NSCLC presents metabolic heterogeneity, and there is still some leeway for EGF stimuli in EGFR-mutated NSCLC

Abstract

BackgroundMetabolic remodeling is crucial in carcinogenesis and cancer progression. Oncogenic mutations may promote metabolic reprogramming in cancer cells to support their energy and biomass requirements. EGFR mutations are commonly found in non-small cell lung cancer (NSCLC) and may induce NSCLC metabolic rewiring. Whether EGFR-driven metabolic reprogramming triggers cell vulnerabilities with therapeutic potential remains unknown. MethodsThe role of EGFR signaling activation by EGF was investigated using NSCLC cell lines with different EGFR and KRAS status: A549 (EGFR WT and KRAS c.34G > A), H292 (EGFR WT and KRAS WT) and PC-9 (EGFR exon 19 E746-A750 deletion and KRAS WT). The effect of EGF on NSCLC cell death and cell cycle was evaluated using flow cytometry, and cell migration was assessed through wound healing. EGFR, HER2, MCT1, and MCT4 expression was analyzed through immunofluorescence or western blotting. We explored the impact of glucose and lactate bioavailability on NSCLC cells' metabolic profile using nuclear magnetic resonance (NMR) spectroscopy. Moreover, the expression of several relevant metabolic genes in NSCLC cells or patient samples was determined by RT-qPCR. ResultsWe showed that cell lines presented different metabolic profiles, and PC-9 cells were the most responsive to EGF stimulus, as they showed higher rates of cell proliferation and migration, together with altered metabolic behavior. By inhibiting EGFR with gefitinib, a decrease in glucose consumption was observed, which may be related to the fact that despite PC-9 harbor EGFR mutation, they still express the EGFR WT allele. The analysis of NSCLC patients' RNA showed a correlation between MCT1/MCT4 and GLUT1 expression in most cases, indicating that the metabolic information can serve as a reference in patients' follow-up. ConclusionTogether, this study shows that NSCLC cell lines have heterogeneous metabolic profiles, which may be underlaid by different genetic profiles, revealing an opportunity to identify and stratify patients who can benefit from metabolism-targeted therapies.