Development and evaluation of the first-in-class nanobody based PET tracer targeting human inducible T-cell co-stimulator receptor

Development and evaluation of the first-in-class nanobody based PET tracer targeting human inducible T-cell co-stimulator receptor | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and evaluation of the first-in-class nanobody based PET tracer targeting human inducible T-cell co-stimulator receptor Jianming Li, Shao Duan, Wei You, Yao Zhao, Weiran Li, Zunyu Xiao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8725887/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Apr, 2026 Read the published version in EJNMMI Research → Version 1 posted 4 You are reading this latest preprint version Abstract Background Inducible T-cell co-stimulator receptor (ICOS) is a conserved biomarker mainly expressed on activated T cells. It had been proved that PET imaging targeting ICOS was a promising strategy for assessment of immune responses in cancer immunotherapy. For further translating this strategy into clinic, in the present study, we had developed the first-in-class ICOS targeting nanobody ICOS-53 via alpaca immunization and yeast display screening. Results The binding affinity of ICOS-53 to recombinant ICOS protein was validated by surface plasmon resonance, and the KD value was 85.3 pM. ICOS-53 was then conjugated to NOTA and radiolabeled with [ 68 Ga]GaCl 3 . In PET imaging study, higher accumulation of [ 68 Ga]Ga-NOTA-ICOS-53 could be observed in CHO-ICOS (CHO transfected by human ICOS) tumors, compared to CHO cohort from all-time points examined. Good correlation between PET imaging quantitative results and biodistribution could be observed (R²=0.627, P < 0.001). Immunofluorescence staining also confirmed the high ICOS expression in CHO-ICOS tumors. Conclusion Our data demonstrated that we had developed a novel nanobody based PET tracer targeting human ICOS, [ 68 Ga]Ga-NOTA-ICOS-53 PET imaging was a promising strategy for tracking ICOS+ cells both in vitro and in vivo. ICOS Nanobody Cancer Immunotherapy Molecular Imaging T Cells Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Benefiting from the breakthroughs in the field of immuno-oncology, cancer treatment had undergone a significant revolution. Immunotherapy strategies, including vaccines, adoptive cell therapy, and immune checkpoint inhibitors had shown promising effects in various solid and hematological cancers. The core mechanism of cancer immunotherapy is to evoke the patients’ immune system, which then attacks and eliminates malignant tumor cells 1 , 2 . Currently, the gold standards for evaluating tumor treatment efficacy in clinical practice are the Response Evaluation Criteria in Solid Tumors (RECIST) and Immune-Related Response Criteria (irRC), which mainly based on computed tomography (CT) and magnetic resonance imaging (MRI) technologies 3 , 4 . Although these imaging techniques can provide high-resolution anatomical information, they failed to capture molecular dynamics within tumor microenvironment 5 , 6 . Plenty of studies had confirmed that in the context of immuno-oncology treatment, these evaluation criteria often failed due to the phenomenon of "pseudoprogression”, a false increase in lesion size on anatomic imaging caused by immune cell infiltration into tumor tissue 3 , 7 . Additionally, after the initiation of treatment, these traditional evaluation methods typically require a 9–12-week gap before assessment which greatly limits the timeliness of clinical decision-making 3 . Therefore, to improve the clinical prognosis of cancer patients, there is an urgent need to develop tools to effectively evaluate novel immunotherapies and accurately monitor therapeutic responses 5 , 8 . Positron emission tomography (PET) is theoretically an ideal solution to this problem, as it possesses the unique advantage of dynamically and non-invasively quantifying systemic molecular information 5 , 9 , 10 . Currently, the most classic PET tracer, 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG), has been widely used in clinical lung cancer diagnosis, and some studies have attempted to apply it to the monitoring of immunotherapy responses 11 – 13 . However, the major limitation of [ 18 F]FDG lies in its insufficient specificity 5 . Since it mainly reflects abnormal metabolic processes, its detection signal includes both tumor cells and proliferatively active immune cells, making it technically difficult to effectively distinguish and resolve the signals generated by these two cell types 11 , 13 . Similarly, other metabolic tracers (e.g., [ 18 F]F-AraG) face the same issue of low specificity as [ 18 F]FDG when applied in immunotherapy settings 14 . Successful cancer immunotherapies mainly consist of T cell activation, migration into tumor region, and cancer killing 1 . Thus, many efforts had focused on developing imaging reagents targeting T cell specific and functional biomarkers 15 . Among those imaging reagents, T cell phenotype biomarker CD8 and secreted biomarker granzyme B have shown great potential in monitoring therapeutic responses in clinical trials 16 , 17 . However, it should be noted that phenotype biomarker CD8 cannot distinguish activated and exhausted T cells 5 . Granzyme B was mainly secreted by cytotoxic T cells, which usually stands for cancer killing, the last step of immunotherapy, and this may lead to missing of monitoring early-stage immune response 17 . Therefore, exploring biomarkers for precisely predicting the efficacy of immunotherapy in the early stages are still needed 5 . Our group mainly focuses on PET imaging targeting T cell co-stimulatory molecules, especially inducible T cell co-stimulator (ICOS, also known as CD278), a key member of the CD28 superfamily 18 . Existing studies had reported that ICOS is mainly expressed on the surface of activated T cells, the activation of the ICOS signaling pathway begins with the specific binding of the receptor to its ligand, ICOS ligand (ICOSL), which is mainly distributed on the surface of B cells, macrophages, and dendritic cells 18 , 19 . After their binding, cascade reactions of downstream signaling pathways are triggered, which not only regulate the proliferative activity and survival cycle of T cells but also promote the secretion of key cytokines such as interleukin-4 (IL-4), interleukin-10 (IL-10), and interferon (IFN), thereby participating in the regulation of immune responses 18 , 19 . In our previous study, we developed an 89 Zr- labeled monoclonal antibody targeting murine ICOS on activated T cells, and ICOS was identified as an ideal molecule for the assessment of cancer immunotherapies in a Lewis lung cancer mouse model. Given the long circulation and poor tissue penetration, such antibody-based PET tracer was limited in clinical translation. To overcome those challenges, in the current study, we developed a nanobody ICOS-53 targeting human ICOS with high affinity at picomole level. ICOS-53 was radiolabeled with 68 Ga, and the capacity of [ 68 Ga]Ga-NOTA-ICOS-53 in capturing human ICOS was both assessed via cell binding and PET imaging studies. Our data demonstrated [ 68 Ga]Ga-NOTA-ICOS-53 as a promising PET tracer in detecting human ICOS with high specificity, and further clinical trials warrant evaluation. Materials and Methods Animal immunization, nanobody screening and affinity determination Before alpaca immunization, 10 mL blood was collected as a negative serum control. Dissolve 0.5 mg of recombinant ICOS extracellular domain protein in 250 μl PBS solution and subcutaneously injected into healthy male alpaca every two weeks, 5 times in total. After the second immunization, collect alpaca coagulant-promoting