Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Individual Assignment of Adult Diffuse Gliomas into the EM/PM Molecular Subtypes Using a TaqMan Low-Density Array [Taqman Low Density Array]

ABSTRACT: Diagnosis of glioma into the hierarchy of human brain development would greatly facilitate mechanistic studies and clinical services in combating glioma. By analysing the multi-dimension big data of glioma genome and clinical manifestations, we have recently developed an EM/PM classification scheme anchored in the brain development and glioma pathogenesis (Sun et al., PNAS 111:3538-3543, 2014). To translate the EM/PM classification into a clinical diagnostic tool, we here designed and validated a TaqMan low-density RT-PCR array (TLDA) and a support vector machine (SVM) based prediction model that enables individual diagnosis of gliomas into the EM/PM subtype with high accuracy and precision. Results of 153 individually diagnosed adult diffuse gliomas of WHO grades II-IV derived from Chinese or Swedish patients confirmed our previous data base investigations: EM and PM gliomas show distinct prognosis, occur at different ages, and harbor mutually exclusive patterns of genomic alterations detected by Sanger sequencing and shallow-coverage whole genome sequencing (WGS). Further, we show that shallow-coverage WGS enabled a systematic identification of clonal and subclonal copy number variations (CNV) in glioma genomes, and the extent and the pattern of CNV can serve as an objective marker of tumor progression in the PM gliomas harboring IDH mutations.

ORGANISM(S): Homo sapiens

PROVIDER: GSE131464 | GEO | 2019/05/21

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Individual Assignment of Adult Diffuse Gliomas into the EM/PM Molecular Subtypes Using a TaqMan Low-Density Array [Human Clariom D array]

Project description:Diagnosis of glioma into the hierarchy of human brain development would greatly facilitate mechanistic studies and clinical services in combating glioma. By analysing the multi-dimension big data of glioma genome and clinical manifestations, we have recently developed an EM/PM classification scheme anchored in the brain development and glioma pathogenesis (Sun et al., PNAS 111:3538-3543, 2014). To translate the EM/PM classification into a clinical diagnostic tool, we here designed and validated a TaqMan low-density RT-PCR array (TLDA) and a support vector machine (SVM) based prediction model that enables individual diagnosis of gliomas into the EM/PM subtype with high accuracy and precision. Results of 153 individually diagnosed adult diffuse gliomas of WHO grades II-IV derived from Chinese or Swedish patients confirmed our previous data base investigations: EM and PM gliomas show distinct prognosis, occur at different ages, and harbor mutually exclusive patterns of genomic alterations detected by Sanger sequencing and shallow-coverage whole genome sequencing (WGS). Further, we show that shallow-coverage WGS enabled a systematic identification of clonal and subclonal copy number variations (CNV) in glioma genomes, and the extent and the pattern of CNV can serve as an objective marker of tumor progression in the PM gliomas harboring IDH mutations.

2019-05-16 | GSE131273 | GEO

Individual Assignment of Adult Diffuse Gliomas into the EM/PM Molecular Subtypes Using a TaqMan Low-Density Array [Taqman Low Density Array]

Project description:Individual Assignment of Adult Diffuse Gliomas into the EM/PM Molecular Subtypes Using a TaqMan Low-Density Array [Taqman Low Density Array]

| PRJNA543777 | ENA

Evaluation of copy number variation detection between high-resolution array CGH and low-coverage short-insert and mate-pair whole-genome sequencing

Project description:In principle, whole-genome sequencing (WGS) of the human genome even at low coverage offers higher resolution for genomic copy number variation (CNV) detection compared to array-based technologies, which is currently the first-tier approach in clinical cytogenetics. There are, however, obstacles in replacing array-based CNV detection with that of low-coverage WGS such as cost, turnaround time, and lack of systematic performance comparisons. With technological advances in WGS in terms of library preparation, instrument platforms, and data analysis algorithms, obstacles imposed by cost and turnaround time are fading. However, a systematic performance comparison between array and low-coverage WGS-based CNV detection has yet to be performed. Here, we compared the CNV detection capabilities between WGS (short-insert, 3kb-, and 5kb-mate-pair libraries) at 1X, 3X, and 5X coverages and standardly used high-resolution arrays in the genome of 1000-Genomes-Project CEU genome NA12878. CNV detection was performed using standard analysis methods, and the results were then compared to a list of Gold Standard NA12878 CNVs distilled from the 1000-Genomes Project. Overall, low-coverage WGS is able to detect drastically more (approximately 5 fold more on average) Gold Standard CNVs compared to arrays and is accompanied with fewer CNV calls without secondary validation. Furthermore, we also show that WGS (at ≥1X coverage) is able to detect all seven validated deletions larger than 100 kb in the NA12878 genome whereas only one of such deletions is detected in most arrays. Finally, we show that the much larger 15 Mbp Cri-du-chat deletion can be clearly seen at even 1X coverage from short-insert WGS.

