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Longitudinal evaluation of five nasopharyngeal carcinoma animal models on the microPET/MR platform

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Abstract

Purpose

We longitudinally evaluated the tumour growth and metabolic activity of three nasopharyngeal carcinoma (NPC) cell line models (C666-1, C17 and NPC43) and two xenograft models (Xeno76 and Xeno23) using a micropositron emission tomography and magnetic resonance (microPET/MR). With a better understanding of the interplay between tumour growth and metabolic characteristics of these NPC models, we aim to provide insights for the selection of appropriate NPC cell line/xenograft models to assist novel drug discovery and evaluation.

Methods

Mice were imaged by 18F-deoxyglucose ([18F]FDG) microPET/MR twice a week for consecutive 3–7 weeks. [18F]FDG uptake was quantified by standardized uptake value (SUV) and presented as SUVmean tumour-to-liver ratio (SUVRmean). Longitudinal tumour growth patterns and metabolic patterns were recorded. SUVRmean and histological characteristics were compared across the five NPC models. Cisplatin was administrated to one selected optimal tumour model, C17, to evaluate our imaging platform.

Results

We found variable tumour growth and metabolic patterns across different NPC tumour types. C17 has an optimal growth rate and higher tumour metabolic activity compared with C666-1. C666-1 has a fast growth rate but is low in SUVRmean at endpoint due to necrosis as confirmed by H&E. NPC43 and Xeno76 have relatively slow growth rates and are low in SUVRmean, due to severe necrosis. Xeno23 has the slowest growth rate, and a relative high SUVRmean. Cisplatin showed the expected therapeutic effect in the C17 model in marked reduction of tumour size and metabolism.

Conclusion

Our study establishes an imaging platform that characterizes the growth and metabolic patterns of different NPC models, and the platform is well able to demonstrate drug treatment outcome supporting its use in novel drug discovery and evaluation for NPC.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Not applicable.

References

  1. Okekpa SI, S M N Mydin RB, Mangantig E, Azmi NSA, Zahari SNS, Kaur G, et al. Nasopharyngeal carcinoma (NPC) risk factors: a systematic review and meta-analysis of the association with lifestyle, diets, socioeconomic and sociodemographic in Asian Region. Asian Pac J Cancer Prev. 2019;20:3505–14. https://doi.org/10.31557/APJCP.2019.20.11.3505.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians. 2021;71:209–49. https://doi.org/10.3322/caac.21660.

    Article  Google Scholar 

  3. Lee A, Chow JCH, Lee NY. Treatment deescalation strategies for nasopharyngeal cancer: a review. JAMA Oncol. 2020. https://doi.org/10.1001/jamaoncol.2020.6154.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Lee AWM, Ng WT, Chan JYW, Corry J, Mäkitie A, Mendenhall WM, et al. Management of locally recurrent nasopharyngeal carcinoma. Cancer Treat Rev. 2019;79: 101890. https://doi.org/10.1016/j.ctrv.2019.101890.

    Article  PubMed  Google Scholar 

  5. Lee VHF, Lam KO, Lee AWM. Chapter 10 - Standard of care for nasopharyngeal carcinoma (2018–2020) Lee AWM ML Lung WT Ng editors. Carcinoma: Academic Press Nasopharyngeal; 2019. p. 205–38.

    Google Scholar 

  6. Chan JYW, Lam TC, Ng WT. Chapter 13 - Salvage of local recurrence. In: Lung ML, Ng WT, editors. Lee AWM. Nasopharyngeal Carcinoma: Academic Press; 2019. p. 289–312.

    Google Scholar 

  7. Huang B, Wong C-YO, Lai V, Kwong DL-W, Khong P-L. Prognostic value of 18 F-FDG PET-CT in nasopharyngeal carcinoma: is dynamic scanning helpful? BioMed Research International. 2015;2015:582614. https://doi.org/10.1155/2015/582614.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Alessi A, Lorenzoni A, Cavallo A, Padovano B, Iacovelli NA, Bossi P, et al. Role of pretreatment 18F-FDG PET/CT parameters in predicting outcome of non-endemic EBV DNA-related nasopharyngeal cancer (NPC) patients treated with IMRT and chemotherapy. Radiol Med (Torino). 2019;124:414–21. https://doi.org/10.1007/s11547-018-0980-6.

