Application of non-invasive imaging on cell tracking of adoptive cell therapy: A systemic review
-
GRAPHICAL ABSTRACT
DownLoad:
Full size image
-
PUBLIC SUMMARY
-
Adoptive cell therapy (ACT) displays considerable promise for treating advanced solid cancers.
Molecular imaging advances ACT by revealing the in vivo properties and biological behavior of ACT.
Molecular imaging technology can act as a switch to turn off ACT side effects, enhancing its safety.
-
-
ABSTRACT
Adoptive cell therapy (ACT) has emerged as a promising treatment for advanced or recurrent cancer patients. It is a challenge to dynamically monitor and modulate the biological behavior and function of infused cells to predict the efficacy of ACT and manage its adverse effects. Numerous studies have endeavored to monitor ACT non-invasively using diverse imaging modalities. Despite each modality's inherent advantages and disadvantages, there remains substantial room for advancement in in vivo cell tracking, making non-invasive cell tracking a continuing research priority. This review examines the current state of radionuclide, optical, magnetic resonance, and magnetic particle imaging in cell tracking, analyzing their adaptability across various application scenarios and discussing the challenges each modality encounters. Finally, combined with the current research status, the review proposes prospective directions for non-invasive molecular imaging to address the dynamic monitoring requirements of high security, high sensitivity, versatility, and longevity in cell tracking. The advancement of molecular imaging technology is expected to enhance understanding of ACT function mechanisms, optimize combination treatment strategies, and bolster the efficacy and safety of ACT. -
-
REFERENCES
[1] Saez-Ibañez A. R., Upadhaya S., Partridge T., et al. (2024). The changing landscape of cancer cell therapies: Clinical trials and real-world data. Nat. Rev. Drug Discov. 23:736−737. DOI:10.1038/d41573-024-00094-4 [2] Seitter S. J., Sherry R. M., Yang J. C., et al. (2021). Impact of prior treatment on the efficacy of adoptive transfer of tumor-infiltrating lymphocytes in patients with metastatic melanoma. Clin. Cancer Res. 27:5289−5298. DOI:10.1158/1078-0432.CCR-21-1171 [3] Lacerna L. V., Jr., Stevenson G. W. and Stevenson H. C. (1988). Adoptive cancer immunotherapy utilizing lymphokine activated killer cells and gamma interferon activated killer monocytes. Pharmacol. Ther. 38:453−465. DOI:10.1016/0163-7258(88)90014-9 [4] Sommaggio R., Cappuzzello E., Dalla Pieta A., et al. (2020). Adoptive cell therapy of triple negative breast cancer with redirected cytokine-induced killer cells. Oncoimmunology 9:1777046. DOI:10.1080/2162402X.2020.1777046 [5] Palucka K. and Banchereau J. (2012). Cancer immunotherapy via dendritic cells. Nat. Rev. Cancer 12:265−277. DOI:10.1038/nrc3258 [6] Maskalenko N. A., Zhigarev D. and Campbell K. S. (2022). Harnessing natural killer cells for cancer immunotherapy: Dispatching the first responders. Nat. Rev. Drug Discov. 21:559−577. DOI:10.1038/s41573-022-00413-7 [7] Nagarsheth N. B., Norberg S. M., Sinkoe A. L., et al. (2021). TCR-engineered T cells targeting E7 for patients with metastatic HPV-associated epithelial cancers. Nat. Med. 27:419−425. DOI:10.1038/s41591-020-01225-1 [8] Han X., Wang Y., Wei J., et al. (2019). Multi-antigen-targeted chimeric antigen receptor T cells for cancer therapy. J. Hematol. Oncol. 12:128. DOI:10.1186/s13045-019-0813-7 [9] Xie G., Dong H., Liang Y., et al. (2020). CAR-NK cells: A promising cellular immunotherapy for cancer. EBioMedicine 59:102975. DOI:10.1016/j.ebiom.2020.102975 [10] Klichinsky M., Ruella M., Shestova O., et al. (2020). Human chimeric antigen receptor macrophages for cancer immunotherapy. Nat. Biotechnol. 