Computational and Experimental Characterization of Patient Derived Mutations Reveal an Unusual Mode of Regulatory Spine Assembly and Drug Sensitivity in EGFR Kinase

Computational Analysis of Activation of Dimerized Epidermal Growth Factor Receptor Kinase Using the String Method and Markov State Model

Epidermal growth factor receptor (EGFR) activation is accompanied by dimerization. During the activation of the intracellular kinase domain, two EGFR kinases form an asymmetric dimer, and one side of the dimer (receiver) is activated. Using the string method and Markov state model (MSM), we performed a computational analysis of the structural changes in the activation of the EGFR dimer in this study. The string method reveals the minimum free-energy pathway (MFEP) from the inactive to active structure. The MSM was constructed from numerous trajectories of molecular dynamics simulations around the MFEP, which revealed the free-energy map of structural changes. In the activation of the receiver kinase, the unfolding of the activation loop (A-loop) is followed by the rearrangement of the C-helix, as observed in other kinases. However, unlike other kinases, the free-energy map of EGFR at the asymmetric dimer showed that the active state yielded the highest stability and revealed how interactions at the dimer interface induced receiver activation. As the H-helix of the activator approaches the C-helix of the receiver during activation, the A-loop unfolds. Subsequently, L782 of the receiver enters the pocket between the G- and H-helices of the activator, leading to a rearrangement of the hydrophobic residues around L782 of the receiver, which constitutes a structural rearrangement of the C-helix of the receiver from an outward to an inner position. The MSM analysis revealed long-time scale trajectories via kinetic Monte Carlo.

Dark kinase annotation, mining, and visualization using the Protein Kinase Ontology

The Protein Kinase Ontology (ProKinO) is an integrated knowledge graph that conceptualizes the complex relationships among protein kinase sequence, structure, function, and disease in a human and machine-readable format. In this study, we have significantly expanded ProKinO by incorporating additional data on expression patterns and drug interactions. Furthermore, we have developed a completely new browser from the ground up to render the knowledge graph visible and interactive on the web. We have enriched ProKinO with new classes and relationships that capture information on kinase ligand binding sites, expression patterns, and functional features. These additions extend ProKinO's capabilities as a discovery tool, enabling it to uncover novel insights about understudied members of the protein kinase family. We next demonstrate the application of ProKinO. Specifically, through graph mining and aggregate SPARQL queries, we identify the p21-activated protein kinase 5 (PAK5) as one of the most frequently mutated dark kinases in human cancers with abnormal expression in multiple cancers, including a previously unappreciated role in acute myeloid leukemia. We have identified recurrent oncogenic mutations in the PAK5 activation loop predicted to alter substrate binding and phosphorylation. Additionally, we have identified common ligand/drug binding residues in PAK family kinases, underscoring ProKinO's potential application in drug discovery. The updated ontology browser and the addition of a web component, ProtVista, which enables interactive mining of kinase sequence annotations in 3D structures and Alphafold models, provide a valuable resource for the signaling community. The updated ProKinO database is accessible at https://prokino.uga.edu.

Regulatory spine RS3 residue of protein kinases: a lipophilic bystander or a decisive element in the small-molecule kinase inhibitor binding?

In recent years, protein kinases have been one of the most pursued drug targets. These determined efforts have resulted in ever increasing numbers of small-molecule kinase inhibitors reaching to the market, offering novel treatment options for patients with distinct diseases. One essential component related to the activation and normal functionality of a protein kinase is the regulatory spine (R-spine). The R-spine is formed of four conserved residues named as RS1–RS4. One of these residues, RS3, located in the C-terminal part of αC-helix, is usually accessible for the inhibitors from the ATP-binding cavity as its side chain is lining the hydrophobic back pocket in many protein kinases. Although the role of RS3 has been well acknowledged in protein kinase function, this residue has not been actively considered in inhibitor design, even though many small-molecule kinase inhibitors display interactions to this residue. In this minireview, we will cover the current knowledge of RS3, its relationship with the gatekeeper, and the role of RS3 in kinase inhibitor interactions. Finally, we comment on the future perspectives how this residue could be utilized in the kinase inhibitor design.

Repurposing FDA Drug Compounds against Breast Cancer by Targeting EGFR/HER2

Repurposing studies have identified several FDA-approved compounds as potential inhibitors of the intracellular domain of epidermal growth factor receptor 1 (EGFR) and human epidermal receptor 2 (HER2). EGFR and HER2 represent important targets for the design of new drugs against different types of cancer, and recently, differences in affinity depending on active or inactive states of EGFR or HER2 have been identified. In this study, we first identified FDA-approved compounds with similar structures in the DrugBank to lapatinib and gefitinib, two known inhibitors of EGFR and HER2. The selected compounds were submitted to docking and molecular dynamics MD simulations with the molecular mechanics generalized Born surface area approach to discover the conformational and thermodynamic basis for the recognition of these compounds on EGFR and HER2. These theoretical studies showed that compounds reached the ligand-binding site of EGFR and HER2, and some of the repurposed compounds did not interact with residues involved in drug resistance. An in vitro assay performed on two different breast cancer cell lines, MCF-7, and MDA-MB-23, showed growth inhibitory activity for these repurposed compounds on tumorigenic cells at micromolar concentrations. These repurposed compounds open up the possibility of generating new anticancer treatments by targeting HER2 and EGFR.

