Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Proteomic cellular signatures of kinase inhibitor-induced cardiotoxicity

ABSTRACT: Drug Toxicity Signature Generation Center (DToxS) at the Icahn School of Medicine at Mount Sinai is an integral part of the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. A key aim of DToxS is to generate both proteomic and transcriptomic signatures that cab predict adverse effects, especially cardiotoxicity, of drugs approved by the Food and Drug Administration. Towards this goal, high throughput shot-gun proteomics experiments (308 cell line/drug combinations + 64 HeLa control lysates + 9 auxiliary treatment samples) have been conducted at the Center for Advanced Proteomics Research at Rutgers-New Jersey Medical School. The integrated proteomic and transcriptomic signatures have been used for computational network analysis to identify cellular signatures of cardiotoxicity that may predict drug-induced toxicity and possible mitigation of such toxicities by mixing different drugs. Both raw and processed proteomics data have been carefully controlled for quality and have been made publicly available via the PRoteomics IDEntifications (PRIDE) database. As such, this broad drug-stimulated proteomic dataset is valuable for the prediction drug toxicities and their mitigation.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture, Cardiac Muscle Cell

SUBMITTER: Yuguang Xiong

LAB HEAD: Hong Li [1] and Evren U. Azeloglu [2]

PROVIDER: PXD014791 | Pride | 2021-10-18

REPOSITORIES: Pride

ACCESS DATA

Json Xml

Similar Datasets

Proteomic cellular signatures of kinase inhibitor-induced cardiotoxicity

Project description:Drug Toxicity Signature Generation Center (DToxS) at the Icahn School of Medicine at Mount Sinai is an integral part of the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. A key aim of DToxS is to generate both proteomic and transcriptomic signatures that cab predict adverse effects, especially cardiotoxicity, of drugs approved by the Food and Drug Administration. Towards this goal, high throughput shot-gun proteomics experiments on 308 cell line/drug combinations (from four primary human cardiomyocyte cell lines purchased from PromoCell), 64 HeLa control lysates, and 9 auxiliary treatment samples have been conducted at the Center for Advanced Proteomics Research at Rutgers-New Jersey Medical School. The integrated proteomic and transcriptomic signatures have been used for computational network analysis to identify cellular signatures of cardiotoxicity that may predict drug-induced toxicity and possible mitigation of such toxicities by mixing different drugs. Both raw and processed proteomics data have been carefully controlled for quality and have been made publicly available via the PRoteomics IDEntifications (PRIDE) database. As such, this broad drug-stimulated proteomic dataset is valuable for the prediction drug toxicities and their mitigation. All data are available through CC BY 4.0 open license.

2020-03-02 | LDS-1575 | LINCS

Drug Combination Signatures for Prediction and Mitigation of Toxicity.

Project description:Drug Combination Signatures for Prediction and Mitigation of Toxicity.

| PRJNA731502 | ENA

Drug Combination Signatures for Prediction and Mitigation of Toxicity

Project description:Drug Combination Signatures for Prediction and Mitigation of Toxicity

| PRJNA657744 | ENA

Drug Combination Signatures for Prediction and Mitigation of Toxicity [single cell]

Project description:Drug Combination Signatures for Prediction and Mitigation of Toxicity [single cell]

| PRJNA733493 | ENA

Major MLL fusion partners regulate promoter-proximal pausing and release of RNA polymerase II

Project description:1Sheng Yushou Center of Cell Biology and Immunology, Department of Genetics and Developmental Biology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. 2Laboratory of Biochemistry and Molecular Biology, The Rockefeller University, New York, NY 10065, USA. 3Systems Biology Center, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA. 4CCTS Bioinformatic Program, The Rockefeller University, New York, NY 10065, USA. 5State Key Laboratory of Genetic Engineering & Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200438, China

2021-06-30 | GSE84157 | GEO

Enterococcus faecalis reconfigure the activation of its transcriptional regulatory networks under different copper exposure levels