blood, isolate serum, and use ELISA to evaluate the immune response. After the fifth immunization, peripheral blood was collected and blood lymphocytes were isolated. B cell mRNA was reversely transcript into DNA, following the establishment of yeast display library for ICOS nanobody screening. Typically, ICOS nanobody screening was based on the standard magnet selection and FACS sorting. After DNA sequencing, ICOS-53 was expressed according to the standard procedures as previously described 20, 21 . For affinity determination, surface plasmon resonance (SPR) assay was performed between ICOS-53 and ICOS extracellular protein using a Biacore T200 system (GE Healthcare). The running buffer comprised 120 mM NaCl, 10 mM phosphate (pH 7.4), 50 μM EDTA, and 0.05% Tween 20. Firstly, biotinylated ICOS extracellular proteins were immobilized onto a CAP sensor chip via the Biotin CAPture Kit (Cytiva), reaching approximately 750 response units (RU). Subsequently, ICOS-specific Nbs samples were injected over the chip surface at a flow rate of 30 µL/min with a contact time of 120 s followed by a dissociation phase of 150 s. The response (RU) was recorded in real time. Finally, the sensor chip was regenerated using the Biotin CAPture Kit (Cytiva). For serially diluted samples, each sample was tested by repeating the above procedure. The equilibrium dissociation constant was derived using Biacore T200 evaluation software. Cell culture and FACS analysis of ICOS expression in both CHO and CHO-ICOS cells. The CHO-ICOS and CHO cell lines were purchased from SHANGHAIYAJI BIOTECHNOLOGYCO, LTD. CHO-ICOS and CHO cell were cultured according to standard protocols in Dulbecco’smodified Eagle’smedium (DMEM), supplemented with 10% (v/v) FCS and 1% (v/v) penicillin/streptomycin (all Thermo Fisher Scientific). ICOS expression was validated via flow cytometry. The Brilliant Violet 421™ anti-human CD278 (ICOS) Antibody was purchased from Biolegend. [ 68 Ga]Ga-NOTA-ICOS-53 synthesis, characterization and cell uptake study The site-specific conjugated NOTA-ICOS-53 (100 µg) was diluted in 1 mL of 1 M NaOAc buffer (pH 4.5–4.7) and incubated with 1 mL [ 68 Ga]Ga3+ eluate (780-870 MBq, Galli Ad, IRE ELiT) for 10 min at 50°C. The crude radiolabeled VHH reaction mixture was purified by SEC using a PD-10 desalting column (GE Healthcare) eluted with sterile 0.01 M phosphate-buffered saline (PBS, pH 7.4). Radiochemical purity was evaluated using radio-iTLC (0.1 M sodium citrate buffer (pH 5.0); [ 68 Ga]Ga-NOTA-anti-ICOS VHH: Rf = 0; [ 68 Ga]Ga3+: Rf = 1) and SEC as described above. The non-decay-corrected radiochemical yield was calculated based on the activity obtained after PD-10 purification. The stability assessment was performed as described in the supplementary information. For cell uptake studies, 2 μCi [ 68 Ga]Ga-NOTA-ICOS-53 were incubated with 10 5 CHO and CHO-ICOS cells for 1 hour under 37°C in serum free DMEM, respectively. After 3 washes, cells were collected and the radioactivity was tested via gamma counter. Animal model establishment All research involving animal subjects was approved by the Local Ethical Committee of Harbin Medical University Animal Care and Use. 5×10 6 CHO cells and CHO-ICOS cells in 50 μL PBS were injected subcutaneously in the left shoulder and right shoulder of female NSG mice, respectively. Tumor volumes were recorded every other day by calipers and tumor volumes were calculated using the formula (π/6) length×width×height. PET/CT imaging and Biodistribution study All PET scans were performed on a Siemens Inveon MM-PET/CT. After anesthetization by 1.5%–2% isoflurane gas, 200μCi [ 68 Ga]Ga-NOTA-ICOS-53 was administered via mouse tail vein. Static PET scans were acquired immediately after CT imaging at 30 minutes, 60 minutes, and 120 minutes, which were used to provide anatomical references and attenuation correction for PET signals. After the final PET scan at 120 minutes, NSG mice were euthanized, organs including heart, liver, spleen, lungs, kidneys, CHO tumors, CHO-ICOS tumors, muscle, bone, and tail were collected and wet weighted. An auto-matic gamma counter was used for determining the activity in different organs. Both ROI and biodistribution data were normalized to %ID/g. Immunofluorescence staining After the mice were sacrificed, the two types of tumor tissues were fixed with 4% paraformaldehyde. After embedding in paraffin After depa-raffinization, antigen retrieval, and BSA blockade, tumor tissues were stained overnight at 4°C with ICOS antibody. Secondary antibodies were then added and incubated for 1 h. After staining the nuclei with DAPI (Beyotime, Shanghai,China), the sections were examined under a fluorescent microscope (Nikon, Japan). Data analysis All statistical analyses were conducted with GraphPad Prism10.1.2 software and R language (version 4.5.0). Quantitative data are presented as the mean ± standard deviation. Paired two-tailed Student’s T-tests were employed for comparisons of paired samples between two groups. For multi-group comparisons, one-way analysis of variance (ANOVA) was initially performed to assess overall group differences; upon detecting significant effects, Tukey’s honestly significant difference (HSD) post hoc test was utilized for pairwise comparisons. Pearson correlation analysis was used to evaluate correlations between variables, while linear regression analysis was conducted to confirm linear relationships. In the figures, statistical significance is denoted as P < 0.05 (*), P < 0.01 (**), P < 0.001 (***), and P < 0.0001 (***). Differences were considered statistically significant at P < 0.05. Results ICOS was highly correlated with therapeutic responses of cancer immunotherapy across many types of human cancers To validate the potential of ICOS as a biomarker in assessing immune responses of cancer immunotherapy, we integrated its expression dynamics, prognostic relevance, and predictive capacity across multiple malignancies. Our analysis incorporated transcriptomic from four major cancers—breast, liver, lung, and melanoma—sourced from the CTR‑DB platform (v2.0). ICOS mRNA expression was much higher in responsive patients receiving immunotherapy than that in non-responder group (Fig 1A-1D, upper panel). The receiver operating characteristic (AUC) curve analysis validated the robust role of ICOS in predicting responses of cancer immunotherapy, with area under the curve (AUC) values ranging from 0.71 to 0.77 (Fig 1A-1D, lower panel). Further omics datasets from TCGA (The Cancer Genome Atlas Program) demonstrated that ICOS is downregulated in LUAD tumors compared to the paired adjacent normal tissues (P < 0.001), which was probably associated with the absence of ICOS + activated T cells within the tumor microenvironment (Fig 1E). Additional Kaplan–Meier analysis indicated that LUAD patients with high ICOS expression have longer overall survival rate (P=0.019; hazard ratio [HR]=0.619) (Fig 1F), indicating the protective role of ICOS. We also assessed immune infiltration in the tumor microenvironment using seven computational tools (e.g., xCell, TIMER). ICOS expression showed positive correlations with anti-tumor subsets—including CD8⁺ T cells (effector and central memory), CD4⁺ memory T cells, and M1 macrophages (r = 0.2–0.5)—and negative correlations with pro-tumor populations such as regulatory T cells (Tregs) and M2 macrophages (r = −0.1 to −0.4) (Fig 1G). These associations suggest that ICOS may contribute to a more favorable immune microenvironment in LUAD, possibly by facilitating the recruitment of anti-tumor effectors while restraining immunosuppressive components. Animal immunization and Nb screening To generate ICOS-specific