Allosteric activation of functionally asymmetric RAF kinase dimers.

Although RAF kinases are critical for controlling cell growth, their mechanism of activation is incompletely understood. Recently, dimerization was shown to be important for activation. Here we show that the dimer is functionally asymmetric with one kinase functioning as an activator to stimulate activity of the partner, receiver kinase. The activator kinase did not require kinase activity but did require N-terminal phosphorylation that functioned allosterically to induce cis-autophosphorylation of the receiver kinase. Based on modeling of the hydrophobic spine assembly, we also engineered a constitutively active mutant that was independent of Ras, dimerization, and activation-loop phosphorylation. As N-terminal phosphorylation of BRAF is constitutive, BRAF initially functions to activate CRAF. N-terminal phosphorylation of CRAF was dependent on MEK, suggesting a feedback mechanism and explaining a key difference between BRAF and CRAF. Our work illuminates distinct steps in RAF activation that function to assemble the active conformation of the RAF kinase.

Decreasing alloimmunization-specific mortality in sickle cell disease in the United States: Cost-effectiveness of a shared transfusion resource.

Red blood cell alloimmunization and consequent delayed hemolytic transfusion reaction (DHTR) incidence and mortality in patients with sickle cell disease (SCD) are high. A shared transfusion resource has decreased both in other countries, while in the United States cost concerns persist. We conducted a Markov cohort simulation of a birth cohort of alloimmunized patients with SCD to estimate lifetime DHTR incidence, DHTR-specific mortality, quality-adjusted life expectancy (QALE), and costs with the implementation of a shared transfusion resource to identify antibody history versus without (i.e., status quo). We conducted our analysis using a lifetime analytic time horizon and from a United States health system perspective. Implementation of shared transfusion resource projects to decrease cumulative DHTR-specific mortality by 26% for alloimmunized patients with SCD in the United States, relative to the status quo. For an average patient population of 32 000, this intervention would generate a discounted increment of 4000 QALYs at an incremental discounted cost of $0.3 billion, resulting in an incremental cost-effectiveness ratio of $75 600/QALY [95% credible interval $70 200-81 400/QALY]. The results are most sensitive to the baseline lifetime medical expenditure of patients with SCD. Alloantibody data exchange is cost-effective in 100% of 10 000 Monte Carlo simulations. The resource would theoretically need a minimum patient population of 1819 patients or cost no more than $5.29 million annually to be cost-effective. By reducing DHTR-specific mortality, a shared transfusion resource in the United States projects to be a life-saving and cost-effective intervention for patients with SCD in the United States.

KRAS-Mutated, Estrogen Receptor-Positive Low-Grade Serous Ovarian Cancer: Unraveling an Exceptional Response Mystery.

We report on a woman with aggressive estrogen receptor-positive, KRAS-mutated ovarian cancer who achieved a remarkable response to combination therapy with the MEK inhibitor (trametinib) and the aromatase inhibitor (letrozole), even though the disease had failed to respond to a combination of a PI3K inhibitor and different MEK inhibitor, as well as to trametinib and the estrogen modulator, tamoxifen, and to letrozole by itself. The mechanism of action for exceptional response was elucidated by in vitro experiments that demonstrated that the fact that tamoxifen can have an agonistic effect in addition to antagonist activity, whereas letrozole results only in estrogen depletion was crucial to the response achieved when letrozole was combined with an MEK inhibitor. Our current observations indicate that subtle variations in mechanisms of action of outwardly similar regimens may have a major impact on outcome and that such translational knowledge is critical for optimizing a precision medicine strategy. KEY POINTS: This report describes the remarkable response of a patient with KRAS-mutated, estrogen receptor-positive low-grade serous ovarian cancer treated with trametinib (MEK inhibitor) and letrozole (aromatase inhibitor), despite prior progression on similar agents including tamoxifen (estrogen modulator). In vitro investigation revealed that tamoxifen can have agonistic in addition to antagonistic effects, which could be the reason for the patient not responding to the combination of trametinib and tamoxifen. The current observations suggest that drugs with different mechanisms of action targeting the same receptor may have markedly different anticancer activity when used in combinations.

Discernment between candidate mechanisms for KRAS G13D colorectal cancer sensitivity to EGFR inhibitors.