Project description:The microarrays experiments was performed with the purpose of identify transcriptional networks activated by copper. This experiment correspond the work tituled Enterococcus faecalis reconfigure the activation of its transcriptional regulatory networks under different copper exposure levels (work in preparation).Mauricio Latorrea,b, Jessica Galloway-Peñac,d,e, Jung Hyeo Rhoc,d, Marko Budinichf, Barbara E. Murrayc,d,e, Alejandro Maassb,f, Mauricio Gonzáleza,b,f*. a INTA, Laboratorio de Bioinformática y Expresión Génica, INTA, Universidad de Chile, Santiago, Chile. b Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile. c Division of Infectious Disease, Department of Medicine, University of Texas Medical School, Houston, Texas, United States of America. d Center for the Study of Emerging and Reemerging Pathogens, University of Texas Medical School, Houston, Texas, United States of America. e Department of Microbiology and Molecular Genetics, University of Texas Medical School, Houston, Texas, United States of America f Mathomics, Center for Mathematical Modeling (UMI2807CNRS), Santiago, Chile. * Corresponding author. Address: El Líbano 5524, Santiago 11, Chile. Fax: +56 (2) 2214030.

2013-11-12 | GSE45131 | GEO

Identification of drug associated genes in ovarian cancer by RNA profiling

Project description:Two expression profilings were conducted in order to identify drug-associated genes in ovarian cancers. The first expression profiling was performed between RNAs from chemosensitive ovarian cancers and chemoresistant ovarian cancers. The second analysis was using a drug sensitive ovarian cancer cell line and a multi-drug resistant ovarian cancer cell line.

2010-06-30 | E-GEOD-2058 | biostudies-arrayexpress

Prinary breast cancers and brain metastasis

Project description:Sixteen paired matched samples from primary breast cancers and brain metastases diagnosed between April 1, 2001 and December 31, 2012 were collected from 8 institutions. Brain metastases were identified based on magnetic resonance imaging and/or computed tomography findings. The clinical characteristics of all the patients were obtained from their medical records. This study was approved by the institutional review board of each participating institute (Tokai University School of Medicine; National Hospital Organization Osaka National Hospital; Kinki University School of Medicine; Niigata Cancer Center Hospital; Shizuoka General Hospital; Hokkaido Cancer Center; National Hospital Organization, Tokyo Medical Center; and Gunma Prefectural Cancer Center). Matching primary breast cancers and brain metastases Formalin-Fixed Paraffin-Embedded (FFPE) specimens for gene expression analysis were collected into RNA. RNA from specimens was isolated, and quantity and quality of the each RNA was using an Agilent 2100 Bioanalyzer (Agilent Technologies). Genome-wide expression levels of transcripts were analyzed using the Affymetrix U133A gene chips (Affymetrix) according to the manufacture’s instructions.

2019-09-25 | GSE125989 | GEO

Total protein profiles of human induced pluripotent stem cell-derived cardiomyocytes in response to four tyrosine kinase inhibitors

Project description:Drug-induced cardiotoxicity is a widespread clinical issue affecting numerous drug classes and remains difficult to treat. One such drug class is Tyrosine Kinase Inhibitors (TKIs), which cause varying degrees of contraction-related cardiotoxicity usually after weeks of exposure. Understanding molecular mechanisms underlying both acute and chronic toxicity of TKIs could help identify new treatment opportunities. Here, we presented transcriptome responses to four TKIs (Sunitinib, Sorafenib, Lapatinib and Erlotinib) across 3 doses and 4 time points in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). Gene expression evolved continually under drug treatment and revealed changes in several biological networks that were associated with cardiac metabolism and contraction. These changes were confirmed by proteomics and resulted in metabolic and structural remodeling of hiPSC-CMs. One of the metabolic remodeling was the increased aerobic glycolysis induced by Sorafenib, which is an adaptive response in preserving cell survival under Sorafenib treatment. Drug adaptation in cardiac cells may represent new targets for managing chronic forms of TKI-induced cardiotoxicity.

2019-05-16 | PXD012043 | Pride

RNA-seq analysis revealed that the cardiotoxicity caused by herceptin is due to the release of PTX3.

Project description:Herceptin, also known as trastuzumab, is currently approved as a targeted therapy for breast cancer and advanced gastric cancer. However, the clinical application of hercptin was limited by its severe cardiotoxicity. We found that the cardiotoxicity caused by Herceptin is due to the release of PTX3 by vascular endothelial cells, which leads to the decline of cardiac systolic function. This study found the role and mechanism of vascular endothelial cells in indirectly regulating cardiotoxicity, and provided a new idea for the study of clinical drug-induced cardiotoxicity.

2022-12-01 | GSE200325 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

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