nanobodies, alpacas was immunized with 0.5mg recombinant human ICOS extracellular domain protein (amino acid residues 21–141) for five times in total. On day 56, one week after the final immunization, the alpaca's peripheral blood lymphocytes (PBLs) were collected, the mRNA was reverse transcribed into cDNA, and a nanobody (Nb) yeast library representing the VHH repertoire of this alpaca was established (Fig 2A). To test the binding capability of ICOS nanobody candidates, the yeast library was inducted with galactose, AF488 labeled anti-C-Myc antibody was employed to validate the expression of ICOS nanobodies. Subsequently, inducted yeast cells were incubated with BV-650-labeled recombinant human ICOS extracellular domain protein, via flow cytometry analysis, ICOS-53 was identified as the optimal candidate nanobody (Fig 2B). To evaluate the binding affinity of ICOS-53, we performed surface plasmon resonance (SPR) analysis between ICOS-53 and human ICOS extracellular domain protein, and ICOS-53 showed a considerably strong affinity, as indicated by a KD value of 85.3 pM (Fig 2C). Synthesis and Characterization of [ 68 Ga]Ga-NOTA-ICOS-53 To validate ICOS expression, flow cytometry assay was performed between CHO-ICOS and CHO cells. High ICOS expression could be detected on CHO-ICOS cells, compared to CHO cell line, P<0.0001 (Fig 3A). For tracer radiolabeling, ICOS-53 was conjugated with p-SCN-Bn-NOTA，[ 68 Ga]GaCl 3 was added to NOTA-ICOS-53, and the pH of mixture was adjusted to 4.0, followed by incubation at approximately 37℃ for 30 minutes (Fig 3B). Right after the reaction, the radiochemical purity of [ 68 Ga]Ga-NOTA-ICOS-53 was tested by radio-iTLC. To validate the specificity of ICOS-53 to human ICOS protein, we employed CHO-ICOS cell line stable expressing human ICOS for in vitro cell uptake study. ICOS expression was first validated via flow cytometry. For cell uptake study, 2μCi of [ 68 Ga]Ga-NOTA-ICOS-53 was added to CHO-ICOS and CHO cells, respectively. After 1 hour incubation and 3 washes, the radioactivity in both cell lines was determined by a gamma counter. Higher activity retention was detected in CHO-ICOS cells (P<0.001), indicating the specificity of [ 68 Ga]Ga-NOTA-ICOS-53 to human ICOS protein (Fig 3C). [ 68 Ga]Ga-NOTA-ICOS-53 PET/CT imaging study in CHO-ICOS and CHO tumor models To assess the ability of [ 68 Ga]Ga-NOTA-ICOS-53 in detecting human ICOS protein in vivo , PET/CT imaging study was performed in CHO-ICOS and CHO dual tumor models. PET data acquisition was at 30, 60, and 120 minutes after [ 68 Ga]Ga-NOTA-ICOS-53 administration. PET images were shown in Fig 4A, significant PET signal could be observed at CHO-ICOS tumors (purple arrow) from all time-points examined. To quantify the tracer uptake in both tumors, region of interests (ROIs) was drawn via 3D mode. Tracer uptake rate (%ID/g) in CHO-ICOS tumors was significantly higher than that in the CHO tumor (30min: CHO-ICOS 4.99±1.59%ID/g, CHO 2.05±0.29%ID/g; 60min: CHO-ICOS 3.32±1.02%ID/g, CHO 1.29±0.24%ID/g; 120min:CHO-ICOS 2.43±0.77%ID/g, CHO 1.09±0.14%ID/g) at all time points, indicating the specificity of [ 68 Ga]Ga-NOTA-ICOS-53 to ICOS-positive cells and tissues (Fig 4B). For testing the pharmacokinetics of this novel PET tracer, ROI quantification of major organs was also recorded, and the time course pharmacokinetic profiles were listed in Fig 4C. Similar with most nanobody imaging studies, [ 68 Ga]Ga-NOTA-ICOS-53 was fast clearance from blood pool and other major organs, this was potentially mediated by urinary system excretion. Ex vivo biodistribution and immunofluorescence staining Right after the last PET scan, all mice were euthanized, and biodistribution study was performed in major organs (Fig 5A). Higher accumulation of [ 68 Ga]Ga-NOTA-ICOS-53 could be detected in CHO-ICOS tumors (1.67±0.60%ID/g), compared to that in CHO tumors (0.70±0.12%ID/g) (P<0.05), this was in line with tumor ROI measurements. To validate the accuracy of ROI measurements, further linear regression analysis was also performed. Good correlation could be observed between the log₁₀(biodistribution quantification) and log 10 (PET ROI measurements) across all major organs selected (R²=0.627, P<0.001, Fig 5B). Additional immunofluorescence staining assay confirmed the differences of ICOS expression between CHO-ICOS and CHO tumors, (Fig 5C), indicating that the higher uptake of [ 68 Ga]Ga-NOTA-ICOS-53 in CHO-ICOS tumors should be attributed to ICOS protein expression. Discussion Cancer immunotherapy has revolutionized the therapeutic landscape of clinical malignancies 1 . However, delayed efficacy evaluation tools have severely hampered clinical decision-making 5 . Anatomical imaging techniques fail to capture molecular dynamics in the tumor microenvironment 3 . They are also highly susceptible to the "pseudoprogression" phenomenon. Moreover, these evaluation methods often require 2–3 months waiting period after treatment initiation 3 . The classic metabolic PET tracer [ 18 F]FDG shows insufficient specificity 5 . It cannot distinguish signals from tumor cells and activated immune cells 11 . Existing T cell-related biomarkers also have inherent drawbacks 5 . CD8 cannot differentiate between activated and exhausted T cells 14 . Granzyme B only reflects the terminal stage of immune response 17 . Neither meets the demand for early efficacy monitoring 8 . Therefore, developing highly specific and dynamically detectable molecular imaging tools is now an urgent need in immunotherapy. T cell co-stimulatory receptor molecules, such as ICOS, OX40 and 4-1BB play a pivotal role in the process of T cell activation, immune defense and immune memories 19 , 22 . Upon the second activation signal, those molecules immediately upregulate on the surface of activated T cells, making them as ideal targets for monitoring T cell mediated immune responses at early stages 14 , 23 . In the CTR-DB database, our bioinformatics analyses had validated that ICOS mRNA expression from responder cohort was significantly higher than that in non-responder groups across four cancer types, demonstrating ICOS was an indicator for cancer immunotherapy. Additional data from the TCGA LUAD cohort demonstrated downregulated ICOS expression in tumor tissues, compared with adjacent normal tissues, and patients with high ICOS expression also had significantly prolonged overall survival, emphasizing the protective role of ICOS in LUAD. Immune infiltration analysis confirmed positive correlations between ICOS expression and anti-tumor cell subsets. These subsets include CD8⁺ effector T cells and M1 macrophages. ICOS expression was also negatively correlated with pro-tumor populations, such as regulatory T cells and M2 macrophages. All these findings indicate that ICOS was an ideal imaging biomarker and a protective factor in cancer immunotherapies. In our previous study, the 89 Zr-labeled anti-ICOS monoclonal antibody are limited in clinical translation. The large size and molecular weight of antibodies often leads to poor tissue penetration and prolonged blood circulation, which may result in more radiation exposure. In contrast, the nanobody ICOS-53 developed in this study has multiple distinct advantages. The small size and low molecular weight confer strong tissue penetration and fast in vivo clearance, which were usually considered as great advantages for PET imaging probes. For nanobody screening, alpaca immunization with recombinant protein of certain targets could easily generate plenty of nanobody candidates. Under the help of phage or yeast display platforms, we may gain targeting nanobodies with high binding affinity, and this has streamlined the protocol of binder