    Article  Google Scholar 

  9. Chan S-C, Yeh C-H, Chang JT-C, Chang K-P, Wang J-H, Ng S-H. Combing MRI perfusion and 18F-FDG PET/CT metabolic biomarkers helps predict survival in advanced nasopharyngeal carcinoma: a prospective multimodal imaging study. Cancers. 2021;13:1550.

    Article  CAS  Google Scholar 

  10. Cheng Y, Bai L, Shang J, Tang Y, Ling X, Guo B, et al. Preliminary clinical results for PET/MR compared with PET/CT in patients with nasopharyngeal carcinoma. Oncol Rep. 2020;43:177–87. https://doi.org/10.3892/or.2019.7392.

    Article  CAS  PubMed  Google Scholar 

  11. Chan S-C, Yeh C-H, Yen T-C, Ng S-H, Chang JT-C, Lin C-Y, et al. Clinical utility of simultaneous whole-body 18F-FDG PET/MRI as a single-step imaging modality in the staging of primary nasopharyngeal carcinoma. European Journal of Nuclear Medicine and Molecular Imaging. 2018;45:1297–308. https://doi.org/10.1007/s00259-018-3986-3.

    Article  PubMed  Google Scholar 

  12. Piao Y, Cao C, Xu Y, Huang S, Jiang F, Jin T, et al. Detection and staging of recurrent or metastatic nasopharyngeal carcinoma in the era of FDG PET/MR. Eur Arch Otorhinolaryngol. 2021. https://doi.org/10.1007/s00405-021-06779-5.

    Article  PubMed  Google Scholar 

  13. Chomet M, Schreurs M, Vos R, Verlaan M, Kooijman EJ, Poot AJ, et al. Performance of nanoScan PET/CT and PET/MR for quantitative imaging of 18F and 89Zr as compared with ex vivo biodistribution in tumor-bearing mice. EJNMMI Res. 2021;11:57. https://doi.org/10.1186/s13550-021-00799-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Weerasekera A, Crabbé M, Tomé SO, Gsell W, Sima D, Casteels C, et al. Non-invasive characterization of amyotrophic lateral sclerosis in a hTDP-43A315T mouse model: A PET-MR study. NeuroImage: Clinical. 2020;27:102327. https://doi.org/10.1016/j.nicl.2020.102327.

    Article  Google Scholar 

  15. Kim K, Kim H, Bae S-H, Lee S-Y, Kim Y-H, Na J, et al. [(18)F]CB251 PET/MR imaging probe targeting translocator protein (TSPO) independent of its polymorphism in a neuroinflammation model. Theranostics. 2020;10:9315–31. https://doi.org/10.7150/thno.46875.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Busson P, Ganem G, Flores P, Mugneret F, Clausse B, Caillou B, et al. Establishment and characterization of three transplantable EBV-containing nasopharyngeal carcinomas. Int J Cancer. 1988;42:599–606. https://doi.org/10.1002/ijc.2910420422.

    Article  CAS  PubMed  Google Scholar 

  17. Huang DP, Ho JHC, Chan WK, Lau WH, Lui M. Cytogenetics of undifferentiated nasopharyngeal carcinoma xenografts from southern chinese. Int J Cancer. 1989;43:936–9. https://doi.org/10.1002/ijc.2910430535.

    Article  CAS  PubMed  Google Scholar 

  18. Cheung ST, Huang DP, Hui ABY, Lo KW, Ko CW, Tsang YS, et al. Nasopharyngeal carcinoma cell line (C666–1) consistently harbouring Epstein-Barr virus. Int J Cancer. 1999;83:121–6. https://doi.org/10.1002/(SICI)1097-0215(19990924)83:1%3c121::AID-IJC21%3e3.0.CO;2-F.

    Article  CAS  PubMed  Google Scholar 

  19. Yip YL, Lin W, Deng W, Jia L, Lo KW, Busson P, et al. Establishment of a nasopharyngeal carcinoma cell line capable of undergoing lytic Epstein-Barr virus reactivation. Lab Invest. 2018;98:1093–104. https://doi.org/10.1038/s41374-018-0034-7.

