Array-based gene expression, CGH and tissue data define a 12q24 gain in neuroblastic tumors with prognostic implication
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Maija Wolf
PROVIDER: E-GEOD-18144 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Publications
Array-based gene expression, CGH and tissue data defines a 12q24 gain in neuroblastic tumors with prognostic implication.
Wolf Maija M Korja Miikka M Karhu Ritva R Edgren Henrik H Kilpinen Sami S Ojala Kalle K Mousses Spyro S Kallioniemi Anne A Haapasalo Hannu H
BMC cancer 20100505
<h4>Background</h4>Neuroblastoma has successfully served as a model system for the identification of neuroectoderm-derived oncogenes. However, in spite of various efforts, only a few clinically useful prognostic markers have been found. Here, we present a framework, which integrates DNA, RNA and tissue data to identify and prioritize genetic events that represent clinically relevant new therapeutic targets and prognostic biomarkers for neuroblastoma.<h4>Methods</h4>A single-gene resolution aCGH ...[more]
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Project description:Title: Array-based gene expression, CGH and tissue data define a 12q24 gain in neuroblastic tumors with prognostic implication. Background: Neuroblastoma has successfully served as a model system for the identification of neuroectoderm-derived oncogenes. However, in spite of various efforts, only a few clinically useful prognostic markers have been found. Here, we present a framework, which integrates DNA, RNA and tissue data to identify and prioritize genetic events that represent clinically relevant new therapeutic targets and prognostic biomarkers for neuroblastoma. Methods: A single-gene resolution aCGH profiling was integrated with microarray-based gene expression profiling data to distinguish genetic copy number alterations that were strongly associated with transcriptional changes in two neuroblastoma cell lines. FISH analysis using a hotspot tumour tissue microarray of 37 paraffin-embedded neuroblastoma samples and in silico data mining for gene expression information obtained from previously published studies including up to 445 healthy nervous system samples and 123 neuroblastoma samples were used to evaluate the clinical significance and transcriptional consequences of the detected alterations and to identify subsequently activated gene(s). Results: In addition to the anticipated high-level amplification and subsequent overexpression of MYCN, MEIS1, CDK4 and MDM2 oncogenes, the aCGH analysis revealed numerous other genetic alterations, including microamplifications at 2p and 12q24.11. Most interestingly, we identified and investigated the clinical relevance of a previously poorly characterized amplicon at 12q24.31. FISH analysis showed low-level gain of 12q24.31 in 14 of 33 (42%) neuroblastomas. Patients with the low-level gain had an intermediate prognosis in comparison to patients with MYCN amplification (poor prognosis) and to those with no MYCN amplification or 12q24.31 gain (good prognosis) (P = 0.001). Using the in silico data mining approach, we identified elevated expression of five genes located at the 12q24.31 amplicon in neuroblastoma (DIABLO, ZCCHC8, RSRC2, KNTC1 and MPHOSPH9). Among these, DIABLO showed the strongest activation suggesting a putative role in neuroblastoma progression. Conclusions: The presented systematic and rapid framework, which integrates aCGH, gene expression and tissue data to obtain novel targets and biomarkers for cancer, identified a low-level gain of the 12q24.31 as a potential new biomarker for neuroblastoma progression. Furthermore, results of in silico data mining suggest a new neuroblastoma target gene, DIABLO, within this region, whose functional and therapeutic role remains to be elucidated in follow-up studies. High-resolution aCGH was utilized to identify novel genetic alterations in two neuroblastoma cell lines, NGP and IMR-32. Through the integration of gene copy number and gene expression data, the impact of copy number changes on expression levels was determined. Fluorescence in situ hybridization (FISH) on a tissue microarray (TMA) format was used to assess the clinical significance of the identified copy number increase at 12q24.31 in neuroblastoma patients. Finally, we used in silico data mining of publicly available transcriptomics data, to evaluate the transcriptional consequences of the detected 12q24.31 alteration and to identify subsequently activated gene(s). Here, aCGH of the NGP and IMR-32 cell lines was performed. Male genomic DNA was used as reference.