selection. Following this protocol, ICOS-53 with high binding affinity at picomole level was generated. PET imaging with [ 68 Ga]Ga-NOTA-ICOS-53 could distinguish ICOS + tumors from ICOS − tumors, validating the high specificity of this probe to ICOS proteins. Further ex vivo biodistribution and IF staining had confirmed this viewpoint. Despite the advantages listed above, there are still limitations in this study. To assess the capacity of [ 68 Ga]Ga-NOTA-ICOS-53 in detecting human ICOS, we employed ICOS stable expression cell line CHO-ICOS and subcutaneous xenograft tumor models in immunodeficient NSG mice. These models lack a functionally intact immune system. They cannot recapitulate the complexity of the tumor microenvironment. Further work should be assessed in a human ICOS gene transgenic or human hematopoietic stem cells reconstruction mouse model. Conclusions In this proof-of-concept work, we successfully developed and validated the first-in-class nanobody-based PET tracer targeting human ICOS–[ 68 Ga]Ga-NOTA-ICOS-53. [ 68 Ga]Ga-NOTA-ICOS-53 could noninvasively detect human ICOS+ cells and tumors with high specificity and affinity, further PET imaging studies in assessing immune responses are warranted. Abbreviations ICOS Inducible T-cell co-stimulator receptor PET Positron emission tomography CT Computed tomography Nb Nanobody SPR Surface plasmon resonance ROI Region of interest IF Immunofluorescence CHO Chinese hamster ovary NSG NOD scid gamma Declarations Acknowledgements We thank Liang Yong for his technical assistance during the experiments. Author contributions JYL and ZYX conceived and supervised the study. JML and SD performed nanobody screening, radiotracer synthesis, in vitro experiments, and conducted PET/CT imaging and animal studies. WY, YZ, and WRL contributed to immunofluorescence staining and biodistribution experiments. JML carried out bioinformatics and statistical analyses. JML and SD drafted the manuscript. ZYX and JYL critically revised the manuscript. All authors reviewed and approved the final version. Funding National Natural Science Foundation of China(82272056)(2023.01—2026.12) Joint Fund of Zhejiang Provincial Natural Science Foundation of China (ZCLKLZ25H1602)(2025.01—2027.12) The Ningbo Major Research and Development Plan Project(2024Z214)(2024.06—2027.05) Ningbo Natural Science Foundation(2024J043)(2024.06—2027.05) Ningbo Yongjiang Talent Programme(2023)(2024.01—2028.12) Harbin Medical University Research Project（SYDW2025-003）（2025.01--2027.12） Data availability The data supporting the findings of this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate All research involving animal subjects was approved by the Local Ethical Committee of Harbin Medical University Animal Care and Use. Consent for publication Not applicable. Competing interests The authors declare no competing interests AUTHOR INFORMATION Corresponding Authors Jianyu Liu- Department of Orthopedics, the Second Affiliated Hospital of Harbin Medical University, Harbin, China Zunyu Xiao- Department of Nuclear Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, China Authors Jianming Li- Department of Orthopedics, the Second Affiliated Hospital of Harbin Medical University, Harbin, China; Department of Nuclear Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, China Shao Duan- Department of Orthopedics, the Second Affiliated Hospital of Harbin Medical University, Harbin, China; Department of Nuclear Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, China Wei You- Department of Nuclear Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, China Yao Zhao- Department of Orthopedics, the Second Affiliated Hospital of Harbin Medical University, Harbin, China; Department of Nuclear Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, China Weiran Li- Department of Orthopedics, the Second Affiliated Hospital of Harbin Medical University, Harbin, China References Waldman, A. 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Seth, S.; Chen, R.; Liu, Y.; Fujimoto, J.; Hong, L.; Reuben, A.; Varghese, S.; Behrens, C.; McDowell, T.; Soto, L. S.; Haymaker, C.; Weissferdt, A.; Kalhor, N.; Wu, J.; Le, X.; Vokes, N. I.; Cheng, C.; Heymach, J. V.; Gibbons, D. L.; Futreal, P. A.; Wistuba, II; Kadara, H.; Zhang, J.; Moran, C.; Zhang, J., Integrative genomic and transcriptomic profiling of pulmonary sarcomatoid carcinoma identifies molecular subtypes associated with distinct immune features and clinical outcomes. Cancer innovation 2024, 3 (3), e112. Cite Share Download PDF Status: Published Journal Publication published 19 Apr, 2026 Read the published version in EJNMMI Research → Version 1 posted Reviewers agreed at journal 16 Feb, 2026 Reviewers invited by journal 13 Feb, 2026 Editor assigned by journal 13 Feb, 2026 First submitted to journal 12 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8725887","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590918955,"identity":"8cf7e59d-1419-4974-8da5-803f3c203f2e","order_by":0,"name":"Jianming Li","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jianming","middleName":"","lastName":"Li","suffix":""},{"id":590918956,"identity":"7a764485-166c-4616-b483-21cce289e64e","order_by":1,"name":"Shao Duan","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shao","middleName":"","lastName":"Duan","suffix":""},{"id":590918957,"identity":"746ae80f-6688-40ef-a6c4-ac901ca2760b","order_by":2,"name":"Wei You","email":"","orcid":"","institution":"The First Affiliated Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"You","suffix":""},{"id":590918958,"identity":"a6ce4eb8-5993-4423-a4a7-7c3a61799802","order_by":3,"name":"Yao Zhao","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Zhao","suffix":""},{"id":590918959,"identity":"96f12c75-e682-40c0-9f96-0862e1053b99","order_by":4,"name":"Weiran Li","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weiran","middleName":"","lastName":"Li","suffix":""},{"id":590918960,"identity":"ca24b2b2-6398-4475-bcc4-8527af1729ab","order_by":5,"name":"Zunyu Xiao","email":"","orcid":"","institution":"The First Affiliated Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Zunyu","middleName":"","lastName":"Xiao","suffix":""},{"id":590918961,"identity":"e9de3724-84a7-4db6-81ec-579bc98297b3","order_by":6,"name":"jianyu liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACPmYGBoMEMPPwgQMffhChhQ2uhfFY4sGZPcRogbOYzxgf5mDDoxSuhZ3HoOBBzZ3EDcfOfDjMwMMgzy92gJDDeAwMEo49S9xw5uyGwwUWDIYzZycQo4XtcOKGG0AtM3gYEgxuE6XlH1DL/TcPDvOwEaslsQ2o5cAZBmK1sBUYJPYdNp554JgBMJAlCPuFn//wNsMf3w7L9h04/PjDhx828vzSBLSALDIAEo4NEI4EQeUgwPwASNgTpXQUjIJRMApGJgAAz1FKUXxWQbQAAAAASUVORK5CYII=","orcid":"","institution":"The 2nd Affiliated Hospital of Harbin Medical University: Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"jianyu","middleName":"","lastName":"liu","suffix":""}],"badges":[],"createdAt":"2026-01-29 01:29:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8725887/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8725887/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13550-026-01432-w","type":"published","date":"2026-04-19T15:58:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102992856,"identity":"3ebfc278-c005-4c32-b2e0-7d073976a74f","added_by":"auto","created_at":"2026-02-19 11:41:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1214761,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eICOS was highly correlated with therapeutic responses of cancer immunotherapy across many types of human cancers\u003c/strong\u003e.