    Article  PubMed  Google Scholar 

  20. Lin W, Yip YL, Jia L, Deng W, Zheng H, Dai W, et al. Establishment and characterization of new tumor xenografts and cancer cell lines from EBV-positive nasopharyngeal carcinoma. Nat Commun. 2018;9:4663. https://doi.org/10.1038/s41467-018-06889-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Prasetyanti PR, van Hooff SR, van Herwaarden T, de Vries N, Kalloe K, Rodermond H, et al. Capturing colorectal cancer inter-tumor heterogeneity in patient-derived xenograft (PDX) models. Int J Cancer. 2019;144:366–71. https://doi.org/10.1002/ijc.31767.

    Article  CAS  PubMed  Google Scholar 

  22. Yang Z, Shi Q, Zhang Y, Pan H, Yao Z, Hu S, et al. Pretreatment 18 F-FDG uptake heterogeneity can predict survival in patients with locally advanced nasopharyngeal carcinoma——a retrospective study. Radiat Oncol. 2015;10:4. https://doi.org/10.1186/s13014-014-0268-5.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Chan S-C, Chang K-P, Fang Y-HD, Tsang N-M, Ng S-H, Hsu C-L, et al. Tumor heterogeneity measured on F-18 fluorodeoxyglucose positron emission tomography/computed tomography combined with plasma Epstein-Barr Virus load predicts prognosis in patients with primary nasopharyngeal carcinoma. The Laryngoscope. 2017;127:E22–8. https://doi.org/10.1002/lary.26172.

    Article  CAS  PubMed  Google Scholar 

  24. Bankhead P, Loughrey MB, Fernández JA, Dombrowski Y, McArt DG, Dunne PD, et al. QuPath: open source software for digital pathology image analysis. Sci Rep. 2017;7:16878. https://doi.org/10.1038/s41598-017-17204-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wong T-L, Ng K-Y, Tan KV, Chan L-H, Zhou L, Che N, et al. CRAF Methylation by PRMT6 regulates aerobic glycolysis–driven hepatocarcinogenesis via ERK-dependent PKM2 nuclear relocalization and activation. Hepatology. 2020;71:1279–96. https://doi.org/10.1002/hep.30923.

    Article  CAS  PubMed  Google Scholar 

  26. Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med. 2009;50:122S-S150. https://doi.org/10.2967/jnumed.108.057307.

    Article  CAS  PubMed  Google Scholar 

  27. Shiono S, Abiko M, Okazaki T, Chiba M, Yabuki H, Sato T. Positron emission tomography for predicting recurrence in stage I lung adenocarcinoma: standardized uptake value corrected by mean liver standardized uptake value. Eur J Cardiothorac Surg. 2011;40:1165–9. https://doi.org/10.1016/j.ejcts.2011.02.041.

    Article  PubMed  Google Scholar 

  28. Lee JY, Kim M-S, Kim EH, Chung N, Jeong YK. Retrospective growth kinetics and radiosensitivity analysis of various human xenograft models. Laboratory animal research. 2016;32:187–93.

    Article  Google Scholar 

  29. Hai W, Wu X, Shi S, Yang Y, Yang Z, Li B, et al. The effects of season change and fasting on Brown adipose tissue FDG-PET in mice. Biochem Biophys Res Commun. 2020;529:398–403.

    Article  CAS  Google Scholar 

  30. Fueger BJ, Czernin J, Hildebrandt I, Tran C, Halpern BS, Stout D, et al. Impact of animal handling on the results of 18F-FDG PET studies in mice. J Nucl Med. 2006;47:999–1006.

    CAS  PubMed  Google Scholar 

  31. Eschbach RS, Kazmierczak PM, Heimer MM, Todica A, Hirner-Eppeneder H, Schneider MJ, et al. (18)F-FDG-PET/CT and diffusion-weighted MRI for monitoring a BRAF and CDK 4/6 inhibitor combination therapy in a murine model of human melanoma. Cancer Imaging. 2018;18:2. https://doi.org/10.1186/s40644-018-0135-y.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Mergenthaler P, Meisel A. Chapter 8 - Animal models: value and translational potency. In: Wehling M, editor. Principles of Translational Science in Medicine. 3rd ed. Boston: Academic Press; 2021. p. 95–103.

    Chapter  Google Scholar 

  33. Landi M, Everitt J, Berridge B. Bioethical, reproducibility, and translational challenges of animal models. Ilar j. 2021. https://doi.org/10.1093/ilar/ilaa027.

