNA Archive Kit (Applied Biosystems, Foster City, CA). PCR primers of 18 to 25 bp in length were designed by Primer Express Version 2.0 (Applied Biosystems) software. Real-time quantitative PCR was then carried out using an ABI Prism 7000 Sequence Detection System (Applied Biosystems). A volume of0.5 µl of cDNA was used in a20-µl PCR reaction containing the appropriate primers and 1x SYBR Green PCR master mix (PE Applied Biosystems). Parallel experiments were done using an 18S primer set. Each sample was run in triplicate, and each PCR experiment included three no-template negative control wells. The comparative CT method (PE Applied Biosystems) was used to determine the relative ratio of gene expression for each gene corrected using 18S rRNA and referenced to normal cerebellum RNA (Ambion). The sequences of the primers used are as follows:

18S-For, CGGTTCTATTTTGTTGGTTTTCG;
18S-Rev, GCTCTGGTCCGTCTTGCG;
Sema5A-For, GATCTATGGCATCTTTACCACCAA;
Sema5A-Rev, TGGCGCTCAGGTTGAAGAC;
PYSL3-For, CGAACCCACCTGTGAGGAAT;
DPYSL3-Rev, CGAACCCCCTCATCCACTT;
SCRG1-For, TGTGAGATGATCTGTTACTGCAACTT;
SCRG1-Rev, GAGATCTTTGGTCCAAAGAAAACG;
PTPRZ1-For, GAAGGACAAAAATTTCCACTTGAGAT;
PTPRZ1-Rev, CGGTCCGCATCAAAGCAG;
ASCL1-For, AGTCAGCGCCCAAGCAAGT;
ASCL1-Rev, AGCCAAAGCCGCTGAAGTT.

Data Analysis. Raw images (dat files) from Affymetrix GeneChip scanner were processed with dChip 1.3 software (5). The raw signal of individual probes for the 25 arrays were normalized against the chip with median raw signal intensity, and is based on a set of probes called an "invariant set" that consists of points from nondifferentially expressed genes. After normalization, the expression values of each gene in all samples were computed using a perfect match–only model followed by outlier detection algorithm (5). After expression values were computed, genes with extremely low values were assigned a value of 60, which is equivalent to the average value of the lowest 10th percentile of all the genes that are called absent. This will prevent the overestimation of fold changes for weakly expressed genes. Probe sets with intensities absent in more than five samples were also removed from further analysis. Unsupervised hierarchical clustering was done with dChip 1.3 (5) as discussed in Results.

Identification of Differentially Expressed Genes. Differentially expressed genes were identified by supervised analysis with the Significance Analysis of Microarrays (SAM) software (6). SAM analysis is similar to t statistics but with permutations to calculate the false discovery rate. Normalized expression values from dChip analysis were used for a two-class unpaired SAM analysis. The SAMsoftware estimated the false discovery rate and generated a qvalue for each gene. The q value for each gene represents the probability that it is falsely called differentially expressed. Similar to a P value, a smaller q value indicates a more significant differential expression.