\u003cstrong\u003e(A–D) \u003c/strong\u003eThe upper panel, higher mRNA expression of ICOS could be detected in responders, compared to non-responders from patients receiving cancer immunotherapy. The lower panel, receiver operating characteristic (ROC) curve demonstrated the great potential of ICOS in predicting responses of cancer immunotherapy, with area under the curve (AUC) values ranging from 0.71 to 0.77. All data above was from the CTR‑DB platform (v2.0): breast cancer (CTR_Microarray_170-1, 175-1, 41-1); liver cancer (CTR_Microarray_119-1,); lung; melanoma (CTR_RNAseq_11-1,370-1,593-1); lung cancer (CTR_RNAseq_197-1, 539-1). \u003cstrong\u003e(E)\u003c/strong\u003e ICOS expression and survival in lung adenocarcinoma (LUAD). ICOS is significantly downregulated in LUAD tumors (n=530) compared to the paired adjacent normal tissues (n=347; ***P \u0026lt; 0.001; TCGA data).\u003cstrong\u003e (F)\u003c/strong\u003e Kaplan–Meier analysis indicates that patients with high ICOS expression (defined by optimal cutoff) have longer overall survival (P=0.019; hazard ratio [HR]=0.619). \u003cstrong\u003e(G)\u003c/strong\u003e ICOS correlates with immune infiltration in the LUAD microenvironment. Spearman correlation coefficients between ICOS expression and immune cell abundances (estimated by seven algorithms, e.g., xCell, TIMER) are shown. ICOS expression positively correlates (r = 0.2–0.5) with anti‑tumor subsets such as CD8⁺ T cells and M1 macrophages and negatively correlates (r = −0.1 to −0.4) with pro‑tumor populations including regulatory T cells and M2 macrophages. P \u0026lt; 0.05 was considered significant.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8725887/v1/bacef14aa3a7b7014d114fc2.png"},{"id":102992854,"identity":"ca9bed96-3fbe-4010-b026-c76038104152","added_by":"auto","created_at":"2026-02-19 11:41:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":497813,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnimal immunization, nanobody screening, and affinity determination\u003c/strong\u003e. \u003cstrong\u003e(A)\u003c/strong\u003e Schematic workflow for anti-ICOS nanobody generation. \u003cstrong\u003e(B)\u003c/strong\u003e Sequence alignment and flow cytometric analysis of ICOS nanobody candidates, Framework region 3 (FR3), complementarity-determining region 3 (CDR3), and framework region 4 (FR4) amino acid sequences of four candidate nanobodies (ICOS-345, ICOS-130, ICOS-404, ICOS-53) were aligned. Corresponding flow cytometric profiles confirmed the surface display efficacy and ICOS-binding activity of each candidate. \u003cstrong\u003e(C)\u003c/strong\u003eSurface plasmon resonance (SPR) assay between ICOS-53 and recombinant human ICOS (hICOS) extracellular domain, yielding an equilibrium dissociation constant (KD) of 85.3 pM.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8725887/v1/98c6c2f9a43b99d7ef633621.png"},{"id":102992855,"identity":"4692d5c7-345d-4bf0-94c0-0b6e30533d04","added_by":"auto","created_at":"2026-02-19 11:41:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":290028,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSynthesis and characterization of [\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e68\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eGa]Ga-NOTA-ICOS-53.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Flow cytometry analysis of ICOS expression on CHO and CHO-ICOS cells. (B) Scheme of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 radiolabeling. \u003cstrong\u003e(C)\u003c/strong\u003e Radio-iTLC (mobile phase: 50 mM EDTA) and cellular uptake of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 in CHO-ICOS and CHO cells. Data are presented as mean ± standard deviation (SD) unless otherwise indicated. Statistical analysis was conducted with an unpaired two-tailed Student’s t-test, P \u0026lt; 0.05, P \u0026lt; 0.01, P \u0026lt; 0.001, P \u0026lt; 0.0001, ns, not significant.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8725887/v1/380980668b519406b8d4723b.png"},{"id":102992853,"identity":"8f7acd2a-bd31-497e-9a75-468087858e54","added_by":"auto","created_at":"2026-02-19 11:41:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":372786,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e[\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e68\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eGa]Ga-NOTA-ICOS-53 in vivo PET/CT imaging and ROI quantification in CHO-ICOS/CHO dual tumor models\u003c/strong\u003e. \u003cstrong\u003e(A)\u003c/strong\u003e Serial [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 PET/CT fusion images at 30-, 60-, and 120- min post tracer administration (purple arrows: CHO-ICOS tumors; blue arrows: CHO tumors). \u003cstrong\u003e(B)\u003c/strong\u003e ROI quantification of tracer uptake (%ID/g) in CHO and CHO-ICOS tumors at the indicated time points, quantified via PET image segmentation. \u003cstrong\u003e(C)\u003c/strong\u003e [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 pharmacokinetics at all imaging time points in major organs (heart, liver, spleen, lung, muscle, bone, kidney). Data are presented as mean ± standard deviation (SD) unless otherwise indicated. Statistical analysis was conducted with an unpaired two-tailed Student’s t-test, P \u0026lt; 0.05, P \u0026lt; 0.01, P \u0026lt; 0.001, P \u0026lt; 0.0001, ns, not significant.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8725887/v1/19364c7ff0abafd115ecaa23.png"},{"id":102992857,"identity":"247dd355-234b-4417-9eb7-e53d93076f05","added_by":"auto","created_at":"2026-02-19 11:41:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":494847,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEx vivo biodistribution and immunofluorescence staining\u003c/strong\u003e. \u003cstrong\u003e(A)\u003c/strong\u003e Ex vivo biodistribution of[\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 at 120 min time point. \u003cstrong\u003e(B)\u003c/strong\u003e Linear regression analysis between ex vivo biodistribution data and PET ROI measurements, yielded a coefficient of determination (R²= 0.627) and significant correlation (P \u0026lt; 0.001). \u003cstrong\u003e(C)\u003c/strong\u003e ICOS immunofluorescence staining of CHO-ICOS (left) and CHO (right) tumor tissues (DAPI: Blue, ICOS: Yellow). Data are presented as mean ± standard deviation (SD) unless otherwise indicated. Statistical analysis was conducted with an unpaired two-tailed Student’s t-test, P \u0026lt; 0.05, P \u0026lt; 0.01, P \u0026lt; 0.001, P \u0026lt; 0.0001, ns, not significant.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8725887/v1/e2a9ce258ca28a99078282f3.png"},{"id":107350792,"identity":"6b7c2553-8fc8-499f-9317-d9c4a231fc10","added_by":"auto","created_at":"2026-04-20 16:04:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3348767,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8725887/v1/d14e4e84-924b-48c9-b4c0-a172789d88ea.pdf"}],"financialInterests":"","formattedTitle":"Development and evaluation of the first-in-class nanobody based PET tracer targeting human inducible T-cell co-stimulator receptor","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBenefiting from the breakthroughs in the field of immuno-oncology, cancer treatment had undergone a significant revolution. Immunotherapy strategies, including vaccines, adoptive cell therapy, and immune checkpoint inhibitors had shown promising effects in various solid and hematological cancers. The core mechanism of cancer immunotherapy is to evoke the patients\u0026rsquo; immune system, which then attacks and eliminates malignant tumor cells\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Currently, the gold standards for evaluating tumor treatment efficacy in clinical practice are the Response Evaluation Criteria in Solid Tumors (RECIST) and Immune-Related Response Criteria (irRC), which mainly based on computed tomography (CT) and magnetic