Abstract
Background
Cisplatin (CDDP) is a mainstay treatment for advanced head and neck squamous cell carcinomas (HNSCC) despite a high frequency of innate and acquired resistance. We hypothesised that tumours acquire CDDP resistance through an enhanced reductive state dependent on metabolic rewiring.
Methods
To validate this model and understand how an adaptive metabolic programme might be imprinted, we performed an integrated analysis of CDDP-resistant HNSCC clones from multiple genomic backgrounds by whole-exome sequencing, RNA-seq, mass spectrometry, steady state and flux metabolomics.
Results
Inactivating KEAP1 mutations or reductions in KEAP1 RNA correlated with Nrf2 activation in CDDP-resistant cells, which functionally contributed to resistance. Proteomics identified elevation of downstream Nrf2 targets and the enrichment of enzymes involved in generation of biomass and reducing equivalents, metabolism of glucose, glutathione, NAD(P), and oxoacids. This was accompanied by biochemical and metabolic evidence of an enhanced reductive state dependent on coordinated glucose and glutamine catabolism, associated with reduced energy production and proliferation, despite normal mitochondrial structure and function.
Conclusions
Our analysis identified coordinated metabolic changes associated with CDDP resistance that may provide new therapeutic avenues through targeting of these convergent pathways.
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Data availability
The data generated in this study are available within the article and its supplementary data files.
References
Bauml JM, Vinnakota R, Anna Park YH, Bates SE, Fojo T, Aggarwal C, et al. Cisplatin every 3 weeks versus weekly with definitive concurrent radiotherapy for squamous cell carcinoma of the head and neck. J Natl Cancer Inst. 2019;111:490–7.
Cepeda V, Fuertes MA, Castilla J, Alonso C, Quevedo C, Perez JM. Biochemical mechanisms of cisplatin cytotoxicity. Anticancer Agents Med Chem. 2007;7:3–18.
Chen HH, Song IS, Hossain A, Choi MK, Yamane Y, Liang ZD, et al. Elevated glutathione levels confer cellular sensitization to cisplatin toxicity by up-regulation of copper transporter hCtr1. Mol Pharm. 2008;74:697–704.
Drayton RM, Catto JW. Molecular mechanisms of cisplatin resistance in bladder cancer. Expert Rev Anticancer Ther. 2012;12:271–81.
Du H, Chen B, Jiao NL, Liu YH, Sun SY, Zhang YW. Elevated glutathione peroxidase 2 expression promotes cisplatin resistance in lung adenocarcinoma. Oxid Med Cell Longev. 2020;2020:7370157.
Mehanna H, Robinson M, Hartley A, Kong A, Foran B, Fulton-Lieuw T, et al. Radiotherapy plus cisplatin or cetuximab in low-risk human papillomavirus-positive oropharyngeal cancer (De-ESCALaTE HPV): an open-label randomised controlled phase 3 trial. Lancet. 2019;393:51–60.
Mezencev R, Matyunina LV, Wagner GT, McDonald JF. Acquired resistance of pancreatic cancer cells to cisplatin is multifactorial with cell context-dependent involvement of resistance genes. Cancer Gene Ther. 2016;23:446–53.
Yu W, Chen Y, Dubrulle J, Stossi F, Putluri V, Sreekumar A, et al. Cisplatin generates oxidative stress which is accompanied by rapid shifts in central carbon metabolism. Sci Rep. 2018;8:4306.
Yu W, Chen Y, Putluri N, Coarfa C, Robertson MJ, Putluri V, et al. Acquisition of cisplatin resistance shifts head and neck squamous cell carcinoma metabolism toward neutralization of oxidative stress. Cancers. 2020;12:1670.
Zhu H, Luo H, Zhang W, Shen Z, Hu X, Zhu X. Molecular mechanisms of cisplatin resistance in cervical cancer. Drug Des Dev Ther. 2016;10:1885–95.
Galluzzi L, Senovilla L, Vitale I, Michels J, Martins I, Kepp O, et al. Molecular mechanisms of cisplatin resistance. Oncogene. 2012;31:1869–83.
Makovec T. Cisplatin and beyond: molecular mechanisms of action and drug resistance development in cancer chemotherapy. Radio Oncol. 2019;53:148–58.
