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Updated: May 27, 2025
A Method for Screening and Validation of Resistant Mutations Against Kinase Inhibitors
Published on: December 7, 2014
Mapping kinase domain resistance mechanisms for the MET receptor tyrosine kinase via deep mutational scanning
Gabriella O Estevam1,2, Edmond Linossi3,4, Jingyou Rao5
1Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, United States.
Abstract:
Mutations in the kinase and juxtamembrane domains of the MET Receptor Tyrosine Kinase are responsible for oncogenesis in various cancers and can drive resistance to MET-directed treatments. Determining the most effective inhibitor for each mutational profile is a major challenge for MET-driven cancer treatment in precision medicine. Here, we used a deep mutational scan (DMS) of ~5764 MET kinase domain variants to profile the growth of each mutation against a panel of 11 inhibitors that are reported to target the MET kinase domain. We validate previously identified resistance mutations, pinpoint common resistance sites across type I, type II, and type I ½ inhibitors, unveil unique resistance and sensitizing mutations for each inhibitor, and verify non-cross-resistant sensitivities for type I and type II inhibitor pairs. We augment a protein language model with biophysical and chemical features to improve the predictive performance for inhibitor-treated datasets. Together, our study demonstrates a pooled experimental pipeline for identifying resistance mutations, provides a reference dictionary for mutations that are sensitized to specific therapies, and offers insights for future drug development.
Insights
This study profiles MET kinase domain mutations against 11 inhibitors to identify resistance and sensitivity patterns. Findings aid precision medicine by predicting effective treatments for MET-driven cancers.
Area of Science:
- Oncology
- Molecular Biology
- Pharmacology
Background:
- Mutations in the MET Receptor Tyrosine Kinase drive cancer and treatment resistance.
- Personalized medicine requires matching inhibitors to specific MET mutational profiles.
Purpose of the Study:
- To comprehensively profile MET kinase domain variants against MET inhibitors.
- To identify resistance and sensitizing mutations for various MET inhibitors.
- To improve predictive models for MET-targeted therapies.
Main Methods:
- Deep mutational scanning of ~5764 MET kinase domain variants.
- Testing mutation growth against 11 MET kinase domain inhibitors.
- Augmenting a protein language model with biophysical and chemical features.
Main Results:
- Validated known MET inhibitor resistance mutations.
- Identified common resistance sites across different inhibitor types (I, II, I ½).
- Discovered unique resistance and sensitizing mutations for individual inhibitors.
- Verified non-cross-resistant sensitivities for type I and type II inhibitor combinations.
- Enhanced a protein language model for predicting inhibitor efficacy.
Conclusions:
- Developed a pooled experimental pipeline for identifying MET inhibitor resistance mutations.
- Created a reference dictionary of mutations sensitive to specific MET-targeted therapies.
- Provided insights for developing next-generation MET inhibitors and personalized cancer treatments.
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