Oncobox Bioinformatical Platform for Selecting Potentially Effective Combinations of Target Cancer Drugs Using High-Throughput Gene Expression Data

National Research Centre “Kurchatov Institute”, Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, 1 Akademika Kurchatova pl., Moscow 123182, Russia

Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow 119991, Russia

OmicsWay Corp., Walnut, CA 91798, USA

⁴

Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia

⁵

D. Rogachev Federal Research Center of Pediatric Hematology, Oncology and Immunology, 1 Samory Mashela str., Moscow 117997, Russia

⁶

Orekhovich Institute of Biomedical Chemistry, Moscow 119832, Russia

⁷

Center of Personalized Oncology, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow 119991, Russia

⁸

Clinical Center Vitamed, Moscow 121309, Russia

⁹

Kaluga Regional Oncological Hospital, Kaluga 248007, Russia

¹⁰

N.N. Blokhin Russian Cancer Research Center, Moscow 115478, Russia

¹¹

State Research Center-Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency, Moscow 123098, Russia

Show full affiliation list

Hide full affiliation list

Author to whom correspondence should be addressed.

Cancers 2018, 10(10), 365; https://doi.org/10.3390/cancers10100365

Received: 29 August 2018 / Revised: 24 September 2018 / Accepted: 25 September 2018 / Published: 29 September 2018

(This article belongs to the Special Issue Drug Resistance in Cancers)

View Full-Text Download PDF

Browse Figures

Citation Export

Abstract

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. View Full-Text

Keywords: target therapeutics; tyrosine kinase inhibitors; rapalogues; cancer; synergistic combinations; drug resistant tumor cells; bioinformatics; molecular pathways

▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Supplementary Material

Supplementary File 1:
ZIP-Document (ZIP, 486 KiB)

Share and Cite

MDPI and ACS Style

Sorokin, M.; Kholodenko, R.; Suntsova, M.; Malakhova, G.; Garazha, A.; Kholodenko, I.; Poddubskaya, E.; Lantsov, D.; Stilidi, I.; Arhiri, P.; Osipov, A.; Buzdin, A. Oncobox Bioinformatical Platform for Selecting Potentially Effective Combinations of Target Cancer Drugs Using High-Throughput Gene Expression Data. Cancers 2018, 10, 365. https://doi.org/10.3390/cancers10100365

AMA Style

Sorokin M, Kholodenko R, Suntsova M, Malakhova G, Garazha A, Kholodenko I, Poddubskaya E, Lantsov D, Stilidi I, Arhiri P, Osipov A, Buzdin A. Oncobox Bioinformatical Platform for Selecting Potentially Effective Combinations of Target Cancer Drugs Using High-Throughput Gene Expression Data. Cancers. 2018; 10(10):365. https://doi.org/10.3390/cancers10100365

Chicago/Turabian Style

Sorokin, Maxim, Roman Kholodenko, Maria Suntsova, Galina Malakhova, Andrew Garazha, Irina Kholodenko, Elena Poddubskaya, Dmitriy Lantsov, Ivan Stilidi, Petr Arhiri, Andreyan Osipov, and Anton Buzdin. 2018. "Oncobox Bioinformatical Platform for Selecting Potentially Effective Combinations of Target Cancer Drugs Using High-Throughput Gene Expression Data" Cancers 10, no. 10: 365. https://doi.org/10.3390/cancers10100365

Find Other Styles

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Abstract views Pdf views Html views

Article Menu

Oncobox Bioinformatical Platform for Selecting Potentially Effective Combinations of Target Cancer Drugs Using High-Throughput Gene Expression Data

Abstract

Supplementary Material

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI