Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA175450 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Gene expression Classification of Colon Cancer defines six molecular subtypes with distinct clinical, molecular and survival characteristics [Expression]
| PRJNA171160 | ENA
Project description:From a clinical and molecular perspective, colon cancer (CC) is a heterogeneous disease but to date no classification based on high-density transcriptome data has been established. The aim of this study was to build up a robust molecular classification of mRNA expression profiles (Affymetrix U133Plus2) of a large series of 443 CC and to validate it on an independent serie of 123 CC and 906 public dataset. We identified and validated six molecular subtypes in this large cohort as a combination of multiple molecular processes that complement current disease stratification based on clinicopathological variables and molecular markers. The biological relevance of these subtypes was consolidated by significant differences in survival. These insights open new perspectives for improving prognostic models and targeted therapies. Among a large series of colon cancers collected for the Cartes d'Identité des Tumeurs (CIT) program from the French Ligue Nationale Contre le Cancer (http://cit.ligue-cancer.net), 566 were analyzed for mRNA expression profiles using Affymetrix U133plus2 chip and, among theses, 463 could also be analyzed for DNA alteration profiles using the CGH Array ( CIT-CGHarray V6). The 566 tumors was divided into a discovery dataset of 443 CC and a validation dataset of 123 CC.
2013-05-22 | E-GEOD-39582 | biostudies-arrayexpress
Project description:From a clinical and molecular perspective, colon cancer (CC) is a heterogeneous disease but to date no classification based on high-density transcriptome data has been established. The aim of this study was to build up a robust molecular classification of mRNA expression profiles (Affymetrix U133Plus2) of a large series of 443 CC and to validate it on an independent serie of 123 CC and 906 public dataset. We identified and validated six molecular subtypes in this large cohort as a combination of multiple molecular processes that complement current disease stratification based on clinicopathological variables and molecular markers. The biological relevance of these subtypes was consolidated by significant differences in survival. These insights open new perspectives for improving prognostic models and targeted therapies. Among a large series of colon cancers collected for the Cartes d'Identité des Tumeurs (CIT) program from the French Ligue Nationale Contre le Cancer (http://cit.ligue-cancer.net), 566 were analyzed for mRNA expression profiles using Affymetrix U133plus2 chip and, among theses, 463 could also be analyzed for DNA alteration profiles using the CGH Array ( CIT-CGHarray V6). The 566 tumors was divided into a discovery dataset of 443 CC and a validation dataset of 123 CC.
2013-05-22 | E-GEOD-40966 | biostudies-arrayexpress
Project description:From a clinical and molecular perspective, colon cancer (CC) is a heterogeneous disease but to date no classification based on high-density transcriptome data has been established. The aim of this study was to build up a robust molecular classification of mRNA expression profiles (Affymetrix U133Plus2) of a large series of 443 CC and to validate it on an independent serie of 123 CC and 906 public dataset. We identified and validated six molecular subtypes in this large cohort as a combination of multiple molecular processes that complement current disease stratification based on clinicopathological variables and molecular markers. The biological relevance of these subtypes was consolidated by significant differences in survival. These insights open new perspectives for improving prognostic models and targeted therapies.
2013-05-22 | GSE40966 | GEO
Project description:From a clinical and molecular perspective, colon cancer (CC) is a heterogeneous disease but to date no classification based on high-density transcriptome data has been established. The aim of this study was to build up a robust molecular classification of mRNA expression profiles (Affymetrix U133Plus2) of a large series of 443 CC and 19 non-tumoral colorectal mucosas, and to validate it on an independent serie of 123 CC and 906 public dataset. We identified and validated six molecular subtypes in this large cohort as a combination of multiple molecular processes that complement current disease stratification based on clinicopathological variables and molecular markers. The biological relevance of these subtypes was consolidated by significant differences in survival. These insights open new perspectives for improving prognostic models and targeted therapies.