Phase three clinical trial evidence suggests that colorectal cancers with the KRAS G13D mutation may benefit from EGFR inhibitors, like cetuximab, in contrast to the other most common KRAS mutations. A mechanism to explain why this mutation behaves differently from other KRAS mutations had long been lacking. Two recent studies have reproduced KRAS G13D specific sensitivity to cetuximab in cellular models, and both have implicated the tumor suppressor NF1 as a critical variable in determining sensitivity and resistance. One study proposes a mechanism that focuses on the inhibition of active, GTP-bound wild-type RAS, which is proposed to occur to a greater extent in KRAS G13D tumors due to the inability of KRAS G13D to bind NF1 well. The other study suggests NF1 can convert GTP-bound KRAS G13D to inactive, GDP-bound KRAS G13D. Here, we report an inability to reproduce cellular and biophysical studies that suggested NF1 has strong GTPase activity on KRAS G13D. We also report additional data that further suggests only WT RAS-GTP levels are reduced with EGFR inhibition and that KRAS G13D is impaired in binding to NF1. These new experiments further support a mechanism in which cetuximab inhibits wild-type (HRAS and NRAS) signals in KRAS G13D colorectal cancers. Video Abstract.

Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor.

Receptor tyrosine kinases (RTKs) typically contain multiple autophosphorylation sites in their cytoplasmic domains. Once activated, these autophosphorylation sites can recruit downstream signaling proteins containing Src homology 2 (SH2) and phosphotyrosine-binding (PTB) domains, which recognize phosphotyrosine-containing short linear motifs (SLiMs). These domains and SLiMs have polyspecific or promiscuous binding activities. Thus, multiple signaling proteins may compete for binding to a common SLiM and vice versa. To investigate the effects of competition on RTK signaling, we used a rule-based modeling approach to develop and analyze models for ligand-induced recruitment of SH2/PTB domain-containing proteins to autophosphorylation sites in the insulin-like growth factor 1 (IGF1) receptor (IGF1R). Models were parameterized using published datasets reporting protein copy numbers and site-specific binding affinities. Simulations were facilitated by a novel application of model restructuration, to reduce redundancy in rule-derived equations. We compare predictions obtained via numerical simulation of the model to those obtained through simple prediction methods, such as through an analytical approximation, or ranking by copy number and/or KD value, and find that the simple methods are unable to recapitulate the predictions of numerical simulations. We created 45 cell line-specific models that demonstrate how early events in IGF1R signaling depend on the protein abundance profile of a cell. Simulations, facilitated by model restructuration, identified pairs of IGF1R binding partners that are recruited in anti-correlated and correlated fashions, despite no inclusion of cooperativity in our models. This work shows that the outcome of competition depends on the physicochemical parameters that characterize pairwise interactions, as well as network properties, including network connectivity and the relative abundances of competitors.

Proceedings of the fifth international RASopathies symposium: When development and cancer intersect.

This report summarizes and highlights the fifth International RASopathies Symposium: When Development and Cancer Intersect, held in Orlando, Florida in July 2017. The RASopathies comprise a recognizable pattern of malformation syndromes that are caused by germ line mutations in genes that encode components of the RAS/mitogen-activated protein kinase (MAPK) pathway. Because of their common underlying pathogenetic etiology, there is significant overlap in their phenotypic features, which includes craniofacial dysmorphology, cardiac, cutaneous, musculoskeletal, gastrointestinal and ocular abnormalities, neurological and neurocognitive issues, and a predisposition to cancer. The RAS pathway is a well-known oncogenic pathway that is commonly found to be activated in somatic malignancies. As in somatic cancers, the RASopathies can be caused by various pathogenetic mechanisms that ultimately impact or alter the normal function and regulation of the MAPK pathway. As such, the RASopathies represent an excellent model of study to explore the intersection of the effects of dysregulation and its consequence in both development and oncogenesis.

Use of mechanistic models to integrate and analyze multiple proteomic datasets.

