Internal validation cohort of breast cancers for development of ClinicoMolecular Triad Classification
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE45725 | GEO | 2014/06/19
SECONDARY ACCESSION(S): PRJNA196133
REPOSITORIES: GEO
Similar Datasets
Project description:In our early study ([GEO Accession: GSE16987], [PMID: 21939527]), we have created a ClinicoMolecular Triad Classification (CMTC) to improve prediction and prognostication of breast cancer by using a training cohort contained 161 breast cancer patients (2003 to 2008). Here, a supplemental internal validation cohort contained 340 breast cancer patients was collected (2008 to 2010) for development of the CMTC. Total 340 fine-needle aspirates (FNA) specimens from breast tumors without replicate were collected and prepared for microarray examination. Finally, 284 invasive breast cancers were used in this study.
2014-06-19 | E-GEOD-45725 | biostudies-arrayexpress
Project description:When making treatment decisions, oncologists often stratify breast cancers into a low-risk group (ER+, low grade); an intermediate-risk group (ER+, high grade); and a high-risk group that includes Her2+ and triple-negative (ER-/PR-/Her2-) tumors. None of the currently available gene signatures correlates to this clinical classification. We aimed to develop a test that is practical for the oncologists, that offers both molecular characterization of BCs, and improved prediction of prognosis and treatment response. We investigated the molecular basis of such clinical practice by grouping Her2+ and triple-negative breast cancers together during clustering analyses on the genome-wide gene expression profiles of our training cohort, mostly derived from fine needle aspiration biopsies (FNABs) of 149 consecutive evaluable Breast cancers. The analyses consistently divided these tumors into a three-cluster pattern, similar to clinical risk-stratification groups, that was reproducible in published microarray databases (n=2487) annotated with clinical outcomes. The clinicopathologic parameters of each of these three molecular groups were also similar to clinical classification. The low-risk group had good outcomes and benefited from endocrine therapy. Both intermediate- and high-risk groups had poor outcomes and were resistant to endocrine therapy. The latter demonstrated the highest rate of complete pathological response to neoadjuvant chemotherapy; the highest activities in MYC, E2F1, Ras, β-Catenin and IFN-γ pathways; and poor prognosis predicted by 14 independent prognostic signatures. Based on a multivariate analysis, this new gene signature, termed ClinicoMolecular Triad Classification, predicted recurrence and treatment response better than all pathologic parameters and other prognostic signatures. 149 invasive breast cancers from the 172 specimens contained 161 tumors were used in this study. Expression data of the 11 tumors with replicate was separately combined before analysis.
2011-09-28 | E-GEOD-16987 | biostudies-arrayexpress
Project description:TRIAD: a transposition-based approach for gene mutagenesis by random short in-frame insertions and deletions for directed protein evolution
| PRJEB28011 | ENA
Project description:This study examined how transcriptomics tools can be included in a Triad-based soil quality assessment to assess the toxicity of soils from river banks polluted by metals. To that end we measured chemical soil properties and used the standardized ISO guideline for ecotoxicological tests and a newly developed microarray for gene expression in the indicator soil arthropod, Folsomia candida. Microarray analysis revealed that the oxidative stress response pathway was significantly affected in all soils except one. The data indicate that changes in cell redox homeostasis are a significant signature of metal stress. Finally, 32 genes showed significant dose-dependent expression with metal concentrations. They are promising genetic markers providing an early indication of the need for higher tier testing in soil quality. One of the least polluted soils showed toxicity in the bioassay that could be removed by sterilization. The gene expression profile for this soil did not show a metal-related signature, confirming that another factor than metals (most likely of biological origin) caused the toxicity. This study demonstrates the feasibility and advantages of integrating transcriptomics into Triad-based soil quality assessment. Combining molecular and organismal life-history traitM-bM-^@M-^Ys stress responses helps identifying causes of adverse effect in bioassays. Further validation is needed for verifying the set of genes with dose-dependent expression patterns linked with toxic stress. We used a one-color microarray design where each sample was hybridized to a single array
2014-02-01 | E-GEOD-52393 | biostudies-arrayexpress
Project description:This study examined how transcriptomics tools can be included in a Triad-based soil quality assessment to assess the toxicity of soils from river banks polluted by metals. To that end we measured chemical soil properties and used the standardized ISO guideline for ecotoxicological tests and a newly developed microarray for gene expression in the indicator soil arthropod, Folsomia candida. Microarray analysis revealed that the oxidative stress response pathway was significantly affected in all soils except one. The data indicate that changes in cell redox homeostasis are a significant signature of metal stress. Finally, 32 genes showed significant dose-dependent expression with metal concentrations. They are promising genetic markers providing an early indication of the need for higher tier testing in soil quality. One of the least polluted soils showed toxicity in the bioassay that could be removed by sterilization. The gene expression profile for this soil did not show a metal-related signature, confirming that another factor than metals (most likely of biological origin) caused the toxicity. This study demonstrates the feasibility and advantages of integrating transcriptomics into Triad-based soil quality assessment. Combining molecular and organismal life-history trait’s stress responses helps identifying causes of adverse effect in bioassays. Further validation is needed for verifying the set of genes with dose-dependent expression patterns linked with toxic stress.