38:947−953. DOI:10.1038/s41587-020-0462-y [11] Huang Y., Wang Z., Zhong C., et al. (2024). CRISPRa engineered Elite macrophages enable adoptive cell therapy for rheumatoid arthritis. Innov. Med. 2:100050. DOI:10.59717/j.xinn-med.2024.100050 [12] Zhang Y. and Zhang Z. (2020). The history and advances in cancer immunotherapy: Understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications. Cell. Mol. Immunol. 17:807−821. DOI:10.1038/s41423-020-0488-6 [13] Saez-Ibañez A. R., Upadhaya S., Partridge T., et al. (2022). Landscape of cancer cell therapies: Trends and real-world data. Nat. Rev. Drug Discov. 21:631−632. DOI:10.1038/d41573-022-00095-1 [14] Eberlein T. J., Rosenstein M. and Rosenberg S. A. (1982). Regression of a disseminated syngeneic solid tumor by systemic transfer of lymphoid cells expanded in interleukin 2. J. Exp. Med. 156:385−397. DOI:10.1084/jem.156.2.385 [15] Harao M., Forget M. A., Roszik J., et al. (2017). 4-1BB-enhanced expansion of CD8(+) TIL from triple-negative breast cancer unveils mutation-specific CD8(+) T cells. Cancer Immunol. Res. 5:439−445. DOI:10.1158/2326-6066.CIR-16-0364 [16] Wallstabe L., Gottlich C., Nelke L. C., et al. (2019). ROR1-CAR T cells are effective against lung and breast cancer in advanced microphysiologic 3D tumor models. JCI Insight 4:e126345. DOI:10.1172/jci.insight.126345 [17] Leidner R., Sanjuan Silva N., Huang H., et al. (2022). Neoantigen T-cell receptor gene therapy in pancreatic cancer. N. Engl. J. Med. 386:2112−2119. DOI:10.1056/NEJMoa2119662 [18] Rosenberg S. A., Yang J. C., Sherry R. M., et al. (2011). Durable complete responses in heavily pretreated patients with metastatic melanoma using T-cell transfer immunotherapy. Clin. Cancer Res. 17:4550−4557. DOI:10.1158/1078-0432.Ccr-11-0116 [19] Andersen R., Donia M., Ellebaek E., et al. (2016). Long-lasting complete responses in patients with metastatic melanoma after adoptive cell therapy with tumor-infiltrating lymphocytes and an attenuated IL2 regimen. Clin. Cancer Res. 22:3734−3745. DOI:10.1158/1078-0432.Ccr-15-1879 [20] Chen X., Wang S., Chen Y., et al. (2023). Non-invasive activation of intratumoural gene editing for improved adoptive T-cell therapy in solid tumours. Nat. Nanotechnol. 18:933−944. DOI:10.1038/s41565-023-01378-3 [21] Xiao Z., Wang J., Yang J., et al. (2025). Dendritic cells instruct T cell anti-tumor immunity and immunotherapy response. Innov. Med. 3:100128. DOI:10.59717/j.xinn-med.2025.100128 [22] Zhou X., Zhang B., Hu J., et al. (2024). Igniting cold tumors of intrahepatic cholangiocarcinoma: An insight into immune evasion and tumor immune microenvironment. Innov. Med. 2:100052. DOI:10.59717/j.xinn-med.2024.100052 [23] Lu X., Cheng L., Yang C., et al. (2024). Crosstalk between bladder cancer and the tumor microenvironment: Molecular mechanisms and targeted therapy. Innov. Med. 2:100094. DOI:10.59717/j.xinn-med.2024.100094 [24] Hiam-Galvez K. J., Allen B. M. and Spitzer M. H. (2021). Systemic immunity in cancer. Nat. Rev. Cancer 21:345−359. DOI:10.1038/s41568-021-00347-z [25] Hegde P. S. and Chen D. S. (2020). Top 10 challenges in cancer immunotherapy. Immunity 52:17−35. DOI:10.1016/j.immuni.2019.12.011 [26] Kircher M. F., Gambhir S. S. and Grimm J. (2011). Noninvasive cell-tracking methods. Nat. Rev. Clin. Oncol. 