Gene expression profiling of 151 pediatric low-grade gliomas
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Rameen Beroukhim
PROVIDER: E-GEOD-60898 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:We performed gene expression profiling on 151 paraffin-embedded PLGGs from different locations, ages, histological subtypes as well as BRAF genetic status We also compared molecular differences to normal pediatric brain expression profiles to observe whether those patterns were mirrored in normal brain expression.
2014-08-29 | GSE60898 | GEO
Project description:Background and Aims Formalin-fixed, paraffin-embedded (FFPE) tissue is the most commonly available form of archived clinical specimens, which are often stored as thin sections on glass slides. RNA isolated from such archived section (AS) of FFPE tissue is more degraded compared to freshly cut (FC) FFPE section because of prolonged air exposure. In this study, we evaluated performance of transcriptome profiling-based disease classification in AS-FFPE tissue. Methods Genome-wide gene-expression profiles of AS-FFPE tissues of 83 hepatocellular carcinoma (HCC) and 47 liver cirrhosis samples were generated by using whole-genome DASL assay (Illumina), and compared with the profiles previously produced by using FC tissue sections from the same FFPE blocks. Quality of the profiles and performance of gene signature-based class prediction were systematically evaluated. Results RNA quality and assay reproducibility of AS-FFPE RNA were comparable to intermediate ~ poor quality FC-FFPE samples (R2 as high as 0.93). Gene-expression signal was detected in lower number of probes in AS FFPE samples compared to FC-FFPE samples (proportion of probes with present signal (%P-call): 10%~60% and 70%~90% in AS- and FC-FFPE profiles, respectively). Based on %P-call quality threshold of 20%, 64/88 (77%) HCC and 37/48 (77%) liver profiles were judged as having relatively good quality data with comparable inter-sample correlation. Inter-sample correlation coefficient, as a measure to detect outlier profiles due to poor RNA quality, was also lower in AS-FFPE (0.4~0.9) compared to FC-FFPE (0.6~1.0). In the genome-wide profiling analysis, previously identified molecular subclasses of HCC tumors were reproduced in 67/83 (81%) samples, which was improved to 43/48 (90%) samples when we focused on statistically confident predictions (p<0.05). A 186-gene prognostic signature in liver cirrhosis was reproduced in 32/47 (68%) samples, which was slightly improved to 11/16 (69%) when focused on statistically significant predictions. Conclusions We observed decay of genome-wide transcriptional profiles in AS-FFPE tissues in quantitative manner. However, disease classification was still possible, which suggests potential of AS-FFPE material for clinical diagnosis and prognosis. FFPE tissue sections (10 micron-thick) sliced from 5~16-year-old FFPE blocks and archived for 6~7 years on glass slide Gene-expression profiles of archived section of formalin-fixed paraffin-embedded (AS-FFPE) liver tissues from HCC patients: 47 samples Gene-expression profiles of archived section of formalin-fixed paraffin-embedded (AS-FFPE) tumor tissues from HCC patients: 83 samples
2014-01-31 | E-GEOD-46444 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the following subset Series: GSE32488: Expression profiling of formalin-fixed, paraffin-embedded (FFPE) breast cancer metastases of the lymph node and autopsy tissues [DASL HT-12 samples] GSE32489: Expression profiling of formalin-fixed, paraffin-embedded (FFPE) breast cancer metastases of the lymph node and autopsy tissues [DASL HumanRef-v3 samples] Refer to individual Series
2012-10-02 | E-GEOD-32490 | biostudies-arrayexpress
Project description:Over 20 million archival tissue samples are stored annually in the United States as formalin-fixed, paraffin-embedded (FFPE) tissue blocks, but only recently has whole-genome expression profiling from these samples become technically feasible. Here, we introduce novel general methods for assessing, summarizing, and visualizing expression data quality from archival samples. We validated these methods in technical study of 144 clinical breast cancer and autopsy samples and in an overview of all current publicly available FFPE whole-genome expression data. We additionally performed a case study incorporating over 1,000 colorectal cancer (CRC) samples collected from patients across the United States over a period of more than 25 years, integrating clinicopathological information, tumor molecular data, and archival tissue gene expression on an unparalleled scale. Both large-scale clinical studies presented a much greater range of data quality than previous smaller studies, emphasizing the need for rigorous quality control in translational applications of archival tissue gene expression profiling. This series includes 1,003 FFPE-preserved CRC tumors, assayed by Illumina HumanRef-v3 WG-DASL microarray.
