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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

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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

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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.

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

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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.

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

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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).

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