|
Advertisement |
|
|
||
 |
|
|||||
|
|||||
|
 |
||||||
|
||||||
|
||||||
|
Originally published as JCO Early Release 10.1200/JCO.2005.04.4974 on March 27 2006 © 2006 American Society of Clinical Oncology. Genomics Identifies Medulloblastoma Subgroups That Are Enriched for Specific Genetic Alterations
From the St Jude Children's Research Hospital, Memphis, TN; Texas Children's Hospital, Houston, TX; Royal Children's Hospital, Melbourne; The Children's Hospital at Westmead and University of Sydney, Sydney, Australia; and Hospital for Sick Children, Toronto, Ontario, Canada Address reprint requests to Richard J. Gilbertson, MD, PhD, Department of Developmental Neurobiology, St Jude Children's Research Hospital, 332 N Lauderdale St, Memphis, TN 38105; e-mail: Richard.Gilbertson{at}stjude.org
PURPOSE: Traditional genetic approaches to identify gene mutations in cancer are expensive and laborious. Nonetheless, if we are to avoid rejecting effective molecular targeted therapies, we must test these drugs in patients whose tumors harbor mutations in the drug target. We hypothesized that gene expression profiling might be a more rapid and cost-effective method of identifying tumors that contain specific genetic abnormalities. MATERIALS AND METHODS: Gene expression profiles of 46 samples of medulloblastoma were generated using the U133av2 Affymetrix oligonucleotide array and validated using real-time reverse transcriptase polymerase chain reaction (RT-PCR) and immunohistochemistry. Genetic abnormalities were confirmed using fluorescence in situ hybridization (FISH) and direct sequencing. RESULTS: Unsupervised analysis of gene expression profiles partitioned medulloblastomas into five distinct subgroups (subgroups A to E). Gene expression signatures that distinguished these subgroups predicted the presence of key molecular alterations that we subsequently confirmed by gene sequence analysis and FISH. Subgroup-specific abnormalities included mutations in the Wingless (WNT) pathway and deletion of chromosome 6 (subgroup B) and mutations in the Sonic Hedgehog (SHH) pathway (subgroup D). Real-time RT-PCR analysis of gene expression profiles was then used to predict accurately the presence of mutations in the WNT and SHH pathways in a separate group of 31 medulloblastomas. CONCLUSION: Genome-wide expression profiles can partition large tumor cohorts into subgroups that are enriched for specific genetic alterations. This approach may assist ultimately in the selection of patients for future clinical trials of molecular targeted therapies.
Gene mutations are promising targets for new anticancer therapies.1-3 However, so far, only a handful of genetic abnormalities have been targeted successfully in the clinic. If we are to extend these early successes to all cancers, we must identify additional targets and the patient populations in whom these drugs are likely to be most effective. Clinical experience in the use of molecular targeted therapies is at an early stage; nevertheless, available data suggest that these drugs are more effective against tumors that contain mutant rather than wild-type proteins. For example, gefitinib, an inhibitor of the epidermal growth factor receptor (EGFR), is highly effective against non–small-cell lung cancers containing mutations in the kinase domain of EGFR, but it is relatively ineffective against tumors expressing the wild-type protein.2,4 Thus, to avoid rejecting effective new treatments of cancer, molecular targeted therapies must be tested in patients whose tumors harbor mutations in the corresponding drug target. Traditionally, mutations have been identified in cancer by direct gene sequence analysis; however, this technique is likely to prove too time consuming and laborious for selecting patients for trials of molecular targeted therapies. Thus, the oncology community must develop and validate alternative techniques that can rapidly and accurately detect mutations in tumor samples. As well as developing tools to detect known molecular drug targets, we must also increase the rate at which we discover new mutations. The Human Cancer Genome Project has suggested that new cancer-causing mutations could be identified in tumors by analyzing the coding sequences of groups of genes that are thought to play a role in cancer (eg, protein kinases).5 However, such broadly applied direct sequencing strategies are laborious, extremely costly (sequencing the genomes of the most common human tumors will cost an estimated $12 billion6), and unlikely to uncover new molecular targets with equal efficiency in all cancers. For example, although a study of 340 serine/threonine protein kinases in colorectal carcinomas identified a number of gene mutations,7 similar studies of 540 kinases in breast cancers,8 lung cancers,9 and testicular germ cell tumors10 have failed to detect somatic mutations that might represent useful drug targets. Here, by studying a large cohort of medulloblastomas, we demonstrate that genome-wide patterns of gene expression can detect the presence of underlying genetic alterations in tumors. Thus, gene expression signatures can provide clinicians with a more expedient tool to triage patients for trials of molecular targeted treatments of cancer.
