Most human non-GCIMP glioblastoma subtypes evolve from a common proneural-like precursor glioma.
Ontology highlight
ABSTRACT:
SUBMITTER: Ozawa T
PROVIDER: S-EPMC4143139 | biostudies-literature | 2014 Aug
REPOSITORIES: biostudies-literature
Publications
Most human non-GCIMP glioblastoma subtypes evolve from a common proneural-like precursor glioma.
Ozawa Tatsuya T Riester Markus M Cheng Yu-Kang YK Huse Jason T JT Squatrito Massimo M Helmy Karim K Charles Nikki N Michor Franziska F Holland Eric C EC
Cancer cell 20140801 2
To understand the relationships between the non-GCIMP glioblastoma (GBM) subgroups, we performed mathematical modeling to predict the temporal sequence of driver events during tumorigenesis. The most common order of evolutionary events is 1) chromosome (chr) 7 gain and chr10 loss, followed by 2) CDKN2A loss and/or TP53 mutation, and 3) alterations canonical for specific subtypes. We then developed a computational methodology to identify drivers of broad copy number changes, identifying PDGFA (ch ...[more]
Similar Datasets
Project description:In order to understand the relationships between the human non-GCIMP glioblastoma subgroups, we performed computational analysis of human genomic data to predict the temporal sequence in which the driver events arise during tumorigenesis. The order of evolutionary events for non-GCIMP GBM is 1) chr 7 gain and loss of chr 10, followed by 2) CDKN2A loss and/or TP53 mutation, and 3) alterations canonical for specific subtypes such as NF1 loss or focal amplification of PDGFRα or EGFR. We then developed a computational methodology to identify the drivers of broad copy number changes, identifying PDGF-A (chr 7) and PTEN (chr 10) as driving the initial non-disjunction events. These predictions were validated using mouse modeling, showing PDGF-A is sufficient to induce PN-like gliomas that are enhanced by loss of Ink4a-Arf, Tp53 or Pten. Additional Nf1 loss converts PN to the MES subtype. Our findings suggest non-GCIMP GBMs arise as, and evolve from, a common proneural-like precursor. hGBM_sphere: Three different human GBM sphere lines expressing two different NF1-shRNAs or empty shRNA-control were generated by a lentiviral infection with the relevant pLKO.1 vectors. The total RNAs were obtained from the GBM sphere lines and the gene expression profile of the NF1-shRNA cells was then compared to the control cells. hGBM_rapamycin: Two human GBM sphere lines (TS543 and TS667) expressing the NF1-shRNAs were generated by a lentiviral infection with two different pLKO.1-NF1-shRNA vectors (two different target sequence; #2 and #5). The total RNAs were obtained from the NF1-shRNA lines treated with a 1nM rapamycin or 0.1% DMSO control for 5 hours and the gene expression profile was then compared between the both groups. Murine_gliomas: Murine brain tumors were generated by an injection of DF1 cells producing the relevant RCAS virus into newborn pups or adult mice brain. When mice presented any symptoms of disease, the mice were sacrificed and the brain tumor tissues were macroscopically dissected. Then total RNAs were extracted from the murine brain tumors or normal brain tissues and the gene expression profile was compared between various tumor types and/or normal brain tissues. In this study, two different RCAS-shNf1 (GR249 and GF249) and shp53 (R696 and mR696) was used for the generation of the murine brain tumors. Each RCAS vector has different structure but the target sequence against the Nf1 and p53 is same and similar knockdown effect was observed. Tumor grade were determined with the residual sections after a piece of tumor tissue were excised for the RNA extraction. The detail information of the experiment was described in the associated-publication. Murine_shere: Murine neurosphere lines were generated by a retroviral infection with the relevant RCAS viruses. The total RNAs were obtained from the murine neurosphere lines expressing the RCAS-shGL2, shNf1, shp53 or shNf1+shp53 and the gene expression profiles of the shNf1, shp53 or shNf1/p53 cells were then compared to the control shGL2 cells.