8:677−688. DOI:10.1038/nrclinonc.2011.141 [27] Galli F., Varani M., Lauri C., et al. (2021). Immune cell labelling and tracking: Implications for adoptive cell transfer therapies. EJNMMI Radiopharm. Chem. 6:7. DOI:10.1186/s41181-020-00116-7 [28] Volpe A., Adusumilli P. S., Schöder H., et al. (2022). Imaging cellular immunotherapies and immune cell biomarkers: From preclinical studies to patients. J. Immunother. Cancer 10:e004902. DOI:10.1136/jitc-2022-004902 [29] He X., Du Y., Wang Z., et al. (2020). Upfront dose-reduced chemotherapy synergizes with immunotherapy to optimize chemoimmunotherapy in squamous cell lung carcinoma. J. Immunother. Cancer 8:e000807. DOI:10.1136/jitc-2020-000807 [30] Du Y., Qi Y., Jin Z., et al. (2019). Noninvasive imaging in cancer immunotherapy: The way to precision medicine. Cancer Lett. 466:13−22. DOI:10.1016/j.canlet.2019.08.009 [31] Kiraga Ł., Kucharzewska P., Paisey S., et al. (2021). Nuclear imaging for immune cell tracking in vivo - Comparison of various cell labeling methods and their application. Coord. Chem. Rev. 445:214008. DOI:10.1016/j.ccr.2021.214008 [32] Du Y., Sun T., Liang X., et al. (2017). Improved resection and prolonged overall survival with PD-1-IRDye800CW fluorescence probe-guided surgery and PD-1 adjuvant immunotherapy in 4T1 mouse model. Int. J. Nanomedicine 12:8337−8351. DOI:10.2147/ijn.S149235 [33] Ashmore-Harris C., Iafrate M., Saleem A., et al. (2020). Non-invasive reporter gene imaging of cell therapies, including T cells and stem cells. Mol. Ther. 28:1392−1416. DOI:10.1016/j.ymthe.2020.03.016 [34] Harvell-Smith S., Tung L. D. and Thanh N. T. K. (2022). Magnetic particle imaging: Tracer development and the biomedical applications of a radiation-free, sensitive, and quantitative imaging modality. Nanoscale 14:3658−3697. DOI:10.1039/d1nr05670k [35] Liu X., Zhang S., Shao L., et al. (2024). Improving prediction of treatment response and prognosis in colorectal cancer with AI-based medical image analysis. Innov. Med. 2:100069. DOI:10.59717/j.xinn-med.2024.100069 [36] Dai J., Xu H., Chen T., et al. (2025). Artificial intelligence for medicine 2025: Navigating the endless frontier. Innov. Med. 3:100120. DOI:10.59717/j.xinn-med.2025.100120 [37] Stewart M. P., Langer R. and Jensen K. F. (2018). Intracellular delivery by membrane disruption: Mechanisms, strategies, and concepts. Chem. Rev. 118:7409−7531. DOI:10.1021/acs.chemrev.7b00678 [38] Lemaster J. E., Chen F., Kim T., et al. (2018). Development of a trimodal contrast agent for acoustic and magnetic particle imaging of stem cells. ACS Appl. Nano. Mater. 1:1321−1331. DOI:10.1021/acsanm.8b00063 [39] Van Hoeck J., Vanhove C., De Smedt S. C., et al. (2022). Non-invasive cell-tracking methods for adoptive T cell therapies. Drug Discov. Today 27:793−807. DOI:10.1016/j.drudis.2021.10.012 [40] Bulte J. W. M., Wang C. and Shakeri-Zadeh A. (2022). In vivo cellular magnetic imaging: Labeled vs. unlabeled cells. Adv. Funct. Mater. 32:2207626. DOI:10.1002/adfm.202207626 [41] Gawne P. J., Man F., Blower P. J., et al. (2022). Direct cell radiolabeling for in vivo cell tracking with PET and SPECT imaging. Chem. Rev. 122:10266−10318. DOI:10.1021/acs.chemrev.1c00767 [42] Minn I., Huss D. J., Ahn H. H., et al. (2019). Imaging CAR T cell therapy with PSMA-targeted positron emission tomography. Sci. Adv. 5:eaaw5096. DOI:10.1126/sciadv.aaw5096 [43] Mercier-Letondal P., Deschamps M., Sauce D., et al. (2008). Early immune response against retrovirally transduced herpes simplex virus thymidine kinase-expressing gene-modified T cells coinfused with a T cell-depleted marrow graft: an altered immune response. Hum. Gene Ther. 19:937−950. DOI:10.1089/hum.2007.156 [44] Keu K. V., Witney T. H., Yaghoubi S., et al. (2017). Reporter gene imaging of targeted T cell immunotherapy in recurrent glioma. Sci. Transl. Med. 9:eaag2196. DOI:10.1126/scitranslmed.aag2196 [45] Zhu L., Ploessl K. and Kung H. F. (2014). PET/SPECT imaging agents for neurodegenerative diseases. Chem. Soc. Rev. 43:6683−6691. DOI:10.1039/c3cs60430f [46] Livieratos L. (2015). Technical pitfalls and limitations of SPECT/CT. Semin. Nucl. Med. 45:530−540. DOI:10.1053/j.semnuclmed.2015.06.002 [47] Fisher B., Packard B. S., Read E. J., et al. (1989). Tumor localization of adoptively transferred indium-111 labeled tumor infiltrating lymphocytes in patients with metastatic melanoma. J. Clin. Oncol. 