2010-05-05 | E-GEOD-18143 | biostudies-arrayexpress
Project description:Title: Array-based gene expression, CGH and tissue data define a 12q24 gain in neuroblastic tumors with prognostic implication. Background: Neuroblastoma has successfully served as a model system for the identification of neuroectoderm-derived oncogenes. However, in spite of various efforts, only a few clinically useful prognostic markers have been found. Here, we present a framework, which integrates DNA, RNA and tissue data to identify and prioritize genetic events that represent clinically relevant new therapeutic targets and prognostic biomarkers for neuroblastoma. Methods: A single-gene resolution aCGH profiling was integrated with microarray-based gene expression profiling data to distinguish genetic copy number alterations that were strongly associated with transcriptional changes in two neuroblastoma cell lines. FISH analysis using a hotspot tumour tissue microarray of 37 paraffin-embedded neuroblastoma samples and in silico data mining for gene expression information obtained from previously published studies including up to 445 healthy nervous system samples and 123 neuroblastoma samples were used to evaluate the clinical significance and transcriptional consequences of the detected alterations and to identify subsequently activated gene(s). Results: In addition to the anticipated high-level amplification and subsequent overexpression of MYCN, MEIS1, CDK4 and MDM2 oncogenes, the aCGH analysis revealed numerous other genetic alterations, including microamplifications at 2p and 12q24.11. Most interestingly, we identified and investigated the clinical relevance of a previously poorly characterized amplicon at 12q24.31. FISH analysis showed low-level gain of 12q24.31 in 14 of 33 (42%) neuroblastomas. Patients with the low-level gain had an intermediate prognosis in comparison to patients with MYCN amplification (poor prognosis) and to those with no MYCN amplification or 12q24.31 gain (good prognosis) (P = 0.001). Using the in silico data mining approach, we identified elevated expression of five genes located at the 12q24.31 amplicon in neuroblastoma (DIABLO, ZCCHC8, RSRC2, KNTC1 and MPHOSPH9). Among these, DIABLO showed the strongest activation suggesting a putative role in neuroblastoma progression. Conclusions: The presented systematic and rapid framework, which integrates aCGH, gene expression and tissue data to obtain novel targets and biomarkers for cancer, identified a low-level gain of the 12q24.31 as a potential new biomarker for neuroblastoma progression. Furthermore, results of in silico data mining suggest a new neuroblastoma target gene, DIABLO, within this region, whose functional and therapeutic role remains to be elucidated in follow-up studies. High-resolution aCGH was utilized to identify novel genetic alterations in two neuroblastoma cell lines, NGP and IMR-32. Through the integration of gene copy number and gene expression data, the impact of copy number changes on expression levels was determined. Fluorescence in situ hybridization (FISH) on a tissue microarray (TMA) format was used to assess the clinical significance of the identified copy number increase at 12q24.31 in neuroblastoma patients. Finally, we used in silico data mining of publicly available transcriptomics data, to evaluate the transcriptional consequences of the detected 12q24.31 alteration and to identify subsequently activated gene(s). Here, gene expression analysis of the NGP and IMR-32 cell lines was performed. A pooled sample of 16 cancer cell lines was used as reference.
2010-05-27 | E-GEOD-18139 | biostudies-arrayexpress
Project description:The expression profiles of 64 neuroblastic tumors (mainly neuroblastoma) were determined on Affymetrix chips HG U133 Plus 2.0.
2008-10-16 | GSE12460 | GEO
Project description:The expression profiles of 64 neuroblastic tumors (mainly neuroblastoma) were determined on Affymetrix chips HG U133 Plus 2.0. Experiment Overall Design: 64 neuroblastic tumors
2008-10-26 | E-GEOD-12460 | biostudies-arrayexpress
Project description:Detection of DNA hypermethylation across all UCSC annotated CpG islands and refseq promoter sequences in neuroblatoma cell lines and neuroblastic tumors
2011-01-21 | E-TABM-965 | biostudies-arrayexpress
Project description:Tumor tissue heterogeneity is a well known feature of several solid tumors. Neuroblastic Tumors (NTs) is a group of paediatric cancers with a great tissue heterogeneity. Most of NTs are composed of undifferentiated, poorly differentiated or differentiating neuroblastic (Nb) cells with very few or absent Schwannian stromal (SS) cells: these tumors are grouped as Neuroblastoma (Schwannian stroma-poor). The remaining NTs are composed of abundant SS cells and classified as Ganglioneuroblastoma (Schwannian stroma-rich) intermixed or nodular and Ganglioneuroma. The importance to understand Nb and SS gene signatures in NTs, is to clarify the complex network mechanism of tumor growth and progression. In order to identify the Nb and SS cells gene signatures, we analyzed the gene expression profiling of 19 cases of neuroblastic tumors: 10 stroma poor (NTs-SP) and 9 stroma rich (NTs-SR), by high density oligonucleotide microarrays. Moreover, the analysis was performed in parallel on both whole and laser microdissected tumor samples: from 4 of 19 cases, was isolated different areas all composed of pure cellular populations. We performed genome wide expression analysis by using Affymetrix technology and we used two different approaches for data analysis: SAM (Significance Analysis of Microarrays) and a method based on Game Theory (GT), to identify genes differently expressed in SS and Nb cells. Differently from the SAM method, the analysis based on GT (Moretti et al. (2006)) gives the advantage of selecting relevant genes not only according to the expression profile of each single gene, but considering also gene interaction. Keywords: Gene signature of Neuroblastic and Schwannian stromal cells.
