Document Type

Article

Abstract

The epidermal growth factor receptor (EGFR) is a frequently mutated protein kinase found in patients suffering from non-small cell lung cancer (NSCLC). Although therapeutic drugs targeting EGFR are available, not all patients respond to these drugs, and it is not yet known whether some clinically detected mutations are actionable. Although the oncogenicity of some mutants is well established, many mutants found in patients are not yet classified or classified as Variants of Uncertain Significance. For newly discovered rare mutants, strong evidence supporting their classification into oncogenic or benign is often lacking. Under such situations, multiple pieces of evidence can still help classification, even if each individual piece of evidence is weaker. Computational predictions have been considered as supporting information by the Standard Operating Procedure (SOP) of the Clinical Genome Resource/Cancer Genomics Consortium/Variant Interpretation for Cancer Consortium (ClinGen/CGC/VICC). The SOP considers predictions from multiple computational programs as providing only one piece of supporting information to avoid double counting because these programs share common approaches. A computational approach that explicitly accounts for the conformational effects of mutations via molecular dynamics (MD) simulation could add another piece of complementary supporting information. We tested the hypothesis that features of structural dynamics observed from MD simulation could help distinguish oncogenic from benign mutants. To this end, we performed three independent MD simulations lasting 1 μs each for wild-type EGFR and 32 of its oncogenic or benign mutants. We found that random forest machine-learning models constructed using suitable structural features were able to distinguish oncogenic and benign mutations effectively. Although we did not supply features of structural motifs commonly used by scientists to distinguish active and inactive conformations, our machine-learning approach identified and utilized some features associated with these motifs for classifying EGFR mutants.

Publication Date

9-22-2026

Publication Title

ACS Omega

Volume

11

Issue

37

First Page

56609

Last Page

56622

Comments

PDF checked with Adobe Accessibility Checker and did not pass all elements of reading order, tables, and annotations. Alternate text generated by Microsoft Copilot (Basic) (October 2026).

DOI

https://doi.org/10.1021/acsomega.6c08155

Funding Number

CA224033

Funding Sponsor

U.S. National Institutes of Health

Download

Included in

Chemistry Commons

Share

COinS