Mathematical investigation of how oncogenic ras mutants promote ras signaling

Computational Random Mutagenesis to Investigate RAS Mutant Signaling

This chapter describes how mathematical models can be used to investigate the possible range of behaviors for mutant forms of a protein. A mathematical model of the RAS signaling network that has previously been developed and applied to specific RAS mutants will be adapted for the process of computational random mutagenesis. By using this model to computationally investigate the range of RAS signaling outputs that would be anticipated over a wide range of the relevant parameter space, one can gain intuition about the types of behaviors that would be demonstrated by biological RAS mutants.

Identification of RAS mutant biomarkers for EGFR inhibitor sensitivity using a systems biochemical approach

Mutations can be important biomarkers that influence the selection of specific cancer treatments. We recently combined mathematical modeling of RAS signaling network biochemistry with experimental cancer cell biology to determine why KRAS G13D is a biomarker for sensitivity to epidermal growth factor receptor (EGFR)-targeted therapies. The critical mechanistic difference between KRAS G13D and the other most common KRAS mutants is impaired binding to tumor suppressor Neurofibromin (NF1). Here, we hypothesize that impaired binding to NF1 is a "biophysical biomarker" that defines other RAS mutations that retain therapeutic sensitivity to EGFR inhibition. Both computational and experimental investigations support our hypothesis. By screening RAS mutations for this biophysical characteristic, we identify 10 additional RAS mutations that appear to be biomarkers for sensitivity to EGFR inhibition. Altogether, this work suggests that personalized medicine may benefit from migrating from gene-based and allele-based biomarker strategies to biomarkers based on biophysically defined subsets of mutations.

Mathematical Modeling to Study KRAS Mutant-Specific Responses to Pathway Inhibition

This chapter will describe how mathematical modeling allows the RAS pathway to be studied with computational experiments. The mathematical model utilized simulates the biochemical reactions that regulate RAS signaling. This type of model incorporates knowledge of reaction mechanisms, including measured quantitative parameters that characterize these reactions for both wild-type and mutant RAS proteins. For an illustrative example, this chapter focuses on how modeling provided new insights that helped solve a problem that challenged the RAS community for nearly a decade: why do colorectal cancers with the KRAS G13D mutation, but not the other common KRAS mutations, benefit from EGFR inhibition? The methods described include computational dose-response experiments and the use of "computational chimeric" RAS mutants.

A systems mechanism for KRAS mutant allele-specific responses to targeted therapy

Cancer treatment decisions are increasingly guided by which specific genes are mutated within each patient's tumor. For example, agents inhibiting the epidermal growth factor receptor (EGFR) benefit many colorectal cancer (CRC) patients, with the general exception of those whose tumor includes a KRAS mutation. However, among the various KRAS mutations, that which encodes the G13D mutant protein (KRAS^G13D) behaves differently; for unknown reasons, KRAS^G13D CRC patients benefit from the EGFR-blocking antibody cetuximab. Controversy surrounds this observation, because it contradicts the well-established mechanisms of EGFR signaling with regard to RAS mutations. Here, we identified a systems-level, mechanistic explanation for why KRAS^G13D cancers respond to EGFR inhibition. A computational model of RAS signaling revealed that the biophysical differences between the three most common KRAS mutants were sufficient to generate different sensitivities to EGFR inhibition. Integrated computation with experimentation then revealed a nonintuitive, mutant-specific dependency of wild-type RAS activation by EGFR that is determined by the interaction strength between KRAS and the tumor suppressor neurofibromin (NF1). KRAS mutants that strongly interacted with and competitively inhibited NF1 drove wild-type RAS activation in an EGFR-independent manner, whereas KRAS^G13D weakly interacted with and could not competitively inhibit NF1 and, thus, KRAS^G13D cells remained dependent on EGFR for wild-type RAS activity. Overall, our work demonstrates how systems approaches enable mechanism-based inference in genomic medicine and can help identify patients for selective therapeutic strategies.

Quantitative Systems Pharmacology Analysis of KRAS G12C Covalent Inhibitors

KRAS has proven difficult to target pharmacologically. Two strategies have recently been described for covalently targeting the most common KRAS mutant in lung cancer, KRAS G12C. Previously, we developed a computational model of the processes that regulate Ras activation. Here, we use this model to investigate KRAS G12C covalent inhibitors. We updated the model to include Ras protein turnover, and validation demonstrates that our model performs well in areas of G12C targeting where conventional wisdom struggles. We then used the model to investigate possible strategies to improve KRAS G12C inhibitors and identified GEF loading as a mechanism that could improve efficacy. Our simulations also found resistance‐promoting mutations may reverse which class of KRAS G12C inhibitor inhibits the system better, suggesting that there may be value to pursuing both types of KRAS G12C inhibitors. Overall, this work demonstrates areas in which systems biology approaches can inform Ras drug development.

Cooperation between Noncanonical Ras Network Mutations

Cancer develops after the acquisition of a collection of mutations that together create the cancer phenotype. How collections of mutations work together within a cell and whether there is selection for certain combinations of mutations are not well understood. We investigated this problem with a mathematical model of the Ras signaling network, including a computational random mutagenesis. Modeling and subsequent experiments revealed that mutations of the tumor suppressor gene NF1 can amplify the effects of other Ras pathway mutations, including weakly activating, noncanonical Ras mutants. Furthermore, analyzing recently available, large, cancer genomic data sets uncovered increased co-occurrence of NF1 mutations with mutations in other Ras network genes. Overall, these data suggest that combinations of Ras pathway mutations could serve the role of cancer "driver." More generally, this work suggests that mutations that result in network instability may promote cancer in a manner analogous to genomic instability.