7:250−261. DOI:10.1200/jco.1989.7.2.250 [48] Aarntzen E. H., Srinivas M., Bonetto F., et al. (2013). Targeting of 111In-labeled dendritic cell human vaccines improved by reducing number of cells. Clin. Cancer Res. 19:1525−1533. DOI:10.1158/1078-0432.Ccr-12-1879 [49] Gildehaus F. J., Haasters F., Drosse I., et al. (2011). Impact of indium-111 oxine labelling on viability of human mesenchymal stem cells in vitro, and 3D cell-tracking using SPECT/CT in vivo. Mol. Imaging Biol. 13:1204−1214. DOI:10.1007/s11307-010-0439-1 [50] Gholamrezanezhad A., Mirpour S., Ardekani J. M., et al. (2009). Cytotoxicity of 111In-oxine on mesenchymal stem cells: A time-dependent adverse effect. Nucl. Med. Commun. 30:210−216. DOI:10.1097/MNM.0b013e328318b328 [51] Krekorian M., Fruhwirth G. O., Srinivas M., et al. (2019). Imaging of T-cells and their responses during anti-cancer immunotherapy. Theranostics 9:7924−7947. DOI:10.7150/thno.37924 [52] Lu D., Wang Y., Zhang T., et al. (2021). Metabolic radiolabeling and in vivo PET imaging of cytotoxic T lymphocytes to guide combination adoptive cell transfer cancer therapy. J. Nanobiotechnol. 19:175. DOI:10.1186/s12951-021-00924-2 [53] Wei W., Jiang D., Ehlerding E. B., et al. (2018). Noninvasive PET imaging of T cells. Trends Cancer 4:359−373. DOI:10.1016/j.trecan.2018.03.009 [54] Wolfs E., Struys T., Notelaers T., et al. (2013). 18F-FDG labeling of mesenchymal stem cells and multipotent adult progenitor cells for PET imaging: Effects on ultrastructure and differentiation capacity. J. Nucl. Med. 54:447−454. DOI:10.2967/jnumed.112.108316 [55] Man F., Lim L., Volpe A., et al. (2019). In vivo PET tracking of (89)Zr-labeled Vgamma9Vdelta2 T cells to mouse xenograft breast tumors activated with liposomal alendronate. Mol. Ther. 27:219−229. DOI:10.1016/j.ymthe.2018.10.006 [56] Sato N., Wu H., Asiedu K. O., et al. (2015). (89)Zr-oxine complex PET cell imaging in monitoring cell-based therapies. Radiology 275:490−500. DOI:10.1148/radiol.15142849 [57] Sato N., Stringaris K., Davidson-Moncada J. K., et al. (2020). In vivo tracking of adoptively transferred natural killer cells in rhesus macaques using (89)Zirconium-oxine cell labeling and PET imaging. Clin. Cancer Res. 26:2573−2581. DOI:10.1158/1078-0432.Ccr-19-2897 [58] Leland P., Kumar D., Nimmagadda S., et al. (2023). Characterization of chimeric antigen receptor modified T cells expressing scFv-IL-13Rα2 after radiolabeling with (89)Zirconium oxine for PET imaging. J. Transl. Med. 21:367. DOI:10.1186/s12967-023-04142-2 [59] Shuch B., Pantuck A. J., Bernhard J.-C., et al. (2024). [89Zr]Zr-girentuximab for PET–CT imaging of clear-cell renal cell carcinoma: A prospective, open-label, multicentre, phase 3 trial. Lancet Oncol. 25:1277−1287. DOI:10.1016/S1470-2045(24)00402-9 [60] Merkx R. I. J., Lobeek D., Konijnenberg M., et al. (2021). Phase I study to assess safety, biodistribution and radiation dosimetry for (89)Zr-girentuximab in patients with renal cell carcinoma. Eur. J. Nucl. Med. Mol. Imaging 48:3277−3285. DOI:10.1007/s00259-021-05271-w [61] Gao T., Wang P., Gong T., et al. (2022). Reporter genes for brain imaging using MRI, SPECT and PET. Int. J. Mol. Sci. 23:8443. DOI:10.3390/ijms23158443 [62] Emami-Shahri N., Foster J., Kashani R., et al. (2018). Clinically compliant spatial and temporal imaging of chimeric antigen receptor T-cells. Nat. Commun. 