Acquired rare recurrent EGFR mutations as mechanisms of resistance to Osimertinib in lung cancer and in silico structural modelling

A growing number of progression on Osimertinib among EGFR-mutated lung cancers represents a great challenge clinically. Our study aims to gain insights into novel mechanisms of acquired resistance to Osimertinib. We performed genomic studies on 2 large independent cohorts of lung cancer patients with progressed diseases on different tyrosine kinase inhibitors (TKIs). In silico modeling was used to study the structural mechanism of selected EGFR mutations. Compared with the 1^st-TKIs-resistant group, EGFR mutations C797S/G, L718Q/V, L792F/H were significantly more enriched in the Osimertinib-resistant cohort, whose sensitivities to Osimertinib were successfully predicted. Importantly, a total of 14 low-frequency EGFR mutations were exclusively or significantly observed in the Osimertinib-resistant group, 7 were predicted to dramatically reduce the binding affinity of EGFR to Osimertinib (G796S, V802F, T725M, Q791L/H, P794S/R). Analysis of pre-Osimertinib treatment samples of two patients supported that EGFR V802F and G796S were acquired during the treatment. In addition, EGFR G796S was predicted to be susceptible to gefitinib. This study represented the largest real-world data so far investigating Osimertinib resistance in EGFR-mutated lung cancer. We identified a collection of coexistent EGFR rare mutations and provided possible guidance for those patients who progressed on the first-line treatment of Osimertinib.

A Novel Acquired Exon 20 EGFR M766Q Mutation in Lung Adenocarcinoma Mediates Osimertinib Resistance but is Sensitive to Neratinib and Poziotinib

INTRODUCTION

Osimertinib is an effective third-generation tyrosine kinase inhibitor (TKI) for EGFR-mutant lung cancers. However, treatment for patients with acquired resistance to osimertinib remains challenging. We characterized a novel EGFR mutation in exon 20 that was acquired while on osimertinib.

METHODS

A 79-year-old woman had disease progression during third-line treatment with osimertinib for an EGFR L858R/T790M-mutant lung cancer. Sequencing of circulating cell-free DNA showed EGFR L858R, an acquired novel EGFR M766Q mutation in exon 20, and no evidence of EGFR T790M. Homology modeling was performed to investigate the effects of M766Q on binding to osimertinib. L858R and L858R/M766Q mutations were retrovirally introduced into Ba/F3 and NIH/3T3 cells and evaluated for sensitivity to first-generation (erlotinib), second-generation (afatinib, neratinib, and poziotinib), and third-generation TKIs (osimertinib) by cell viability and colony-formation assays. EGFR-mediated signaling pathways were interrogated by western blotting.

RESULTS

Modeling suggested that EGFR M766Q could disrupt osimertinib binding. L858R/M766Q double-mutant cells were 12-fold more resistant to osimertinib, and more than 250-fold more resistant to erlotinib and afatinib, as compared to L858R-mutant cells. In contrast, double-mutant cells remained sensitive to neratinib and poziotinib at clinically relevant doses (concentration that inhibits 50%, 4.3 and 1.3 nM, respectively). This was corroborated by the effects of the TKIs on colony formation and EGFR signaling.

CONCLUSIONS

Acquisition of EGFR M766Q exon 20 mutation is a novel mechanism of acquired resistance to osimertinib. EGFR-mutant lung cancers with an acquired EGFR M766Q mutation in the setting of osimertinib resistance may be sensitive to neratinib and poziotinib.

Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare EGFR mutations

A variety of rare mutations account for 10–20% of EGFR mutations in nonsmall cell lung cancer. However, due to high diversity, proper medication for patients with such mutations is impossible in daily clinic. To appropriately treat lung cancer patients harboring such rare EGFR mutations, a robust prediction model to predict sensitivities of rare EGFR mutants to existing drugs is strongly needed. Using molecular dynamics simulation-based model, we successfully predicted diverse sensitivities of EGFR exon 20 insertion mutants to existing inhibitors. The findings suggest the usefulness of in silico simulation to overcome mutation diversity at a clinically relevant level. The present in silico model will help in selecting effective drugs for these patients.