resonance imaging (MRI) technologies\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Although these imaging techniques can provide high-resolution anatomical information, they failed to capture molecular dynamics within tumor microenvironment\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Plenty of studies had confirmed that in the context of immuno-oncology treatment, these evaluation criteria often failed due to the phenomenon of \"pseudoprogression\u0026rdquo;, a false increase in lesion size on anatomic imaging caused by immune cell infiltration into tumor tissue\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Additionally, after the initiation of treatment, these traditional evaluation methods typically require a 9\u0026ndash;12-week gap before assessment which greatly limits the timeliness of clinical decision-making\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Therefore, to improve the clinical prognosis of cancer patients, there is an urgent need to develop tools to effectively evaluate novel immunotherapies and accurately monitor therapeutic responses\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePositron emission tomography (PET) is theoretically an ideal solution to this problem, as it possesses the unique advantage of dynamically and non-invasively quantifying systemic molecular information\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Currently, the most classic PET tracer, 2-deoxy-2-[\u003csup\u003e18\u003c/sup\u003eF]fluoro-D-glucose ([\u003csup\u003e18\u003c/sup\u003eF]FDG), has been widely used in clinical lung cancer diagnosis, and some studies have attempted to apply it to the monitoring of immunotherapy responses\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, the major limitation of [\u003csup\u003e18\u003c/sup\u003eF]FDG lies in its insufficient specificity\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Since it mainly reflects abnormal metabolic processes, its detection signal includes both tumor cells and proliferatively active immune cells, making it technically difficult to effectively distinguish and resolve the signals generated by these two cell types\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Similarly, other metabolic tracers (e.g., [\u003csup\u003e18\u003c/sup\u003eF]F-AraG) face the same issue of low specificity as [\u003csup\u003e18\u003c/sup\u003eF]FDG when applied in immunotherapy settings\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Successful cancer immunotherapies mainly consist of T cell activation, migration into tumor region, and cancer killing\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Thus, many efforts had focused on developing imaging reagents targeting T cell specific and functional biomarkers\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Among those imaging reagents, T cell phenotype biomarker CD8 and secreted biomarker granzyme B have shown great potential in monitoring therapeutic responses in clinical trials\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, it should be noted that phenotype biomarker CD8 cannot distinguish activated and exhausted T cells\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Granzyme B was mainly secreted by cytotoxic T cells, which usually stands for cancer killing, the last step of immunotherapy, and this may lead to missing of monitoring early-stage immune response\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Therefore, exploring biomarkers for precisely predicting the efficacy of immunotherapy in the early stages are still needed\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur group mainly focuses on PET imaging targeting T cell co-stimulatory molecules, especially inducible T cell co-stimulator (ICOS, also known as CD278), a key member of the CD28 superfamily\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Existing studies had reported that ICOS is mainly expressed on the surface of activated T cells, the activation of the ICOS signaling pathway begins with the specific binding of the receptor to its ligand, ICOS ligand (ICOSL), which is mainly distributed on the surface of B cells, macrophages, and dendritic cells\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. After their binding, cascade reactions of downstream signaling pathways are triggered, which not only regulate the proliferative activity and survival cycle of T cells but also promote the secretion of key cytokines such as interleukin-4 (IL-4), interleukin-10 (IL-10), and interferon (IFN), thereby participating in the regulation of immune responses\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In our previous study, we developed an \u003csup\u003e89\u003c/sup\u003eZr- labeled monoclonal antibody targeting murine ICOS on activated T cells, and ICOS was identified as an ideal molecule for the assessment of cancer immunotherapies in a Lewis lung cancer mouse model. Given the long circulation and poor tissue penetration, such antibody-based PET tracer was limited in clinical translation. To overcome those challenges, in the current study, we developed a nanobody ICOS-53 targeting human ICOS with high affinity at picomole level. ICOS-53 was radiolabeled with \u003csup\u003e68\u003c/sup\u003eGa, and the capacity of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 in capturing human ICOS was both assessed via cell binding and PET imaging studies. Our data demonstrated [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 as a promising PET tracer in detecting human ICOS with high specificity, and further clinical trials warrant evaluation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eAnimal immunization, nanobody screening and affinity determination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore alpaca immunization, 10 mL blood was collected as a negative serum control. Dissolve 0.5 mg of recombinant ICOS extracellular domain protein in 250 \u0026mu;l PBS solution and subcutaneously injected into healthy male alpaca every two weeks, 5 times in total. After the second immunization, collect alpaca coagulant-promoting blood, isolate serum, and use ELISA to evaluate the immune response. After the fifth immunization, peripheral blood was collected and blood lymphocytes were isolated. B cell mRNA was reversely transcript into DNA, following the establishment of yeast display library for ICOS nanobody screening. Typically, ICOS nanobody screening was based on the standard magnet selection and FACS sorting. After DNA sequencing, ICOS-53 was expressed according to the standard procedures as previously described\u003csup\u003e20, 21\u003c/sup\u003e. For affinity determination, surface plasmon resonance (SPR) assay was performed between ICOS-53 and ICOS extracellular protein using a Biacore T200 system (GE Healthcare). The running buffer comprised 120 mM NaCl, 10 mM phosphate (pH 7.4), 50 \u0026mu;M EDTA, and 0.05% Tween 20. Firstly, biotinylated ICOS extracellular proteins were immobilized onto a CAP sensor chip via the Biotin CAPture Kit (Cytiva), reaching approximately 750 response units (RU). Subsequently, ICOS-specific Nbs samples were injected over the chip surface at a flow rate of 30 \u0026micro;L/min with a contact time of 120 s followed by a dissociation phase of 150 s. The response (RU) was recorded in real time. Finally, the sensor chip was regenerated using the Biotin CAPture Kit (Cytiva). For serially diluted samples, each sample was tested by repeating the above procedure. The equilibrium dissociation constant was derived using Biacore T200 evaluation software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell culture and FACS analysis of ICOS expression in both CHO and CHO-ICOS cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CHO-ICOS and CHO cell lines were purchased from SHANGHAIYAJI BIOTECHNOLOGYCO, LTD. CHO-ICOS and CHO cell were cultured according to standard protocols in Dulbecco\u0026rsquo;smodified Eagle\u0026rsquo;smedium (DMEM), supplemented with 10% (v/v) FCS and 1% (v/v) penicillin/streptomycin (all Thermo Fisher Scientific). ICOS expression was validated via flow cytometry. The Brilliant Violet 421\u0026trade; anti-human CD278 (ICOS) Antibody was purchased from Biolegend.