Back

A critical role of FAK signaling in Rac1-driven melanoma cell resistance to MAPK pathway inhibition

Riordan, J. D.; Nathanson, T. A.; Varzavand, A.; Hawkins, A. A.; Peplinski, R. M.; Hannan, E. C.; Bibeau, F. A.; Freesmeier, N. J.; Jilek, M. C.; Coma, S.; Pachter, J. A.; Dupuy, A.; Stipp, C. S.

2024-12-23 cancer biology
10.1101/2024.12.22.629990 bioRxiv
Show abstract

The Rac1 P29S hotspot mutation in cutaneous melanoma is associated with resistance to MAPK pathway inhibitors (MAPKi) and worse clinical outcomes. Moreover, activation of Rac1 guanine exchange factors (GEFs) also promotes MAPKi-resistance, particularly in undifferentiated melanoma cells. Here we delineate mechanisms of Rac1-driven MAPKi-resistance and identify strategies to inhibit the growth of this class of cutaneous melanomas. We find that Rac1-driven melanomas manifest pleiotropic resistance mechanisms including (i) reduced dependence on BRAF/MEK, (ii) activation of alternative MAPK pathways utilizing Jun kinase and p38 MAP kinase, and (iii) a partial reliance on YAP/TAZ signaling. Importantly, although Rac1-driven melanoma cells display reduced dependence on BRAF/MEK, they are not completely ERK-independent. Additionally, the presence of activated Rac1 appears to create a dependency on focal adhesion kinase (FAK) signaling in undifferentiated melanoma cells. Therefore, despite the pleiotropic mechanisms of Rac1-driven MAPKi resistance, we find that combined inhibition of RAF and MEK with the RAF/MEK clamp auvotometinib and FAK with the FAK inhibitor defactinib is a promising approach for suppressing the growth of Rac1-driven melanoma cells. Thus, the avutometinib plus defactinib combination, which is currently being investigated for brain metastatic cutaneous melanoma may also have utility against Rac1-driven MAPKi-resistance in heavily pre-treated, advanced disease.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.