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Assessment of Resistance to Tyrosine Kinase Inhibitors by an Interrogation of Signal Transduction Pathways by Antibody Arrays
Published on: September 19, 2018
Exploiting receptor tyrosine kinase co-activation for cancer therapy
Aik-Choon Tan1, Simon Vyse2, Paul H Huang2
1Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Abstract:
Studies over the past decade have shown that many cancers have evolved receptor tyrosine kinase (RTK) co-activation as a mechanism to drive tumour progression and limit the lethal effects of therapy. This review summarises the general principles of RTK co-activation and discusses approaches to exploit this phenomenon in cancer therapy and drug discovery. Computational strategies to predict kinase co-dependencies by integrating drug screening data and kinase inhibitor selectivity profiles will also be described. We offer a perspective on the implications of RTK co-activation on tumour heterogeneity and cancer evolution and conclude by surveying emerging computational and experimental approaches that will provide insights into RTK co-activation biology and deliver new developments in effective cancer therapies.
Insights
Receptor tyrosine kinase (RTK) co-activation drives cancer progression and therapy resistance. This review explores targeting RTK co-activation for novel cancer therapies and drug discovery, using computational methods to predict kinase dependencies.
Area of Science:
- Oncology
- Molecular Biology
- Pharmacology
Background:
- Receptor tyrosine kinases (RTKs) play crucial roles in cell signaling.
- Aberrant RTK signaling, specifically co-activation, is implicated in numerous cancers.
- RTK co-activation contributes to tumor progression and therapeutic resistance.
Purpose of the Study:
- To summarize the principles of RTK co-activation in cancer.
- To discuss therapeutic strategies targeting RTK co-activation.
- To explore computational approaches for predicting kinase co-dependencies.
Main Methods:
- Review of existing literature on RTK co-activation.
- Analysis of computational strategies integrating drug screening and kinase inhibitor profiles.
- Discussion of implications for tumor heterogeneity and cancer evolution.
Main Results:
- RTK co-activation is a significant mechanism in cancer progression and treatment failure.
- Computational methods can predict kinase co-dependencies.
- Understanding RTK co-activation offers insights into tumor evolution.
Conclusions:
- Targeting RTK co-activation presents a promising avenue for cancer therapy and drug discovery.
- Emerging computational and experimental approaches are key to advancing RTK co-activation biology.
- New developments in effective cancer therapies are anticipated.
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