Immunohistochemistry. The Pediatric Division Cooperative Human Tissue Network, a National Cancer Institute–funded tissue banking facility in Columbus, OH, USA, provided paraffin sections from incompletely resected JPA for myelin basic protein (MBP) immunostaining. Sections were deparaffinized, rehydrated, and incubated in 3% hydrogen peroxide in methanol for 30 minutes to remove endogenous peroxidase. Antigen retrieval was done by treating sections in BD Retrievagen A antigen retrieval solution (BD Biosciences PharMingen, San Diego, CA) at 89°C for 20 minutes and cooling back down to room temperature. Sections were then blocked with 1.5% serum and incubated with anti-human MBP monoclonal antibody (QED Bioscience, Inc., San Diego, CA) overnight at 4°C. Staining reactions were done using Vectastain Elite avidin-biotin complex kit (Vector Laboratories, Burlingame, CA) and 3,3'-diaminobenzidine as the substrate.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Identification of Differentially Expressed Genes in JPA. We have analyzed the expression of ~22,000 transcripts using Affymetrix U133A microarrays for 21 JPAs, a normal fetal brain, and three normal cerebella RNA samples. After probe sets with intensities absent in more than five samples were removed from further analysis, we obtained a final list of 16,682 probe sets (supplemental Table 2) for SAM analysis to identify differentially expressed genes in JPA. Using a q value of < 0.2%, a total of 428 significant probe sets were identified to have more than 3-fold changes between the JPA (n = 21) and normal cerebellar tissue (n=3). There were 192 up-regulated and 236 down-regulated probe sets in JPA tumors. With further analysis of these probe sets using the Onto-Express analysis tool (7), we identified statistically significant biological processes that are associated with these deregulated genes. Using a P value of < 0.05, Table 2 lists the biological processes represented by these deregulated genes which include neurogenesis (P = 2.66 x 10–6), cell adhesion (P = 0.0299), synaptic transmission (P = 0.0232), central nervous system development (P = 5.08 x 10–5), potassium ion transport (P = 0.0481), protein dephosphorylation (P = 0.0349), and cell differentiation (P = 0.0339). The P value is the probability that the deregulated genes are associated with a functional category by chance and is based on a binomial probability distribution and gene ontology annotations (7).


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  		Table 2. Statistically significant biological processes represented by differentially expressed genes in juvenile pilocytic astrocytomas

 
Real-time Quantitative Reverse Transcription-PCR. To validate genes that are truly deregulated in the biological processes of neurogenesis and central nervous system development in JPA tumors, real-time quantitative PCR were done on five up-regulated genes (SEMA5A, SCRG1, PTPRZ1, DPYSL3 and ASCL1). The results confirmed that these genes are highly up-regulated in JPA with an average 4-fold to 137- increase versus normal cerebellar tissues (Table 3). The up-regulation of these genes in brain tumors is consistent with our analysis using the SAGE data available from the CGAP project (http://cgap.nci.nih.gov/). The SAGE results are summarized in Table 4.


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  		Table 3. Real-time quantitative reverse transcription-PCR analysis of up-regulated genes in JPA

 

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  		Table 4. Expression analysis with SAGE data from normal brain and glioma tissues

 
Clustering Analysis. To perform unsupervised hierarchical clustering analysis of these 25 samples, 848 probe sets with high covariance (0.5-10) were selected. A dendrogram was generated by using a 1-Pearson correlation distance matrix and the centroid linkage method (5). The dendrogram (Fig. 1) shows that the normal cerebellar samples were clustered together with fetal brain, whereas the JPA samples formed another cluster. Furthermore, the JPA group seems to have two potential subgroups. Supervised analyses of these two subgroups were done with SAM to identify statistically significant genes that are differentially expressed in the two potential JPA subgroups. Using a criterion of at least 2-fold change and a q value of < 9.6%, 89 differentially expressed probe sets were identified. Significant biological processes represented by these differentially expressed probe sets include pathways in cell adhesion, regulation of cell growth, cell motility, nerve ensheathment, and angiogenesis (Table 5). Similar analyses were also done on JPA data (n = 17) previously generated by Dr. Hanash's group (8) using an older Affymetrix GeneChip Hu6800. Clustering analysis of Dr. Hanash's data also indicates two subgroups of JPA (supplemental figure), and 75 genes were found to be differentially expressed between the two subgroups (supplemental table). More importantly, we have similar results in 14 of these 75 genes, which include AMD, COL1A2, COL3A1, COL4A1, EGR3, FN1, MBP, LAMB1, IL8, NPTX2, NR4A2, POSTN, PRG1, and SPP1.



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  		Figure 1. Unsupervised hierarchical clustering analysis. Normal, Nfb, normal fetal brain; Nc739, normal cerebellum; Nc321, normal cerebellum; NcAb, normal cerebellum. JPA-group A, all gross totally resected JPA. JPA-group B, all gross totally resected JPA. *, subtotally resected JPA with progressive diseases. Clustering was done using 1-Pearson correlation coefficient matrix with 848 filtered genes that have expression values with coefficient values from 0.5 to 10 across the samples using the dChip program.