2007-04-17 | GSE7529 | GEO
Project description:This SuperSeries is composed of the following subset Series: GSE17827: Integrated bioinformatic and wet-lab approach to identify potential oncogenic networks in neuroblastoma: MEIS1 GSE18271: Analysis of TALE homeobox genes in neuroblastic tumors: ganglioneuroblastoma and ganglioneuroma Refer to individual Series
2012-09-24 | E-GEOD-18272 | biostudies-arrayexpress
Project description:A large proportion of patients suffering from the malignant pediatric tumor neuroblastoma die of progressive disease despite intensive therapy. Neuroblastomas belong to the group of neuroblastic tumors, together with the more benign, differentiated ganglioneuroblastomas and ganglioneuromas. Little is known of the genes driving the differentiation processes in these tumor types. A search for the transcription factors differentially expressed between ganglioneuromas, ganglioneuroblastomas, and neuroblastomas in a series of 110 neuroblastic tumors (NB110) identified a large number of HOX- and TALE (Three Amino acid Loop Extension)-class homeobox transcription factor genes. The MEIS1-3, PBX1 and -3, and PKNOX1 TALE genes showed highest expression in neuroblastomas and lowest in ganglioneuromas and ganglioneuroblastomas. The PKNOX2 and TGIF1-2 genes showed the opposite expression pattern. This suggests an involvement of TALE genes in neuroblastoma differentiation. Expression of MEIS1, a known oncogene in haematopoietic tumors, was high in all neuroblastomas, and strongly correlated with undifferentiated histology. Consequently, we generated IMR-32 neuroblastoma cells capable of inducible shRNA-mediated MEIS1 knockdown. We observed differentiation, growth arrest and induction of apoptosis upon MEIS1 down-regulation. Affymetrix profiling of time-course experiments using these cells allowed the identification of MEIS1 target genes. Analysis of the target genes in the NB110 series showed that 323 of these were also significantly correlated to MEIS1 expression and to tumor differentiation in neuroblastic tumors. Genes involved in the cell cycle and in developmental pathways were over-represented in this gene set. We conclude that MEIS1 governs several of the signal transduction routes important for neuroblastoma survival and differentiation.
2012-09-24 | GSE18271 | GEO
Project description:Schwann cell plasticity regulates neuroblastic tumor cell differentiation
| PRJNA615860 | ENA
Project description:A large proportion of patients suffering from the malignant pediatric tumor neuroblastoma die of progressive disease despite intensive therapy. Neuroblastomas belong to the group of neuroblastic tumors, together with the more benign, differentiated ganglioneuroblastomas and ganglioneuromas. Little is known of the genes driving the differentiation processes in these tumor types. A search for the transcription factors differentially expressed between ganglioneuromas, ganglioneuroblastomas, and neuroblastomas in a series of 110 neuroblastic tumors (NB110) identified a large number of HOX- and TALE (Three Amino acid Loop Extension)-class homeobox transcription factor genes. The MEIS1-3, PBX1 and -3, and PKNOX1 TALE genes showed highest expression in neuroblastomas and lowest in ganglioneuromas and ganglioneuroblastomas. The PKNOX2 and TGIF1-2 genes showed the opposite expression pattern. This suggests an involvement of TALE genes in neuroblastoma differentiation. Expression of MEIS1, a known oncogene in haematopoietic tumors, was high in all neuroblastomas, and strongly correlated with undifferentiated histology. Consequently, we generated IMR-32 neuroblastoma cells capable of inducible shRNA-mediated MEIS1 knockdown. We observed differentiation, growth arrest and induction of apoptosis upon MEIS1 down-regulation. Affymetrix profiling of time-course experiments using these cells allowed the identification of MEIS1 target genes. Analysis of the target genes in the NB110 series showed that 323 of these were also significantly correlated to MEIS1 expression and to tumor differentiation in neuroblastic tumors. Genes involved in the cell cycle and in developmental pathways were over-represented in this gene set. We conclude that MEIS1 governs several of the signal transduction routes important for neuroblastoma survival and differentiation. 12 ganglioneuroma (gn) and 10 ganglioneuroblastoma (gnb) were analyzed.
2012-09-24 | E-GEOD-18271 | biostudies-arrayexpress