Systems-level Consequences of Low RAF Abundance for EGFR-ERK Signaling

The RAF kinases are central links between RAS, once activated by receptor tyrosine kinases (RTKs), and the extracellular signal-regulated kinases (ERK). In many cancer cells, RAFs are the least abundantly expressed RTK-ERK pathway proteins and can be present at just hundreds of copies per cell at the plasma membrane, but the consequences of limited RAF expression are unclear. By developing continuum and stochastic computational models of the epidermal growth factor receptor (EGFR)-ERK pathway, we showed that low RAF abundance creates stoichiometric bottlenecks between RTKs and ERK with concomitant stochastic RAF dynamics that propagate to weakly expressed downstream pathway proteins. Advanced sensitivity and Sloppiness analyses identified RAS activation and RAS-RAF interactions as strong determinants of signaling in low-RAF settings and revealed an efficient model fitting approach. RAF bottlenecks were predicted to impede ERK activation by oncogenic RAS mutants and explained a tendency for RAF1 membrane localization to be noisy. This work provides quantitative insight into a common, yet unexplored, regime for EGFR-ERK signaling and a systematic approach to develop and characterize dynamic models of receptor-mediated signaling.

RAF kinases connect receptors to the mitogenic ERK signaling pathway by translocating to the plasma membrane, but in a substantial fraction of cancer cell contexts RAFs are greatly outnumbered by other pathway proteins, potentially creating an unrecognized and consequential signaling bottleneck. We trained a novel computational model of EGFR-ERK signaling and characterized it comprehensively using integrated multivariate sensitivity analyses and Sloppiness analysis. The results revealed that low RAF abundance suppresses EGFR-mediated ERK activation, limits the effects of upstream oncogenic RAS mutants, and creates stochastic RAF dynamics that can propagate downstream. Thus, the canonical EGFR-ERK pathway exhibits divergent behaviors in a parameter space representative of a substantial fraction of cancer cell settings.