Pham D, Deter CJ, Reinard MC, Gibson GA, Kiselyov K, Yu W, et al. Using ligand-accelerated catalysis to repurpose fluorogenic reactions for platinum or copper. ACS Cent Sci. 2020;6:1772–88.
Sandulache VC, Chen Y, Feng L, William WN, Skinner HD, Myers JN, et al. Metabolic interrogation as a tool to optimize chemotherapeutic regimens. Oncotarget. 2017;8:18154–65.
Niehr F, Eder T, Pilz T, Konschak R, Treue D, Klauschen F, et al. Multilayered omics-based analysis of a head and neck cancer model of cisplatin resistance reveals intratumoral heterogeneity and treatment-induced clonal selection. Clin Cancer Res. 2018;24:158–68.
Simons AL, Ahmad IM, Mattson DM, Dornfeld KJ, Spitz DR. 2-Deoxy-D-glucose combined with cisplatin enhances cytotoxicity via metabolic oxidative stress in human head and neck cancer cells. Cancer Res. 2007;67:3364–70.
Krupar R, Hautmann MG, Pathak RR, Varier I, McLaren C, Gaag D, et al. Immunometabolic determinants of chemoradiotherapy response and survival in head and neck squamous cell carcinoma. Am J Pathol. 2018;188:72–83.
Kazi MA, Veeramachaneni R, Deng D, Putluri N, Cardinas M, Sikora A, et al. Glutathione peroxidase 2 is a metabolic driver of the tumor immune microenvironment and immune checkpoint inhibitor response. J ImmunoTher Cancer. 2022;10:e004752.
Sandulache VC, Chen Y, Skinner HD, Lu T, Feng L, Court LE, et al. Acute tumor lactate perturbations as a biomarker of genotoxic stress: development of a biochemical model. Mol Cancer Ther. 2015;14:2901–8.
Zhao M, Sano D, Pickering CR, Jasser SA, Henderson YC, Clayman GL, et al. Assembly and initial characterization of a panel of 85 genomically validated cell lines from diverse head and neck tumor sites. Clin Cancer Res. 2011; https://doi.org/10.1158/1078-0432.CCR-11-0690.
Frederick M, Skinner HD, Kazi SA, Sikora AG, Sandulache VC. High expression of oxidative phosphorylation genes predicts improved survival in squamous cell carcinomas of the head and neck and lung. Sci Rep. 2020;10:6380.
Wu M, Neilson A, Swift AL, Moran R, Tamagnine J, Parslow D, et al. Multiparameter metabolic analysis reveals a close link between attenuated mitochondrial bioenergetic function and enhanced glycolysis dependency in human tumor cells. Am J Physiol Cell Physiol. 2007;292:C125–36.
Alhallak K, Rebello LG, Muldoon TJ, Quinn KP, Rajaram N. Optical redox ratio identifies metastatic potential-dependent changes in breast cancer cell metabolism. Biomed Opt Express. 2016;7:4364–74.
Chance B, Schoener B, Oshino R, Itshak F, Nakase Y. Oxidation-reduction ratio studies of mitochondria in freeze-trapped samples. NADH and flavoprotein fluorescence signals. J Biol Chem. 1979;254:4764–71.
Hou J, Wright HJ, Chan N, Tran R, Razorenova OV, Potma EO, et al. Correlating two-photon excited fluorescence imaging of breast cancer cellular redox state with seahorse flux analysis of normalized cellular oxygen consumption. J Biomed Opt. 2016;21:60503.
Kolenc OI, Quinn KP. Evaluating cell metabolism through autofluorescence imaging of NAD(P)H and FAD. Antioxid Redox Signal. 2019;30:875–89.
Lakowicz JR, Szmacinski H, Nowaczyk K, Johnson ML. Fluorescence lifetime imaging of free and protein-bound NADH. Proc Natl Acad Sci USA. 1992;89:1271–5.
Varone A, Xylas J, Quinn KP, Pouli D, Sridharan G, McLaughlin-Drubin ME, et al. Endogenous two-photon fluorescence imaging elucidates metabolic changes related to enhanced glycolysis and glutamine consumption in precancerous epithelial tissues. Cancer Res. 2014;74:3067–75.
