New insights into RAS biology reinvigorate interest in mathematical modeling of RAS signaling

Abstract

RAS is the most frequently mutated gene across human cancers, but developing inhibitors of mutant RAS has proven to be challenging. Given the difficulties of targeting RAS directly, drugs that impact the other components of pathways where mutant RAS operates may potentially be effective. However, the system-level features, including different localizations of RAS isoforms, competition between downstream effectors, and interlocking feedback and feed-forward loops, must be understood to fully grasp the opportunities and limitations of inhibiting specific targets. Mathematical modeling can help us discern the system-level impacts of these features in normal and cancer cells. New technologies enable the acquisition of experimental data that will facilitate development of realistic models of oncogenic RAS behavior. In light of the wealth of empirical data accumulated over decades of study and the advancement of experimental methods for gathering new data, modelers now have the opportunity to advance progress toward realization of targeted treatment for mutant RAS-driven cancers.

Keywords: ERK cascade; Mathematical modeling; Mechanistic modeling; RAS; Systems biology.

Fig. 1

The RAS activation cycle. RAS can bind either GTP or GDP, and is active when bound to GTP. In the active configuration, it is able to interact with downstream effectors. RAS activation/deactivation can occur through multiple processes. Processes of interest are labeled with circled numbers: 1 shows free nucleotide exchange, 2 describes RAS-catalyzed hydrolysis of GTP to GDP, 3 depicts GTP hydrolysis stimulated by GAP, and 4 is GEF-induced GDP release from RAS to facilitate GTP binding. Rate constants for each step are labeled; variable names correspond to those in Table 1.

Fig. 2

Molecularly targeted drugs impacting networks related to RAS signaling. RAS receives activating inputs from EGFR and HER2 triggered by growth factors. Signals propagate from RAS to downstream effectors including RASSF, RAF, RAL-GEF, PLCε, and PI3K, resulting in varied impacts on cellular phenotype, shown with gray text and arrows. Positive feedbacks are indicated with dashed lines and arrows in magenta. Note that the positive feedback from RAS-GTP to SOS is stronger than that of RAS-GDP, as indicated by the thickness of the line. Negative feedbacks are shown with orange lines with blunt ends. Inhibitory drugs are shown in blue with their target indicated by a blue line with a blunt end.

Fig. 3

Positive feedback from RAS to SOS is achieved via binding of RAS to the SOS allosteric site that bridges the RAS exchanger motif (REM) and RAS-GEF domains, which together are responsible for SOS GEF activity. When the allosteric site is empty, SOS has moderate GEF activity, facilitating release of GDP from RAS-GDP bound at the RAS-GEF site with a rate constant k_d,GDP,GEF. When RAS is bound to the REM domain, GEF activity increases to a rate characterized by the rate constant φ*k_d,GDP,GEF , where φ > 1.