2013-05-22 | GSE39582 | GEO
Project description:Histological classification of gliomas guides treatment decisions. Because of the high interobserver variability, we aimed to improve classification by performing gene expression profiling on a large cohort of glioma samples of all histological subtypes and grades. The seven identified intrinsic molecular subtypes are different from histological subgroups and correlate better to patient survival. Our data indicate that distinct molecular subgroups clearly benefit from treatment. Specific genetic changes (EGFR amplification, IDH1 mutation, 1p/19q LOH) segregate in -and may drive- the distinct molecular subgroups. Our findings were validated on three large independent sample cohorts (TCGA, REMBRANDT, and GSE12907). We provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histology. 276 glioma samples of all histology, 8 control samples
2010-04-26 | E-GEOD-16011 | biostudies-arrayexpress
Project description:Purpose: A number of microarray studies have reported distinct molecular profiles of breast cancers (BC): basal-like, ErbB2-like and two to three luminal-like subtypes. These were associated with different clinical outcomes. However, although the basal and the ErbB2 subtypes are repeatedly recognized, identification of estrogen receptor (ER)-positive subtypes has been inconsistent. Refinement of their molecular definition is therefore needed. Materials and methods: We have previously reported a gene-expression grade index (GGI) which defines histological grade based on gene expression profiles. Using this algorithm, we assigned ER-positive BC to either high or low genomic grade subgroups and compared these to previously reported ER-positive molecular classifications. As further validation, we classified 666 ER-positive samples into subtypes and assessed their clinical outcome. Results: Two ER-positive molecular subgroups (high and low genomic grade) could be defined using the GGI. Despite tracking a single biological pathway, these were highly comparable to the previously described luminal A and B classification and significantly correlated to the risk groups produced using the 21-gene recurrence score. The two subtypes were associated with statistically distinct clinical outcome in both systemically untreated and tamoxifen-treated populations. Conclusions: The use of genomic grade can identify two clinically distinct ER-positive molecular subtypes in a simple and highly reproducible manner across multiple datasets. This study emphasizes the important role of proliferation-related genes in predicting prognosis in ER-positive BC. Experiment Overall Design: dataset of microarray experiments from primary breast tumors used to assess the reationship between GGI, molecular subtypes, and tamoxifen resistance. Experiment Overall Design: No replicate, no reference sample.
2008-06-20 | E-GEOD-6532 | biostudies-arrayexpress
Project description:Histological classification of gliomas guides treatment decisions. Because of the high interobserver variability, we aimed to improve classification by performing gene expression profiling on a large cohort of glioma samples of all histological subtypes and grades. The seven identified intrinsic molecular subtypes are different from histological subgroups and correlate better to patient survival. Our data indicate that distinct molecular subgroups clearly benefit from treatment. Specific genetic changes (EGFR amplification, IDH1 mutation, 1p/19q LOH) segregate in -and may drive- the distinct molecular subgroups. Our findings were validated on three large independent sample cohorts (TCGA, REMBRANDT, and GSE12907). We provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histology.
2010-04-26 | GSE16011 | GEO
Project description:Unsupervised classification of gene expression profiles has resulted in the identification of biologically and clinically distinct colon cancer subtypes (CCSs). The subtype that associates with poor clinical outcome displays a mesenchymal gene expression profile. No driver mutation has been identified for this category and patients are heterogeneous with regard to commonly used clinical markers. Here we report a regulatory network consisting of the miR-200 family members that tunes the majority of genes differentially expressed in the poor prognosis CCS, including genes involved in the epithelial-mesenchymal transition (EMT) process. Our data indicate that the epigenetic silencing of the miR-200 family by promoter methylation is identifying the mesenchymal CCS and is predictive of disease-free survival in this malignancy. We demonstrate that the molecular features of poor prognosis colon cancer - expression of EMT-associated genes and miR-200 promoter methylation - can already be installed at the premalignant stage, suggesting a highly malignant potential of specific colon cancer precursor lesions. Four colorectal cancer cell lines that display methylated miR-200 loci have been used to overexpress miR-200 family members from both loci separatedly or simultaneously.
2015-09-01 | E-GEOD-65551 | biostudies-arrayexpress