Gene Expression Microarray Analysis With the approval of the St Jude Children's Research Hospital Institutional Review Board (XPD01-092), 77 samples of snap-frozen pediatric (aged < 21 years) primary medulloblastomas were collected from patients at initial surgery before any adjuvant therapy. Samples were subject to central histopathology review. RNA was extracted using the Trizol reagent (Invitrogen Corp, Carlsbad, CA). Gene expression profiles were generated, as described previously,11 using the U133Av2 array (Affymetrix, Santa Clara, CA) that analyzes the expression level of 18,400 transcripts and variants. Gene expression data were normalized using the MAS5 algorithm and Bioconductor version 1.5. The data were then transformed by taking the logarithm (base 2). Unsupervised hierarchical clustering of expression profiles based on Z scores of 22,212 probes was performed using the Pearson correlation. Probe sets that discriminated the tumor subtypes defined by unsupervised clustering were identified using analysis of variance and t statistics.
Real-Time Reverse Transcriptase Polymerase Chain Reaction
Fluorescence In Situ Hybridization and Immunohistochemistry
Gene Sequence Analysis
Gene Expression Profiling Identifies Clinically and Histologically Distinct Subgroups of Medulloblastoma To uncover molecular subgroups of medulloblastoma, we performed unsupervised hierarchical cluster analysis of the gene expression profiles of 46 primary medulloblastomas. Five principal tumor subgroups were identified (Fig 1A). Analysis of variance suggested that clinical and histologic characteristics were not evenly distributed among these subgroups. For example, the medulloblastomas that comprised subgroup B occurred exclusively in patients older than 3 years and were all of the classic histologic subtype. All subgroup B patients were also alive and disease free at the time of analysis, although the limited patient numbers precluded formal survival analysis. In contrast, medulloblastomas within subgroup D occurred mainly in children aged less than 3 years and were predominantly of the desmoplastic histologic subtype.
Gene Expression Signatures of Medulloblastoma Predict Subgroup-Specific Patterns of Chromosomal Alteration and Gene Mutation Previous studies of medulloblastoma have also linked patterns of gene expression to specific tumor histologies and clinical behaviors, although the molecular basis of these correlations is unknown.17,18 We reasoned that medulloblastoma subgroups might display distinct clinical characteristics and gene expression signatures because they are caused by distinct sets of genetic alterations. Further, scrutiny of these gene expression signatures might reveal the nature of the underlying genetic alterations in the different tumor subtypes. As a first step to determine the molecular basis of the five medulloblastoma subgroups, we performed t tests to identify the signature genes that most discriminated these subgroups. Each tumor subgroup displayed a unique gene expression signature of between 400 and 800 transcripts that were significantly upregulated or downregulated, at the P < .0003 level, when compared with expression levels in the other tumors (see Supplementary Table 2). The gene expression signatures of subgroups B and D were especially robust. In this regard, the top 100 signature genes of subgroup B were differentially expressed in these tumors relative to the other tumors in the study cohort, with an average log ratio of 3.6 with an average significance level of P = 5.5 x 10–10. Similarly, the top 100 signature genes of subgroup D were differentially expressed, with an average log ratio of 2.4 with an average significance of P = 1.5 x 10–7. Interestingly, significant proportions of the signature genes of subgroup B, C, and D tumors are located in just one or two regions of the genome (Fig 1B). Of particular note, 51% (n = 112) of 221 probe sets reporting downregulated gene expression in subgroup B tumors represent genes that are located on chromosome 6. Because chromosome 6 genes account for only 5.8% (n = 1,306 of 22,212) of all probe