Proteins in cell signaling networks tend to interact promiscuously through low-affinity interactions. Consequently, evaluating the physiological importance of mapped interactions can be difficult. Attempts to do so have tended to focus on single, measurable physicochemical factors, such as affinity or abundance. For example, interaction importance has been assessed on the basis of the relative affinities of binding partners for a protein of interest, such as a receptor. However, multiple factors can be expected to simultaneously influence the recruitment of proteins to a receptor (and the potential of these proteins to contribute to receptor signaling), including affinity, abundance, and competition, which is a network property. Here, we demonstrate that measurements of protein copy numbers and binding affinities can be integrated within the framework of a mechanistic, computational model that accounts for mass action and competition. We use cell line-specific models to rank the relative importance of protein-protein interactions in the epidermal growth factor receptor (EGFR) signaling network for 11 different cell lines. Each model accounts for experimentally characterized interactions of six autophosphorylation sites in EGFR with proteins containing a Src homology 2 and/or phosphotyrosine-binding domain. We measure importance as the predicted maximal extent of recruitment of a protein to EGFR following ligand-stimulated activation of EGFR signaling. We find that interactions ranked highly by this metric include experimentally detected interactions. Proteins with high importance rank in multiple cell lines include proteins with recognized, well-characterized roles in EGFR signaling, such as GRB2 and SHC1, as well as a protein with a less well-defined role, YES1. Our results reveal potential cell line-specific differences in recruitment.

The Abundance of KRAS and RAS Gene Mutations in Cancer.

Mutant forms of the RAS genes KRAS, NRAS, and HRAS are important and common drivers of cancer. Recently, two independent teams that integrated cancer genomics with cancer epidemiology estimated that approximately 15-20% of all human cancers harbor a mutation in one of these three RAS genes. These groups also estimate KRAS mutations occur in 11-14% of all human cancers. Although these estimates are lower than many commonly encountered values, these estimates continue to rank KRAS and the ensemble of RAS oncogenes among the most common genetic drivers of cancer across all forms of malignancy.

Cancer research is not correlated with driver gene mutation burdens.

BACKGROUND: Cancer research is pursued with the goal of positively impacting patients with cancer. Decisions regarding how to allocate research funds reflect a complex balancing of priorities and factors. Even though these are subjective decisions, they should be made with consideration of all available objective facts. An accurate estimate of the affected cancer patient population by mutation is one variable that has only recently become available to inform funding decisions. METHODS: We compared the overall incident burden of mutations within each cancer-associated gene with two measures of cancer research efforts: research grant funding amounts and numbers of academic manuscripts. We ask to what degree the aggregate set of cancer research efforts reflects the relative burdens of the different cancer genetic drivers. We thoroughly investigate the design of our queries to ensure that the presented results are robust and conclusions are well justified. FINDINGS: We find cancer research is generally not correlated with the relative burden of mutation within the different genetic drivers of cancer. CONCLUSIONS: We suggest that cancer research would benefit from incorporating, among other factors, an epidemiologically informed mutation-estimate baseline into a larger framework for funding and research allocation decisions. FUNDING: This work was supported in part by the National Institutes of Health (NIH) P30CA014195 and NIH DP2AT011327.

Chemical kinetic mechanistic models to investigate cancer biology and impact cancer medicine.

Traditional experimental biology has provided a mechanistic understanding of cancer in which the malignancy develops through the acquisition of mutations that disrupt cellular processes. Several drugs developed to target such mutations have now demonstrated clinical value. These advances are unequivocal testaments to the value of traditional cellular and molecular biology. However, several features of cancer may limit the pace of progress that can be made with established experimental approaches alone. The mutated genes (and resultant mutant proteins) function within large biochemical networks. Biochemical networks typically have a large number of component molecules and are characterized by a large number of quantitative properties. Responses to a stimulus or perturbation are typically nonlinear and can display qualitative changes that depend upon the specific values of variable system properties. Features such as these can complicate the interpretation of experimental data and the formulation of logical hypotheses that drive further research. Mathematical models based upon the molecular reactions that define these networks combined with computational studies have the potential to deal with these obstacles and to enable currently available information to be more completely utilized. Many of the pressing problems in cancer biology and cancer medicine may benefit from a mathematical treatment. As work in this area advances, one can envision a future where such models may meaningfully contribute to the clinical management of cancer patients.

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.

Theoretical analysis reveals a role for RAF conformational autoinhibition in paradoxical activation.