 

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  		Table 5. Genes that are differentially expressed between two potential subgroups of JPA

 
MBP Immunostaining. Immunostaining for MBP protein showed the presence of a subpopulation of neoplastic cells with nuclear and/ or cytoplasmic positivity for this protein (Fig. 2). Using paraffin sections derived from incompletely resected JPA, Table 6 shows that eight out of ten JPA with progressive diseases were negative on MBP staining. On the other hand, six out of eight JPAs without progressive diseases have MBP-positively stained cells.



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  		Figure 2. Immunohistochemical staining of MBP in paraffin sections derived from incompletely resected tumor tissues. Cases 96-03-P010 and JPA567 (with progressive diseases) do not have nuclear or cytoplasmic positive staining of MBP, except for JPA567, which shows MBP positivity along the residual axonal processes. Cases 99-06-P1313, 2002-11-P2014, 3000-30-P6437, and 3000-30-P6550 (without progressive disease) show varying degrees of nuclear and/or cytoplasmic immunopositivity for MBP.

 

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  		Table 6. Immunostaining of MBP on incompletely resected JPA paraffin sections

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Through the characterization of gene expression profiles for JPA and normal cerebellar tissues, we have identified several major biological processes potentially involved in the pathogenesis of JPA (Table 2). Neurogenesis seems to be one of the major biological processes and has 18 deregulated genes associated with it. We have also confirmed the up-regulation of four neurogenesis-related genes (SEMA5A, SCRG1, DPYSL3, and ASCL1), and one central nervous system development-related gene (PTPRZ1). This result may suggest that JPA is a developmental disease with a defect in neurogenesis.

Up-regulated Genes Involved in Neurogenesis. Neurogenesis is regulated by two major transcription factors of the NEUROD/atonal and ACHAETE SCUTE families (9, 10). NEUROD was not detected in the JPA tissue samples evaluated in this study, which is consistent with a previous report on the expression of neurogenic basic helix-loop-helix genes in glioma (11). Achaete-scute homologue-1 (ACSL1) is the only identified transcription factor involved in neurogenesis that is highly up-regulated in JPA (Table 2), which suggests the involvement of ACSL1 pathways in JPA pathogenesis. ACSL1 (also termed MASH1 in rodents or hASH1 in humans) is a basic helix-loop-helix transcription factor that plays a pivotal role in tissue-specific determination and differentiation. It has been shown that ACSL1 promotes neuronal differentiation during retinal development and is essential for proper ratios of retinal cell types (12). Three other genes, including SEMA5A, SEMA3E, and DYPSL3, are also involved in neurogenesis of the optical pathway. Semaphorin F (SEMA5A), which encodes a positive axonal guidance molecule with a sema protein domain (13), has been implicated in normal optical nerve development and development of thalamocortical connections (14, 15). SEMA5A also contains seven thrombospondin type 1 repeats that have been reported to promote neurite outgrowth and neuronal adhesion (16). Semaphorin 3E precursor (SEMA3E), which also encodes an axonal guidance molecule, has been shown to restrict growth of retinal ganglion cell axons to the optical fiber layer (17). SEMA3E may also be related to tumor cell metastasis (18). Similar to SEMA5A and SEMA3E, dihydropyrimidinase-like 3 (DPYSL3, also called unc-33) is also implicated in axon guidance. DPYSL3 belongs to the Ulip family, which controls neurite elongation and axonal guidance (19). Mutations in DPYSL3 in the nematode have led to severe axon guidance errors in all neurons (20). Although none of the JPA analyzed in this study are from the optical pathway, it is possible that the pathogenesis of JPA may involve a mechanism that mimics the development of the optical pathway. Interestingly, several pediatric gliomas that are associated with the optical nerve, chiasma opticum, and hypothalamic-optical chiasm region (21–27) are JPA. The role of axonal guidance in the pathogenesis of JPA may deserve further studies.

