The impact of the regulatory design on the response of epidermal growth factor receptor-mediated signal transduction towards oncogenic mutations

Systems modeling of oncogenic G-protein and GPCR signaling reveals unexpected differences in downstream pathway activation

Mathematical models of biochemical reaction networks are an important and emerging tool for the study of cell signaling networks involved in disease processes. One promising potential application of such mathematical models is the study of how disease-causing mutations promote the signaling phenotype that contributes to the disease. It is commonly assumed that one must have a thorough characterization of the network readily available for mathematical modeling to be useful, but we hypothesized that mathematical modeling could be useful when there is incomplete knowledge and that it could be a tool for discovery that opens new areas for further exploration. In the present study, we first develop a mechanistic mathematical model of a G-protein coupled receptor signaling network that is mutated in almost all cases of uveal melanoma and use model-driven explorations to uncover and explore multiple new areas for investigating this disease. Modeling the two major, mutually-exclusive, oncogenic mutations (Gαq/11 and CysLT2R) revealed the potential for previously unknown qualitative differences between seemingly interchangeable disease-promoting mutations, and our experiments confirmed oncogenic CysLT2R was impaired at activating the FAK/YAP/TAZ pathway relative to Gαq/11. This led us to hypothesize that CYSLTR2 mutations in UM must co-occur with other mutations to activate FAK/YAP/TAZ signaling, and our bioinformatic analysis uncovers a role for co-occurring mutations involving the plexin/semaphorin pathway, which has been shown capable of activating this pathway. Overall, this work highlights the power of mechanism-based computational systems biology as a discovery tool that can leverage available information to open new research areas.

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.

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.

Feedback activation of neurofibromin terminates growth factor-induced Ras activation

Background

Growth factors induce a characteristically short-lived Ras activation in cells emerging from quiescence. Extensive work has shown that transient as opposed to sustained Ras activation is critical for the induction of mitogenic programs. Mitogen-induced accumulation of active Ras-GTP results from increased nucleotide exchange driven by the nucleotide exchange factor Sos. In contrast, the mechanism accounting for signal termination and prompt restoration of basal Ras-GTP levels is unclear, but has been inferred to involve feedback inhibition of Sos. Remarkably, how GTP-hydrolase activating proteins (GAPs) participate in controlling the rise and fall of Ras-GTP levels is unknown.

Results

Monitoring nucleotide exchange of Ras in permeabilized cells we find, unexpectedly, that the decline of growth factor-induced Ras-GTP levels proceeds in the presence of unabated high nucleotide exchange, pointing to GAP activation as a major mechanism of signal termination. Experiments with non-hydrolysable GTP analogues and mathematical modeling confirmed and rationalized the presence of high GAP activity as Ras-GTP levels decline in a background of high nucleotide exchange. Using pharmacological and genetic approaches we document a raised activity of the neurofibromatosis type I tumor suppressor Ras-GAP neurofibromin and an involvement of Rsk1 and Rsk2 in the down-regulation of Ras-GTP levels.

Conclusions

Our findings show that, in addition to feedback inhibition of Sos, feedback stimulation of the RasGAP neurofibromin enforces termination of the Ras signal in the context of growth-factor signaling. These findings ascribe a precise role to neurofibromin in growth factor-dependent control of Ras activity and illustrate how, by engaging Ras-GAP activity, mitogen-challenged cells play safe to ensure a timely termination of the Ras signal irrespectively of the reigning rate of nucleotide exchange.

Electronic supplementary material

The online version of this article (doi:10.1186/s12964-016-0128-z) contains supplementary material, which is available to authorized users.

Customizing the therapeutic response of signaling networks to promote antitumor responses by drug combinations

Drug resistance, de novo and acquired, pervades cellular signaling networks (SNs) from one signaling motif to another as a result of cancer progression and/or drug intervention. This resistance is one of the key determinants of efficacy in targeted anti-cancer drug therapy. Although poorly understood, drug resistance is already being addressed in combination therapy by selecting drug targets where SN sensitivity increases due to combination components or as a result of de novo or acquired mutations. Additionally, successive drug combinations have shown low resistance potential. To promote a rational, systematic development of combination therapies, it is necessary to establish the underlying mechanisms that drive the advantages of combination therapies, and design methods to determine drug targets for combination regimens. Based on a joint systems analysis of cellular SN response and its sensitivity to drug action and oncogenic mutations, we describe an in silico method to analyze the targets of drug combinations. Our method explores mechanisms of sensitizing the SN through a combination of two drugs targeting vertical signaling pathways. We propose a paradigm of SN response customization by one drug to both maximize the effect of another drug in combination and promote a robust therapeutic response against oncogenic mutations. The method was applied to customize the response of the ErbB/PI3K/PTEN/AKT pathway by combination of drugs targeting HER2 receptors and proteins in the down-stream pathway. The results of a computational experiment showed that the modification of the SN response from hyperbolic to smooth sigmoid response by manipulation of two drugs in combination leads to greater robustness in therapeutic response against oncogenic mutations determining cancer heterogeneity. The application of this method in drug combination co-development suggests a combined evaluation of inhibition effects together with the capability of drug combinations to suppress resistance mechanisms before they become clinically manifest.