RAF kinase inhibitors can, under certain conditions, increase RAF kinase signaling. This process, which is commonly referred to as 'paradoxical activation' (PA), is incompletely understood. We use mathematical and computational modeling to investigate PA and derive rigorous analytical expressions that illuminate the underlying mechanism of this complex phenomenon. We find that conformational autoinhibition modulation by a RAF inhibitor could be sufficient to create PA. We find that experimental RAF inhibitor drug dose-response data that characterize PA across different types of RAF inhibitors are best explained by a model that includes RAF inhibitor modulation of three properties: conformational autoinhibition, dimer affinity, and drug binding within the dimer (i.e., negative cooperativity). Overall, this work establishes conformational autoinhibition as a robust mechanism for RAF inhibitor-driven PA based solely on equilibrium dynamics of canonical interactions that comprise RAF signaling and inhibition.

Making inroads to precision medicine for the treatment of autoimmune diseases: Harnessing genomic studies to better diagnose and treat complex disorders.

Precision Medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle. Autoimmune diseases are those in which the body's natural defense system loses discriminating power between its own cells and foreign cells, causing the body to mistakenly attack healthy tissues. These conditions are very heterogeneous in their presentation and therefore difficult to diagnose and treat. Achieving precision medicine in autoimmune diseases has been challenging due to the complex etiologies of these conditions, involving an interplay between genetic, epigenetic, and environmental factors. However, recent technological and computational advances in molecular profiling have helped identify patient subtypes and molecular pathways which can be used to improve diagnostics and therapeutics. This review discusses the current understanding of the disease mechanisms, heterogeneity, and pathogenic autoantigens in autoimmune diseases gained from genomic and transcriptomic studies and highlights how these findings can be applied to better understand disease heterogeneity in the context of disease diagnostics and therapeutics.

How often is each gene mutated within the cancer patient population?

Genome sequenced samples from cancer patients helped identify roles of different mutation types and enabled targeted therapy development. However, critical questions like what are the gene mutation rates among the patients? or what genes are most commonly mutated, pan-cancer? have only been recently answered. Here, we highlight this recent advance.

Co-targeting KRAS G12C and EGFR reduces both mutant and wild-type RAS-GTP.

The combination of KRAS G12C inhibitors with EGFR inhibitors has reproducibly been shown to be beneficial. Here, we identify another benefit of this combination: it effectively inhibits both wild-type and mutant RAS. We believe that targeting both mutant and wild-type RAS helps explain why this combination of inhibitors is effective.

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.

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.

Cancer gene mutation frequencies for the U.S. population.

Mutations play a fundamental role in the development of cancer, and many create targetable vulnerabilities. There are both public health and basic science benefits from the determination of the proportion of all cancer cases within a population that include a mutant form of a gene. Here, we provide the first such estimates by combining genomic and epidemiological data. We estimate KRAS is mutated in only 11% of all cancers, which is less than PIK3CA (13%) and marginally higher than BRAF (8%). TP53 is the most commonly mutated gene (35%), and KMT2C, KMT2D, and ARID1A are among the ten most commonly mutated driver genes, highlighting the role of epigenetic dysregulation in cancer. Analysis of major cancer subclassifications highlighted varying dependencies upon individual cancer drivers. Overall, we find that cancer genetics is less dominated by high-frequency, high-profile cancer driver genes than studies limited to a subset of cancer types have suggested.

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.

Modeling membrane localization: case study of a Ras signaling model.

Modeling a biological system requires the careful integration of experimental data. It is unclear how best to incorporate rate constants measured in three-dimensional solution for reactions that physiologically occur between reactants confined to the two-dimensional cell membrane. One method adjusts second order rate constants by a factor that is the ratio of the cytoplasmic volume to the volume of a shell which membrane bound proteins can access. The value for this factor has been estimated to be 250. We have previously used this method in our model of the Ras signaling network that made several experimentally confirmed predictions. Here, we investigate if the value of this parameter affects model based predictions. We find that many of our results are robust to the value used. Two predictions appear to be sensitive to the value of the parameter: predicted levels of WT RasGTP after transfection with WT Ras and the experimentally observed increased levels of WT RasGTP when a GTPase Accelerating Protein (GAP) insensitive Ras mutant is present. For these predictions that are sensitive to the value of the membrane localization parameter, we find that the theoretically derived value of 250 results in model predictions that most closely match experimental observations.