Two Potential Subgroups of JPA. It has been reported that nearly 50% of incompletely resected JPA will progress (28, 29). However, most of the 21 JPA samples that we have analyzed are derived from gross totally resected tumors, except for JPA323 and JPA567. It is possible that some of the gross totally resected JPA would have progressed if they were not totally resected. Thus, unsupervised hierarchical clustering strategy without inference from clinical outcome will be the best way to identify subgroups. Unsupervised hierarchical clustering analysis indicates that there are indeed two potential subgroups of JPA (Fig. 1). Using the same analysis, two subgroups of JPAs were also identified with previously reported JPA data generated by an older Affymetrix GeneChip Hu6800 (supplemental figure; ref. 8). Group B includes the two cases of subtotally resected JPA (JPA323 and JPA567) that have progressive diseases. JPA567 progressed 3 months after initial surgery. JPA323 with pilomyxoid features was given chemotherapy after initial surgery for 18 months, but the tumor progressed as soon as cessation of therapy. It is tempting to speculate that the two clusters may represent tumors with different potentials of progression, and tumors associated with JPA323 and JPA567 (JPA of group B) might have progressive disease if they were not completely resected.

Differentially expressed genes between JPA group A and group B were listed in Table 5, and those that are also identified as differentially expressed with Dr. Hanash's data set (8) are highlighted in boldface. JPA from group B has genes with higher expression in biological processes that involve cell adhesion, growth, motility, and angiogenesis. Because cell adhesion and motility are related to invasiveness, the two groups may have different potentials for cell invasion and cell proliferation. JPA of group B also have a higher expression of VEGF, which may suggest higher vascular proliferation. Blinded review of pathologic slides indicate that JPA of group B have more prominent blood vessels than JPA of group A. In a recent report on a small set of pediatric gliomas, a study comparing six low-grade astrocytomas and seven high-grade astrocytomas (30), VEGF did not reach statistical significance although angiogenesis is still the main feature that was used to separate low-grade astrocytoma from high-grade glioblastoma multiform. This may possibly be due to the presence of a subgroup of JPA with higher level of VEGF expression. In a similar adult glioma study (31), a group of genes with angiogenic activity, including VEGF, emerges as a strong predictor of primary glioblastoma multiform from secondary glioblastoma multiform and low-grade glioma. On the other hand, JPA of group B has a lower expression of MBP and proteolipid protein (PLP1) that are involved in nerve ensheathment. Previous studies using in situ hybridization has shown that the number of PLP and MBP positively stained cells in human gliomas decreases markedly with increasing grade, and proposed that the expression of PLP and MBP mRNA could be used to predict the grade and extent of tumor infiltration in astrocytomas (32). To explore the significant of this finding, immunostaining for MBP protein were done on paraffin sections derived from 18 incompletely resected tumor samples. Table 6 shows that eight out of ten JPA with progressive diseases were negative on MBP staining. On the other hand, six out of eight JPA without progressive diseases have MBP-positively stained cells. That is an80% sensitivity and 75% specificity for predicting progression using MBP staining. If the lack of MBP positivity is a marker of poor differentiation and tumor aggressiveness, as has been observed in diffuse astrocytomas (32), one may speculate that JPAs with less MBP-positive cells may be of a more aggressive nature. Further analysis with other potential markers in a large sample size will facilitate the development of a robust prognostic model.

In summary, we have found several genes that are deregulated in JPA. Two subgroups of JPA have emerged with potential differences in the biology and clinical outcomes. Numerous interesting biological processes from this analysis are awaiting further studies to gain additional insights into the biology of pilocytic astrocytoma with regard to neurogenesis and central nervous system development, along with the involvement of cell differentiation, cell adhesion, and potassium transport.