sets on the U133av2 expression array, this genomic region is significantly over-represented among the genes that are downregulated in subgroup B tumors (P < .0001). Thus, we reasoned that chromosome 6 might be deleted in subgroup B tumors. In contrast, subgroup C medulloblastomas displayed a gene expression pattern predictive of concurrent deletion of 17p and gain of 17q (which is compatible with an isochromosome of 17q, a common chromosome alteration in medulloblastoma19), whereas genes on 17q were relatively underexpressed among subgroup D tumors (Fig 1B). We next studied whether aberrant activation of certain cell signal pathways might contribute to medulloblastoma gene expression signatures. To test this, we first reviewed all 33,000 probe sets on the U133av2 expression array and identified 392 that report the expression of genes that are either members or targets of the WNT (n = 240), SHH (n = 29), NOTCH (n = 20), or BMP (n = 103) cell signal pathways (see Supplementary Table 3 for a full list of genes). These four pathways have each been implicated in the development of medulloblastoma.12,15,20-23 Furthermore, anticancer drugs that target the WNT,24 SHH,25 and NOTCH26 pathways are currently being developed for use in the clinic. Thus, defining subgroups of medulloblastoma in which these pathways are aberrantly active is likely to have the most immediate clinical utility. We found that members or targets of the WNT and SHH pathways were significantly over-represented among upregulated genes in subgroup B and D tumors, respectively (Fig 1C). Gene expression signatures did not yield evidence of either NOTCH or BMP pathway activation among any tumor subgroup. To confirm whether genes within the WNT and SHH pathways are upregulated in subgroup B and subgroup D medulloblastoma, respectively, we used real-time RT-PCR and IHC analyses to validate the gene expression signatures of these two subgroups. These studies confirmed that WNT pathway members, including DKK1, DKK2,27 DKK4,27 WNT inhibitory factor 1 (WIF),28 LEF1, and GAD1,29 are indeed selectively upregulated in subgroup B tumors (Figs 2A and 2B). We also identified intense nuclear expression of ß-catenin protein, which is frequently linked with stabilizing mutations in CTNNB1,30 to be an exclusive feature of subgroup B tumors (Fig 2B). Conversely, real-time RT-PCR and IHC confirmed the upregulation of SHH pathway genes among subgroup D tumors (Figs 2A and 2B). These included ATOH1, PTCH2, SFRP1, and GLI1 (Figs 2A and 2B). Each of these genes has been implicated previously in the development of mouse medulloblastoma in which the SHH pathway is activated.31 Interestingly, our real-time RT-PCR and IHC analyses suggested that the SHH pathway is also activated in a proportion of subgroup C tumors (nos. 25 to 29; Figs 2A and 2B). These tumors were located predominately on a separate branch of the dendrogram in our initial hierarchical cluster analysis (Fig 1A) and are hereon referred to as subgroup C.
FISH and Gene Sequence Analysis Confirm the Genetic Alterations Predicted by Gene Expression Microarray Analysis To confirm whether gene expression profiling predicted accurately the presence of chromosome alterations in subgroup B, C, and D tumors, we next performed FISH analysis of chromosome 6p22.1 (CDKAL1), 6q24 (RAB32), 17p13.3 (DPH1), and 17q23.2 (RPS6KB1). In agreement with our gene expression array data, we detected monosomy of chromosome 6 in four of five tested subgroup B tumors but in only two of 36 tested tumors from the other subgroups (P < .01; Fig 3). FISH analysis also confirmed the predicted high incidence of chromosome 17 abnormalities among subgroup C tumors (Fig 3). Ten of 11 tested tumors in subgroup C deleted chromosome 17p; in four cases, this occurred in the context of an isochromosome of 17q. In contrast, deletion of chromosome 17p was relatively rare among the other tumor subgroups (P < .0001). In contrast to subgroups A, C, and E, no subgroup D tumor gained 17q, providing a potential explanation for the comparatively low expression of genes on chromosome 17q among tumors in this subgroup.