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 synthesis, characterization and cell uptake study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe site-specific conjugated NOTA-ICOS-53 (100\u0026nbsp;\u0026micro;g) was diluted in 1\u0026nbsp;mL of 1\u0026nbsp;M NaOAc buffer (pH 4.5\u0026ndash;4.7) and incubated with 1\u0026nbsp;mL [\u003csup\u003e68\u003c/sup\u003eGa]Ga3+\u0026nbsp;eluate (780-870\u0026nbsp;MBq, Galli Ad, IRE ELiT) for 10\u0026nbsp;min at 50\u0026deg;C. The crude radiolabeled VHH reaction mixture was purified by SEC using a PD-10 desalting column (GE Healthcare) eluted with sterile 0.01\u0026nbsp;M phosphate-buffered saline (PBS, pH 7.4). Radiochemical purity was evaluated using radio-iTLC (0.1\u0026nbsp;M sodium citrate buffer (pH 5.0); [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-anti-ICOS VHH: Rf\u0026thinsp;=\u0026thinsp;0; [\u003csup\u003e68\u003c/sup\u003eGa]Ga3+: Rf\u0026thinsp;=\u0026thinsp;1) and SEC as described above. The non-decay-corrected radiochemical yield was calculated based on the activity obtained after PD-10 purification. The stability assessment was performed as described in the supplementary information.\u003c/p\u003e\n\u003cp\u003eFor cell uptake studies, 2 \u0026mu;Ci [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 were incubated with 10\u003csup\u003e5\u003c/sup\u003e CHO and CHO-ICOS cells for 1 hour under 37\u0026deg;C in serum free DMEM, respectively. After 3 washes, cells were collected and the radioactivity was tested via gamma counter.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimal model establishment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll research involving animal subjects was approved by the Local Ethical Committee of Harbin Medical University Animal Care and Use. 5\u0026times;10\u003csup\u003e6\u003c/sup\u003e CHO cells and CHO-ICOS cells in 50 \u0026mu;L PBS were injected subcutaneously in the left shoulder and right shoulder of female NSG mice, respectively. Tumor volumes were recorded every other day by calipers and tumor volumes were calculated using the formula (\u0026pi;/6) length\u0026times;width\u0026times;height.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePET/CT imaging and Biodistribution study\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll PET scans were performed on a Siemens Inveon MM-PET/CT. After anesthetization by 1.5%\u0026ndash;2% isoflurane gas, 200\u0026mu;Ci [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 was administered via mouse tail vein. Static PET scans were acquired immediately after CT imaging at 30 minutes, 60 minutes, and 120 minutes, which were used to provide anatomical references and attenuation correction for PET signals.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAfter the final PET scan at 120 minutes, NSG mice were euthanized, organs including heart, liver, spleen, lungs, kidneys, CHO tumors, CHO-ICOS tumors, muscle, bone, and tail were collected and wet weighted. An auto-matic gamma counter was used for determining the activity in different organs. Both ROI and biodistribution data were normalized to %ID/g.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunofluorescence staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter the mice were sacrificed, the two types of tumor tissues were fixed with 4% paraformaldehyde. After embedding in paraffin After depa-raffinization, antigen retrieval, and BSA blockade, tumor tissues were stained overnight at 4\u0026deg;C with ICOS antibody. Secondary antibodies were then added and incubated for 1 h. After staining the nuclei with DAPI (Beyotime, Shanghai,China), the sections were examined under a fluorescent microscope (Nikon, Japan).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted with GraphPad Prism10.1.2 software and R language (version 4.5.0). Quantitative data are presented as the mean \u0026plusmn; standard deviation. Paired two-tailed Student\u0026rsquo;s T-tests were employed for comparisons of paired samples between two groups. For multi-group comparisons, one-way analysis of variance (ANOVA) was initially performed to assess overall group differences; upon detecting significant effects, Tukey\u0026rsquo;s honestly significant difference (HSD) post hoc test was utilized for pairwise comparisons. Pearson correlation analysis was used to evaluate correlations between variables, while linear regression analysis was conducted to confirm linear relationships. In the figures, statistical significance is denoted as P \u0026lt; 0.05 (*), P \u0026lt; 0.01 (**), P \u0026lt; 0.001 (***), and P \u0026lt; 0.0001 (***). Differences were considered statistically significant at P \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eICOS was highly correlated with therapeutic responses of cancer immunotherapy across many types of human cancers\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo validate the potential of ICOS as a biomarker in assessing immune responses of cancer immunotherapy, we integrated its expression dynamics, prognostic relevance, and predictive capacity across multiple malignancies. Our analysis incorporated transcriptomic from four major cancers\u0026mdash;breast, liver, lung, and melanoma\u0026mdash;sourced from the CTR‑DB platform (v2.0). ICOS mRNA expression was much higher in responsive patients receiving immunotherapy than that in non-responder group (Fig 1A-1D, upper panel). The receiver operating characteristic (AUC) curve analysis validated the robust role of ICOS in predicting responses of cancer immunotherapy, with area under the curve (AUC) values ranging from 0.71 to 0.77 (Fig 1A-1D, lower panel). Further omics datasets from TCGA (The Cancer Genome Atlas Program) demonstrated that ICOS is downregulated in LUAD tumors compared to the paired adjacent normal tissues (P \u0026lt; 0.001), which was probably associated with the absence of ICOS\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eactivated T cells within the tumor microenvironment (Fig 1E). Additional Kaplan\u0026ndash;Meier analysis indicated that LUAD patients with high ICOS expression have longer overall survival rate (P=0.019; hazard ratio [HR]=0.619) (Fig 1F), indicating the protective role of ICOS. We also assessed immune infiltration in the tumor microenvironment using seven computational tools (e.g., xCell, TIMER). ICOS expression showed positive correlations with anti-tumor subsets\u0026mdash;including CD8⁺\u0026nbsp;T cells (effector and central memory), CD4⁺\u0026nbsp;memory T cells, and M1 macrophages (r = 0.2\u0026ndash;0.5)\u0026mdash;and negative correlations with pro-tumor populations such as regulatory T cells (Tregs) and M2 macrophages (r = \u0026minus;0.1 to \u0026minus;0.4) (Fig 1G). These associations suggest that ICOS may contribute to a more favorable immune microenvironment in LUAD, possibly by facilitating the recruitment of anti-tumor effectors while restraining immunosuppressive components.