Back

Binding kinetics enable tumor-selective MAPK and growth suppression by a Type 1 RAF Inhibitor

Desaunay, M.; Peters, T.; Matenoglou, E.; Baars, B.; Gaire, B.; Orive-Ramos, A.; ren, L.; Lyssikatos, J. P.; Burkard, M. R.; Dacus, D.; Sale, M. J.; Aaronson, S. A.; McCormick, F.; Gavathiotis, E.; Gross, S.; Poulikakos, P. I.

2026-10-06 cancer biology
10.64898/2026.10.05.756774 bioRxiv
Show abstract

Therapeutic inhibition of the RAS/MAPK cascade, an oncogenic driver in over one-third of human cancers, has been limited by a core trade-off: effective pathway blockade in tumors versus dose-limiting toxicity in normal tissues. Although Type 1.5 RAF inhibitors successfully mitigated this trade-off in BRAF-monomer-driven tumors, an equivalent strategy has been lacking for RAF-dimer-driven cancers, including those with RAS mutations. Here, we characterize ELV-3111, a next-generation, potent, and selective Type 1 RAF inhibitor that overcomes this limitation via a distinct mechanism of action. ELV-3111 induces paradoxical MAPK hyperactivation in normal tissues by binding RAF and promoting the active conformation (priming) followed by rapid dissociation (fast off-rate), a biochemical feature that spares RAS-mutant tumors, where RAF is already primed. In these and other RAF dimer-driven tumor models, ELV-3111 achieves potent and selective MAPK and growth suppression. When combined with a MEK inhibitor, ELV-3111 enables complementary pharmacology: additive inhibition in tumors and compensatory effects in normal tissues. The combination produced durable regressions across RAS-mutant and BRAF-mutant models, including a RAS-mutant model refractory to current therapies, while maintaining favorable tolerability. Thus, we uncover a generalizable mechanism that enables Type 1 RAF inhibitor plus MEK inhibitor combinations to achieve tumor-selective MAPK suppression, with the potential to inform targeted therapy design.

Matching journals

The top 7 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.