2014-02-01 | GSE52393 | GEO
Project description:When making treatment decisions, oncologists often stratify breast cancers into a low-risk group (ER+, low grade); an intermediate-risk group (ER+, high grade); and a high-risk group that includes Her2+ and triple-negative (ER-/PR-/Her2-) tumors. None of the currently available gene signatures correlates to this clinical classification. We aimed to develop a test that is practical for the oncologists, that offers both molecular characterization of BCs, and improved prediction of prognosis and treatment response. We investigated the molecular basis of such clinical practice by grouping Her2+ and triple-negative breast cancers together during clustering analyses on the genome-wide gene expression profiles of our training cohort, mostly derived from fine needle aspiration biopsies (FNABs) of 149 consecutive evaluable Breast cancers. The analyses consistently divided these tumors into a three-cluster pattern, similar to clinical risk-stratification groups, that was reproducible in published microarray databases (n=2487) annotated with clinical outcomes. The clinicopathologic parameters of each of these three molecular groups were also similar to clinical classification. The low-risk group had good outcomes and benefited from endocrine therapy. Both intermediate- and high-risk groups had poor outcomes and were resistant to endocrine therapy. The latter demonstrated the highest rate of complete pathological response to neoadjuvant chemotherapy; the highest activities in MYC, E2F1, Ras, β-Catenin and IFN-γ pathways; and poor prognosis predicted by 14 independent prognostic signatures. Based on a multivariate analysis, this new gene signature, termed ClinicoMolecular Triad Classification, predicted recurrence and treatment response better than all pathologic parameters and other prognostic signatures.
2011-09-29 | GSE16987 | GEO
Project description:Breast cancer is a leading cause of cancer-death among women, where the clinicopathological features of tumors are used to prognosticate and guide therapy. DNA copy number alterations (CNAs), which occur frequently in breast cancer and define key pathogenetic events, are also potentially useful prognostic or predictive factors. Here, we report a genome-wide array-based comparative genomic hybridization (array CGH) survey of CNAs in 89 breast tumors from a patient cohort with locally advanced disease. Statistical analysis links distinct cytoband loci harboring CNAs to specific clinicopathological parameters, including tumor grade, estrogen receptor status, presence of TP53 mutation, and overall survival. Notably, distinct spectra of CNAs also underlie the different subtypes of breast cancer recently defined by expression-profiling, implying these subtypes develop along distinct genetic pathways. In addition, higher numbers of gains/losses are associated with the "basal-like" tumor subtype, while high-level DNA amplification is more frequent in "luminal-B" subtype tumors, suggesting also that distinct mechanisms of genomic instability might underlie their pathogenesis. The identified CNAs may provide a basis for improved patient prognostication, as well as a starting point to define important genes to further our understanding of the pathobiology of breast cancer.
2006-12-01 | E-SMDB-3878 | biostudies-arrayexpress
Project description:Breast cancer is a leading cause of cancer-death among women, where the clinicopathological features of tumors are used to prognosticate and guide therapy. DNA copy number alterations (CNAs), which occur frequently in breast cancer and define key pathogenetic events, are also potentially useful prognostic or predictive factors. Here, we report a genome-wide array-based comparative genomic hybridization (array CGH) survey of CNAs in 89 breast tumors from a patient cohort with locally advanced disease. Statistical analysis links distinct cytoband loci harboring CNAs to specific clinicopathological parameters, including tumor grade, estrogen receptor status, presence of TP53 mutation, and overall survival. Notably, distinct spectra of CNAs also underlie the different subtypes of breast cancer recently defined by expression-profiling, implying these subtypes develop along distinct genetic pathways. In addition, higher numbers of gains/losses are associated with the "basal-like" tumor subtype, while high-level DNA amplification is more frequent in "luminal-B" subtype tumors, suggesting also that distinct mechanisms of genomic instability might underlie their pathogenesis. The identified CNAs may provide a basis for improved patient prognostication, as well as a starting point to define important genes to further our understanding of the pathobiology of breast cancer.
2006-11-20 | E-SMDB-3877 | biostudies-arrayexpress
Project description:Internal validation cohort of breast cancers for development of ClinicoMolecular Triad Classification
| PRJNA196133 | ENA
Project description:A new gene expression signature, the ClinicoMolecular Triad Classification, may improve prediction and prognostication of breast cancer at the time of diagnosis
| PRJNA117333 | ENA