Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects

Lung cancer is a major cause of cancer deaths worldwide, and has a very low survival rate. Non-small cell lung cancer (NSCLC) is the largest subset of lung cancers, which accounts for about 85% of all cases. It has been well established that a mutation in the epidermal growth factor receptor (EGFR) can lead to lung cancer. EGFR Tyrosine Kinase Inhibitors (TKIs) are developed to target the kinase domain of EGFR. These TKIs produce promising results at the initial stage of therapy, but the efficacy becomes limited due to the development of drug resistance. In this paper, we provide a comprehensive overview of computational methods, for understanding drug resistance mechanisms. The important EGFR mutants and the different generations of EGFR-TKIs, with the survival and response rates are discussed. Next, we evaluate the role of important EGFR parameters in drug resistance mechanism, including structural dynamics, hydrogen bonds, stability, dimerization, binding free energies, and signaling pathways. Personalized drug resistance prediction models, drug response curve, drug synergy, and other data-driven methods are also discussed. Recent advancements in deep learning; such as AlphaFold2, deep generative models, big data analytics, and the applications of statistics and permutation are also highlighted. We explore limitations in the current methodologies, and discuss strategies to overcome them. We believe this review will serve as a reference for researchers; to apply computational techniques for precision medicine, analyzing structures of protein-drug complexes, drug discovery, and understanding the drug response and resistance mechanisms in lung cancer patients.

Radiolabeled EGFR TKI as predictive imaging biomarkers in NSCLC patients – an overview

Non-small cell lung cancer (NSCLC) has one of the highest cancer-related mortality rates worldwide. In a subgroup of NSCLC, tumor growth is driven by epidermal growth factor receptors (EGFR) that harbor an activating mutation. These patients are best treated with EGFR tyrosine kinase inhibitors (EGFR TKI). Identifying the EGFR mutational status on a tumor biopsy or a liquid biopsy using tumor DNA sequencing techniques is the current approach to predict tumor response on EGFR TKI therapy. However, due to difficulty in reaching tumor sites, and varying inter- and intralesional tumor heterogeneity, biopsies are not always possible or representative of all tumor lesions, highlighting the need for alternative biomarkers that predict tumor response. Positron emission tomography (PET) studies using EGFR TKI-based tracers have shown that EGFR mutational status could be identified, and that tracer uptake could potentially be used as a biomarker for tumor response. However, despite their likely predictive and monitoring value, the EGFR TKI-PET biomarkers are not yet qualified to be used in the routine clinical practice. In this review, we will discuss the currently investigated EGFR-directed PET biomarkers, elaborate on the typical biomarker development process, and describe how the advances, challenges, and opportunities of EGFR PET biomarkers relate to this process on their way to qualification for routine clinical practice.

Computationally predicting binding affinity in protein-ligand complexes: free energy-based simulations and machine learning-based scoring functions

Accurately predicting protein-ligand binding affinities can substantially facilitate the drug discovery process, but it remains as a difficult problem. To tackle the challenge, many computational methods have been proposed. Among these methods, free energy-based simulations and machine learning-based scoring functions can potentially provide accurate predictions. In this paper, we review these two classes of methods, following a number of thermodynamic cycles for the free energy-based simulations and a feature-representation taxonomy for the machine learning-based scoring functions. More recent deep learning-based predictions, where a hierarchy of feature representations are generally extracted, are also reviewed. Strengths and weaknesses of the two classes of methods, coupled with future directions for improvements, are comparatively discussed.

Predicting the impacts of mutations on protein-ligand binding affinity based on molecular dynamics simulations and machine learning methods

Purpose

Mutation-induced variation of protein-ligand binding affinity is the key to many genetic diseases and the emergence of drug resistance, and therefore predicting such mutation impacts is of great importance. In this work, we aim to predict the mutation impacts on protein-ligand binding affinity using efficient structure-based, computational methods.