   Acknowledgments
 
Grant support: John S. Dunn Research Foundation and the Robert J. Kleberg, Jr. and Helen C. Kleberg Foundation grants to the Texas Children's Cancer Center, and Children's Oncology Group (COG) Young Investigator Award program.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received 7/13/04. Revised 10/13/04. Accepted 10/22/04.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

  1. Burger PC, Scheithauer BW, Paulus W, Szymas J, Giannini C, Kleihues P. Pilocytic astrocytoma. In: Kleihues P, Cavenee WKs, Kleihues P, Cavenee WKs. Pathology and genetics of tumours of the nervous system. Washington (DC): IARC; 2000. p. 45–51.
  2. Gajjar A, Sanford RA, Heideman R, et al. Low-grade astrocytoma: a decade of experience at St. Jude Children's Research Hospital. J Clin Oncol 1997;15:2792–9.[Abstract]
  3. Fernandez C, Figarella-Branger D, Girard N, et al. Pilocytic astrocytomas in children: prognostic factors—a retrospective study of 80 cases. discussion 54–5.Neurosurgery 2003;53:544–53.[Medline]
  4. Due-Tonnessen BJ, Helseth E, Scheie D, Skullerud K, Aamodt G, Lundar T. Long-term outcome after resection of benign cerebellar astrocytomas in children and young adults (0–19 years): report of 110 consecutive cases. Pediatr Neurosurg 2002;37:71–80.[Medline]
  5. Li C, Wong WH. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci U S A 2001;98:31–6.[Abstract/Free Full Text]
  6. Tusher VG, Tibshirani R, Chu G. Significance Analysis of Microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 2001;98:5116–21.[Abstract/Free Full Text]
  7. Draghici S, Khatri P, Bhavsar P, Shah A, Krawetz SA, Tainsky MA. Onto-Tools, the toolkit of the modern biologist: Onto-Express, Onto-Compare, Onto-Design and Onto-Translate. Nucleic Acids Res 2003;31:3775–81.[Abstract/Free Full Text]
  8. Rickman DS, Bobek MP, Misek DE, et al. Distinctive molecular profiles of high-grade and low-grade gliomas based on oligonucleotide microarray analysis. Cancer Res 2001;61:6885–91.[Abstract/Free Full Text]
  9. Lee JE. NeuroD and neurogenesis. Dev Neurosci 1997;19:27–32.[Medline]
  10. Casarosa S, Fode C, Guillemot F. Mash1 regulates neurogenesis in the ventral telencephalon. Development 1999;126:525–34.[Abstract]
  11. Rostomily RC, Bermingham-McDonogh O, Berger MS, Tapscott SJ, Reh TA, Olson JM. Expression of neurogenic basic helix-loop-helix genes in primitive neuroectodermal tumors. Cancer Res 1997;57:3526–31.[Abstract/Free Full Text]
  12. Tomita K, Nakanishi S, Guillemot F, Kageyama R. Mash1 promotes neuronal differentiation in the retina. Genes Cells 1996;1:765–74.[Abstract]
  13. Adams RH, Betz H, Puschel AW. A novel class of murine semaphorins with homology to thrombospondin is differentially expressed during early embryogenesis. Mech Dev 1996;57:33–45.[CrossRef][Medline]
  14. Oster SF, Bodeker MO, He F, Sretavan DW. Invariant Sema5A inhibition serves an ensheathing function during optic nerve development. Development 2003;130:775–84.[Abstract/Free Full Text]
  15. Jones L, Lopez-Bendito G, Gruss P, Stoykova A, Molnar Z. Pax6 is required for the normal development of the forebrain axonal connections. Development 2002;129:5041–52.[Abstract/Free Full Text]
  16. Adams JC, Tucker RP. The thrombospondin type 1 repeat (TSR) superfamily: diverse proteins with related roles in neuronal development. Dev Dyn 2000;218:280–99.[CrossRef][Medline]
  17. Steinbach K, Volkmer H, Schlosshauer B. Semaphorin 3E/collapsin-5 inhibits growing retinal axons. Exp Cell Res 2002;279:52–61.[CrossRef][Medline]
