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In silico identification of drug target pathways in breast cancer subtypes using pathway cross-talk inhibition

Claudia Cava1, Gloria Bertoli1, Isabella Castiglioni2

  • 1Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.Cervi 93, Segrate, 20090, Milan, Italy.

Journal of Translational Medicine
|June 7, 2018
PubMed
Abstract

Insights

This study introduces a computational method to identify new drug targets in breast cancer (BC) subtypes by analyzing pathway interactions. This approach aims to overcome treatment failure and drug resistance in heterogeneous cancers.

Area of Science:

  • Computational biology
  • Cancer research
  • Genomics

Background:

  • Treatment failure and drug resistance are significant challenges in managing solid cancers like breast cancer (BC).
  • Multiple mechanisms contribute to therapeutic resistance, limiting treatment efficacy.

Purpose of the Study:

  • To optimize a computational method for discovering novel drug target pathways in cancer subtypes.
  • To utilize pathway cross-talk inhibition (PCI) for identifying new therapeutic strategies.

Main Methods:

  • Developed and optimized an in silico method based on detecting and quantifying pathway cross-talk in distinct cancer subtypes.
  • Analyzed The Cancer Genome Atlas (TCGA) breast cancer dataset and validated findings using an independent Gene Expression Omnibus (GEO) dataset.
  • Predicted drug effects by computationally silencing individual or combined subtype-derived pathways.

Main Results:

  • Identified distinct networks of cross-talking pathways specific to different breast cancer subtypes.
  • Discovered a set of novel potential drug target pathways for various BC subtypes.
  • Demonstrated that therapeutic agents acting on these pathways can synergistically inhibit cross-talk and exhibit anti-tumor effects.

Conclusions:

  • In silico methods employing pathway cross-talk inhibition (PCI) offer a promising approach for identifying tailored treatments.
  • This strategy holds potential for improving therapeutic outcomes in heterogeneous cancers like breast cancer.
Keywords:
Breast cancerClassificationDrugsMonte Carlo cross-validationPathway cross-talk inhibitionSubtypes

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