Walsh AJ, Cook RS, Manning HC, Hicks DJ, Lafontant A, Arteaga CL, et al. Optical metabolic imaging identifies glycolytic levels, subtypes, and early-treatment response in breast cancer. Cancer Res. 2013;73:6164–74.
Walsh AJ, Mueller KP, Tweed K, Jones I, Walsh CM, Piscopo NJ, et al. Classification of T-cell activation via autofluorescence lifetime imaging. Nat Biomed Eng. 2021;5:77–88.
Nakashima N, Yoshihara K, Tanaka F, Yagi K. Picosecond fluorescence lifetime of the coenzyme of D-amino acid oxidase. J Biol Chem. 1980;255:5261–63.
Walsh AJ, Skala MC. An automated image processing routine for segmentation of cell cytoplasms in high-resolution autofluorescence images. Proc. SPIE. 2014;8948:161–6.
Henderson YC, Mohamed ASR, Maniakas A, Chen Y, Powell RT, Peng S, et al. A high-throughput approach to identify effective systemic agents for the treatment of anaplastic thyroid carcinoma. J Clin Endocrinol Metab. 2021;106:2962–78.
Osman AA, Neskey DM, Katsonis P, Patel AA, Ward AM, Hsu TK, et al. Evolutionary action score of TP53 coding variants is predictive of platinum response in head and neck cancer patients. Cancer Res. 2015;75:1205–15.
Boot A, Huang MN, Ng AWT, Ho SC, Lim JQ, Kawakami Y, et al. In-depth characterization of the cisplatin mutational signature in human cell lines and in esophageal and liver tumors. Genome Res. 2018;28:654–65.
Hsu TK, Asmussen J, Koire A, Choi BK, Gadhikar MA, Huh E, et al. A general calculus of fitness landscapes finds genes under selection in cancers. Genome Res. 2022;32:916–29.
Katsonis P, Lichtarge O. A formal perturbation equation between genotype and phenotype determines the evolutionary action of protein-coding variations on fitness. Genome Res. 2014;24:2050–8.
Koire A, Katsonis P, Kim YW, Buchovecky C, Wilson SJ, Lichtarge O. A method to delineate de novo missense variants across pathways prioritizes genes linked to autism. Sci Transl Med. 2021;13:eabc1739.
Gillespie M, Jassal B, Stephan R, Milacic M, Rothfels K, Senff-Ribeiro A, et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res. 2022;50:D687–92.
Sandulache VC, Ow TJ, Pickering CR, Frederick MJ, Zhou G, Fokt I, et al. Glucose, not glutamine, is the dominant energy source required for proliferation and survival of head and neck squamous carcinoma cells. Cancer. 2011;117:2926–38.
Sandulache VC, Skinner HD, Wang Y, Chen Y, Dodge CT, Ow TJ, et al. Glycolytic inhibition alters anaplastic thyroid carcinoma tumor metabolism and improves response to conventional chemotherapy and radiation. Mol Cancer Ther. 2012;11:1373–80.
Ramirez MS, Lee J, Walker CM, Sandulache VC, Hennel F, Lai SY, et al. Radial spectroscopic MRI of hyperpolarized [1-(13) C] pyruvate at 7 tesla. Magn Reson Med. 2014;72:986–95.
Sandulache VC, Chen Y, Lee J, Rubinstein A, Ramirez MS, Skinner HD, et al. Evaluation of hyperpolarized [1-(1)(3)C]-pyruvate by magnetic resonance to detect ionizing radiation effects in real time. PLoS ONE. 2014;9:e87031.
Bankson JA, Walker CM, Ramirez MS, Stefan W, Fuentes D, Merritt ME, et al. Kinetic modeling and constrained reconstruction of hyperpolarized [1-13C]-pyruvate offers improved metabolic imaging of tumors. Cancer Res. 2015;75:4708–17.
Lee J, Ramirez MS, Walker CM, Chen Y, Yi S, Sandulache VC, et al. High-throughput hyperpolarized (13)C metabolic investigations using a multi-channel acquisition system. J Magn Reson. 2015;260:20–7.
Simons AL, Parsons AD, Foster KA, Orcutt KP, Fath MA, Spitz DR. Inhibition of glutathione and thioredoxin metabolism enhances sensitivity to perifosine in head and neck cancer cells. J Oncol. 2009;2009:519563.
