Identification of tumor-specific molecular signatures in intracranial ependymoma and association with clinical characteristics.
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SourceJournal of Clinical Oncology, 24, 33, (2006), pp. 5223-5233
Article / Letter to editor
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Journal of Clinical Oncology
SubjectNCMLS 6: Genetics and epigenetic pathways of disease; UMCN 1.2: Molecular diagnosis, prognosis and monitoring
PURPOSE: To delineate clinically relevant molecular signatures of intracranial ependymoma. MATERIALS AND METHODS: We analyzed 24 primary intracranial ependymomas. For genomic profiling, microarray-based comparative genomic hybridization (CGH) was used and results were validated by fluorescent in situ hybridization and loss of heterozygosity mapping. We performed gene expression profiling using microarrays, real-time quantitative reverse transcriptase polymerase chain reaction, and methylation analysis of selected genes. We applied class comparison analyses to compare both genomic and expression profiling data with clinical characteristics. RESULTS: A variable number of genomic imbalances were detected by array CGH, revealing multiple regions of recurrent gain (including 2q23, 7p21, 12p, 13q21.1, and 20p12) and loss (including 5q31, 6q26, 7q36, 15q21.1, 16q24, 17p13.3, 19p13.2, and 22q13.3). An ependymoma-specific gene expression signature was characterized by the concurrent abnormal expression of developmental and differentiation pathways, including NOTCH and sonic hedgehog signaling. We identified specific differentially imbalanced genomic clones and gene expression signatures significantly associated with tumor location, patient age at disease onset, and retrospective risk for relapse. Integrated genomic and expression profiling allowed us to identify genes of which the expression is deregulated in intracranial ependymoma, such as overexpression of the putative proto-oncogene YAP1 (located at 11q22) and downregulation of the SULT4A1 gene (at 22q13.3). CONCLUSION: The present exploratory molecular profiling study allowed us to refine previously reported intervals of genomic imbalance, to identify novel restricted regions of gain and loss, and to identify molecular signatures correlating with various clinical variables. Validation of these results on independent data sets represents the next step before translation into the clinical setting.
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