Next, we investigated whether the gene expression signatures of subgroups B, C, and D might also be driven by activating mutations in the WNT and SHH pathways. The majority of mutations that activate the WNT pathway in sporadic medulloblastoma target residues 32 to 34 of CTNNB1.32,33 Other less frequent mutations occur in the mutation cluster region of APC and in the APC-binding domain of AXIN1.32 Therefore, we sequenced the coding regions of CTNNB1, APC, and AXIN1 in all tumor samples in the cohort. We identified activating mutations in residues 32 to 34 of CTNNB1 in all tested subgroup B tumors but in no other medulloblastoma samples (P < .0001; Fig 4). Thus, the WNT pathway gene expression signature displayed by subgroup B tumors is driven by activating mutations in CTNNB1. Three tumors outside of subgroup B contained mutations in APC (Fig 4). However, these included two nonconservative missense mutations of unknown functional significance (nos. 25 and 30) and one truncating mutation with preservation of the wild-type allele (no. 18). Thus, it remains unclear whether these APC mutations impact the WNT cell signaling pathway. Indeed, these tumors did not share the WNT pathway gene expression signature of subgroup B tumors (Figs 1 and 2A), and they also did not display ß-catenin nuclear immunoreactivity or deletion of chromosome 6 (Figs 2B and 3). Thus, these data suggest that CTNNB1 and APC mutations are mutually exclusive and molecularly distinct in medulloblastoma.
Activating mutations in the SHH pathway predominantly target PTCH and SUFU.32 Five tumors from subgroups C and D contained frameshift mutations in PTCH, and one subgroup D tumor contained a truncating mutation in SUFU (Fig 4). In contrast, we did not detect mutations in either PTCH or SUFU in any other tested tumors (P = .001). Thus, the SHH pathway gene expression signature of most subgroup C and D tumors is driven by inactivating mutations in PTCH and SUFU. Six of 12 tested subgroup C and D tumors did not contain detectable mutations in either PTCH or SUFU (Fig 4). Thus, alternative mechanisms, including mutations in SMO,34 are likely to activate SHH signaling in these tumors.
Real-Time RT-PCR Analysis Rapidly Identifies Medulloblastomas With Mutations in the WNT and SHH Pathways
Despite an aggressive combination of surgery, radiation, and chemotherapy, medulloblastoma remains incurable in a significant proportion of patients.35-37 Thus, similar to most human cancers, there is a great need to identify alternative therapeutic approaches to treat medulloblastoma. Small-molecule inhibitors that target cell signaling pathways that are activated aberrantly in medulloblastoma could have significant utility in the future management of this disease.32 For example, SHHAntag, a drug that binds to and inhibits SMOOTHENED, has shown dramatic activity in Ptch1+/– mouse models of medulloblastoma.25 Although molecular targeted therapies are promising agents for the future treatment of cancer, their successful translation into routine clinical practice will depend on our ability to test these drugs in appropriate patients. Our study of medulloblastoma demonstrates that gene mutations in cell signaling pathways are associated tightly with specific gene expression signatures. We further show that these gene expression signatures can be used to identify patient subgroups that are enriched for the presence of underlying gene mutations. Such mutation-specific gene expression signatures could prove extremely useful in the selection of patients for clinical trials of molecular targeted therapies. In this regard, patient tumor samples could be rapidly screened for mutation-specific gene expression signatures using relatively simple and widely available techniques such as real-time RT-PCR or IHC. Gene sequencing