9:1081. DOI:10.1038/s41467-018-03524-1 [63] Lee S. B., Lee H. W., Lee H., et al. (2017). Tracking dendritic cell migration into lymph nodes by using a novel PET probe (18)F-tetrafluoroborate for sodium/iodide symporter. EJNMMI Res. 7:32. DOI:10.1186/s13550-017-0280-5 [64] Jacob J., Nadkarni S., Volpe A., et al. (2021). Spatiotemporal in vivo tracking of polyclonal human regulatory T cells (Tregs) reveals a role for innate immune cells in Treg transplant recruitment. Mol. Ther. Methods Clin. Dev. 20:324−336. DOI:10.1016/j.omtm.2020.12.003 [65] Kurtz K., Eibler L., Dacek M. M., et al. (2023). Engineering CAR-T cells for radiohapten capture in imaging and radioimmunotherapy applications. Theranostics 13:5469−5482. DOI:10.7150/thno.87489 [66] Emmett L., Crumbaker M., Ho B., et al. (2019). Results of a prospective phase 2 pilot trial of (177)Lu-PSMA-617 therapy for metastatic castration-resistant prostate cancer including imaging predictors of treatment response and patterns of progression. Clin. Genitourin. Cancer 17:15−22. DOI:10.1016/j.clgc.2018.09.014 [67] Ji Y., Jones C., Baek Y., et al. (2020). Near-infrared fluorescence imaging in immunotherapy. Adv. Drug Deliv. Rev. 167:121−134. DOI:10.1016/j.addr.2020.06.012 [68] Li Y., Du Y., Sun T., et al. (2018). PD-1 blockade in combination with zoledronic acid to enhance the antitumor efficacy in the breast cancer mouse model. BMC Cancer 18:669. DOI:10.1186/s12885-018-4412-8 [69] Dehghani M., Gulvin S. M., Flax J., et al. (2020). Systematic evaluation of PKH labelling on extracellular vesicle size by nanoparticle tracking analysis. Sci. Rep. 10:9533. DOI:10.1038/s41598-020-66434-7 [70] Tavri S., Jha P., Meier R., et al. (2009). Optical imaging of cellular immunotherapy against prostate cancer. Mol. Imaging 8:15−26. [71] Lim Y. T., Cho M. Y., Noh Y. W., et al. (2009). Near-infrared emitting fluorescent nanocrystals-labeled natural killer cells as a platform technology for the optical imaging of immunotherapeutic cells-based cancer therapy. Nanotechnology 20:475102. DOI:10.1088/0957-4484/20/47/475102 [72] Kim W., Yoon H. Y., Lim S., et al. (2021). In vivo tracking of bioorthogonally labeled T-cells for predicting therapeutic efficacy of adoptive T-cell therapy. J. Control. Release 329:223−236. DOI:10.1016/j.jconrel.2020.12.002 [73] Szyska M., Herda S., Althoff S., et al. (2018). A transgenic dual-luciferase reporter mouse for longitudinal and functional monitoring of T cells in vivo. Cancer Immunol. Res. 6:110−120. DOI:10.1158/2326-6066.Cir-17-0256 [74] Kuhl C. K. (2019). Abbreviated Magnetic Resonance Imaging (MRI) for breast cancer screening: Rationale, concept, and transfer to clinical practice. Annu. Rev. Med. 70:501−519. DOI:10.1146/annurev-med-121417-100403 [75] Ni D., Bu W., Ehlerding E. B., et al. (2017). Engineering of inorganic nanoparticles as magnetic resonance imaging contrast agents. Chem. Soc. Rev. 46:7438−7468. DOI:10.1039/c7cs00316a [76] Wu L., Liu F., Liu S., et al. (2020). Perfluorocarbons-based (19)F magnetic resonance imaging in biomedicine. Int. J. Nanomedicine 15:7377−7395. DOI:10.2147/ijn.S255084 [77] Zhang C., Xu Z., Di H., et al. (2020). Gadolinium-doped Au@prussian blue nanoparticles as MR/SERS bimodal agents for dendritic cell activating and tracking. Theranostics 10:6061−6071. DOI:10.7150/thno.42114 [78] Oluwasola I. E., Ahmad A. L., Shoparwe N. F., et al. (2022). Gadolinium based contrast agents (GBCAs): Uniqueness, aquatic toxicity concerns, and prospective remediation. J. Contam. Hydrol. 