Journal of Shanghai Jiao Tong University (Medical Science) >

2025 , Vol. 45 >Issue 2: 138 - 149

DOI: https://doi.org/10.3969/j.issn.1674-8115.2025.02.002

Basic research

Metabolic profiling of lung cancer cells with acquired resistance to sotorasib

  • ZOU Peichen ,
  • LIU Hongyu ,
  • AIHEMAITI· Ayinazhaer ,
  • ZHU Liang ,
  • TANG Yabin ,
  • LEI Huimin
Expand
  • 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
TANG Yabin, E-mail: leonyabin2018@shsmu.edu.cn
LEI Huimin, E-mail: leihuimin02@163.com.

Received date: 2024-07-09

  Accepted date: 2024-10-23

  Online published: 2025-02-28

Supported by

National Natural Science Foundation of China(82273950);Natural Science Foundation of Shanghai(21ZR1436700)

Fold

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

Cite this article

ZOU Peichen , LIU Hongyu , AIHEMAITI· Ayinazhaer , ZHU Liang , TANG Yabin , LEI Huimin . Metabolic profiling of lung cancer cells with acquired resistance to sotorasib[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2025 , 45(2) : 138 -149 . DOI: 10.3969/j.issn.1674-8115.2025.02.002

References

1 BRAY F, LAVERSANNE M, SUNG H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3): 229-263.
2 RYAN M B, CORCORAN R B. Therapeutic strategies to target RAS-mutant cancers[J]. Nat Rev Clin Oncol, 2018, 15(11): 709-720.
3 MOORE A R, ROSENBERG S C, MCCORMICK F, et al. RAS-targeted therapies: is the undruggable drugged?[J]. Nat Rev Drug Discov, 2020, 19(8): 533-552.
4 JUDD J, ABDEL KARIM N, KHAN H, et al. Characterization of KRAS mutation subtypes in non-small cell lung cancer[J]. Mol Cancer Ther, 2021, 20(12): 2577-2584.
5 ZHU C X, GUAN X Q, ZHANG X N, et al. Targeting KRAS mutant cancers: from druggable therapy to drug resistance[J]. Mol Cancer, 2022, 21(1): 159.
6 SINGHAL A, LI B T, O'REILLY E M. Targeting KRAS in cancer[J]. Nat Med, 2024, 30(4): 969-983.
7 ZHANG J M, ZHANG J H, LIU Q, et al. Resistance looms for KRAS G12C inhibitors and rational tackling strategies[J]. Pharmacol Ther, 2022, 229: 108050.
8 YAEGER R, MEZZADRA R, SINOPOLI J, et al. Molecular characterization of acquired resistance to KRASG12C-EGFR inhibition in colorectal cancer[J]. Cancer Discov, 2023, 13(1): 41-55.
9 ZHAO Y L, MURCIANO-GOROFF Y R, XUE J Y, et al. Diverse alterations associated with resistance to KRAS(G12C) inhibition[J]. Nature, 2021, 599(7886): 679-683.
10 唐亚斌. 嘌呤代谢重编程ENPP1依赖机制在肺癌EGFR-TKI获得性耐药中的作用[D]. 上海: 上海交通大学, 2020.
  TANG Y B. ENPP1-dependent purine metabolic reprogramming mechanism of EGFR-TKI acquired resistance in lung cancer[D]. Shanghai: Shanghai Jiao Tong University, 2020.
11 RITCHIE M D, HOLZINGER E R, LI R W, et al. Methods of integrating data to uncover genotype-phenotype interactions[J]. Nat Rev Genet, 2015, 16(2): 85-97.
12 ORTMAYR K, DUBUIS S, ZAMPIERI M. Metabolic profiling of cancer cells reveals genome-wide crosstalk between transcriptional regulators and metabolism[J]. Nat Commun, 2019, 10(1): 1841.
13 VANDE VOORDE J, STEVEN R T, NAJUMUDEEN A K, et al. Metabolic profiling stratifies colorectal cancer and reveals adenosylhomocysteinase as a therapeutic target[J]. Nat Metab, 2023, 5(8): 1303-1318.
14 WANG Z Y, ZHU H Y, XIONG W. Advances in mass spectrometry-based multi-scale metabolomic methodologies and their applications in biological and clinical investigations[J]. Sci Bull, 2023, 68(19): 2268-2284.
