Evidence mapPaperPMID 30274248Full record

ArticleCancers2018

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

Maxim Sorokin, Roman Kholodenko, Maria Suntsova, Galina Malakhova, Andrew Garazha, Irina Kholodenko, Elena Poddubskaya, Dmitriy Lantsov, Ivan Stilidi, Petr Arhiri and 2 more

Abstract read
In one paragraph

Article in Cancers, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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  13. Combination of Anti-Cancer Drugs with Molecular Chaperone Inhibitors.International journal of molecular sciences · 2019
    Review
  14. Article
  15. Article
  16. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Maxim SorokinNational 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.
Roman KholodenkoShemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia. khol@mail.ru.ORCID 0000-0001-6083-6588
Maria SuntsovaD. Rogachev Federal Research Center of Pediatric Hematology, Oncology and Immunology, 1 Samory Mashela str., Moscow 117997, Russia. suntsova86@mail.ru.
Galina MalakhovaShemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia. galina_vm@mail.ru.
Andrew GarazhaOmicsWay Corp., Walnut, CA 91798, USA. garazha@oncobox.com.
Irina KholodenkoOrekhovich Institute of Biomedical Chemistry, Moscow 119832, Russia. irkhol@yandex.ru.
Elena PoddubskayaCenter of Personalized Oncology, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow 119991, Russia. podd-elena@ya.ru.
Dmitriy LantsovKaluga Regional Oncological Hospital, Kaluga 248007, Russia. lantsov@mail.ru.
Ivan StilidiN.N. Blokhin Russian Cancer Research Center, Moscow 115478, Russia. info@ponkc.com.
Petr ArhiriN.N. Blokhin Russian Cancer Research Center, Moscow 115478, Russia. arhiri@mail.ru.
Andreyan OsipovState Research Center-Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency, Moscow 123098, Russia. andreyan.osipov@gmail.com.
Anton BuzdinLaboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow 119991, Russia. buzdin@oncobox.com.

Funding

Russian Science Foundation 17-74-10197
6 · The paper itself

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.

Indexed as

bioinformaticscancerdrug resistant tumor cellsmolecular pathwaysrapaloguessynergistic combinationstarget therapeuticstyrosine kinase inhibitors

Identifiers

PMID30274248
PMCPMC6209915

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.