efforts could then be focused to confirm the presence of the underlying mutation in tumors in these patients before their enrollment onto a clinical trial of an appropriate molecular targeted therapy. As well as predicting known gene mutations, we provide proof of principle that expression profiling can guide more focused gene sequencing and chromosome mapping (eg, FISH) efforts to identify unknown genetic alterations in cancer. In the current study, we annotated the U133av2 Affymetrix microarray to hunt for evidence of aberrant signaling within the WNT, SHH, BMP, and NOTCH pathways. Furthermore, because expression array analysis profiles the whole genome, we were able to detect previously unidentified concurrent genetic alterations in medulloblastoma (eg, mutation in CTNNB1 and monosomy 6). Clearly, this approach could be used to screen other types of cancer for mutations in cell signaling pathways or alterations in specific chromosomal regions. The success of this approach will depend on the identification of genes that report the activity of specific signal pathways with a high degree of sensitivity. Such signature genes might by identified by expression profile analysis of cancer cell cultures, xenografts, and fresh tumor samples in which the activation and mutation status of the appropriate cell signal system is known. This approach provides an alternative and potentially more cost-effective method to identify new mutations in cancer than the unbiased genome-wide sequencing approaches recently proposed by the Human Cancer Genome Project.5,6,8-10 Finally, our data provide important new insight into the biology of medulloblastoma. In particular, they demonstrate, for the first time, that activating mutations within the WNT and SHH pathways affect distinct subsets of medulloblastoma. Thus, drugs that target the WNT and SHH pathways are likely to be effective in distinct populations of patients with medulloblastoma. These data underline further the need for a test that can distinguish these tumor subtypes. The finding that activating mutations in CTNNB1 are associated significantly with monosomy of chromosome 6 is a further interesting new finding in the current study. Although monosomy chromosome 6 has been reported previously in a variety of tumor types including medulloblastoma,38,39 to our knowledge, the relationship of this alteration with activating mutations in CTNNB1 has not been reported. We are currently conducting high-density single nucleotide polymorphism mapping array studies to define further the region(s) of chromosome 6 that might harbor genes that suppress the development of CTNNB1 mutant medulloblastoma. Our data demonstrate that genome-wide expression profiles can partition large tumor cohorts into subgroups that are enriched for specific mutations. Furthermore, mutation-specific gene expression signatures that are identified from expression profiles provide a rapid, accurate, and inexpensive tool to select appropriate patients for clinical trials of molecular targeted therapies.
The authors indicated no potential conflicts of interest.
We thank the Tumor Bank and Hartwell Center for Bioinformatics and Biotechnology at St Jude Children's Research Hospital.
Supported by Grant No. NCI CA096832 (R.J.G.), the Sontag Foundation (R.J.G.), the V-Foundation for Cancer Research (R.J.G.), and the American Lebanese Syrian Associated Charities Cancer Center (CORE) Support Grant No. CA 21765. Additional support was provided by the Noyes Foundation, Musicians Against Childhood Cancer, ABC2, and the Pediatric Brain Tumor Foundation. Presented in part at the 37th Annual Congress of the International Society of Pediatric Oncology, Vancouver, British Columbia, Canada, September 21-24, 2005. Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.
