Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Personalized medicine in psoriasis: developing a genomic classifier to predict histological response to Alefacept

ABSTRACT: Alefacept treatment is highly effective in a select group patients with moderate-to-severe psoriasis, and is an ideal candidate to develop systems to predict who will respond to therapy. A clinical trial of 22 patients with moderate to severe psoriasis treated with alefacept (7.5mg weekly i.v. x12 weeks) was conducted in 2002-2003, as a mechanism of action study. Patients were classified as responders or non-responders to alefacept based on histological criteria. Microarray data from PBMCs of 16 of these patients was analyzed to generate a treatment response classifier. We used a discriminant analysis method that performs sample classification from gene expression data, via nearest shrunken centroid method''. A disease response classifier using 23 genes was created to accurately predict response to alefacept (12.3% error rate in favour of responders). This preliminary study may provide a useful tool to predict response of psoriatic patients with alefacept. Keywords: class prediction

ORGANISM(S): Homo sapiens

PROVIDER: GSE18948 | GEO | 2009/12/01

SECONDARY ACCESSION(S): PRJNA120981

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Personalized medicine in psoriasis: developing a genomic classifier to predict histological response to Alefacept

Project description:Alefacept treatment is highly effective in a select group patients with moderate-to-severe psoriasis, and is an ideal candidate to develop systems to predict who will respond to therapy. A clinical trial of 22 patients with moderate to severe psoriasis treated with alefacept (7.5mg weekly i.v. x12 weeks) was conducted in 2002-2003, as a mechanism of action study. Patients were classified as responders or non-responders to alefacept based on histological criteria. Microarray data from PBMCs of 16 of these patients was analyzed to generate a treatment response classifier. We used a discriminant analysis method that performs sample classification from gene expression data, via nearest shrunken centroid method''. A disease response classifier using 23 genes was created to accurately predict response to alefacept (12.3% error rate in favour of responders). This preliminary study may provide a useful tool to predict response of psoriatic patients with alefacept. Keywords: class prediction Microarray data from 16 patients was analyzed to generate a treatment response classifier. We used a discriminant analysis method that performs sample classification from gene expression data, via nearest shrunken centroid method.

2010-10-02 | E-GEOD-18948 | biostudies-arrayexpress

Genome-wide DNA methylation analysis of peripheral blood samples of moderate-to-severe psoriasis patients treated with anti-TNF drugs

Project description:Genome wide DNA methylation profiling of peripheral blood samples of moderate-to-severe psoriasis patients treated with anti-TNF drugs. Patients were distributed on Excellent Responders (ER) if they achieved PASI90 (a 90% reduction with respect to baseline PASI) at 3 and 6 months of treatment with anti-TNF drugs and Partial responders if they did not achieve a PASI75 (a 75% reduction with respect to baseline PASI) at 3 and 6 months of treatment. The Illumina Infinium 450k Human DNA methylation Beadchip v1.2 was used to obtain DNA methylation profiles across approximately 485,000 CpGs in 49 ER and 21 PR which were obtained from peripheral blood samples of anti-TNF drug treated patients. We have searched for pharmaoepigenetic biomarkers of anti-TNF response in moderate-to-severe psoriasis patients.

2020-05-28 | GSE151278 | GEO

Transcription profiling of human whole blood from rheumatoid arthritis patients

Project description:The whole blood was collected pre-treatment from rheumatoid arthritis patients starting the anti_TNF therapy. All patients were naïve to anti_TNFs. The disease activity was measured using the DAS28 score at the pre-treatment visit1 (DAS28_v1) and 14 weeks after treatment visit3 (DAS28_v3). The response to the therapy was evaluated using the EULAR [European League Against Rheumatism] definition of the response. The objective of the data analysis was to identify gene expression coorelating with response as well as to identify genes that differentiate responders versus non-responders pre-treatment. The results of this investigation identified 8 trainscripts that predict responders vs. non-responders with 89% accuracy. Experiment Overall Design: Patients' response to anti-TNF was assessed using EULAR score and patients were classified as responders, moderate responders and non-responders. Genes correlating with the response status have been identified.

