Computational modeling of KRAS mutant resistance mechanisms to targeted therapy

Master Thesis (2023)
Authors

H.T. van der Ent (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Supervisors

L.F.A. Wessels (TU Delft - Pattern Recognition and Bioinformatics)

Bram Thijssen (Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis)

Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright
© 2023 Huub van der Ent
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Huub van der Ent
Graduation Date
28-12-2023
Awarding Institution
Delft University of Technology
Programme
Computer Science
Sponsors
Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
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Abstract

KRAS mutations are very common in several different types of cancer. A promising targeted combination therapy using a MEK and HER inhibitor was proposed based on in vitro finding. The clinical results of this combination were found to be lacking due to emergent treatement resistance. Here we investigate what mechanism is causing this emergent resistance in KRAS mutant cancers. We propose a novel ODE model of the MAPK pathway that can be used to infer kinetic parameter estimates from a population of KRAS mutant cells under drug perturbation. Parameter estimates inferred from FRET biosensor data correctly predict protein activity in an external CyTOF validation dataset. However, the parameter estimates did not recapitulate the known gain-of-function in RAS activity that we would expect. From this we conclude that more experimental observation are required to elucidate the inner working of the resistance mechanism of KRAS mutant cancer to the proposed combination therapy.

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