Methods

Relying on consolidated databases of experimentally determined data we characterize the affinity change upon mutation based on a number of local geometrical features and monitor such feature differences upon mutation during molecular dynamics (MD) simulations. The differences are quantified according to average difference, trajectory-wise distance or time-vary differences. Machine-learning methods are employed to predict the mutation impacts using the resulting conventional or time-series features. Predictions based on estimation of energy and based on investigation of molecular descriptors were conducted as benchmarks.

Results

Our method (machine-learning techniques using time-series features) outperformed the benchmark methods, especially in terms of the balanced F1 score. Particularly, deep-learning models led to the best prediction performance with distinct improvements in balanced F1 score and a sustained accuracy.

Conclusion

Our work highlights the effectiveness of the characterization of affinity change upon mutations. Furthermore, deep-learning techniques are well designed for handling the extracted time-series features. This study can lead to a deeper understanding of mutation-induced diseases and resistance, and further guide the development of innovative drug design.

Impact of Q141K on the Transport of Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors by ABCG2

The ATP-binding cassette transporter ABCG2 is expressed in various organs, such as the small intestine, liver, and kidney, and influences the pharmacokinetics of drugs that are its substrates. ABCG2 is also expressed by cancer cells and mediates resistance to anticancer agents by promoting the efflux of these drugs. In the present study, we investigated the interactions between epidermal growth factor receptor tyrosine kinase inhibitors and ABCG2 by MTT assay, intracellular drug accumulation assay, and FACS. This study showed that four epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs) (gefitinib, erlotinib, lapatinib, and afatinib) were transported from tumor cells as substrates of ABCG2. Q141K is a common single-nucleotide polymorphism of ABCG2 in Asians. We demonstrated that the extracellular efflux of gefitinib, erlotinib, and lapatinib was reduced by Q141K, whereas afatinib transport was not affected. In addition, all four EGFR TKIs inhibited the transport of other substrates by both wild-type and variant ABCG2 at 0.1 μM concentrations. Accordingly, epidermal growth factor receptor tyrosine kinase inhibitors may induce interactions with other drugs that are substrates of ABCG2, and single-nucleotide polymorphisms of ABCG2 may influence both the pharmacokinetics and efficacy of these anticancer agents.

Synonymous EGFR variant p.Q787Q is neither prognostic nor predictive in patients with lung adenocarcinoma

Patients with non-small cell lung cancer (NSCLC) harboring activating mutations in the Epidermal Growth Factor Receptor (EGFR) benefit from targeted therapies. A synonymous polymorphism (rs1050171, p.Q787Q) was shown to be associated with improved overall survival (OS) in colorectal cancer patients. As data in NSCLC are limited, we retrospectively analyzed associations of p.Q787Q with clinicopathological parameters including clinical response and outcome in patients with lung adenocarcinoma (ADC) who received tyrosine kinase inhibitor (TKI) therapy. Of 642 ADC patients whose tumors were profiled by next generation sequencing, 102 (15.9%) carried EGFR mutations targetable by TKIs (30.4% male patients, median age 65.1 y, 19.6% smokers with 12.8 median pack years). Seventy-nine patients (77.5%) received TKI therapy either as a first- or second-line therapy. Of the 102 EGFR-mutant tumors, 72 (70.6%) exhibited the p.Q787Q polymorphism and another 12 (11.8%) cases with p.Q787Q harbored an additional TKI insensitive mutation (p.T790M). The polymorphism was neither associated with classic clinicopathological parameters nor with overall survival (21.1 months vs. 20.1 months; P-value = 0.91) or clinical response (P-value = 0.122). The patients with p.T790M had worse survival compared to EGFR activating mutation carriers with and without p.Q787Q when analyzed as a separate group (27.5 months, P-value = 0.02). In conclusion, p.Q787Q is neither a suitable prognostic nor predictive biomarker for ADC patients receiving anti-EGFR therapy in first- or second-line of therapy. © 2016 Wiley Periodicals, Inc.