2009-09-18 | E-GEOD-15258 | biostudies-arrayexpress

Classifier analysis of gene expression profiles reveals a discriminatory gene set for soil containing different metals

Project description:Environmental pollution is a worldwide problem, and metals are the largest group of contaminants in soil. Microarray toxicogenomic studies with ecologically relevant organisms such as springtails, supplement traditional ecotoxicological research, but are presently rather descriptive. Classifier analysis, a more analytical application of the microarray technique, is able to predict biological classes of unknown samples. We used the uncorrelated shrunken centroid (USC) method to classify gene expression profiles of the springtail Folsomia candida exposed to soil spiked with six different metals (barium, cadmium, cobalt, chromium, lead, and zinc). We identified a gene set (classifier) of 188 genes that can discriminate between six different metals present in soil, which allowed us to predict the correct classes for samples of an independent test set with an accuracy of 83% (error rate = 0.17). This study shows further that in order to apply classifier analysis to actual contaminated field soil samples, more insight and information is needed on the transcriptional responses of soil organisms to different soil types (properties) and mixtures of contaminants.

2010-02-05 | GSE19353 | GEO

Transcription profiling of CD34 positive mononuclear cells from drug naive, chronic myeloid leukemia (CML) patients to determine an expression signature predictive of a major cytogenic response in CML patients treated with Imatinib.

Project description:Newly diagnosed chronic phase chronic myeloid leukemia (CML) patients with a major cytogenetic response (MCyR) after 12 months of imatinib therapy have an excellent long-term outcome, while patients without MCyR have a high progression risk. Since patients with primary cytogenetic resistance may benefit from more intensive therapy up-front, we sought to identify biomarkers to predict MCyR. Keywords: Two group comparison to identify trasncriptomic signature that predicts response to therapy CD34+ cells were isolated from cryopreserved mononuclear cells of chronic phase CML patients with a complete cytogenetic response (CCyR) or >65% Ph-positive metaphases after 12 months of imatinib therapy (training set N=36). Gene expression profiles generated on amplified RNA using Affymetrix HG-U133 Plus 2.0 arrays were compared between responders and non-responders, using the criteria ANOVA p<0.1 and fold difference >I1.5I. A minimal response classifier derived from the comparison was used to predict response in a prospectively collected validation set using same criteria for responders/nonresponders (N=23).

2009-12-31 | E-GEOD-14671 | biostudies-arrayexpress

Molecular profiling of pre-and post-5-azacytidine myelodysplastic syndrome samples identifies predictors of response

Project description:Treatment with the hypomethylating agent 5-azacytidine (AZA) increases survival in high-risk (HR) myelodysplastic syndrome (MDS) patients, but predicting patient response and overall survival remains challenging. To address these issues, we analyzed mutational and transcriptional profiles in CD34+ hematopoietic stem/progenitor cells (HSPCs) before and following AZA therapy in MDS patients. AZA treatment led to a greater reduction in the mutational burden in both blast and hematological responders than non-responders. Blast and hematological responders showed transcriptional evidence of pre-treatment enrichment for pathways such as oxidative phosphorylation, MYC targets, and mTORC1 signaling. While blast non-response was associated with TNFa signaling and leukemia stem cell signature, hematological non-response was associated with cell-cycle related pathways. AZA induced similar transcriptional responses in MDS patients regardless of response type. Comparison of blast responders and non-responders to normal controls, allowed us to generate a transcriptional classifier that could predict AZA response and survival. This classifier outperformed a previously developed gene signature in a second MDS patient cohort, but signatures of hematological responses were unable to predict survival. Overall, these studies characterize the molecular consequences of AZA treatment in MDS HSPCs and identify a potential tool for predicting AZA therapy responses and overall survival prior to initiation of therapy.

