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Cancers (Basel). 2018 Sep 29;10(10). pii: E365. doi: 10.3390/cancers10100365.

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

Author information

1
National Research Centre "Kurchatov Institute", Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, 1 Akademika Kurchatova pl., Moscow 123182, Russia. sorokin.maks@gmail.com.
2
Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow 119991, Russia. sorokin.maks@gmail.com.
3
OmicsWay Corp., Walnut, CA 91798, USA. sorokin.maks@gmail.com.
4
Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia. khol@mail.ru.
5
D. Rogachev Federal Research Center of Pediatric Hematology, Oncology and Immunology, 1 Samory Mashela str., Moscow 117997, Russia. suntsova86@mail.ru.
6
Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia. galina_vm@mail.ru.
7
OmicsWay Corp., Walnut, CA 91798, USA. garazha@oncobox.com.
8
Orekhovich Institute of Biomedical Chemistry, Moscow 119832, Russia. irkhol@yandex.ru.
9
Center of Personalized Oncology, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow 119991, Russia. podd-elena@ya.ru.
10
Clinical Center Vitamed, Moscow 121309, Russia. podd-elena@ya.ru.
11
Kaluga Regional Oncological Hospital, Kaluga 248007, Russia. lantsov@mail.ru.
12
N.N. Blokhin Russian Cancer Research Center, Moscow 115478, Russia. info@ponkc.com.
13
N.N. Blokhin Russian Cancer Research Center, Moscow 115478, Russia. arhiri@mail.ru.
14
State Research Center-Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency, Moscow 123098, Russia. andreyan.osipov@gmail.com.
15
Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow 119991, Russia. buzdin@oncobox.com.
16
OmicsWay Corp., Walnut, CA 91798, USA. buzdin@oncobox.com.
17
Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia. buzdin@oncobox.com.

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.

KEYWORDS:

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

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