Abstract B46: Systems modeling of Ras reveals systems mechanisms that dictate response to treatment

Abstract

Abstract A relative abundance of biochemical and mechanistic data makes the Ras signaling network an ideal “model system” for the application of systems biology approaches to cancer biology. We here describe our application of systems modeling to the previously unexplained situation where colorectal cancer (CRC) patients with a KRAS G13D mutations are more responsive to anti-EGFR agents than patients with other KRAS mutations. Our computational model of Ras signaling was developed in accordance with the well-established network architecture of RasGTP signal regulation. Measured biochemical and biophysical properties comprise the parameters of the model. The previous biochemical characterization of specific oncogenic mutants allows different oncogenic alleles to be modeled. We use the model to find the behaviors that logically follow from what is known, but which the scope and complexity of the network prevent from otherwise being inferred. We simulated anti-EGFR dose responses for the three most common Ras mutants in CRC: G12D, G12V, and G13D. Our simulations unexpectedly found KRAS G13D was considerably more responsive to anti-EGFR agents. This implies that the known biochemical differences between these mutants are sufficient to explain why KRAS G13D CRC is more sensitive to EGFR inhibition. Our model further predicted that wild-type RasGTP levels within KRAS-mutant cancers should decrease much more for KRAS G13D cancers than KRAS G12V and KRAS G12D cancers. Our analysis predicted that these variations in wild-type RasGTP levels follow from mutant-specific differences in their interaction with Ras GAPs like the tumor suppressor neurofibromin (NF1). This mechanism explains why KRAS G13D CRC, but not KRAS G12D and G12V CRC, retains a dependency upon EGFR. We have now completed experiments that confirm this systems-level mechanism that governs responsiveness to treatment. Citation Format: Edward C. Stites. Systems modeling of Ras reveals systems mechanisms that dictate response to treatment [abstract]. In: Proceedings of the AACR Special Conference on Targeting RAS-Driven Cancers; 2018 Dec 9-12; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2020;18(5_Suppl):Abstract nr B46.