ABSTRACT
KRAS is the most frequently mutated oncogene in solid cancers, and inhibitors that specifically target the KRAS-G12C mutant were recently approved for clinical use. The limited availability of experimental data pertaining to the sensitivity of KRAS-non-G12C mutants towards RAS inhibitors made it difficult to predict the response of KRAS-mutated cancers towards RAS-targeted therapies. The current study aims at evaluating sensitivity profiles of KRAS-non-G12C mutations towards clinically approved sotorasib and adagrasib, and experimental RAS inhibitors based on binding energies derived through molecular docking analysis. Computationally predicted sensitivities of KRAS mutants conformed with the available but limited experimental data, thus validating the usefulness of molecular docking approach in predicting clinical response towards RAS inhibitor treatment. Our results indicate differential sensitivity of KRAS mutants towards both clinical and experimental therapeutics; while certain mutants exhibited broad cross-resistance to most inhibitors, some mutants showed resistance towards specific inhibitors. These results thus suggest the potential of emergence of more resistance mutations in future towards RAS-targeted therapy and points to an urgent need to develop novel classes of inhibitors that are able to overcome both primary and secondary drug resistance. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-022-03407-9.
ABSTRACT
Current experimental and clinical data are inadequate to conclusively predict the oncogenicity of uncommon BRAF mutants and their sensitivity towards kinase inhibitors. Therefore, the present study aims at estimating sensitivity profiles of uncommon lung cancer specific BRAF mutations towards clinically approved as well as experimental therapeutics based on computationally derived direct binding energies. Based on the data derived from cBioportal, BRAF mutants displayed significant mutual exclusivity with KRAS and EGFR mutants indicating them as potential drivers in lung cancer. Predicted sensitivity of BRAF-V600E conformed to published experimental and clinical data thus validating the usefulness of computational approach. The BRAF-V600K displayed higher sensitivity to most inhibitors as compared to that of the BRAF-V600E. All the uncommon mutants displayed higher sensitivity than both the wild type and BRAF-V600E towards PLX 8394 and LSN3074753. While V600K, G469R and N581S displayed favorable sensitivity profiles to most inhibitors, V600L/M, G466A/E/V and G469A/V displayed resistance profiles to a variable degree. Notably, molecular dynamic (MD) simulation revealed that increased number of interactions caused enhanced sensitivity of G469R and N581S towards sorafenib. RAF kinase inhibitors were further classified into two groups as per their selectivity (Group I: BRAF-V600E-selective and Group II: CRAF-selective) based on which potential mutation-wise combinations of RAF kinase inhibitors were proposed to overcome resistance. Based on computational inhibitor sensitivity profiles, appropriate treatment strategies may be devised to prevent or overcome secondary drug resistance in lung cancer patients with uncommon mutations.