| S-ECPF-GEOD-45874 | biostudies-other
Project description:In order to understand the relationships between the human non-GCIMP glioblastoma subgroups, we performed computational analysis of human genomic data to predict the temporal sequence in which the driver events arise during tumorigenesis. The order of evolutionary events for non-GCIMP GBM is 1) chr 7 gain and loss of chr 10, followed by 2) CDKN2A loss and/or TP53 mutation, and 3) alterations canonical for specific subtypes such as NF1 loss or focal amplification of PDGFRα or EGFR. We then developed a computational methodology to identify the drivers of broad copy number changes, identifying PDGF-A (chr 7) and PTEN (chr 10) as driving the initial non-disjunction events. These predictions were validated using mouse modeling, showing PDGF-A is sufficient to induce PN-like gliomas that are enhanced by loss of Ink4a-Arf, Tp53 or Pten. Additional Nf1 loss converts PN to the MES subtype. Our findings suggest non-GCIMP GBMs arise as, and evolve from, a common proneural-like precursor.
2014-04-03 | GSE45874 | GEO
Project description:In order to understand the relationships between the human non-GCIMP glioblastoma subgroups, we performed computational analysis of human genomic data to predict the temporal sequence in which the driver events arise during tumorigenesis. The order of evolutionary events for non-GCIMP GBM is 1) chr 7 gain and loss of chr 10, followed by 2) CDKN2A loss and/or TP53 mutation, and 3) alterations canonical for specific subtypes such as NF1 loss or focal amplification of PDGFRα or EGFR. We then developed a computational methodology to identify the drivers of broad copy number changes, identifying PDGF-A (chr 7) and PTEN (chr 10) as driving the initial non-disjunction events. These predictions were validated using mouse modeling, showing PDGF-A is sufficient to induce PN-like gliomas that are enhanced by loss of Ink4a-Arf, Tp53 or Pten. Additional Nf1 loss converts PN to the MES subtype. Our findings suggest non-GCIMP GBMs arise as, and evolve from, a common proneural-like precursor. hGBM_sphere: Three different human GBM sphere lines expressing two different NF1-shRNAs or empty shRNA-control were generated by a lentiviral infection with the relevant pLKO.1 vectors. The total RNAs were obtained from the GBM sphere lines and the gene expression profile of the NF1-shRNA cells was then compared to the control cells. hGBM_rapamycin: Two human GBM sphere lines (TS543 and TS667) expressing the NF1-shRNAs were generated by a lentiviral infection with two different pLKO.1-NF1-shRNA vectors (two different target sequence; #2 and #5). The total RNAs were obtained from the NF1-shRNA lines treated with a 1nM rapamycin or 0.1% DMSO control for 5 hours and the gene expression profile was then compared between the both groups. Murine_gliomas: Murine brain tumors were generated by an injection of DF1 cells producing the relevant RCAS virus into newborn pups or adult mice brain. When mice presented any symptoms of disease, the mice were sacrificed and the brain tumor tissues were macroscopically dissected. Then total RNAs were extracted from the murine brain tumors or normal brain tissues and the gene expression profile was compared between various tumor types and/or normal brain tissues. In this study, two different RCAS-shNf1 (GR249 and GF249) and shp53 (R696 and mR696) was used for the generation of the murine brain tumors. Each RCAS vector has different structure but the target sequence against the Nf1 and p53 is same and similar knockdown effect was observed. Tumor grade were determined with the residual sections after a piece of tumor tissue were excised for the RNA extraction. The detail information of the experiment was described in the associated-publication. Murine_shere: Murine neurosphere lines were generated by a retroviral infection with the relevant RCAS viruses. The total RNAs were obtained from the murine neurosphere lines expressing the RCAS-shGL2, shNf1, shp53 or shNf1+shp53 and the gene expression profiles of the shNf1, shp53 or shNf1/p53 cells were then compared to the control shGL2 cells.