    Article  PubMed  Google Scholar 

  34. Xue Z, Lui VWY, Li Y, Jia L, You C, Li X, et al. Therapeutic evaluation of palbociclib and its compatibility with other chemotherapies for primary and recurrent nasopharyngeal carcinoma. J Exp Clin Cancer Res. 2020;39:262. https://doi.org/10.1186/s13046-020-01763-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Julien S, Merino-Trigo A, Lacroix L, Pocard M, Goéré D, Mariani P, et al. Characterization of a large panel of patient-derived tumor xenografts representing the clinical heterogeneity of human colorectal cancer. Clin Cancer Res. 2012;18:5314–28. https://doi.org/10.1158/1078-0432.Ccr-12-0372.

    Article  CAS  PubMed  Google Scholar 

  36. Dunne LW, Huang Z, Meng W, Fan X, Zhang N, Zhang Q, et al. Human decellularized adipose tissue scaffold as a model for breast cancer cell growth and drug treatments. Biomaterials. 2014;35:4940–9.

    Article  CAS  Google Scholar 

  37. Kapałczyńska M, Kolenda T, Przybyła W, Zajączkowska M, Teresiak A, Filas V, et al. 2D and 3D cell cultures–a comparison of different types of cancer cell cultures. Archives of medical science: AMS. 2018;14:910.

    PubMed  Google Scholar 

  38. Lehuédé C, Dupuy F, Rabinovitch R, Jones RG, Siegel PM. Metabolic plasticity as a determinant of tumor growth and metastasis. Can Res. 2016;76:5201–8.

    Article  Google Scholar 

  39. Hau PM, Lung HL, Wu M, Tsang CM, Wong K-L, Mak NK, et al. Targeting Epstein-Barr virus in nasopharyngeal carcinoma. Front Oncol. 2020;10:600.

    Article  Google Scholar 

  40. Pauli C, Hopkins BD, Prandi D, Shaw R, Fedrizzi T, Sboner A, et al. Personalized in vitro and in vivo cancer models to guide precision medicine. Cancer Discov. 2017;7:462–77.

    Article  Google Scholar 

  41. Kang Y, He W, Ren C, Qiao J, Guo Q, Hu J, et al. Advances in targeted therapy mainly based on signal pathways for nasopharyngeal carcinoma. Signal Transduct Target Ther. 2020;5:245. https://doi.org/10.1038/s41392-020-00340-2.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Cheng T, Zhan X. Pattern recognition for predictive, preventive, and personalized medicine in cancer. EPMA Journal. 2017;8:51–60.

    Article  CAS  Google Scholar 

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Funding

The research was supported by the University of Hong Kong Seed Fund for Basic Research (201910159081) and the Hong Kong Research Grants Council (RGC) Collaborative Research Grant (C7018-14E).

Author information

Author notes

  1. Jingjing Shi and Zhichao Xue contributed to the work equally and should be regarded as co-first authors.

Authors and Affiliations

  1. Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

    Jingjing Shi, Kel Vin Tan, Hui Yuan & Pek-Lan Khong

  2. School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

    Zhichao Xue, Anna Chi Man Tsang & Sai Wah Tsao

  3. Center for Advanced Measurement Science, National Institute of Metrology, Beijing, China

    Zhichao Xue

  4. Department of Nuclear Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China

    Hui Yuan

  5. Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Hong Kong SAR, China

    Anna Chi Man Tsang

  6. Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Pek-Lan Khong

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  1. Jingjing Shi
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Contributions

JS and ZX contributed to the study conception and design. JS conducted the microPET/MR experiments, image analysis and data analysis. ZX conducted the animal model establishment and histological study. The first draft of the manuscript was written by JS and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Pek-Lan Khong.

Ethics declarations

Ethics approval

All animal experiments were conducted under conditions compliant with the animal license issued by the Hong Kong Department of Health and with the approval of the Committee on the Use of Live Animals in Teaching and Research (CULATR) of the University of Hong Kong (CULATR No. 4898).

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Not applicable.

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All authors involved in the study provided their consent to the submission of this article for publication.

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The authors declare no competing interests.

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Shi, J., Xue, Z., Tan, K.V. et al. Longitudinal evaluation of five nasopharyngeal carcinoma animal models on the microPET/MR platform. Eur J Nucl Med Mol Imaging 49, 1497–1507 (2022). https://doi.org/10.1007/s00259-021-05633-4

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