2012-10-02 | E-GEOD-32651 | biostudies-arrayexpress
Project description:Introduction: Hepatocellular carcinoma (HCC) is one of the most aggressive solid tumors and oncogenic pathways (e.g Akt, IGF signaling) are often activated in high proliferating HCC. Among several genetic alterations, epigenetic changes seem to be involved in the development and progression of HCC. DNA hypermethylation of promoter regions is almost always associated with transcriptional silencing and can lead to inactivation of tumor suppressor genes (TSG) in cancer cells. Aim: (1) To identify genes differently methylated in a subclass of HCC associated with proliferation and, (2) To correlate methylation changes with activation of molecular pathways. Methods: gDNA of 20 HCC, 8 cirrhotic and 8 normal liver samples was extracted and the methylation status was detected by the Illumina HumanMethylation27 BeadChip and immunohistochemistry of p-AKT, pIGF IR, p-S6 was analyzed. Results: Unsupervised clustering clearly classified normal livers, cirrhosis and HCC in 3 different groups. 961 genes were significantly hypermethylated in HCC compared to cirrhotic and 3942 genes showed hypermethylation in cirrhotic compared to normal liver tissue. 163 genes showed stepwise significant hypermethylation from normal to cirrhotic to HCC, including well described (p16, SOCS2, SFRP5, RBP1) and potential (SRD5A2, PCDH8, IGF-1R, UCHL1) TSGs, and the miR-10a. 133 genes were specifically hypermethylated in HCC. Among them the transcription factors GATA2, DLX1, and KLF14, all significantly inversely correlated to gene expression (p=0.003, p=0.03, and p=0.007, respectively). The methylation status of SOCS2 (p=0.025) and DLX1 (p=0.025) was significantly correlated to phosphorylation of IGFR1. Samples with RBP1 hypermethylation showed significantly higher AFP serum levels (p=0.018). Conclusion: Whole genome methylation analysis markedly classifies normal, cirrhotic and HCC samples. 8 TSGs play a key role in this stepwise progression of hypermethylation in the development of HCC and could be a promising point of action in anticancer therapy. Genomic DNA extracted from fresh frozen tissue specimens and cell lines was hybridized to genome-wide mthylation beadarray after bisulphite treatment. Keywords: DNA methylation, hepatocellular carcinoma, tissue, cell line
2013-09-27 | E-GEOD-44970 | biostudies-arrayexpress
Project description:BACKGROUND & AIMS: Cholangiocarcinoma, the second most common liver cancer, can be classified as intrahepatic (ICC) or extrahepatic. We performed an integrative genomic analysis of ICC samples from a large series of patients. METHODS: We performed gene expression profile, high-density single nucleotide polymorphism array, and mutation analyses using formalin-fixed ICC samples from 149 patients. Associations with clinico-pathological traits and patient outcomes were examined for 119 cases. Class discovery was based on a non-negative matrix factorization algorithm and significant copy number variations (CNV) were identified by GISTIC analysis. Gene set enrichment analysis was used to identify signaling pathways activated in specific molecular classes of tumors, and to analyze their genomic overlap with hepatocellular carcinoma (HCC). RESULTS: We identified 2 main biological classes of ICC. The inflammation class (38% of ICCs) is characterized by activation of inflammatory signaling pathways, overexpression of cytokines, and STAT3 activation. The proliferation class (62%) is characterized by activation of oncogenic signaling pathways (including RAS, mitogen-activated protein kinase, and MET), DNA amplifications at 11q13.2, deletions at 14q22.1, mutations in KRAS and BRAF, and gene expression signatures previously associated with poor outcomes for patients with HCC. CNV-based clustering was able to further refine these molecular groups. We identified high-level amplifications in 5 regions, including 1p13 (9%) and 11q13.2 (4%), and several focal deletions, such as 9p21.3 (18%) and 14q22.1 (12% in coding regions for the SAV1 tumor suppressor). In a complementary approach, we identified a gene expression signature that was associated with reduced survival times of patients with ICC; this signature was enriched in the proliferation class (P<0.001). CONCLUSIONS: We used an integrative genomic analysis to identify 2 classes of ICC. The proliferation class has specific copy number alterations, many features of the poor-prognosis signatures for HCC, and is associated with worse outcome. Different classes of ICC, based on molecular features, might therefore require different treatment approaches. Gene-expression profiling was performed using formalin-fixed, paraffin-embedded intrahepatic cholangiocarcinoma tissues obtained at the time of surgical resection.
