Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Investigation of the mechansim underlying the inflammatory phenotype in Group A ependymoma

ABSTRACT: Inflammatory response has been identified as a molecular signature of high-risk Group A ependymoma (EPN). To better understand the biology of this phenotype and aid therapeutic development, transcriptomic data from Group A and B EPN patient tumor samples, and additional malignant and normal brain data, were analyzed to identify the mechanism underlying EPN group A inflammation. Gene expression profiles of Group A and B EPN were contrasted to identify inflammatory transcriptional profiles (GSEA analysis). Candidate inflammatory mechanism genes were examined across a broader cohort of pediatric and adult brain tumor types and normal brain. Gene expression profiles were generated from surgical tumor and normal brain samples (n=149) using Affymetrix HG-U133plus2 chips (Platform GPL570).

ORGANISM(S): Homo sapiens

SUBMITTER: Andrew Donson

PROVIDER: E-GEOD-66354 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Interleukin-6/STAT3 Pathway Signaling Drives an Inflammatory Phenotype in Group A Ependymoma.

Griesinger Andrea M AM Josephson Rebecca J RJ Donson Andrew M AM Mulcahy Levy Jean M JM Amani Vladimir V Birks Diane K DK Hoffman Lindsey M LM Furtek Steffanie L SL Reigan Phillip P Handler Michael H MH Vibhakar Rajeev R Foreman Nicholas K NK

Cancer immunology research 20150512 10

Ependymoma (EPN) in childhood is a brain tumor with substantial mortality. Inflammatory response has been identified as a molecular signature of high-risk Group A EPN. To better understand the biology of this phenotype and aid therapeutic development, transcriptomic data from Group A and B EPN patient tumor samples, and additional malignant and normal brain data, were analyzed to identify the mechanism underlying EPN Group A inflammation. Enrichment of IL6 and STAT3 pathway genes were found to d ...[more]

PMID: 25968456

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Project description:Introduction: Pediatric adamantinomatous craniopharyngioma (ACP) is a histologically benign but clinically aggressive brain tumor that arises from the sellar/suprasellar region. Despite a high survival rate with current surgical and radiation therapy (75-95% at 10 years), ACP is associated with debilitating visual, endocrine, neurocognitive and psychological morbidity, resulting in exceptionally poor quality of life for survivors. Identification of an effective pharmacological therapy would drastically decrease morbidity and improve long term outcomes for children with ACP. Results: Using microarray analysis of 15 ACP patient samples, we have found several pharmaceutical targets that are significantly and consistently overexpressed in our panel of ACP relative to other pediatric brain tumors, pituitary tumors, normal pituitary and normal brain tissue. Among the most highly expressed are several targets of the kinase inhibitor dasatinib -- LCK, EPHA2 and SRC; EGFR pathway targets -- AREG, EGFR and ERBB3; and other potentially actionable cancer targets -- SHH, MMP9 and MMP12. We confirm by Western blot that a subset of these targets is highly expressed in ACP primary tumor samples. Discussion: We report here the first microarray gene expression analysis for ACP and the identification of targets for rational therapy. Experimental drugs targeting each of these gene products are currently being tested clinically and pre-clinically for the treatment of other tumor types. This study provides a rationale for further pre-clinical and clinical studies of novel pharmacological treatments for ACP. Development of pre-clinical mouse and cell culture models for ACP will further enable the translation of these targets from the lab to the clinic. Gene expression profiles were generated from surgical tumor and normal brain samples (n=210) using Affymetrix HG-U133 Plus 2.0 chips. Gene expression profiles of adamantinomatous craniopharyngioma (ACP) were compared to a cohort of other tumor samples and normal brain tissues, and analyzed via gene set enrichment (GSEA analysis) and hierarchical clustering analysis to identify high expression of potential drug targets. The sample metadata and processed data for the complete dataset, which includes reanalysis of 23 Samples from GSE26966, 33 Samples from GSE35493, and 42 Samples from GSE50385, are linked below as supplementary files.

Project description:Background. Most colorectal cancers (CRC) arise in a progression through adenoma to carcinoma phenotypes as a consequence of altered genetic information. Clinical progression of CRC may occur in parallel with distinctive signaling alterations. We designed multidirectional analyses integrating microarray-based data with biostatistics and bioinformatics to elucidate the signaling and metabolic alterations underlying CRC development in the adenoma-carcinoma sequence. Methodology/Principal Findings. Studies were performed on normal mucosa, adenoma, and CRC samples obtained during surgery or colonoscopy. Collections of cryostat sections prepared from the tissue samples were evaluated by a pathologist to control the relative cell type content. RNA was isolated from 105 macro- and 40 microdissected specimens. The measurements were done using Affymetrix GeneChip HG-U133plus2, and probe set data were generated using two normalization algorithms: MAS5 and GCRMA with LVS. The data were evaluated using pair-wise comparisons and data decomposition into SVD modes. The method selected for the functional analysis used the Kolmogorov-Smirnov test. Based on a consensus of the results obtained by two tissue handling procedures, two normalization algorithms, and two probe set sorting criteria, we identified six KEGG signaling and metabolic pathways (cell cycle, DNA replication, p53 signaling pathway, purine metabolism, pyrimidine metabolism, and RNA polymerase) that are significantly altered in both macro- and microdissected tumor samples compared to normal colon. On the other hand, pathways altered between benign and malignant tumors were identified only in the macrodissected tissues. Conclusion/Significance. Multidirectional analyses of microarray data allow the identification of essential signaling alterations underlying CRC development. Although the proposed strategy is computationally complex and labor–intensive, it may reduce the number of false results. Gene expression profiles of 40 tissue samples representing distant full-thickness normal colon, normal colon mucosa dissected from tumor, adenoma, and carcinoma. Gene expression profiles of 105 tissue samples representing colon carcinomas, adenocarcinomas, and normal colon samples.

Dataset Information

Investigation of the mechansim underlying the inflammatory phenotype in Group A ependymoma

Publications

Interleukin-6/STAT3 Pathway Signaling Drives an Inflammatory Phenotype in Group A Ependymoma.

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