Genetic interactions and tissue specificity modulate the association of mutations with drug response
In oncology, biomarkers are widely used to predict subgroups of patients that respond to a given drug. Although clinical decisions often rely on single gene biomarkers, machine learning approaches tend to generate complex multi-gene biomarkers that are hard to interpret. Models predicting drug respo...
Gespeichert in:
| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2020
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| In: |
Molecular cancer therapeutics
Year: 2019, Jahrgang: 19, Heft: 3, Pages: 927-936 |
| ISSN: | 1538-8514 |
| DOI: | 10.1158/1535-7163.MCT-19-0045 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1158/1535-7163.MCT-19-0045 Verlag, lizenzpflichtig, Volltext: https://mct.aacrjournals.org/content/19/3/927 
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| Verfasserangaben: | Dina Cramer, Johanna Mazur, Octavio Espinosa, Matthias Schlesner, Daniel Hübschmann, Roland Eils, and Eike Staub |
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| 245 | 1 | 0 | |a Genetic interactions and tissue specificity modulate the association of mutations with drug response |c Dina Cramer, Johanna Mazur, Octavio Espinosa, Matthias Schlesner, Daniel Hübschmann, Roland Eils, and Eike Staub |
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| 520 | |a In oncology, biomarkers are widely used to predict subgroups of patients that respond to a given drug. Although clinical decisions often rely on single gene biomarkers, machine learning approaches tend to generate complex multi-gene biomarkers that are hard to interpret. Models predicting drug response based on multiple altered genes often assume that the effects of single alterations are independent. We asked whether the association of cancer driver mutations with drug response is modulated by other driver mutations or the tissue of origin. We developed an analytic framework based on linear regression to study interactions in pharmacogenomic data from two large cancer cell line panels. Starting from a model with only covariates, we included additional variables only if they significantly improved simpler models. This allows to systematically assess interactions in small, easily interpretable models. Our results show that including mutation-mutation interactions in drug response prediction models tends to improve model performance and robustness. For example, we found that TP53 mutations decrease sensitivity to BRAF inhibitors in BRAF-mutated cell lines and patient tumors, suggesting a therapeutic benefit of combining inhibition of oncogenic BRAF with reactivation of the tumor suppressor TP53. Moreover, we identified tissue-specific mutation-drug associations and synthetic lethal triplets where the simultaneous mutation of two genes sensitizes cells to a drug. In summary, our interaction-based approach contributes to a holistic view on the determining factors of drug response. | ||
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