Dannenmann B, Lehle S, Hildebrand DG, Kubler A, Grondona P, Schmid V, et al. High glutathione and glutathione peroxidase-2 levels mediate cell-type-specific DNA damage protection in human induced pluripotent stem cells. Stem Cell Rep. 2015;4:886–98.
Brigelius-Flohe R, Maiorino M. Glutathione peroxidases. Biochimica et Biophysica Acta. 2013;1830:3289–303.
Osman AA, Arslan E, Bartels M, Michikawa C, Lindemann A, Tomczak K, et al. Dysregulation and epigenetic reprogramming of NRF2 signaling axis promote acquisition of cisplatin resistance and metastasis in head and neck squamous cell carcinoma. Clin Cancer Res. 2023; https://doi.org/10.1158/1078-0432.CCR-22-2747.
Funding
This work was supported by the National Institute of Dental and Craniofacial Research through R03DE028858 and the National Cancer Institute through U54CA274321. VCS, SYL, YC and JAB were supported by the Cancer Prevention and Research Institute of Texas (CPRIT) grant RP170366. NP is supported by the CPRIT Proteomics and Metabolomics Core Facility (RP210227), NIH (P30 CA125123 R01CA220297, R01CA216426, P42ES027725) and Dan L. Duncan Cancer Center. This work was supported by the National Institutes of Health (AG068214-01, AG061105, GM066099, and AG074009 to OL). JA was supported by a training fellowship from the Gulf Coast Consortia, on the NLM Training Program in Biomedical Informatics & Data Science (T15LM007093). Work performed through the Mouse Metabolism and Phenotyping Core (Seahorse) is supported by NIH UM1HG006348 and NIH R01DK114356 and flowing NIH grant P30ES030285 (CW) GVE is a Cancer Prevention and Research Institute of Texas (CPRIT) Scholar in Cancer Research. GVE is supported by CPRIT RR200009; NIH 1K22CA241113-01, and a Breast Cancer Alliance Young Investigator Grant. AJW is supported by CPRIT GCC Combinatorial Drug Discovery Program (RP200668) and NIH NIGMS (R35GM142990). CC is partially supported by CPRIT (RP210227 and RP200504), and NIH P30ES030285 and P42 ES0327725 grants. CS and RTP are supported by CRPIT CFSA core grants RP150578 and RP200668. XQ is supported by NIH R43-GM137665 and JW is supported by NIH R01-GM115622. The content is solely the responsibility of the authors and does not necessarily represent the official views of their sponsors.
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Conceptualisation: WY, MJF, VCS and JNM; funding: VCS and JNM; reagent generation: WY, YC, JAB, SYL, AO and AJW; assay design and development: WY, YC, NP, VP, CC, MJF, AHMK, JKA, PK, OL, and MDM; data generation: WY, YC, NP, AO, CC, VP, AHMK, JKA, PK, WL, CCS, RTP, JK, KMA, LU, AT, MTD, JAN, TW, JD, KRP, MAH, MLB, GVE, XQ, JW, AIF, AJW, OL, VCS and MJF; data analysis: WY, YC, NP, AO, CC, VP, AHMK, JKA, PK, WL, CCS, RTP, JK, KMA, LU, AT, MTD, JAN, MLB, GVE, XQ, JW, AIF, AJW, OL, VCS and MJF; manuscript drafting: WY, YC, NP, CC, VCS, JNM and MJF; manuscript review: WY, YC, NP, AO, CC, VP, AHMK, JKA, PK, WL, CCS, RTP, JK, KMA, LU, AT, MTD, JAN, MLB, GVE, XQ, JW, AIF, AJW, OL, VCS, MJF, HDS and FMJ.
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The authors report no competing interests relevant to the work summarised in the current manuscript. JW is the founder of Chemical Biology Probes LLC and holds a patent related to the GSH probes used in this study. JW also serves as a consultant for CoRegen Inc.
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Yu, W., Chen, Y., Putluri, N. et al. Evolution of cisplatin resistance through coordinated metabolic reprogramming of the cellular reductive state. Br J Cancer 128, 2013–2024 (2023). https://doi.org/10.1038/s41416-023-02253-7
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DOI: https://doi.org/10.1038/s41416-023-02253-7