1. Druker BJ, Sawyers CL, Kantarjian H, et al: Activity of a specific inhibitor of the BCR-ABL tyrosine kinase in the blast crisis of chronic myeloid leukemia and acute lymphoblastic leukemia with the Philadelphia chromosome. N Engl J Med 344:1038-1042, 2001 2. Paez JG, Janne PA, Lee JC, et al: EGFR mutations in lung cancer: Correlation with clinical response to gefitinib therapy. Science 304:1497-1500, 2004 3. Vogel CL, Cobleigh MA, Tripathy D, et al: Efficacy and safety of trastuzumab as a single agent in first-line treatment of HER2-overexpressing metastatic breast cancer. J Clin Oncol 20:719-726, 2002 4. Lynch TJ, Bell DW, Sordella R, et al: Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 350:2129-2139, 2004 5. Kaiser J: National Institutes of Health: NCI gears up for Cancer Genome Project. Science 307:1182a, 2005 6. Gabor Miklos GL: The Human Cancer Genome Project: One more misstep in the war on cancer. Nat Biotechnol 23:535-537, 2005[Medline] 7. Parsons DW, Wang T-L, Samuels Y, et al: Colorectal cancer: Mutations in a signalling pathway. Nature 436:792, 2005[CrossRef][Medline] 8. Stephens P, Edkins S, Davies H, et al: A screen of the complete protein kinase gene family identifies diverse patterns of somatic mutations in human breast cancer. Nat Genet 37:590-592, 2005[CrossRef][Medline] 9. Davies H, Hunter C, Smith R, et al: Somatic mutations of the protein kinase gene family in human lung cancer. Cancer Res 65:7591-7595, 2005 10. Bignell G, Smith R, Hunter C, et al: Sequence analysis of the protein kinase gene family in human testicular germ-cell tumors of adolescents and adults. Genes Chromosomes Cancer 45:42-46, 2006[CrossRef][Medline] 11. Hernan R, Fasheh R, Calabrese C, et al: ERBB2 up-regulates S100A4 and several other prometastatic genes in medulloblastoma. Cancer Res 63:140-148, 2003 12. Taylor MD, Liu L, Raffel C, et al: Mutations in SUFU predispose to medulloblastoma. Nat Genet 31:306-310, 2002[CrossRef][Medline] 13. Hahn H, Wicking C, Zaphiropoulous PG, et al: Mutations of the human homolog of Drosophila patched in the nevoid basal cell carcinoma syndrome. Cell 85:841-851, 1996[CrossRef][Medline] 14. Satoh S, Daigo Y, Furukawa Y, et al: AXIN1 mutations in hepatocellular carcinomas, and growth suppression in cancer cells by virus-mediated transfer of AXIN1. Nat Genet 24:245-250, 2000[CrossRef][Medline] 15. Huang H, Mahler-Araujo BM, Sankila A, et al: APC mutations in sporadic medulloblastomas. Am J Pathol 156:433-437, 2000 16. Burns AS, Jaros E, Cole M, et al: The molecular pathology of p53 in primitive neuroectodermal tumours of the central nervous system. Br J Cancer 86:1117-1123, 2002[CrossRef][Medline] 17. MacDonald TJ, Brown KM, LaFleur B, et al: Expression profiling of medulloblastoma: PDGFRA and the RAS/MAPK pathway as therapeutic targets for metastatic disease. Nat Genet 29:143-152, 2001[CrossRef][Medline] 18. Pomeroy SL, Tamayo P, Gaasenbeek M, et al: Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415:436-442, 2002[CrossRef][Medline] 19. Ellison DW, Clifford SC, Gajjar A, et al: What's new in neuro-oncology? Recent advances in medulloblastoma. Eur J Paediatr Neurol 7:53-66, 2003[CrossRef][Medline] 20. Wolter M, Reifenberger J, Sommer C, et al: Mutations in the human homologue of the Drosophila segment polarity gene patched (PTCH) in sporadic basal cell carcinomas of the skin and primitive neuroectodermal tumors of the central nervous system. Cancer Res 57:2581-2585, 1997 21. Zurawel RH, Chiappa SA, Allen C, et al: Sporadic medulloblastomas contain oncogenic beta-catenin mutations. Cancer Res 58:896-899, 1998 22. Hallahan AR, Pritchard JI, Hansen S, et al: The SmoA1 mouse model reveals that notch signaling is critical for