Project description:Classification of a large micro-array dataset. Algorithm comparison and analysis of drug signatures. These data support the publication titled "Classification of a large micro-array dataset. Algorithm comparison and analysis of drug signatures.". Some of the calculations in the publication were derived from an older version of the data available at http://www.iconixpharm.com Copyright (c) 2005 by Iconix Pharmaceuticals, Inc. Guidelines for commercial use: http://www.iconixbiosciences.com/guidelineCommUse.pdf Keywords: other
Project description:Classification of a large micro-array dataset. Algorithm comparison and analysis of drug signatures. These data support the publication titled "Classification of a large micro-array dataset. Algorithm comparison and analysis of drug signatures.". Some of the calculations in the publication were derived from an older version of the data available at http://www.iconixpharm.com Copyright (c) 2005 by Iconix Pharmaceuticals, Inc. Guidelines for commercial use: http://www.iconixbiosciences.com/guidelineCommUse.pdf
Project description:we present a novel path to drug repurposing to identify new immunotherapies for ASCVD. The integration of time of-flight mass cytometry (CyTOF) and RNA-sequencing identified unique inflammatory signatures in peripheral blood mononuclear cells (PBMCs) stimulated with ASCVD plasma. By comparing these inflammatory signatures to large-scale gene expression data from the LINCS L1000 dataset, we identified drugs that could reverse this inflammatory response. In conclusion, a systems immunology-driven drug repurposing with pre- clinical validation strategy can aid the development of new cardiovascular immunotherapies.
Project description:we present a novel path to drug repurposing to identify new immunotherapies for ASCVD. The integration of time of-flight mass cytometry (CyTOF) and RNA-sequencing identified unique inflammatory signatures in peripheral blood mononuclear cells (PBMCs) stimulated with ASCVD plasma. By comparing these inflammatory signatures to large-scale gene expression data from the LINCS L1000 dataset, we identified drugs that could reverse this inflammatory response. In conclusion, a systems immunology-driven drug repurposing with pre- clinical validation strategy can aid the development of new cardiovascular immunotherapies.
Project description:we present a novel path to drug repurposing to identify new immunotherapies for ASCVD. The integration of time of-flight mass cytometry (CyTOF) and RNA-sequencing identified unique inflammatory signatures in peripheral blood mononuclear cells (PBMCs) stimulated with ASCVD plasma. By comparing these inflammatory signatures to large-scale gene expression data from the LINCS L1000 dataset, we identified drugs that could reverse this inflammatory response. In conclusion, a systems immunology-driven drug repurposing with pre- clinical validation strategy can aid the development of new cardiovascular immunotherapies.
Project description:Small regulatory RNAs including small interfering RNAs (siRNAs) and microRNAs (miRNAs) guide Argonaute (Ago) proteins to specific target RNAs leading to mRNA destabilization or translational repression. We recently reported the identification of Importin 8 (Imp8) as a novel component of miRNA-guided regulatory pathways. Imp8 interacts with Ago proteins and localizes to cytoplasmic processing bodies (P-bodies), structures involved in RNA metabolism. For this micro-array dataset, we used immunoprecipitations of Ago2-associated mRNAs followed by micro-array analysis. The results demonstrate that Imp8 is required for recruiting Ago protein complexes to a large set of Ago2-associated target mRNAs allowing for efficient and specific gene silencing. Therefore, we provide evidence that Imp8 is required for cytoplasmic miRNA-guided gene silencing.
Project description:Histone modification H3K9me2 is associated with gene silencing and forming large heterochromatin domains. But the micro-structure within large H3K9me2 domains and their relationship with DNA methylation remains unclear. This dataset was generated to compare with genome-wide DNA methylation data.
Project description:Tumor heterogeneity is a major challenge to the treatment of colorectal cancer (CRC). Recently, a transcriptome-based classification was developed, segregating CRC into four consensus molecular subtypes (CMS) with distinct biological and clinical characteristics. Here, we applied the CMS classification on CRC cell lines to identify novel subtype-specific drug vulnerabilities. We combined publicly available transcriptome data from multiple resources to assign 159 CRC cell lines to CMS. By integrating results from large scale drug screens, we discovered that CMS1 cancer is highly vulnerable to the survivin suppressor YM155. We confirmed our results using an independent panel of CRC cell lines and demonstrate a 100-fold higher sensitivity of CMS1 lines. This vulnerability was specific to YM155 and not observed for commonly used chemotherapeutic agents. In CMS1 cancer, low concentrations of YM155 induced apoptosis and expression signatures associated with NFkappaB and ER stress mediated apoptosis signaling. Using a genome-wide CRISPR/Cas9 screen, we discovered a novel role of genes involved in LDL-receptor recycling as modulators of YM155 response in CMS1 CRC. Our work shows that combining drug response data with CMS classification in cell lines can reveal specific vulnerabilities and propose YM155 as novel CMS1 specific drug.
Project description:In an effort to develop a genomics-based approach to the prediction of drug response, we have developed an algorithm for classification of cell line chemosensitivity based on gene expression profiles alone. Using oligonucleotide microarrays, the expression levels of 6,817 genes were measured in a panel of 60 human cancer cell lines (the NCI-60) for which the chemosensitivity profiles of thousands of chemical compounds have been determined. We sought to determine whether the gene expression signatures of untreated cells were sufficient for the prediction of chemosensitivity. Gene expression-based classifiers of sensitivity or resistance for 232 compounds were generated and then evaluated on independent sets of data. The classifiers were designed to be independent of the cells' tissue of origin. The accuracy of chemosensitivity prediction was considerably better than would be expected by chance. Eighty-eight of 232 expression-based classifiers performed accurately (with P < 0.05) on an independent test set, whereas only 12 of the 232 would be expected to do so by chance. These results suggest that at least for a subset of compounds genomic approaches to chemosensitivity prediction are feasible. golub-00299 Assay Type: Gene Expression Provider: Affymetrix Array Designs: Hu6800 Organism: Homo sapiens (ncbitax) Tissue Sites: Kidney, Brain, Blood, Lung, Skin, Colon, Breast, Prostate, Ovary Material Types: cell, synthetic_RNA, whole_organism, total_RNA Disease States: Carcinoma, Glioblastoma, Lymphoma, Adenocarcinoma, Melanoma, Amelanotic Melanoma, Large Cell Carcinoma, Precursor Lymphoblastic Leukemia, Carcinosarcoma, Myeloid Leukemia, Plasma Cell Myeloma, Bronchogenic Carcinoma, Chronic Myelogenous Leukemia, Squamous Cell Carcinoma