上海交通大学学报（医学版）

上海交通大学学报（医学版） >

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

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

论著 · 基础研究

索托拉西布获得性耐药肺癌细胞的代谢轮廓分析

  • 邹沛辰 ,
  • 刘鸿宇 ,
  • 阿衣娜扎尔·艾合买提 ,
  • 朱亮 ,
  • 唐亚斌 ,
  • 雷绘敏
展开
  • 1.上海交通大学基础医学院药理学与化学生物学系，上海 200025
    2.上海交通大学医学院附属胸科医院呼吸内科，上海 200030
邹沛辰（1997—），男，硕士生；电子信箱：peichenzou1997@163.com。
唐亚斌，高级实验师，博士；电子信箱：leonyabin2018@shsmu.edu.cn
雷绘敏，高级实验师，博士；电子信箱：leihuimin02@163.com。

收稿日期: 2024-07-09

  录用日期: 2024-10-23

  网络出版日期: 2025-02-28

基金资助

国家自然科学基金项目(82273950);上海市自然科学基金(21ZR1436700)

收起

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

摘要

目的·探究KRAS靶向抑制剂索托拉西布（sotorasib）获得性耐药肺癌细胞的代谢特征及代谢重编程规律。方法·构建肺癌细胞H2122和H358的索托拉西布获得性耐药细胞模型（H2122-SR和H358-SR细胞），并采用CCK-8法加以验证。应用超高效液相色谱串联四极杆飞行时间质谱（UPLC-QTOF/MS）分析获得性耐药肺癌细胞及其同源亲本细胞的代谢轮廓，利用主成分分析法和偏最小二乘法-判别分析法等统计学方法进行非靶向代谢组学分析及代谢表征，筛选并鉴定索托拉西布获得性耐药相关的差异代谢物，并对所筛选到的差异代谢物进行通路富集分析。通过绘制热图比较分析主要差异代谢通路上的代谢物在耐药细胞与亲本细胞之间的差异。结果·成功构建H2122和H358细胞的获得性耐药细胞模型，对索托拉西布的半数抑制浓度均较亲本细胞提升50倍以上。与亲本细胞相比，耐药细胞代谢轮廓存在显著差异。H2122-SR与H2122细胞之间发现48种差异代谢物，其中变量投影重要程度（variable importance in the projection，VIP）值排名前10位的差异代谢物为尿嘧啶、黄苷酸、吲哚-3-甲酸、烟酸、黄苷、黄嘌呤、N-甲基烟酰胺、次黄嘌呤、葫芦巴碱、半乳糖醛酸；H358-SR与H358细胞之间发现79种差异代谢物，其中VIP值排名前10位的差异代谢物为还原型谷胱甘肽、黄苷、α-酮戊二酸、羧甲基赖氨酸、胸苷、嘌呤、核黄素、3-吲哚丙烯酸、吲哚-3-丙酮酸、二氢尿嘧啶。2种肺癌细胞系的差异代谢通路主要涉及嘌呤代谢和糖酵解/糖异生，其中嘌呤代谢变化最为显著。热图分析显示，耐药细胞嘌呤代谢途径多数代谢物水平升高。结论·索托拉西布获得性耐药的肺癌细胞嘌呤代谢增强。

关键词： 肺癌; 索托拉西布; 耐药性; 代谢轮廓; 超高效液相色谱串联高分辨质谱; 嘌呤代谢

本文引用格式

邹沛辰 , 刘鸿宇 , 阿衣娜扎尔·艾合买提 , 朱亮 , 唐亚斌 , 雷绘敏 . 索托拉西布获得性耐药肺癌细胞的代谢轮廓分析[J]. 上海交通大学学报（医学版）, 2025 , 45(2) : 138 -149 . DOI: 10.3969/j.issn.1674-8115.2025.02.002

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

参考文献

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
文章导航

/

〈 〉