250:104057. DOI:10.1016/j.jconhyd.2022.104057 [79] Bulte J. W. M. and Shakeri-Zadeh A. (2022). In vivo MRI tracking of tumor vaccination and antigen presentation by dendritic cells. Mol. Imaging Biol. 24:198−207. DOI:10.1007/s11307-021-01647-4 [80] Sehl O. C., Makela A. V., Hamilton A. M., et al. (2019). Trimodal cell tracking in vivo: Combining iron- and fluorine-based magnetic resonance imaging with magnetic particle imaging to monitor the delivery of mesenchymal stem cells and the ensuing inflammation. Tomography 5:367−376. DOI:10.18383/j.tom.2019.00020 [81] Richards J. M., Shaw C. A., Lang N. N., et al. (2012). In vivo mononuclear cell tracking using superparamagnetic particles of iron oxide: Feasibility and safety in humans. Circ. Cardiovasc. Imaging 5:509−517. DOI:10.1161/circimaging.112.972596 [82] de Vries I. J., Lesterhuis W. J., Barentsz J. O., et al. (2005). Magnetic resonance tracking of dendritic cells in melanoma patients for monitoring of cellular therapy. Nat. Biotechnol. 23:1407−1413. DOI:10.1038/nbt1154 [83] Meier R., Golovko D., Tavri S., et al. (2011). Depicting adoptive immunotherapy for prostate cancer in an animal model with magnetic resonance imaging. Magn. Reson. Med. 65:756−763. DOI:10.1002/mrm.22652 [84] Lian J., Li M., Duan M., et al. (2024). NK-92 cells labeled with Fe(3)O(4)-PEG-CD56/Avastin@Ce6 nanoprobes for the targeted treatment and noninvasive therapeutic evaluation of breast cancer. J. Nanobiotechnol. 22:313. DOI:10.1186/s12951-024-02599-x [85] Hingorani D. V., Chapelin F., Stares E., et al. (2020). Cell penetrating peptide functionalized perfluorocarbon nanoemulsions for targeted cell labeling and enhanced fluorine-19 MRI detection. Magn. Reson. Med. 83:974−987. DOI:10.1002/mrm.27988 [86] Ahrens E. T., Helfer B. M., O'Hanlon C. F., et al. (2014). Clinical cell therapy imaging using a perfluorocarbon tracer and fluorine-19 MRI. Magn. Reson. Med. 72:1696−1701. DOI:10.1002/mrm.25454 [87] Guo R., Li Q., Yang F., et al. (2018). In vivo MR imaging of dual MRI reporter genes and deltex-1 gene-modified human mesenchymal stem cells in the treatment of closed penile fracture. Mol. Imaging Biol. 20:417−427. DOI:10.1007/s11307-017-1128-0 [88] Kim H. S., Woo J., Lee J. H., et al. (2015). In vivo tracking of dendritic cell using MRI reporter gene, ferritin. PLoS ONE 10:e0125291. DOI:10.1371/journal.pone.0125291 [89] Gleich B. and Weizenecker J. (2005). Tomographic imaging using the nonlinear response of magnetic particles. Nature 435:1214−1217. DOI:10.1038/nature03808 [90] Knopp T., Gdaniec N. and Möddel M. (2017). Magnetic particle imaging: From proof of principle to preclinical applications. Phys. Med. Biol. 62:R124−R178. DOI:10.1088/1361-6560/aa6c99 [91] Li Y., Hui H., Zhang P., et al. (2023). Modified Jiles-Atherton model for dynamic magnetization in X-space magnetic particle imaging. IEEE Trans. Biomed. Eng. 70:2035−2045. DOI:10.1109/tbme.2023.3234256 [92] Li Y., Li G., Zhang P., et al. (2024). Modified Jiles–Atherton model-based system matrix generation method for magnetic particle imaging. IEEE Trans. Instrum. Meas. 73:1−9. DOI:10.1109/TIM.2024.3381694 [93] Zhou X. Y., Tay Z. W., Chandrasekharan P., et al. (2018). Magnetic particle imaging for radiation-free, sensitive and high-contrast vascular imaging and cell tracking. Curr. Opin. Chem. Biol. 45:131−138. DOI:10.1016/j.cbpa.2018.04.014 [94] Graeser M., Thieben F., Szwargulski P., et al. (2019). Human-sized magnetic particle imaging for brain applications. Nat. Commun. 