2018-06-26 | GSE105092 | GEO

sWGS on paired cfDNA and tissue DNA from pediatric cancer patients

Project description:We performed shallow whole genome sequencing (WGS) on circulating free (cf)DNA extracted from plasma or cerebrospinal fluid (CSF), and shallow WGS on the tissue DNA extracted from the biopsy in order to evaluate the correlation between the two biomaterials. After library construction and sequencing (Hiseq3000 or Ion Proton), copy number variations were called with WisecondorX.

2021-11-17 | E-MTAB-10548 | biostudies-arrayexpress

Individual Assignment of Adult Diffuse Gliomas into the EM/PM Molecular Subtypes Using a TaqMan Low-Density Array [Human Clariom D array]

Project description:Individual Assignment of Adult Diffuse Gliomas into the EM/PM Molecular Subtypes Using a TaqMan Low-Density Array [Human Clariom D array]

| PRJNA543103 | ENA

Affymetrix CytoScan 750K and HD data for developmental delay and/or physical disability phenotypes samples

Project description:Chromosomal copy number variations (CNV) have been associated with various neurological and developmental disorders and chromosomal microarray (CMA) is a method of choice to diagnose Copy Number Gain/Loss syndromes. Recently, next-generation sequencing (NGS)-based low-coverage whole genome sequencing (LC-WGS) has been applied to detect Copy Number Gain/Loss syndromes. This dataset is intended to be used as a “Golden standard data set” for development of LC-WGS analysis method. It consists of patients (n=63) who have a mental delay and/or physical disability phenotype and normal (n=20) phenotype.

2018-09-29 | GSE120624 | GEO

Individual Assignment of Adult Diffuse Gliomas into the EM/PM Molecular Subtypes

Project description:Individual Assignment of Adult Diffuse Gliomas into the EM/PM Molecular Subtypes

| PRJNA543775 | ENA

Metabolic changes related to the IDH1 mutation in gliomas preserve TCA-cycle activity: an investigation at the protein level

Project description:The discovery of the IDH1 R132H (IDH1 mut) mutation in low-grade glioma and the associated change in function of the IDH1 enzyme has increased the interest in glioma metabolism. In an earlier study, we found that changes in expression of genes involved in the aerobic glycolysis and the TCA-cycle are associated with IDH1 mut. Here we apply proteomics to FFPE samples of diffuse gliomas with or without IDH1 mutations, in order to map changes in protein levels associated with this mutation. We observed significant changes in the enzyme abundance associated with aerobic glycolysis, glutamate metabolism and the TCA-cycle in IDH1 mut gliomas. Specifically, the enzymes involved in the metabolism of glutamate, lactate and enzymes involved in the conversion of α-ketoglutarate were increased in IDH1 mut gliomas. In addition, the bicarbonate transporter (SLC4A4) was increased in IDH1 mut gliomas, supporting the idea that a mechanism preventing intracellular acidification is active. We also found that enzymes that convert proline, valine, leucine and isoleucine into glutamate were increased in IDH1 mut glioma. We conclude that in IDH1 mut glioma metabolism is rewired (increased input of lactate and glutamate) to preserve TCA cycle activity in IDH1 mut gliomas.

2020-03-09 | PXD016698 | Pride

Metabolic changes related to the IDH1 mutation in gliomas preserve TCA-cycle activity: an investigation at the protein level

2020-03-09 | PXD016697 | Pride

Analysis of copy number variation landscape of glioma by shallow coverage whole genome sequencing

Project description:We reported shallow coverage whole genome sequencing (WGS) is an economic method for identification of whole genome somatic copy number variation (SCNV). Combining with a Taqman low density array (TLDA) designed for molecular subtyping of glioma, it enabled classification of glioma into biological entities with different cellular origin and genomic alterations. Further, we also showed shallow-coverage WGS enabled systematic identification of clonal and subclonal SCNV in glioma genomes and the burden and pattern of SCNV can serve as an objective marker of tumor progression and predict risk and relapse.

| EGAS00001003690 | EGA

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data