Journal of Shanghai Jiao Tong University (Medical Science) ›› 2025, Vol. 45 ›› Issue (2): 138-149.doi: 10.3969/j.issn.1674-8115.2025.02.002

• Basic research • Previous Articles    

Metabolic profiling of lung cancer cells with acquired resistance to sotorasib

ZOU Peichen1(), LIU Hongyu2, AIHEMAITI· Ayinazhaer1, ZHU Liang1, TANG Yabin1(), LEI Huimin1()   

  1. 1.Department of Pharmacology and Chemical Biology, Shanghai Jiao Tong University College of Basic Medical Sciences, Shanghai 200025, China
    2.Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
  • Received:2024-07-09 Accepted:2024-10-23 Online:2025-02-28 Published:2025-02-28
  • Contact: TANG Yabin, LEI Huimin E-mail:peichenzou1997@163.com;leonyabin2018@shsmu.edu.cn;leihuimin02@163.com
  • Supported by:
    National Natural Science Foundation of China(82273950);Natural Science Foundation of Shanghai(21ZR1436700)

Abstract:

Objective ·To explore the metabolic profiling and metabolic reprogramming patterns of lung cancer cells with acquired resistance to sotorasib, a specific inhibitor to KRAS. Methods ·The H2122 and H358 lung cancer cell models with acquired resistance to sotorasib (H2122-SR and H358-SR cells) were established and validated by CCK-8 assay. Ultra-performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) was employed to acquire the metabolic profiling of the resistant lung cancer cells and their homologous parental cells. Untargeted metabolomics studies and metabolic characterizations were conducted with multi-dimensional methods, including principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), to identify differential metabolites associated with acquired resistance to sotorasib. Then these differential metabolites were subjected to pathway enrichment analysis. Heatmap analysis was used to compare the changes in metabolites in major differential metabolic pathways between the resistant and parental cells. Results ·The cell models of H2122 and H358 with acquired resistance were successfully constructed, with half-maximal inhibitory concentrations (IC50) of sotorasib being 50 times higher than those of the parental cells. Besides, the metabolic profiling was significantly different between the resistant and parental cells. A total of 48 differential metabolites were identified between H2122-SR and H2122 cells. The top 10 differential metabolites, ranked by VIP values, were uridine, xanthylic acid, indole-3-carboxylic acid, nicotinic acid, xanthosine, xanthine, N-methylnicotinamide, hypoxanthine, trigonelline, and galactonic acid. Between H358-SR and H358 cells, a total of 79 differential metabolites were identified. The top 10 differential metabolites, ranked by VIP values, were glutathione, xanthosine, 2-ketoglutaric acid, carboxyethyl lysine, thymidine, purine, riboflavin, 3-indoleacrylic acid, indole-3-pyruvic acid, and dihydrouracil. The differential metabolites in the two lung cancer cell lines mainly participated in purine metabolism and glycolysis/gluconeogenesis, and purine metabolism was the most significantly altered metabolic pathway. Heatmap analysis showed that many metabolites in the purine metabolism were elevated in the sotorasib-resistant cells. Conclusion ·The lung cancer cells with acquired resistance to sotorasib show enhanced purine metabolism.

Key words: lung cancer, sotorasib, drug resistance, metabolic profiling, ultra-performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS), purine metabolism

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