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Oncobox Bioinformatical Platform for Selecting Potentially Effective Combinations of Target Cancer Drugs Using High-Throughput Gene Expression Data
Sequential courses of anticancer target therapy lead to selection of drug-resistant cells, which results in continuous decrease of clinical response. Here we present a new approach for predicting effective combinations of target drugs, which act in a synergistic manner. Synergistic combinations of d...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6209915/ https://www.ncbi.nlm.nih.gov/pubmed/30274248 http://dx.doi.org/10.3390/cancers10100365 |
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author | Sorokin, Maxim Kholodenko, Roman Suntsova, Maria Malakhova, Galina Garazha, Andrew Kholodenko, Irina Poddubskaya, Elena Lantsov, Dmitriy Stilidi, Ivan Arhiri, Petr Osipov, Andreyan Buzdin, Anton |
author_facet | Sorokin, Maxim Kholodenko, Roman Suntsova, Maria Malakhova, Galina Garazha, Andrew Kholodenko, Irina Poddubskaya, Elena Lantsov, Dmitriy Stilidi, Ivan Arhiri, Petr Osipov, Andreyan Buzdin, Anton |
author_sort | Sorokin, Maxim |
collection | PubMed |
description | Sequential courses of anticancer target therapy lead to selection of drug-resistant cells, which results in continuous decrease of clinical response. Here we present a new approach for predicting effective combinations of target drugs, which act in a synergistic manner. Synergistic combinations of drugs may prevent or postpone acquired resistance, thus increasing treatment efficiency. We cultured human ovarian carcinoma SKOV-3 and neuroblastoma NGP-127 cancer cell lines in the presence of Tyrosine Kinase Inhibitors (Pazopanib, Sorafenib, and Sunitinib) and Rapalogues (Temsirolimus and Everolimus) for four months and obtained cell lines demonstrating increased drug resistance. We investigated gene expression profiles of intact and resistant cells by microarrays and analyzed alterations in 378 cancer-related signaling pathways using the bioinformatical platform Oncobox. This revealed numerous pathways linked with development of drug resistant phenotypes. Our approach is based on targeting proteins involved in as many as possible signaling pathways upregulated in resistant cells. We tested 13 combinations of drugs and/or selective inhibitors predicted by Oncobox and 10 random combinations. Synergy scores for Oncobox predictions were significantly higher than for randomly selected drug combinations. Thus, the proposed approach significantly outperforms random selection of drugs and can be adopted to enhance discovery of new synergistic combinations of anticancer target drugs. |
format | Online Article Text |
id | pubmed-6209915 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-62099152018-11-02 Oncobox Bioinformatical Platform for Selecting Potentially Effective Combinations of Target Cancer Drugs Using High-Throughput Gene Expression Data Sorokin, Maxim Kholodenko, Roman Suntsova, Maria Malakhova, Galina Garazha, Andrew Kholodenko, Irina Poddubskaya, Elena Lantsov, Dmitriy Stilidi, Ivan Arhiri, Petr Osipov, Andreyan Buzdin, Anton Cancers (Basel) Article Sequential courses of anticancer target therapy lead to selection of drug-resistant cells, which results in continuous decrease of clinical response. Here we present a new approach for predicting effective combinations of target drugs, which act in a synergistic manner. Synergistic combinations of drugs may prevent or postpone acquired resistance, thus increasing treatment efficiency. We cultured human ovarian carcinoma SKOV-3 and neuroblastoma NGP-127 cancer cell lines in the presence of Tyrosine Kinase Inhibitors (Pazopanib, Sorafenib, and Sunitinib) and Rapalogues (Temsirolimus and Everolimus) for four months and obtained cell lines demonstrating increased drug resistance. We investigated gene expression profiles of intact and resistant cells by microarrays and analyzed alterations in 378 cancer-related signaling pathways using the bioinformatical platform Oncobox. This revealed numerous pathways linked with development of drug resistant phenotypes. Our approach is based on targeting proteins involved in as many as possible signaling pathways upregulated in resistant cells. We tested 13 combinations of drugs and/or selective inhibitors predicted by Oncobox and 10 random combinations. Synergy scores for Oncobox predictions were significantly higher than for randomly selected drug combinations. Thus, the proposed approach significantly outperforms random selection of drugs and can be adopted to enhance discovery of new synergistic combinations of anticancer target drugs. MDPI 2018-09-29 /pmc/articles/PMC6209915/ /pubmed/30274248 http://dx.doi.org/10.3390/cancers10100365 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sorokin, Maxim Kholodenko, Roman Suntsova, Maria Malakhova, Galina Garazha, Andrew Kholodenko, Irina Poddubskaya, Elena Lantsov, Dmitriy Stilidi, Ivan Arhiri, Petr Osipov, Andreyan Buzdin, Anton Oncobox Bioinformatical Platform for Selecting Potentially Effective Combinations of Target Cancer Drugs Using High-Throughput Gene Expression Data |
title | Oncobox Bioinformatical Platform for Selecting Potentially Effective Combinations of Target Cancer Drugs Using High-Throughput Gene Expression Data |
title_full | Oncobox Bioinformatical Platform for Selecting Potentially Effective Combinations of Target Cancer Drugs Using High-Throughput Gene Expression Data |
title_fullStr | Oncobox Bioinformatical Platform for Selecting Potentially Effective Combinations of Target Cancer Drugs Using High-Throughput Gene Expression Data |
title_full_unstemmed | Oncobox Bioinformatical Platform for Selecting Potentially Effective Combinations of Target Cancer Drugs Using High-Throughput Gene Expression Data |
title_short | Oncobox Bioinformatical Platform for Selecting Potentially Effective Combinations of Target Cancer Drugs Using High-Throughput Gene Expression Data |
title_sort | oncobox bioinformatical platform for selecting potentially effective combinations of target cancer drugs using high-throughput gene expression data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6209915/ https://www.ncbi.nlm.nih.gov/pubmed/30274248 http://dx.doi.org/10.3390/cancers10100365 |
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