10:1936. DOI:10.1038/s41467-019-09704-x [95] Wang Q., Ma X., Liao H., et al. (2020). Artificially engineered cubic iron oxide nanoparticle as a high-performance magnetic particle imaging tracer for stem cell tracking. ACS Nano 14:2053−2062. DOI:10.1021/acsnano.9b08660 [96] Tay Z. W., Chandrasekharan P., Fellows B. D., et al. (2021). Magnetic particle imaging: An emerging modality with prospects in diagnosis, targeting and therapy of cancer. Cancers (Basel) 13:5285. DOI:10.3390/cancers13215285 [97] Rivera-Rodriguez A., Hoang-Minh L. B., Chiu-Lam A., et al. (2021). Tracking adoptive T cell immunotherapy using magnetic particle imaging. Nanotheranostics 5:431−444. DOI:10.7150/ntno.55165 [98] Kiru L., Zlitni A., Tousley A. M., et al. (2022). In vivo imaging of nanoparticle-labeled CAR T cells. Proc. Natl. Acad. Sci. U. S. A. 119:e2102363119. DOI:10.1073/pnas.2102363119 [99] Wu W. E., Chang E., Jin L., et al. (2023). Multimodal in vivo tracking of chimeric antigen receptor T cells in preclinical glioblastoma models. Invest. Radiol. 58:388−395. DOI:10.1097/rli.0000000000000946 [100] Schwenck J., Sonanini D., Cotton J. M., et al. (2023). Advances in PET imaging of cancer. Nat. Rev. Cancer 23:474−490. DOI:10.1038/s41568-023-00576-4 [101] Karimian-Jazi K., Münch P., Alexander A., et al. (2020). Monitoring innate immune cell dynamics in the glioma microenvironment by magnetic resonance imaging and multiphoton microscopy (MR-MPM). Theranostics 10:1873−1883. DOI:10.7150/thno.38659 [102] Pfeifer R., Henze J., Wittich K., et al. (2022). A multimodal imaging workflow for monitoring CAR T cell therapy against solid tumor from whole-body to single-cell level. Theranostics 12:4834−4850. DOI:10.7150/thno.68966 [103] Li J., Guo L., Feng Y., et al. (2025). Optical-magnetic imaging for optimizing lymphodepletion-TIL combination therapy in breast cancer. Mol. Imaging Biol. 27:260-273. DOI:10.1007/s11307-025-01985-7 [104] Bensch F., van der Veen E. L., Lub-de Hooge M. N., et al. (2018). (89)Zr-atezolizumab imaging as a non-invasive approach to assess clinical response to PD-L1 blockade in cancer. Nat. Med. 24:1852−1858. DOI:10.1038/s41591-018-0255-8 [105] Xing Y., Chand G., Liu C., et al. (2019). Early phase I study of a (99m)Tc-labeled anti-programmed death ligand-1 (PD-L1) single-domain antibody in SPECT/CT assessment of PD-L1 expression in non-small cell lung cancer. J. Nucl. Med. 60:1213−1220. DOI:10.2967/jnumed.118.224170 [106] Pittet M. J., Grimm J., Berger C. R., et al. (2007). In vivo imaging of T cell delivery to tumors after adoptive transfer therapy. Proc. Natl. Acad. Sci. U. S. A. 104:12457−12461. DOI:10.1073/pnas.0704460104 [107] Wang Z. and Kelley S. O. (2025). Microfluidic technologies for enhancing the potency, predictability and affordability of adoptive cell therapies. Nature Biomed. Eng. Online ahead of print. DOI:10.1038/s41551-024-01315-2 [108] Wang L. L., Janes M. E., Kumbhojkar N., et al. (2021). Cell therapies in the clinic. Bioeng. Transl. Med. 6:e10214. DOI:10.1002/btm2.10214 [109] Dzhandzhugazyan K. N., Guldberg P. and Kirkin A. F. (2018). Adoptive T cell cancer therapy. Nat. Mater. 17:475−477. DOI:10.1038/s41563-018-0094-5 [110] Seymour L., Bogaerts J., Perrone A., et al. (2017). iRECIST: Guidelines for response criteria for use in trials testing immunotherapeutics. Lancet Oncol. 18:e143−e152. DOI:10.1016/s1470-2045(17)30074-8 [111] Song G., Chen M., Zhang Y., et al. (2018). Janus iron oxides @ semiconducting polymer nanoparticle tracer for cell tracking by magnetic particle imaging. Nano Lett. 18:182−189. DOI:10.1021/acs.nanolett.7b03829 [112] Azoulay É., Castro P., Maamar A., et al. (2021). Outcomes in patients treated with chimeric antigen receptor T-cell therapy who were admitted to intensive care (CARTTAS): An international, multicentre, observational cohort study. Lancet Haematol. 8:e355−e364. DOI:10.1016/S2352-3026(21)00060-0 [113] Song X., Zhang Y., Lv X., et al. (2023). Noninvasive longitudinal PET/CT imaging of CAR T cells using PSMA reporter gene. Eur. J. Nucl. Med. Mol. Imaging 51:965−977. DOI:10.1007/s00259-023-06508-6 [114] Kann B. H., Hosny A. and Aerts H. (2021). Artificial intelligence for clinical oncology. Cancer Cell 39:916−927. DOI:10.1016/j.ccell.2021.04.002 [115] Bera K., Braman N., Gupta A., et al. (2022). Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat. Rev. Clin. Oncol. 19:132−146. DOI:10.1038/s41571-021-00560-7 [116] Yin X., Liao H., Yun H., et al. (2022). Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer. Semin. Cancer Biol. 86:146−159. DOI:10.1016/j.semcancer.2022.08.002 [117] Liu L., Ma C., Zhang Z., et al. (2022). Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse. J. Immunother. Cancer 10:e005360. DOI:10.1136/jitc-2022-005360 [118] Ao Z., Cai H., Wu Z., et al. (2022). Microfluidics guided by deep learning for cancer immunotherapy screening. Proc. Natl. Acad. Sci. U. S. A. 119:e2214569119. DOI:10.1073/pnas.2214569119 [119] Joy D. A., Libby A. R. G. and McDevitt T. C. (2021). Deep neural net tracking of human pluripotent stem cells reveals intrinsic behaviors directing morphogenesis. Stem Cell Rep. 16:1317−1330. DOI:10.1016/j.stemcr.2021.04.008 [120] Julve M., Lythgoe M. P., Larkin J., et al. (2024). Lifileucel: The first cellular therapy approved for solid tumours. Trends Cancer 10:475−477. DOI:10.1016/j.trecan.2024.04.003 [121] Sznol M., Dutcher J. P., Atkins M. B., et al. (1989). Review of interleukin-2 alone and interleukin-2/LAK clinical trials in metastatic malignant melanoma. Cancer Treat. Rev. 16 Suppl A:29-38. DOI:10.1016/0305-7372(89)90020-0 [122] López-Díaz de Cerio A., García-Muñoz R., Pena E., et al. (2020). Maintenance therapy with ex vivo expanded lymphokine-activated killer cells and rituximab in patients with follicular lymphoma is safe and may delay disease progression. Br. J. Haematol. 189:1064−1073. DOI:10.1111/bjh.16474 [123] Ying Li C. M., Li R., Drew P., et al. (2024). Clinical application of cytokine-induced killer (CIK) cell therapy in colorectal cancer: Current strategies and future challenges. Cancer Treat. Rev. 122:102665. DOI:10.1016/j.ctrv.2023.102665 [124] Thara E., Dorff T. B., Pinski J. K., et al. (2011). Vaccine therapy with sipuleucel-T (Provenge) for prostate cancer. Maturitas 69:296−303. DOI:10.1016/j.maturitas.2011.04.012 [125] Shimasaki N., Jain A. and Campana D. (2020). NK cells for cancer immunotherapy. Nat. Rev. Drug Discov. 19:200−218. DOI:10.1038/s41573-019-0052-1 [126] Keam S. J. (2024). Afamitresgene autoleucel: First approval. Mol. Diagn. Ther. 28:861−866. DOI:10.1007/s40291-024-00749-3 [127] Ma S., Li X., Wang X., et al. (2019). Current progress in CAR-T cell therapy for solid tumors. Int. J. Biol. Sci. 15:2548−2560. DOI:10.7150/ijbs.34213 [128] Shin M. H., Oh E., Kim Y., et al. (2023). Recent advances in CAR-based solid tumor immunotherapy. Cells 12:1606. DOI:10.3390/cells12121606 -
ABOUT THIS ARTICLE
Cite this article:
Li J., Ma J., Wang L., et al. (2025). Application of non-invasive imaging on cell tracking of adoptive cell therapy: A systemic review. The Innovation Medicine 3:100137. https://doi.org/10.59717/j.xinn-med.2025.100137
Welcome!
To request copyright permission to republish or share portions of our works, please visit Copyright Clearance Center's (CCC) Marketplace website at marketplace.copyright.com.
Export File
Citation
Li J., Ma J., Wang L., et al. (2025). Application of non-invasive imaging on cell tracking of adoptive cell therapy: A systemic review. The Innovation Medicine 3:100137. https://doi.org/10.59717/j.xinn-med.2025.100137
Format
Content
-
Figure 1.
Schematic illustration of cell tracking imaging techniques using direct and indirect labeling.
-
Figure 2.
Cell tracking by nuclear-based imaging
-
Figure 3.
Cell tracking by OI
-
Figure 4.
Cell tracking by MRI
-
Figure 5.
Cell tracking by MPI
-
Figure 6.
Main features of various imaging techniques.