Parameter space exploration within dynamic simulations of signaling networks

We started offering an introduction to very basic aspects of molecular biology, for the reader coming from computer sciences, information technology, mathematics. Similarly we offered a minimum of information about pathways and networks in graph theory, for a reader coming from the bio-medical sector. At the crossover about the two different types of expertise, we offered some definition about Systems Biology. The core of the article deals with a Molecular Interaction Map (MIM), a network of biochemical interactions involved in a small signaling-network sub-region relevant in breast cancer. We explored robustness/sensitivity to random perturbations. It turns out that our MIM is a non-isomorphic directed graph. For non physiological directions of propagation of the signal the network is quite resistant to perturbations. The opposite happens for biologically significant directions of signal propagation. In these cases we can have no signal attenuation, and even signal amplification. Signal propagation along a given pathway is highly unidirectional, with the exception of signal-feedbacks, that again have a specific biological role and significance. In conclusion, even a relatively small network like our present MIM reveals the preponderance of specific biological functions over unspecific isomorphic behaviors. This is perhaps the consequence of hundreds of millions of years of biological evolution.

Mechanistic modeling to investigate signaling by oncogenic Ras mutants

Mathematical models based on biochemical reaction mechanisms can be a powerful complement to experimental investigations of cell signaling networks. In principle, such models have the potential to find the behaviors that result from well-understood component interactions and their measurable properties, such as concentrations and rate constants. As cancer results from the acquisition of mutations that alter the expression level and/or the biochemistry of proteins encoded by mutated genes, mathematical models of cell signaling networks would also seem to have the potential to predict how these changes alter cell signaling to produce a cancer phenotype. Ras is commonly found in cancer and has been extensively characterized at the level of detail needed to develop such models. Here, we consider how biochemical mechanism-based models have been used to study mutant Ras signaling. These models demonstrate that it is clearly possible to use observable properties of individual reactions to predict how the entire system behaves to produce the high levels of signal that drive the cancer phenotype. These models also demonstrate differences in how models are developed and studied. Their evaluation suggests which approaches are most promising for future work.

Epidermal growth factor receptor mutation in combination with expression of MIG6 alters gefitinib sensitivity

BACKGROUND

Epidermal growth factor receptor (EGFR) signaling plays an important role in the regulation of cell proliferation, survival, metastasis, and invasion in various tumors. Earlier studies showed that the EGFR is frequently overexpressed in non-small-cell lung cancer (NSCLC) and EGFR mutations at specific amino acid residues in the kinase domain induce altered responsiveness to gefitinib, a small molecule EGFR tyrosine kinase inhibitor. However, the mechanism underlying the drug response modulated by EGFR mutation is still largely unknown. To elucidate drug response in EGFR signal transduction pathway in which complex dynamics of multiple molecules involved, a systematic approach is necessary. In this paper, we performed experimental and computational analyses to clarify the underlying mechanism of EGFR signaling and cell-specific gefitinib responsiveness in three H1299-derived NSCLC cell lines; H1299 wild type (H1299WT), H1299 with an overexpressed wild type EGFR (H1299EGFR-WT), and H1299 with an overexpressed mutant EGFR L858R (H1299L858R; gefitinib sensitive mutant).

RESULTS

We predicted and experimentally verified that Mig6, which is a known negative regulator of EGFR and specifically expressed in H1299L858R cells, synergized with gefitinib to suppress cellular growth. Computational analyses indicated that this inhibitory effect is amplified at the phosphorylation/dephosphorylation steps of MEK and ERK.

CONCLUSIONS

Thus, we showed that L858R receptor mutation in combination with expression of its negative regulator, Mig6, alters signaling outcomes and results in variable drug sensitivity.

Pathway sensitivity analysis for detecting pro-proliferation activities of oncogenes and tumor suppressors of epidermal growth factor receptor-extracellular signal-regulated protein kinase pathway at altered protein levels

BACKGROUND

Mathematic models and sensitivity analyses of biologic pathways have been used for exploring the dynamics and for detecting the key components of signaling pathways.

