Molecular Systems BiologySystems BiologyPeer-reviewed journal article
Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance
Fabian FröhlichiD, Luca GerosaiD, Jeremy L. MuhlichiD, Peter Karl SorgeriD
Abstract
BRAFV600E is prototypical of oncogenic mutations that can be targeted therapeutically and treatment of BRAF-mutant melanomas with RAF and MEK inhibitors results in rapid tumor regression. However, drug-induced rewiring causes BRAFV600E melanoma cells to rapidly acquire a drug-adapted state. In patients this is thought to promote acquisition or selection for resistance mutations and disease recurrence. In this paper we use an energy-based implementation of ordinary differential equations in combination with proteomic, transcriptomic and imaging data from melanoma cells, to model the precise mechanisms responsible for adaptive rewiring. We demonstrate the presence of two parallel MAPK (RAF-MEK-ERK kinase) reaction channels in BRAFV600E melanoma cells that are differentially sensitive to RAF and MEK inhibitors. This arises from differences in protein oligomerization and allosteric regulation induced by oncogenic mutations and drug binding. As a result, the RAS-regulated MAPK channel can be active under conditions in which the BRAFV600E-driven channel is fully inhibited. Causal tracing demonstrates that this provides a sufficient quantitative explanation for initial and acquired responses to multiple different RAF and MEK inhibitors individually and in combination. HighlightsO_LIA thermodynamic framework enables structure-based description of allosteric interactions in the EGFR and MAPK pathways C_LIO_LICausal decomposition of efficacy of targeted drugs elucidates rewiring of MAPK channels C_LIO_LIModel-based extrapolation from type I[1/2] RAF inhibitors to type II RAF inhibitors C_LIO_LIA unified mechanistic explanation for adaptive and genetic resistance across BRAF-cancers C_LI
- organism: human
- assay: proteomics
- assay: bulk RNA-seq
- assay: imaging
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 47
- FWCI
- 4.00
- Field percentile
- top 4%
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