Combinatorial high-throughput experimental and bioinformatic approach identifies molecular pathways linked with the sensitivity to anticancer target drugs
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Larisa Venkova1,2, Alexander Aliper1,3, Maria Suntsova1,2,3, Roman Kholodenko4, Denis Shepelin1,4, Nicolas Borisov1,2, Galina Malakhova4, Raif Vasilov5, Sergey Roumiantsev3,6,7, Alex Zhavoronkov3,8, Anton Buzdin3,4,5
1Drug Research and Design Department, Pathway Pharmaceuticals, Wan Chai, Hong Kong, Hong Kong SAR
2Department of Personalized Medicine, First Oncology Research and Advisory Center, Moscow, Russia
3Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
4Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
5National Research Centre “Kurchatov Institute”, Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, Moscow, Russia
6Pirogov Russian National Research Medical University, Department of Oncology, Hematology and Radiotherapy, Moscow, Russia
7Moscow Institute of Physics and Technology, Department of Translational and Regenerative Medicine, Dolgoprudny, Moscow Region, Russia
8Insilico Medicine, Inc, ETC, Johns Hopkins University, Baltimore, MD, USA
Anton Buzdin, e-mail: firstname.lastname@example.org
Keywords: anticancer target drugs, cancer, signaling pathway, metabolic pathway, gene expression
Received: January 17, 2015 Accepted: July 17, 2015 Published: July 30, 2015
Effective choice of anticancer drugs is important problem of modern medicine. We developed a method termed OncoFinder for the analysis of new type of biomarkers reflecting activation of intracellular signaling and metabolic molecular pathways. These biomarkers may be linked with the sensitivity to anticancer drugs. In this study, we compared the experimental data obtained in our laboratory and in the Genomics of Drug Sensitivity in Cancer (GDS) project for testing response to anticancer drugs and transcriptomes of various human cell lines. The microarray-based profiling of transcriptomes was performed for the cell lines before the addition of drugs to the medium, and experimental growth inhibition curves were built for each drug, featuring characteristic IC50 values. We assayed here four target drugs - Pazopanib, Sorafenib, Sunitinib and Temsirolimus, and 238 different cell lines, of which 11 were profiled in our laboratory and 227 - in GDS project. Using the OncoFinder-processed transcriptomic data on ~600 molecular pathways, we identified pathways showing significant correlation between pathway activation strength (PAS) and IC50 values for these drugs. Correlations reflect relationships between response to drug and pathway activation features. We intersected the results and found molecular pathways significantly correlated in both our assay and GDS project. For most of these pathways, we generated molecular models of their interaction with known molecular target(s) of the respective drugs. For the first time, our study uncovered mechanisms underlying cancer cell response to drugs at the high-throughput molecular interactomic level.
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