2014-04-03 | E-GEOD-45874 | biostudies-arrayexpress
Project description:Human non-GCIMP gioblastoma subtypes evolve from a common proneural-like precursor glioma
| PRJNA196865 | ENA
Project description:Glioblastomas (GBM)-the most common, therapy-resistant, and lethal tumors driven by populations of glioma stem cells (GSCs) are still on the list of the most complicated pathologies. Thus, deeper understanding and characterization of GSCs is indispensable to find suitable targets and develop more effective therapies. In the present study, we applied native glioblastoma cells and GSCs sequencing, screened for GSC-specific targets and checked if the signature is related to GBM patient pathological, clinical data as well as molecular subtypes applying TCGA cohort. Data analysis revealed that tumors of proneural and mesenchymal subtypes are branching in separate clusters based on screened gene expression. Samples of the same subtype revealed significantly different patient survival prognosis as well as recurrence chance between the clusters. Recently, different subpopulations of mesenchymal GSC demonstrating different properties were shown, which indicates possible internal heterogeneity of GBM subtypes as well. Current findings also revealed branching of molecular GBM subtypes that were significantly linked to patient outcome and that might be decided by distinct GSC subpopulations.
| S-EPMC8124327 | biostudies-literature
Project description:Accumulating evidence suggests that the stem cell markers CD133 and CD44 indicate molecular subtype in Glioblastoma Multiforme (GBM). Gene coexpression analysis of The Cancer Genome Atlas GBM dataset was undertaken to compare markers of the Glioblastoma Stem-Progenitor Cell (GSPC) phenotype. Pearson correlation identified genes coexpressed with stem cell markers, which were then used to build a gene signature that classifies patients based on a CD133 coexpression module signature (CD133-M) or CD44-M subtype. CD133-M tumors were enriched for the Proneural (PN) GBM subtype compared to Mesenchymal (MES) subtype for CD44-M tumors. Gene set enrichment identified DNA replication/cell cycle genes in the CD133-M and invasion/migration in CD44-M, while functional experiments showed enhanced cellular growth in CD133 expressing cells and enhanced invasion in cells expressing CD44. As with the 4 major molecular subtypes of GBM, there was no long-term survival difference between CD44-M and CD133-M patients, although CD44-M patients responded better to temozolomide while CD133-M patients benefited from radiotherapy. The use of a targeted coexpression approach to predict functional properties of surface marker expressing cells is novel, and in the context of GBM, supports accumulating evidence that CD133 and CD44 protein marker expression correlates with molecular subtype.
| S-EPMC4467436 | biostudies-literature
Project description:Germ cell tumors (GCTs) are the most common cancer in men between the ages of 15 and 40. Although most patients are cured, those with disease arising in the mediastinum have distinctly poor outcomes. One in every 17 patients with primary mediastinal nonseminomatous GCTs develop an incurable hematologic malignancy and prior data intriguingly suggest a clonal relationship exists between hematologic malignancies and GCTs in these cases. To date, however, the precise clonal relationship between GCTs and the diverse additional somatic malignancies arising in such individuals have not been determined. Here, we traced the clonal evolution and characterized the genetic features of each neoplasm from a cohort of 15 patients with GCTs and associated hematologic malignancies. We discovered that GCTs and hematologic malignancies developing in such individuals evolved from a common shared precursor, nearly all of which harbored allelically imbalanced p53 and/or RAS pathway mutations. Hematologic malignancies arising in this setting genetically resembled mediastinal GCTs rather than de novo myeloid neoplasms. Our findings argue that this scenario represents a unique clinical syndrome, distinct from de novo GCTs or hematologic malignancies, initiated by an ancestral precursor that gives rise to the parallel evolution of GCTs and blood cancers in these patients.
| S-EPMC7685753 | biostudies-literature
Project description:Genome-wide association studies (GWAS) have transformed our understanding of glioma susceptibility, but individual studies have had limited power to identify risk loci. We performed a meta-analysis of existing GWAS and two new GWAS, which totaled 12,496 cases and 18,190 controls. We identified five new loci for glioblastoma (GBM) at 1p31.3 (rs12752552; P = 2.04 × 10-9, odds ratio (OR) = 1.22), 11q14.1 (rs11233250; P = 9.95 × 10-10, OR = 1.24), 16p13.3 (rs2562152; P = 1.93 × 10-8, OR = 1.21), 16q12.1 (rs10852606; P = 1.29 × 10-11, OR = 1.18) and 22q13.1 (rs2235573; P = 1.76 × 10-10, OR = 1.15), as well as eight loci for non-GBM tumors at 1q32.1 (rs4252707; P = 3.34 × 10-9, OR = 1.19), 1q44 (rs12076373; P = 2.63 × 10-10, OR = 1.23), 2q33.3 (rs7572263; P = 2.18 × 10-10, OR = 1.20), 3p14.1 (rs11706832; P = 7.66 × 10-9, OR = 1.15), 10q24.33 (rs11598018; P = 3.39 × 10-8, OR = 1.14), 11q21 (rs7107785; P = 3.87 × 10-10, OR = 1.16), 14q12 (rs10131032; P = 5.07 × 10-11, OR = 1.33) and 16p13.3 (rs3751667; P = 2.61 × 10-9, OR = 1.18). These data substantiate that genetic susceptibility to GBM and non-GBM tumors are highly distinct, which likely reflects different etiology.
| S-EPMC5558246 | biostudies-literature
Project description:BackgroundGliomas are the most common primary malignant brain tumors in adults with great heterogeneity in histopathology and clinical course. The intent was to evaluate the relevance of known glioblastoma (GBM) expression and methylation based subtypes to grade II and III gliomas (ie. lower grade gliomas).MethodsGene expression array, single nucleotide polymorphism (SNP) array and clinical data were obtained for 228 GBMs and 176 grade II/II gliomas (GII/III) from the publically available Rembrandt dataset. Two additional datasets with IDH1 mutation status were utilized as validation datasets (one publicly available dataset and one newly generated dataset from MD Anderson). Unsupervised clustering was performed and compared to gene expression subtypes assigned using the Verhaak et al 840-gene classifier. The glioma-CpG Island Methylator Phenotype (G-CIMP) was assigned using prediction models by Fine et al.ResultsUnsupervised clustering by gene expression aligned with the Verhaak 840-gene subtype group assignments. GII/IIIs were preferentially assigned to the proneural subtype with IDH1 mutation and G-CIMP. GBMs were evenly distributed among the four subtypes. Proneural, IDH1 mutant, G-CIMP GII/III s had significantly better survival than other molecular subtypes. Only 6% of GBMs were proneural and had either IDH1 mutation or G-CIMP but these tumors had significantly better survival than other GBMs. Copy number changes in chromosomes 1p and 19q were associated with GII/IIIs, while these changes in CDKN2A, PTEN and EGFR were more commonly associated with GBMs.ConclusionsGBM gene-expression and methylation based subtypes are relevant for GII/III s and associate with overall survival differences. A better understanding of the association between these subtypes and GII/IIIs could further knowledge regarding prognosis and mechanisms of glioma progression.
| S-EPMC3948818 | biostudies-literature
Project description:There are distinct cell subpopulations in normal epithelial tissue, including stem cells, progenitor cells, and more differentiated cells, all of which have been extensively studied for their susceptibility to tumorigenesis. However, normal cells usually have to progress through a precancerous lesion state before becoming a full-blown tumor. Precancerous early lesions are heterogeneous, and the cell subset that is the primary source of the eventual tumor remains largely unknown. By using mouse models that are tailored to address this question, we identified a keratin 6a-expressing precancerous stem cell (PcSC) subset and a more differentiated whey acidic protein-positive (WAP+) cell subset in mammary precancerous lesions initiated by the Wnt1 oncogene. Both cell subsets rapidly progressed to cancer upon introduction of constitutively active versions of either HRAS or BRAF. However, the resulting tumors were dramatically different in protein profiles and histopathology: keratin 6a+ precancerous cells gave rise to adenocarcinoma, whereas WAP+ cells yielded metaplastic carcinoma with severe squamous differentiation and more robust activation of MEK/ERK signaling. Therefore, both stem and non-stem cells in mammary precancerous lesions can contribute to the eventual cancers, but their differentiation status determines the resulting cancer phenotype. This work identifies a previously unknown player in cancer heterogeneity and suggests that cancer prevention should target precancerous cells broadly and not be limited to PcSC. SIGNIFICANCE: This work uses a novel mouse mammary gland cancer model to show that tumors initiated from different precancerous mammary epithelial cells are distinct.
| S-EPMC6318055 | biostudies-literature