Exploiting vulnerabilities in cancer signalling networks to combat targeted therapy resistance
Peter T Harrison1, Paul H Huang2
1Division of Molecular Pathology, The Institute of Cancer Research, London, U.K.
Abstract:
Drug resistance remains one of the greatest challenges facing precision oncology today. Despite the vast array of resistance mechanisms that cancer cells employ to subvert the effects of targeted therapy, a deep understanding of cancer signalling networks has led to the development of novel strategies to tackle resistance both in the first-line and salvage therapy settings. In this review, we provide a brief overview of the major classes of resistance mechanisms to targeted therapy, including signalling reprogramming and tumour evolution; our discussion also focuses on the use of different forms of polytherapies (such as inhibitor combinations, multi-target kinase inhibitors and HSP90 inhibitors) as a means of combating resistance. The promise and challenges facing each of these polytherapies are elaborated with a perspective on how to effectively deploy such therapies in patients. We highlight efforts to harness computational approaches to predict effective polytherapies and the emerging view that exceptional responders may hold the key to better understanding drug resistance. This review underscores the importance of polytherapies as an effective means of targeting resistance signalling networks and achieving durable clinical responses in the era of personalised cancer medicine.
Insights
Drug resistance is a major hurdle in precision oncology. This review explores resistance mechanisms and highlights polytherapies, including inhibitor combinations, as key strategies for durable cancer treatment responses.
Area of Science:
- Oncology
- Pharmacology
- Molecular Biology
Background:
- Drug resistance is a significant challenge in precision oncology.
- Cancer cells utilize diverse mechanisms to evade targeted therapies.
- Understanding cancer signaling networks is crucial for developing new treatment strategies.
Purpose of the Study:
- To review major drug resistance mechanisms in targeted cancer therapy.
- To discuss polytherapies as a strategy to overcome resistance.
- To explore computational approaches and the role of exceptional responders in combating resistance.
Main Methods:
- Literature review of resistance mechanisms.
- Analysis of various polytherapy approaches (inhibitor combinations, multi-target inhibitors, HSP90 inhibitors).
- Discussion of computational methods for predicting effective polytherapies.
Main Results:
- Identified major resistance mechanisms including signaling reprogramming and tumor evolution.
- Evaluated the potential and challenges of different polytherapies.
- Highlighted the importance of computational approaches and exceptional responders.
Conclusions:
- Polytherapies are effective in targeting resistance signaling networks.
- Strategic deployment of polytherapies can achieve durable clinical responses.
- Further research into computational prediction and exceptional responders is warranted for personalized cancer medicine.
More Related Videos
Related Concept Videos
02:57Targeted Cancer Therapies
There are several types of targeted therapies against...
02:49Cancer Therapies
However, cancer treatments can pose several challenges, as therapies used to kill cancer cells are generally also toxic to normal cells. Moreover, cancer cells mutate rapidly and can develop resistance to chemical agents or radiation therapy. Besides, all types of cancer cells may not respond to the same therapy. Some cancer cells respond to one...
02:56Treatment Resistant Cancers
03:03mTOR Signaling and Cancer Progression
The mTOR pathway or the...
02:26Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
02:18Network Covalent Solids
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...