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Updated: Mar 11, 2026
A Method for Screening and Validation of Resistant Mutations Against Kinase Inhibitors
Published on: December 7, 2014
HUMAN KINASES DISPLAY MUTATIONAL HOTSPOTS AT COGNATE POSITIONS WITHIN CANCER
Jonathan Gallion1, Angela D Wilkins, Olivier Lichtarge
1Structural Computational Biology and Molecular Biophysics, Baylor College of Medicine, One Baylor Plaza Houston, TX, 77030, USA†The authors gratefully acknowledge support from the National Institutes of Health (GM066099 and GM079656), from the National Science Foundation (DBI-1356569), and from DARPA (N66001-15-C-4042), Jonathan.Gallion@bcm.edu.
Abstract:
The discovery of driver genes is a major pursuit of cancer genomics, usually based on observing the same mutation in different patients. But the heterogeneity of cancer pathways plus the high background mutational frequency of tumor cells often cloud the distinction between less frequent drivers and innocent passenger mutations. Here, to overcome these disadvantages, we grouped together mutations from close kinase paralogs under the hypothesis that cognate mutations may functionally favor cancer cells in similar ways. Indeed, we find that kinase paralogs often bear mutations to the same substituted amino acid at the same aligned positions and with a large predicted Evolutionary Action. Functionally, these high Evolutionary Action, non-random mutations affect known kinase motifs, but strikingly, they do so differently among different kinase types and cancers, consistent with differences in selective pressures. Taken together, these results suggest that cancer pathways may flexibly distribute a dependence on a given functional mutation among multiple close kinase paralogs. The recognition of this "mutational delocalization" of cancer drivers among groups of paralogs is a new phenomena that may help better identify relevant mechanisms and therefore eventually guide personalized therapy.
Insights
Cancer driver mutations can be hidden among passenger mutations. Grouping mutations in kinase paralogs reveals a new "mutational delocalization" phenomenon, aiding in identifying cancer drivers for personalized therapy.
Area of Science:
- Genomics
- Molecular Biology
- Cancer Research
Background:
- Identifying cancer driver genes is crucial but challenging due to pathway heterogeneity and high background mutation rates.
- Distinguishing true drivers from passenger mutations is difficult in cancer genomics.
Purpose of the Study:
- To develop a novel method for identifying cancer driver mutations by analyzing mutations in kinase paralogs.
- To investigate the hypothesis that cognate mutations in kinase paralogs functionally support cancer cells similarly.
Main Methods:
- Grouped mutations from closely related kinase paralogs.
- Analyzed mutations for shared substituted amino acids at aligned positions with high predicted Evolutionary Action.
- Assessed functional impact on kinase motifs across different cancer types.
Main Results:
- Identified non-random mutations in kinase paralogs affecting known kinase motifs.
- Observed that these mutations impact motifs differently across kinase types and cancers, reflecting varied selective pressures.
- Found evidence of "mutational delocalization" where cancer driver dependence is distributed among paralogs.
Conclusions:
- Cancer pathways can flexibly distribute functional mutation dependence across kinase paralogs.
- Recognizing "mutational delocalization" enhances the identification of cancer drivers and mechanisms.
- This approach may improve the development of personalized cancer therapies.
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