2013-01-24 | E-GEOD-32225 | biostudies-arrayexpress
Project description:BACKGROUND & AIMS: Cholangiocarcinoma, the second most common liver cancer, can be classified as intrahepatic (ICC) or extrahepatic. We performed an integrative genomic analysis of ICC samples from a large series of patients. METHODS: We performed gene expression profile, high-density single nucleotide polymorphism array, and mutation analyses using formalin-fixed ICC samples from 149 patients. Associations with clinico-pathological traits and patient outcomes were examined for 119 cases. Class discovery was based on a non-negative matrix factorization algorithm and significant copy number variations (CNV) were identified by GISTIC analysis. Gene set enrichment analysis was used to identify signaling pathways activated in specific molecular classes of tumors, and to analyze their genomic overlap with hepatocellular carcinoma (HCC). RESULTS: We identified 2 main biological classes of ICC. The inflammation class (38% of ICCs) is characterized by activation of inflammatory signaling pathways, overexpression of cytokines, and STAT3 activation. The proliferation class (62%) is characterized by activation of oncogenic signaling pathways (including RAS, mitogen-activated protein kinase, and MET), DNA amplifications at 11q13.2, deletions at 14q22.1, mutations in KRAS and BRAF, and gene expression signatures previously associated with poor outcomes for patients with HCC. CNV-based clustering was able to further refine these molecular groups. We identified high-level amplifications in 5 regions, including 1p13 (9%) and 11q13.2 (4%), and several focal deletions, such as 9p21.3 (18%) and 14q22.1 (12% in coding regions for the SAV1 tumor suppressor). In a complementary approach, we identified a gene expression signature that was associated with reduced survival times of patients with ICC; this signature was enriched in the proliferation class (P<0.001). CONCLUSIONS: We used an integrative genomic analysis to identify 2 classes of ICC. The proliferation class has specific copy number alterations, many features of the poor-prognosis signatures for HCC, and is associated with worse outcome. Different classes of ICC, based on molecular features, might therefore require different treatment approaches. This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2013-01-24 | E-GEOD-33327 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2014-08-22 | E-GEOD-54102 | biostudies-arrayexpress
Project description:Gene-expression profiles of formalin-fixed, paraffin-embedded human hepatocellular carcinoma tissues obtained at the time of surgical resection. Gene-expression profiling was performed using formalin-fixed, paraffin-embedded hepatocellular carcinoma tissues obtained at the time of surgical resection.
2011-02-17 | E-GEOD-19977 | biostudies-arrayexpress
Project description:BACKGROUND & AIMS: In approximately 70% of patients with hepatocellular carcinoma (HCC) treated by resection or ablation, disease recurs within 5 years. Although gene expression signatures have been associated with outcome, there is no method to predict recurrence based on combined clinical, pathology, and genomic data (from tumor and cirrhotic tissue). We evaluated gene expression signatures associated with outcome in a large cohort of patients with early stage (Barcelona-Clinic Liver Cancer 0/A), single-nodule HCC and heterogeneity of signatures within tumor tissues. METHODS: We assessed 287 HCC patients undergoing resection and tested genome-wide expression platforms using tumor (n = 287) and adjacent nontumor, cirrhotic tissue (n = 226). We evaluated gene expression signatures with reported prognostic ability generated from tumor or cirrhotic tissue in 18 and 4 reports, respectively. In 15 additional patients, we profiled samples from the center and periphery of the tumor, to determine stability of signatures. Data analysis included Cox modeling and random survival forests to identify independent predictors of tumor recurrence. RESULTS: Gene expression signatures that were associated with aggressive HCC were clustered, as well as those associated with tumors of progenitor cell origin and those from nontumor, adjacent, cirrhotic tissues. On multivariate analysis, the tumor-associated signature G3-proliferation (hazard ratio [HR], 1.75; P = .003) and an adjacent poor-survival signature (HR, 1.74; P = .004) were independent predictors of HCC recurrence, along with satellites (HR, 1.66; P = .04). Samples from different sites in the same tumor nodule were reproducibly classified. CONCLUSIONS: We developed a composite prognostic model for HCC recurrence, based on gene expression patterns in tumor and adjacent tissues. These signatures predict early and overall recurrence in patients with HCC, and complement findings from clinical and pathology analyses. Center and peripheral portions of hepatocellular carcinoma tumors as well as surrounding non-tumor cirrhotic liver tissues were obtained from fresh frozen surgically resected tissues, and isolated total RNA samples were analyzed for genome-wide expression profiles.
2014-03-25 | E-GEOD-56140 | biostudies-arrayexpress