  18. Christensen CR, Klingelhofer J, Tarabykina S, Hulgaard EF, Kramerov D, Lukanidin E. Transcription of a novel mouse semaphorin gene, M-semaH, correlates with the metastatic ability of mouse tumor cell lines. Cancer Res 1998;58:1238–44.[Abstract/Free Full Text]
  19. Byk T, Ozon S, Sobel A. The Ulip family phosphoproteins—common and specific properties. Eur J Biochem 1998;254:14–24.[Medline]
  20. Quinn CC, Gray GE, Hockfield S. A family of proteins implicated in axon guidance and outgrowth. J Neurobiol 1999;41:158–64.[CrossRef][Medline]
  21. Kocks W, Kalff R, Reinhardt V, Grote W, Hilke J. Spinal metastasis of pilocytic astrocytoma of the chiasma opticum. Childs Nerv Syst 1989;5:118–20.[Medline]
  22. Pennelli N, Montaguti A, Carteri A, Midena E, Boccato P. Juvenile pilocytic astrocytoma of the optic nerve diagnosed by fine needle aspiration biopsy. Acta Cytol 1988;32:395–8.[Medline]
  23. Bowers DC, Krause TP, Aronson LJ, et al. Second surgery for recurrent pilocytic astrocytoma in children. Pediatr Neurosurg 2001;34:229–34.[Medline]
  24. Perilongo G, Carollo C, Salviati L, et al. Diencephalic syndrome and disseminated juvenile pilocytic astrocytomas of the hypothalamic-optic chiasm region. Cancer 1997;80:142–6.[CrossRef][Medline]
  25. Silva MM, Goldman S, Keating G, Marymont MA, Kalapurakal J, Tomita T. Optic pathway hypothalamic gliomas in children under three years of age: the role of chemotherapy. Pediatr Neurosurg 2000;33:151–8.[Medline]
  26. Janss AJ, Grundy R, Cnaan A, et al. Optic pathway and hypothalamic/chiasmatic gliomas in children younger than age 5 years with a 6-year follow-up. Cancer 1995;75:1051–9.[CrossRef][Medline]
  27. Koenig SB, Naidich TP, Zaparackas Z. Optic glioma masquerading as spasmus nutans. J Pediatr Ophthalmol Strabismus 1982;19:20–4.[Medline]
  28. Sutton LN, Cnaan A, Klatt L, et al. Postoperative surveillance imaging in children with cerebellar astrocytomas. J Neurosurg 1996;84:721–5.[Medline]
  29. Dirven CM, Mooij JJ, Molenaar WM. Cerebellar pilocytic astrocytoma: a treatment protocol based upon analysis of 73 cases and a review of the literature. Childs Nerv Syst 1997;13:17–23.[CrossRef][Medline]
  30. Khatua S, Peterson KM, Brown KM, et al. Overexpression of the EGFR/FKBP12/HIF-2{alpha} pathway identified in childhood astrocytomas by angiogenesis gene profiling. Cancer Res 2003;63:1865–70.[Abstract/Free Full Text]
  31. Godard S, Getz G, Delorenzi M, et al. Classification of human astrocytic gliomas on the basis of gene expression: a correlated group of genes with angiogenic activity emerges as a strong predictor of subtypes. Cancer Res 2003;63:6613–25.[Abstract/Free Full Text]
  32. Landry CF, Verity MA, Cherman L, et al. Expression of oligodendrocytic mRNAs in glial tumors: changes associated with tumor grade and extent of neoplastic infiltration. Cancer Res 1997;57:4098–104.[Abstract/Free Full Text]
  33. Boon K, Osorio EC, Greenhut SF, et al. An anatomy of normal and malignant gene expression. Proc Natl Acad Sci U S A 2002;99:11287–92.[Abstract/Free Full Text]



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[Abstract] [PDF]

Home page
 Clin. Cancer Res.Home page
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Distinct Genetic Signatures among Pilocytic Astrocytomas Relate to Their Brain Region Origin
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Matrilin-2 expression distinguishes clinically relevant subsets of pilocytic astrocytoma
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 NeurologyHome page
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