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Freij-affy-human-91666

ABSTRACT: Diffuse infiltrating gliomas are the most common primary brain malignancy found in adults, and Glioblastoma multiforme, the highest grade glioma, is associated with a median survival of 7 months. Transcriptional profiling has been applied to 85 gliomas from 74 patients to elucidate glioma biology, prognosticate survival, and define tumor sub-classes. These studies reveal that transcriptional profiling of gliomas is more accurate at predicting survival than traditional pathologic grading, and that gliomas characteristically express coordinately regulated genes of one of four molecular signatures: neurogenesis, synaptic transmission, mitotic, or extra-cellular matrix. Elucidation of these survival associated molecular signatures will aid in tumor prognostication and define targets for future directed therapy. Evaluate a large number of diffuse infiltrating gliomas through transcriptional profiling. Glioma tumor sub-classes may be identified through large scale gene expression studies. All patients undergoing surgical treatment at the University of California, Los Angeles for primary brain cancers between 1996 and 2003 were invited to participate in this Institutional Review Board approved study. 74 of the patients participating in this broad protocol were analyzed as part of this study if their initial tumor was diagnosed as a grade III (n=24) or IV (n=50) glioma of any histologic type on initial surgical treatment and fresh frozen material was obtained. Only grade III and IV gliomas were included in this study as the distinction between these grades is subtle and prone to misclassification. The time in days elapsed from resection to the day of death, or if the patient has remained alive, to the current day was recorded for all samples studied. Patient ages at diagnosis varied from 18 to 82 years. There were 46 females and 28 males. Probes were prepared using standard Affymetrix protocols, and hybridized to Affymetrix HG-U133A and HG-U133B arrays. Keywords: disease state analysis

ORGANISM(S): Homo sapiens

PROVIDER: GSE4412 | GEO | 2006/03/08

SECONDARY ACCESSION(S): PRJNA94645

REPOSITORIES: GEO

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Project description:Diffuse infiltrating gliomas are the most common primary brain malignancy found in adults, and Glioblastoma multiforme, the highest grade glioma, is associated with a median survival of 7 months. Transcriptional profiling has been applied to 85 gliomas from 74 patients to elucidate glioma biology, prognosticate survival, and define tumor sub-classes. These studies reveal that transcriptional profiling of gliomas is more accurate at predicting survival than traditional pathologic grading, and that gliomas characteristically express coordinately regulated genes of one of four molecular signatures: neurogenesis, synaptic transmission, mitotic, or extra-cellular matrix. Elucidation of these survival associated molecular signatures will aid in tumor prognostication and define targets for future directed therapy. Evaluate a large number of diffuse infiltrating gliomas through transcriptional profiling. Glioma tumor sub-classes may be identified through large scale gene expression studies. All patients undergoing surgical treatment at the University of California, Los Angeles for primary brain cancers between 1996 and 2003 were invited to participate in this Institutional Review Board approved study. 74 of the patients participating in this broad protocol were analyzed as part of this study if their initial tumor was diagnosed as a grade III (n=24) or IV (n=50) glioma of any histologic type on initial surgical treatment and fresh frozen material was obtained. Only grade III and IV gliomas were included in this study as the distinction between these grades is subtle and prone to misclassification. The time in days elapsed from resection to the day of death, or if the patient has remained alive, to the current day was recorded for all samples studied. Patient ages at diagnosis varied from 18 to 82 years. There were 46 females and 28 males. Probes were prepared using standard Affymetrix protocols, and hybridized to Affymetrix HG-U133A and HG-U133B arrays.

Project description:Migrated from 1.6 id: 1015897590491013 GEDP id: 760 In current clinical practice, histology-based grading of diffuse infiltrative gliomas is the best predictor of patient survival time. Yet histology provides little insight into the underlying biology of gliomas and is limited in its ability to identify and guide new molecularly targeted therapies. We have performed large-scale gene expression analysis using the Affymetrix HG U133 oligonucleotide arrays on 85 diffuse infiltrating gliomas of all histologic types to assess whether a gene expression-based, histology-independent classifier is predictive of survival and to determine whether gene expression signatures provide insight into the biology of gliomas. We found that gene expression-based grouping of tumors is a more powerful survival predictor than histologic grade or age. The poor prognosis samples could be grouped into three different poor prognosis groups, each with distinct molecular signatures. We further describe a list of 44 genes whose expression patterns reliably classify gliomas into previously unrecognized biological and prognostic groups: these genes are outstanding candidates for use in histology-independent classification of high-grade gliomas. The ability of the large scale and 44 gene set expression signatures to group tumors into strong survival groups was validated with an additional external and independent data set from another institution composed of 50 additional gliomas. This demonstrates that large-scale gene expression analysis and subset analysis of gliomas reveals unrecognized heterogeneity of tumors and is efficient at selecting prognosis-related gene expression differences which are able to be applied across institutions. nelso-00262 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG-U133A, HG-U133B Organism: Homo sapiens (ncbitax) Material Types: total RNA, synthetic_RNA, organism_part, whole_organism Disease States: Glioma, Glioblastoma, Oligodendroglial Tumor, astrocytomas

Project description:Ribba2012 - Low-grade gliomas, tumour growth inhibition model Using longitudinal mean tumour diameter (MTD) data, this model describe the size evolution of low-grade glioma (LGG) in patients treated with chemotherapy or radiotherapy. This model is described in the article: A tumour growth inhibition model for low-grade glioma treated with chemotherapy or radiotherapy Ribba B, Kaloshi G, Peyre M, Ricard D, Calvez V, Tod M, Cajavec-Bernard B, Idbaih A, Psimaras D, Dainese L, Pallud J, Cartalat-Carel S, Delattre JY, Honnorat J, Grenier E, Ducray F. Clin. Cancer Res. 2012 Sep; 18(18): 5071-5080 Abstract: PURPOSE: To develop a tumor growth inhibition model for adult diffuse low-grade gliomas (LGG) able to describe tumor size evolution in patients treated with chemotherapy or radiotherapy. EXPERIMENTAL DESIGN: Using longitudinal mean tumor diameter (MTD) data from 21 patients treated with first-line procarbazine, 1-(2-chloroethyl)-3-cyclohexyl-l-nitrosourea, and vincristine (PCV) chemotherapy, we formulated a model consisting of a system of differential equations, incorporating tumor-specific and treatment-related parameters that reflect the response of proliferative and quiescent tumor tissue to treatment. The model was then applied to the analysis of longitudinal tumor size data in 24 patients treated with first-line temozolomide (TMZ) chemotherapy and in 25 patients treated with first-line radiotherapy. RESULTS: The model successfully described the MTD dynamics of LGG before, during, and after PCV chemotherapy. Using the same model structure, we were also able to successfully describe the MTD dynamics in LGG patients treated with TMZ chemotherapy or radiotherapy. Tumor-specific parameters were found to be consistent across the three treatment modalities. The model is robust to sensitivity analysis, and preliminary results suggest that it can predict treatment response on the basis of pretreatment tumor size data. CONCLUSIONS: Using MTD data, we propose a tumor growth inhibition model able to describe LGG tumor size evolution in patients treated with chemotherapy or radiotherapy. In the future, this model might be used to predict treatment efficacy in LGG patients and could constitute a rational tool to conceive more effective chemotherapy schedules. This model is hosted on BioModels Database and identified by: BIOMD0000000521 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Freij-affy-human-91666

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