Protemic Cellular Signatures of Kinase Inhibitor-Induced Cardiotoxicity: Mount Sinai DToxS LINCS Center Dataset

Examples of protein quantification via SDS-PAGE. Twenty five micrograms of HeLa cell proteins was loaded along with the proteins recovered from the KI-drug treated cell line A and separated on 10% SDS-PAGE gels. The gel was stained with CBB and the densities of the CBB stain from all lanes are measured. The protein CBB densities in drug-treated samples were compared to that of the HeLa cells, and the protein amounts in each sample was then estimated as described in Supplementary Table 2.

Protemic Cellular Signatures of Kinase Inhibitor-Induced Cardiotoxicity: Mount Sinai…

Abstract  The Drug Toxicity Signature Generation Center (DToxS) at the Icahn School of Medicine at Mount Sinai is one of the centers 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 can predict adverse effects, especially cardiotoxicity, of kinase inhibitors approved by the Food and Drug Administration. Towards this goal, high throughput shot-gun proteomics experiments (317 cell line/drug combinations + 64 control lysates) have been conducted at the Center for Advanced Proteomics Research at Rutgers University – New Jersey Medical School. Using computational network analyses, these proteomic data can be integrated with transcriptomic signatures generated in tandem to identify cellular signatures of cardiotoxicity that may predict kinase inhibitor-induced toxicity and possible mitigation. Both raw and processed proteomics data have been carefully screened for quality and made publicly available via the PRIDE database. As such, this broad protein kinase inhibitor-stimulated cardiomyocyte proteomic data and signature set is valuable for the prediction of drug toxicities.


Proteomic cellular signatures of kinase inhibitor-induced cardiotoxicity: Mount Sinai DToxS LINCS Center Dataset, Yuguang Xiong, Tong Liu, Tong Chen, Jens Hansen, Bin Hu, Yibang Chen, Gomathi Jayaraman, Stephan Schürer, Dusica Vidovic, Joseph Goldfarb, Eric A. Sobie, Marc R. Birtwistle, Ravi Iyengar, Hong Li, Evren U. Azeloglu bioRxiv 2020.02.26.966606; doi: