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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
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Oncobox Bioinformatical Platform for Selecting Potentially Effective Combinations of Target Cancer Drugs Using

Maxim Sorokin1,2,3, Roman Kholodenko4, Maria Suntsova5

  • 1National 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.

Cancers
|October 3, 2018
PubMed

Insights

This study introduces a novel method to predict synergistic drug combinations for cancer therapy, aiming to overcome drug resistance. The approach identifies key proteins in upregulated pathways of resistant cells, significantly improving treatment efficacy.

Area of Science:

  • Oncology
  • Pharmacology
  • Bioinformatics

Background:

  • Sequential anticancer targeted therapies often lead to drug resistance, reducing clinical effectiveness.
  • Acquired resistance in cancer cells necessitates novel therapeutic strategies to maintain treatment efficacy.

Purpose of the Study:

  • To develop and validate a new approach for predicting synergistic anticancer drug combinations.
  • To identify drug combinations that can prevent or delay acquired drug resistance.

Main Methods:

  • Cultured human ovarian carcinoma (SKOV-3) and neuroblastoma (NGP-127) cell lines with Tyrosine Kinase Inhibitors and Rapalogues to induce resistance.
  • Analyzed gene expression profiles of sensitive and resistant cells using microarrays.
  • Utilized the bioinformatic platform Oncobox to analyze 378 cancer-related signaling pathways and identify upregulated pathways in resistant cells.
  • Tested predicted synergistic drug combinations against random combinations.

Main Results:

  • Identified numerous signaling pathways associated with the development of drug-resistant phenotypes.
  • The proposed approach, targeting proteins in multiple upregulated pathways, yielded significantly higher synergy scores compared to random drug combinations.
  • Predicted combinations demonstrated superior synergy over random selections.

Conclusions:

  • The novel approach effectively predicts synergistic anticancer drug combinations.
  • This method can enhance the discovery of new drug combinations to combat acquired resistance and improve cancer treatment outcomes.
Keywords:
bioinformaticscancerdrug resistant tumor cellsmolecular pathwaysrapaloguessynergistic combinationstarget therapeuticstyrosine kinase inhibitors

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