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

Abstract

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.

Keywords: in silico prediction model; mutation diversity; nonsmall cell lung cancer; osimertinib; rare EGFR mutation.

Fig. 1.

Frequency and distribution of variants of unknown significance in EGFR. (A) Pie chart showing the frequency of genetic alterations of indicated genes. (B) Distribution of variants of unknown significance in the EGFR gene. (C) Oncopanel illustrating the genetic alterations in NSCLC patients with variants of unknown significance in EGFR.

Fig. 2.

Calculation of binding energy values for EGFR exon 20 insertion mutations. (A) Structures of mutated EGFR kinase domains for EGFR A763_Y764insFQEA, A767_V769dupASV, D770_N771insNPG, and N771_P772insPGD were modeled using wild-type EGFR data (Protein Data Bank ID code 4ZAU). The structures were extracted from the trajectories of 50-ns molecular dynamics simulations that were used for binding affinity calculations. The structures in gray and in colors are wild-type and mutated EGFRs, respectively. Mutated amino acids in EGFR structures are indicated in red. Osimertinib molecules are shown as sticks (white, carbon; blue, nitrogen; red, oxygen). (B) Plot of ΔG_bind values against negative log-transformed IC₅₀ values. Each mutated EGFR is indicated by a dot. Dashed line represents a linear fit with squared correlation coefficient R².

Fig. 3.

Biological confirmation of sensitivity of N771_P772insPGD to osimertinib. (A) A positron emission tomography-computed tomography scan showing 18F-fluorodeoxyglucose accumulation in the right lung filed of a NSCLC patient. (B) Results of Sanger sequencing of EGFR from patient-derived xenograft tumor DNA showing three amino acid (PGD) insertion in N771_P772. (C) Effect of osimertinib (25 or 50 mg/kg) on the size of patient-derived xenograft tumors in nonobese diabetic/severe combined immunodeficiency mice. Each group n = 5. Data are presented as the mean ± SD. **P < 0.01 for the combination of osmertinib (25 mg/kg or 50 mg/kg) versus control (Upper). Pictures of the tumors (Lower).