METHODS

The authors previously developed a mathematic model of the epidermal growth factor receptor-extracellular signal-regulated protein kinase (EGFR-ERK) pathway using ordinary differential equations from existing EGFR-ERK pathway models. By using prolonged ERK activation as an indicator that may lead to cell proliferation under certain circumstances, in the current study, a pathway sensitivity analysis was performed to test its capability of detecting pro-proliferative activities through altered protein levels to examine the effects on ERK activation.

RESULTS

The analysis revealed that 12 of 20 oncoproteins and 4 of 5 tumor suppressors were detected, consistent with reported experimental works. Because pathway dynamics depend on many factors, some of which were not included in the current models, failure to detect all known oncogenes and tumor suppressors can be because of the failure to include relevant crosstalk to other pathway components.

CONCLUSIONS

Overall, the current results indicated that pathway sensitivity analysis is a useful approach for detecting and distinguishing pro-proliferation activities of oncoproteins and suppressed proliferative activities of tumor suppressors at altered protein levels at least in the EGFR-ERK model.

Cell type-specific importance of ras-c-raf complex association rate constants for MAPK signaling

We generated 17 c-Raf (RAF proto-oncogene serine-threonine protein kinase) mutants with altered Ras-Raf association and dissociation rates to investigate the role of electrostatically driven Ras-Raf association rates on epidermal growth factor (EGF)-activated mitogen-activated protein kinase (MAPK) signal transduction. Some of these mutants had compensating changes in association and dissociation rates, enabling the effects of changes in association rate to be distinguished from those of changes in affinity. In rabbit kidney (RK13) cells, these mutants affected downstream signaling, with changes in Ras-c-Raf association rates having a greater effect on MAPK signaling than did similar changes in dissociation rates. Mutants with compensating decreases in both association and dissociation rates stimulated less extracellular signal-regulated kinase (ERK)-dependent reporter activity than did wild-type c-Raf, whereas the converse was true for mutants with increased association and dissociation rates. In marked contrast, the mutants had little or no effect on signaling in human embryonic kidney (HEK) 293 cells. These two cell lines also showed distinct patterns of EGF-dependent ERK phosphorylation and signaling: ERK activation and signaling were transient in HEK293 cells and sustained in RK13 cells, with the difference resulting from the lack of negative feedback from ERK to Sos (Son of Sevenless) in the latter. Computer simulation revealed that, in the presence of negative feedback, changes in the rate of Ras-c-Raf binding have little effect on ERK activation. Thus, EGF-MAPK activation kinetics and feedback regulation is cell type specific and depends on the network topology.

A systems perspective of ras signaling in cancer

The development of cancer reflects the complex interactions and properties of many proteins functioning as part of large biochemical networks within the cancer cell. Although traditional experimental models have provided us with wonderful insights on the behavior of individual proteins within a cancer cell, they have been deficient in simultaneously keeping track of many proteins and their interactions in large networks. Computational models have emerged as a powerful tool for investigating biochemical networks due to their ability to meaningfully assimilate numerous network properties. Using the well-studied Ras oncogene as an example, we discuss the use of models to investigate pathologic Ras signaling and describe how these models could play a role in the development of new cancer drugs and the design of individualized treatment regimens.

Epidermal growth factor receptor (EGFR) status and K-Ras mutations in colorectal cancer

BACKGROUND

In advanced colorectal cancer, K-Ras somatic mutations predict resistance to mAbs targeting epidermal growth factor receptor (EGFR). Relationships between K-Ras mutations and EGFR status have not been examined so far. We analyzed relationships between K-Ras mutations and EGFR tumoral status based on EGFR germinal polymorphisms, gene copy number and expression.

METHODS

Eighty colorectal tumors (stage 0-IV) and 39 normal mucosas were analyzed. K-Ras mutations at codons 12 and 13 were detected by a sensitive enrichment double PCR-restriction fragment length polymorphism (RFLP) assay. EGFR gene polymorphisms at positions -216G>T, -191C>A and 497Arg>Lys were analyzed (PCR-RFLP), along with CA repeat polymorphism in intron 1 (fluorescent genotyping) and EGFR gene copy number (PCR amplification). EGFR expression was quantified by Scatchard binding assay.

RESULTS

The number of EGFR high-affinity sites, dissociation constant (Kd), gene copy number, intron 1, -216G>T, -191C>A or 497Lys>Arg genotypes was not different between K-Ras-mutated or K-Ras-non-mutated tumors. No relationship was observed between any of the analyzed EGFR genotypes and EGFR expression. EGFR expression was not related to gene copy number. EGFR gene copy number in tumor and normal tissue was not correlated. The mean value of the tumor/normal mucosa gene copy number ratio was 1.16.

CONCLUSIONS

Present data clearly show that EGFR status is independent of K-Ras mutations in colorectal tumors.