2024-07-21 | GSE272173 | GEO

Classifier analysis of gene expression profiles reveals a discriminatory gene set for soil containing different metals

2010-02-04 | E-GEOD-19353 | biostudies-arrayexpress

Cellular and Molecular Changes in Psoriasis Lesions Induced by Ustekinumab: Distinct Differences in Responders vs. Non-Responders

Project description:A gene expression profiling sub-study was conducted in which skin biopsy samples (n=192) were collected for RNA extraction and hybridization to microarrays from patients with moderate-to-severe psoriasis who participated in ACCEPT, an IRB-approved Phase 3, multicenter, randomized trial. This analysis identified gene expressions significantly modulated in psoriasis lesions (LS) following ustekinumab or etanercept treatment at week 12 compared to baseline. Molecular expression of mRNA was found to be different in ustekinumab PASI75 responders vs. nonresponders. Differential modulation of selected mRNAs was also observed between ustekinumab and etanercept PASI75 responders.

2019-02-01 | GSE106992 | GEO

Specific molecular signatures underlie response to decitabine in CMML [RNA-seq]

Project description:Myelodysplastic syndromes and chronic myelomonocytic leukemia (CMML) are characterized by mutations in epigenetic modifiers and aberrant DNA methylation. DNA methyltransferase inhibitors (DMTis) are used to treat these disorders, but response is highly variable with few means to predict which patients will benefit. To develop a molecular means of predicting response at diagnosis, we examined baseline differences in mutations, DNA methylation, and gene expression in 40 CMML patients responsive and resistant to decitabine (DAC). While somatic mutations did not differentiate responders and non-responders, we were able to identify for the first time 158 differentially methylated regions (DMRs) at baseline between responders and non-responders using next-generation sequencing. These DMRs were primarily localized to non-promoter regions and overlapped with distal regulatory enhancers. Using the methylation profiles, we developed an epigenetic classifier that accurately predicted DAC response at the time of diagnosis. We also found 53 differentially expressed genes between responders and non-responders. Genes up-regulated in responders were enriched in the cell cycle, potentially contributing to effective DAC incorporation. Two chemokines overexpressed in non-responders -- CXCL4 and CXCL7 -- were able to block the effect of DAC on normal CD34+ and primary CMML cells in vitro, suggesting their up-regulation contributes to primary DAC resistance. mRNA profiling in bone marrow mononuclear cells (BM MNC) from 14 CMML patients (8 decitabine responders vs. 6 non-responders).

2015-03-31 | E-GEOD-61162 | biostudies-arrayexpress

Specific molecular signatures underlie response to decitabine in CMML [ERRBS]

Project description:Myelodysplastic syndromes and chronic myelomonocytic leukemia (CMML) are characterized by mutations in epigenetic modifiers and aberrant DNA methylation. DNA methyltransferase inhibitors (DMTis) are used to treat these disorders, but response is highly variable with few means to predict which patients will benefit. To develop a molecular means of predicting response at diagnosis, we examined baseline differences in mutations, DNA methylation, and gene expression in 40 CMML patients responsive and resistant to decitabine (DAC). While somatic mutations did not differentiate responders and non-responders, we were able to identify for the first time 158 differentially methylated regions (DMRs) at baseline between responders and non-responders using next-generation sequencing. These DMRs were primarily localized to non-promoter regions and overlapped with distal regulatory enhancers. Using the methylation profiles, we developed an epigenetic classifier that accurately predicted DAC response at the time of diagnosis. We also found 53 differentially expressed genes between responders and non-responders. Genes up-regulated in responders were enriched in the cell cycle, potentially contributing to effective DAC incorporation. Two chemokines overexpressed in non-responders -- CXCL4 and CXCL7 -- were able to block the effect of DAC on normal CD34+ and primary CMML cells in vitro, suggesting their up-regulation contributes to primary DAC resistance. DNA methylation profiling in bone marrow mononuclear cells (BM MNC) from 39 CMML patients (19 decitabine responders vs. 20 non-responders).

2015-03-31 | E-GEOD-61161 | biostudies-arrayexpress

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