Pathological Mechanistic Studies of Osimertinib Resistance in Non-Small-Cell Lung Cancer Cells Using an Integrative Metabolomics-Proteomics Analysis

Abstract

Background. Osimertinib is the first-line therapeutic option for the T790M-mutant non-small-cell lung cancer and the acquired resistance obstructs its application. It is an urgent challenge to identify the potential mechanisms of osimertinib resistance for uncovering some novel therapeutic approaches. Methods. In the current study, the cell metabolomics based on ultra-high-performance liquid chromatography coupled with linear ion trap-Orbitrap mass spectrometry and the qualitative and tandem mass tags quantitative proteomics were performed. Results. 54 differential metabolites and 195 differentially expressed proteins were, respectively, identified. The amino acids metabolisms were significantly altered. HIF-1 signaling pathway modulating P-glycoproteins expression, PI3K-Akt pathway regulating survivin expression, and oxidative phosphorylation were upregulated, while arginine and proline metabolism regulating NO production and glycolysis/gluconeogenesis were downregulated during osimertinib resistance. Conclusion. The regulation of HIF-1 and PI3K-Akt signaling pathway, energy supply process, and amino acids metabolism are the promising therapeutic tactics for osimertinib resistance.

Integrated microbiome, metabolome, and proteome analysis identifies a novel interplay among commensal bacteria, metabolites and candidate targets in non‐small cell lung cancer

“…Additionally, intersected metabolic pathways of differential metabolites in serum‐/tissues‐based metabolomics were closely related to arginine, proline, retinol, and caffeine metabolism. Amino acid metabolism is one of the basic metabolic pathways in cancer growth, 48 and a study has clarified that amino acid metabolism was significantly altered in the T790M‐mutant NSCLC 49 . Retinol (vitamin A), a fat‐soluble nutrient obtained from diet, can affect cell differentiation, proliferation, and apoptosis‐related BPs 50 .…”

“…Amino acid metabolism is one of the basic metabolic pathways in cancer growth, 48 and a study has clarified that amino acid metabolism was significantly altered in the T790M‐mutant NSCLC. 49 Retinol (vitamin A), a fat‐soluble nutrient obtained from diet, can affect cell differentiation, proliferation, and apoptosis‐related BPs. 50 Caffeine (1,3,7‐trimethylxanthine) is a xanthine alkaloid found in a wide variety of dietary products.…”

Whole-exome sequencing reveals potential mechanisms of drug resistance to FGFR3-TACC3 targeted therapy and subsequent drug selection: towards a personalized medicine

“…Drug-driven resistance is common, which reflects the complicated course of tumor genomic alteration under the drug pressure. It has been observed and explored in multiple types of cancer, including lung cancer [ 12 ], pancreatic cancer [ 13 ], colorectal cancer [ 14 ] and so on. And researchers tried to find out the deep drug-driven resistance mechanisms.…”

A Serum Metabolomic Study Reveals Changes in Metabolites During the Treatment of Lung Cancer-Bearing Mice with Anlotinib

“…For example, Ma et al conducted cell metabolomics to explore the potential mechanism of acquired resistance to osimertinib, demonstrating that the regulation of amino acid metabolism, the energy supply process and the PI3K-Akt and HIF-1 signaling pathways were highly related to osimertinib resistance. 12 The HIF-1 signaling pathway, which modulates P-glycoprotein expression, the PI3K-Akt pathway, which regulates survivin expression, and oxidative phosphorylation were all upregulated, while arginine and proline metabolism, which regulates NO production and glycolysis/gluconeogenesis, were downregulated in osimertinib resistance. Peng et al examined 43 lung cancer patients with different responses to cisplatin to explore the mechanisms of cisplatin resistance by metabolomics and screened 8 novel biomarkers significantly associated with the platinum chemotherapy response, and a prediction model with these biomarkers was employed to predict chemotherapy response in a validation set, exhibiting 100% sensitivity and specificity.…”