Next generation sequencing (NGS)-based tumor profiling identified an overwhelming number of uncharacterized somatic mutations, also known as variants of unknown significance (VUS). The therapeutic significance of EGFR mutations outside mutational hotspots, consisting of >50 types, in nonsmall cell lung carcinoma (NSCLC) is largely unknown. In fact, our pan-nation screening of NSCLC without hotspot EGFR mutations (n = 3,779) revealed that the majority (>90%) of cases with rare EGFR mutations, accounting for 5.5% of the cohort subjects, did not receive EGFR-tyrosine kinase inhibitors (TKIs) as a first-line treatment. To tackle this problem, we applied a molecular dynamics simulation-based model to predict the sensitivity of rare EGFR mutants to EGFR-TKIs. The model successfully predicted the diverse in vitro and in vivo sensitivities of exon 20 insertion mutants, including a singleton, to osimertinib, a third-generation EGFR-TKI (R² = 0.72, P = 0.0037). Additionally, our model showed a higher consistency with experimentally obtained sensitivity data than other prediction approaches, indicating its robustness in analyzing complex cancer mutations. Thus, the in silico prediction model will be a powerful tool in precision medicine for NSCLC patients carrying rare EGFR mutations in the clinical setting. Here, we propose an insight to overcome mutation diversity in lung cancer.

Altered conformational landscape and dimerization dependency underpins the activation of EGFR by αC-β4 loop insertion mutations

Mutational activation of epidermal growth factor receptor (EGFR) in human cancers involves both point mutations and complex mutations (insertions and deletions). In particular, short in-frame insertion mutations within a conserved αC-β4 loop in the EGFR kinase domain are frequently observed in tumor samples and patients harboring these mutations are insensitive to first-generation EGFR inhibitors. Despite the prevalence and clinical relevance of insertion mutations, the mechanisms by which these mutations regulate EGFR activity and contribute to drug sensitivity are poorly understood. Using cell-based mutation screening, we find that the precise location, length, and sequence of the inserted segment are critical for ligand-independent EGFR activation and downstream signaling. We identify three insertion mutations (N771_P772insN, D770_N771insG, and D770>GY) that activate EGFR in a unique way by relying more on the "acceptor" interface for kinase activation. Our drug inhibition studies indicate that these activating insertion mutations respond more favorably to osimertinib, a recently Food and Drug Administration-approved EGFR inhibitor for T790M-positive patients with lung cancer. Molecular dynamics simulations and umbrella sampling of WT and mutant EGFR suggest a model in which activating insertion mutations increase catalytic activity by relieving key autoinhibitory interactions associated with αC-helix movement and by lowering the transition free energy ([Formula: see text]) between active and inactive states. Our studies also identify a transition state sampled by activating insertion mutations that can be exploited in the design of mutant-selective EGFR inhibitors.

Integrative annotation and knowledge discovery of kinase post-translational modifications and cancer-associated mutations through federated protein ontologies and resources

Many bioinformatics resources with unique perspectives on the protein landscape are currently available. However, generating new knowledge from these resources requires interoperable workflows that support cross-resource queries. In this study, we employ federated queries linking information from the Protein Kinase Ontology, iPTMnet, Protein Ontology, neXtProt, and the Mouse Genome Informatics to identify key knowledge gaps in the functional coverage of the human kinome and prioritize understudied kinases, cancer variants and post-translational modifications (PTMs) for functional studies. We identify 32 functional domains enriched in cancer variants and PTMs and generate mechanistic hypotheses on overlapping variant and PTM sites by aggregating information at the residue, protein, pathway and species level from these resources. We experimentally test the hypothesis that S768 phosphorylation in the C-helix of EGFR is inhibitory by showing that oncogenic variants altering S768 phosphorylation increase basal EGFR activity. In contrast, oncogenic variants altering conserved phosphorylation sites in the ‘hydrophobic motif’ of PKCβII (S660F and S660C) are loss-of-function in that they reduce kinase activity and enhance membrane translocation. Our studies provide a framework for integrative, consistent, and reproducible annotation of the cancer kinomes.

Structural and energetic basis for the molecular recognition of dual synthetic vs. natural inhibitors of EGFR/HER2

Activation of EGFR starts by ligand binding at the extracellular domain which results in homo and heterodimerization, leading to phosphorylation, activation of downstream signaling pathways which upregulate expression of genes, proliferation and angiogenesis. Abnormalities in the expression of EGFR play a critical role in the development of different types of cancer. HER2 is the preferred heterodimerization partner for EGFR; this biological characteristic together with the high percentage of structural homology has been exploited in the design of dual synthetic inhibitors against EGFR/HER2. Herein we combined structural data and molecular dynamics (MD) simulations coupled to an MMGBSA approach to provide insight into the binding mechanism between two dual synthetics (lapatinib and TAK-285) and one dual natural inhibitor (EGCG) which target EGFR/HER2. In addition, we proposed some EGCG derivatives which were filtered through in silico screening. Structural analysis demonstrated that the coupling of synthetic, natural or newly designed compounds impacts the conformational space of EGFR and HER2 differently. Energetic analysis points out that lapatinib and TAK-285 have better affinity for inactive EGFR than the active EGFR state or HER2, whereas some EGCG derivatives seem to form binding affinities similar to those observed for lapatinib or TAK-285.