15 HANAHAN D. Hallmarks of cancer: new dimensions[J]. Cancer Discov, 2022, 12(1): 31-46.
16 FAUBERT B, SOLMONSON A, DEBERARDINIS R J. Metabolic reprogramming and cancer progression[J]. Science, 2020, 368(6487): eaaw5473.
17 PANG Z Q, LU Y, ZHOU G Y, et al. MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation[J]. Nucleic Acids Res, 2024, 52(W1): W398-W406.
18 JIMéNEZ-CARVELO A M, MARTíN-TORRES S, ORTEGA-GAVILáN F, et al. PLS-DA vs sparse PLS-DA in food traceability. A case study: authentication of avocado samples[J]. Talanta, 2021, 224: 121904.
19 SHAKEEL M, MAJEED M I, NAWAZ H, et al. Surface-enhanced Raman spectroscopy for the characterization of pellets of biofilm forming bacterial strains of Staphylococcus epidermidis[J]. Photodiagnosis Photodyn Ther, 2022, 40: 103145.
20 LIN Z Y, LI J W, ZHANG J, et al. Metabolic reprogramming driven by IGF2BP3 promotes acquired resistance to EGFR inhibitors in non-small cell lung cancer[J]. Cancer Res, 2023, 83(13): 2187-2207.
21 NIE M, CHEN N, PANG H H, et al. Targeting acetylcholine signaling modulates persistent drug tolerance in EGFR-mutant lung cancer and impedes tumor relapse[J]. J Clin Invest, 2022, 132(20): e160152.
22 SHI D D, SAVANI M R, ABDULLAH K G, et al. Emerging roles of nucleotide metabolism in cancer[J]. Trends Cancer, 2023, 9(8): 624-635.
23 MULLEN N J, SINGH P K. Nucleotide metabolism: a pan-cancer metabolic dependency[J]. Nat Rev Cancer, 2023, 23(5): 275-294.
24 SHUKLA S K, PUROHIT V, MEHLA K, et al. MUC1 and HIF-1α signaling crosstalk induces anabolic glucose metabolism to impart gemcitabine resistance to pancreatic cancer[J]. Cancer Cell, 2017, 32(3): 392.
25 HUANG Y T, CHAN S, CHEN S N, et al. Wnt/β-catenin signalling activates IMPDH2-mediated purine metabolism to facilitate oxaliplatin resistance by inhibiting caspase-dependent apoptosis in colorectal cancer[J]. J Transl Med, 2024, 22(1): 133.
26 ZHOU W H, YAO Y Y, SCOTT A J, et al. Purine metabolism regulates DNA repair and therapy resistance in glioblastoma[J]. Nat Commun, 2020, 11(1): 3811.
27 BEN-SAHRA I, HOXHAJ G, RICOULT S J H, et al. mTORC1 induces purine synthesis through control of the mitochondrial tetrahydrofolate cycle[J]. Science, 2016, 351(6274): 728-733.
28 HUANG F, NI M, CHALISHAZAR M D, et al. Inosine monophosphate dehydrogenase dependence in a subset of small cell lung cancers[J]. Cell Metab, 2018, 28(3): 369-382.e5.
29 TORRENCE M E, MACARTHUR M R, HOSIOS A M, et al. The mTORC1-mediated activation of ATF4 promotes protein and glutathione synthesis downstream of growth signals[J]. eLife, 2021, 10: e63326.
30 ALI E S, SAHU U, VILLA E, et al. ERK2 phosphorylates PFAS to mediate posttranslational control of de novo purine synthesis[J]. Mol Cell, 2020, 78(6): 1178-1191.e6.
31 XIAO Y, YU T J, XU Y, et al. Emerging therapies in cancer metabolism[J]. Cell Metab, 2023, 35(8): 1283-1303.
32 LABRIE M, BRUGGE J S, MILLS G B, et al. Therapy resistance: opportunities created by adaptive responses to targeted therapies in cancer[J]. Nat Rev Cancer, 2022, 22(6): 323-339.
33 WU Q, BA-ALAWI W, DEBLOIS G, et al. GLUT1 inhibition blocks growth of RB1-positive triple negative breast cancer[J]. Nat Commun, 2020, 11(1): 4205.
34 LIU H, LYU H, JIANG G M, et al. ALKBH5-mediated m6A demethylation of GLUT4 mRNA promotes glycolysis and resistance to HER2-targeted therapy in breast cancer[J]. Cancer Res, 2022, 82(21): 3974-3986.
Options
Outlines

/

〈 〉