the growth and survival of sonic hedgehog-induced medulloblastomas. Cancer Res 64:7794-7800, 2004 23. Hallahan AR, Pritchard JI, Chandraratna RA, et al: BMP-2 mediates retinoid-induced apoptosis in medulloblastoma cells through a paracrine effect. Nat Med 9:1033-1038, 2003[CrossRef][Medline] 24. Moon RT, Kohn AD, Ferrari GVD, et al: WNT and [beta]-catenin signaling: Diseases and therapies. Nat Rev Genet 5:691-701, 2004[CrossRef][Medline] 25. Romer JT, Kimura H, Magdaleno S, et al: Suppression of the SHH pathway using a small molecule inhibitor eliminates medulloblastoma in Ptc1(+/)p53(-/-) mice. Cancer Cell 6:229-240, 2004[CrossRef][Medline] 26. Nickoloff BJ, Osborne BA, Miele L: Notch signaling as a therapeutic target in cancer: A new approach to the development of cell fate modifying agents. Oncogene 22:6598-6608, 2003[CrossRef][Medline] 27. van de Wetering M, Sancho E, Verweij C, et al: The beta-catenin/TCF-4 complex imposes a crypt progenitor phenotype on colorectal cancer cells. Cell 111:241-250, 2002[CrossRef][Medline] 28. Nelson WJ, Nusse R: Convergence of WNT, beta-catenin, and cadherin pathways. Science 303:1483-1487, 2004 29. Schwartz DR, Wu R, Kardia SL, et al: Novel candidate targets of beta-catenin/T-cell factor signaling identified by gene expression profiling of ovarian endometrioid adenocarcinomas. Cancer Res 63:2913-2922, 2003 30. Eberhart CG, Tihan T, Burger PC: Nuclear localization and mutation of beta-catenin in medulloblastomas. J Neuropathol Exp Neurol 59:333-337, 2000[Medline] 31. Lee Y, Miller HL, Jensen P, et al: A molecular fingerprint for medulloblastoma. Cancer Res 63:5428-5437, 2003 32. Gilbertson RJ: Medulloblastoma: Signalling a change in treatment. Lancet Oncol 5:209-218, 2004[CrossRef][Medline] 33. Wu G, Xu G, Schulman BA, et al: Structure of a beta-TrCP1-Skp1-beta-catenin complex: Destruction motif binding and lysine specificity of the SCF(beta-TrCP1) ubiquitin ligase. Mol Cell 11:1445-1456, 2003[CrossRef][Medline] 34. Reifenberger J, Wolter M, Weber RG, et al: Missense mutations in SMOH in sporadic basal cell carcinomas of the skin and primitive neuroectodermal tumors of the central nervous system. Cancer Res 58:1798-1803, 1998 35. Strother D, Ashley D, Kellie SJ, et al: Feasibility of four consecutive high-dose chemotherapy cycles with stem-cell rescue for patients with newly diagnosed medulloblastoma or supratentorial primitive neuroectodermal tumor after craniospinal radiotherapy: Results of a collaborative study. J Clin Oncol 19:2696-2704, 2001 36. Taylor RE, Bailey CC, Robinson K, et al: Results of a randomized study of preradiation chemotherapy versus radiotherapy alone for nonmetastatic medulloblastoma: The International Society of Paediatric Oncology/United Kingdom Children's Cancer Study Group PNET-3 Study. J Clin Oncol 21:1581-1591, 2003 37. Zeltzer PM, Boyett JM, Finlay JL, et al: Metastasis stage, adjuvant treatment, and residual tumor are prognostic factors for medulloblastoma in children: Conclusions from the Children's Cancer Group 921 randomized phase III study. J Clin Oncol 17:832-845, 1999 38. Zeller C, Hinzmann B, Seitz S, et al: SASH1: A candidate tumor suppressor gene on chromosome 6q24.3 is downregulated in breast cancer. Oncogene 22:2972-2983, 2003[Medline] 39. Thomas GA, Raffel C: Loss of heterozygosity on 6q, 16q, and 17p in human central nervous system primitive neuroectodermal tumors. Cancer Res 51:639-643, 1991 Submitted October 21, 2005; accepted February 13, 2006.
This article has been cited by other articles:
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
|||||||||||
|
|||||||||||
|
|
Copyright © 2006 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
|
TOP
ABSTRACT
INTRODUCTION