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimal immunization and Nb screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo generate ICOS-specific nanobodies, alpacas was immunized with 0.5mg recombinant human ICOS extracellular domain protein (amino acid residues 21\u0026ndash;141) for five times in total. On day 56, one week after the final immunization, the alpaca\u0026apos;s peripheral blood lymphocytes (PBLs) were collected, the mRNA was reverse transcribed into cDNA, and a nanobody (Nb) yeast library representing the VHH repertoire of this alpaca was established (Fig 2A). To test the binding capability of ICOS nanobody candidates, the yeast library was inducted with galactose, AF488 labeled anti-C-Myc antibody was employed to validate the expression of ICOS nanobodies. Subsequently, inducted yeast cells were incubated with BV-650-labeled recombinant human ICOS extracellular domain protein, via flow cytometry analysis, ICOS-53 was identified as the optimal candidate nanobody (Fig 2B). To evaluate the binding affinity of ICOS-53, we performed surface plasmon resonance (SPR) analysis between ICOS-53 and human ICOS extracellular domain protein, and ICOS-53 showed a considerably strong affinity, as indicated by a KD value of 85.3 pM (Fig 2C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSynthesis and Characterization of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate ICOS expression, flow cytometry assay was performed between CHO-ICOS and CHO cells. High ICOS expression could be detected on CHO-ICOS cells, compared to CHO cell line, P\u0026lt;0.0001 (Fig 3A). For tracer radiolabeling, ICOS-53 was conjugated with p-SCN-Bn-NOTA，[\u003csup\u003e68\u003c/sup\u003eGa]GaCl\u003csub\u003e3\u003c/sub\u003e was added to NOTA-ICOS-53, and the pH of mixture was adjusted to 4.0, followed by incubation at approximately 37℃ for 30 minutes (Fig 3B). Right after the reaction, the radiochemical purity of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewas tested by radio-iTLC. To validate the specificity of ICOS-53 to human ICOS protein, we employed CHO-ICOS cell line stable expressing human ICOS for in vitro cell uptake study. ICOS expression was first validated via flow cytometry. For cell uptake study, 2\u0026mu;Ci of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 was added to CHO-ICOS and CHO cells, respectively. After 1 hour incubation and 3 washes, the radioactivity in both cell lines was determined by a gamma counter. Higher activity retention was detected in CHO-ICOS cells (P\u0026lt;0.001), indicating the specificity of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 to human ICOS protein (Fig 3C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 PET/CT imaging study in CHO-ICOS and CHO tumor models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the ability of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 in detecting human ICOS protein \u003cem\u003ein vivo\u003c/em\u003e, PET/CT imaging study was performed in CHO-ICOS and CHO dual tumor models. PET data acquisition was at 30, 60, and 120 minutes after [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 administration. PET images were shown in Fig 4A, significant PET signal could be observed at CHO-ICOS tumors (purple arrow) from all time-points examined. To quantify the tracer uptake in both tumors, region of interests (ROIs) was drawn via 3D mode. Tracer uptake rate (%ID/g) in CHO-ICOS tumors was significantly higher than that in the CHO tumor (30min: CHO-ICOS 4.99\u0026plusmn;1.59%ID/g, CHO 2.05\u0026plusmn;0.29%ID/g; 60min: CHO-ICOS 3.32\u0026plusmn;1.02%ID/g, CHO 1.29\u0026plusmn;0.24%ID/g; 120min:CHO-ICOS 2.43\u0026plusmn;0.77%ID/g, CHO 1.09\u0026plusmn;0.14%ID/g) at all time points, indicating the specificity of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 to ICOS-positive cells and tissues (Fig 4B). For testing the pharmacokinetics of this novel PET tracer, ROI quantification of major organs was also recorded, and the time course pharmacokinetic profiles were listed in Fig 4C. Similar with most nanobody imaging studies, [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 was fast clearance from blood pool and other major organs, this was potentially mediated by urinary system excretion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEx vivo biodistribution and immunofluorescence staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRight after the last PET scan, all mice were euthanized, and biodistribution study was performed in major organs (Fig 5A). Higher accumulation of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 could be detected in CHO-ICOS tumors (1.67\u0026plusmn;0.60%ID/g), compared to that in CHO tumors (0.70\u0026plusmn;0.12%ID/g) (P\u0026lt;0.05), this was in line with tumor ROI measurements. To validate the accuracy of ROI measurements, further linear regression analysis was also performed. Good correlation could be observed between the log₁₀(biodistribution quantification) and log\u003csub\u003e10\u003c/sub\u003e(PET ROI measurements) across all major organs selected (R\u0026sup2;=0.627, P\u0026lt;0.001, Fig 5B). Additional immunofluorescence staining assay confirmed the differences of ICOS expression between CHO-ICOS and CHO tumors, (Fig 5C), indicating that the higher uptake of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 in CHO-ICOS tumors should be attributed to ICOS protein expression.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCancer immunotherapy has revolutionized the therapeutic landscape of clinical malignancies\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. However, delayed efficacy evaluation tools have severely hampered clinical decision-making\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Anatomical imaging techniques fail to capture molecular dynamics in the tumor microenvironment\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. They are also highly susceptible to the \"pseudoprogression\" phenomenon. Moreover, these evaluation methods often require 2\u0026ndash;3 months waiting period after treatment initiation\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The classic metabolic PET tracer [\u003csup\u003e18\u003c/sup\u003eF]FDG shows insufficient specificity\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. It cannot distinguish signals from tumor cells and activated immune cells\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Existing T cell-related biomarkers also have inherent drawbacks\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. CD8 cannot differentiate between activated and exhausted T cells\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Granzyme B only reflects the terminal stage of immune response\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Neither meets the demand for early efficacy monitoring\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Therefore, developing highly specific and dynamically detectable molecular imaging tools is now an urgent need in immunotherapy.\u003c/p\u003e \u003cp\u003eT cell co-stimulatory receptor molecules, such as ICOS, OX40 and 4-1BB play a pivotal role in the process of T cell activation, immune defense and immune memories\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Upon the second activation signal, those molecules immediately upregulate on the surface of activated T cells, making them as ideal targets for monitoring T cell mediated immune responses at early stages\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In the CTR-DB database, our bioinformatics analyses had validated that ICOS mRNA expression from responder cohort was significantly higher than that in non-responder groups across four cancer types, demonstrating ICOS was an indicator for cancer immunotherapy. Additional data from the TCGA LUAD cohort demonstrated downregulated ICOS expression in tumor tissues, compared with adjacent normal tissues, and patients with high ICOS expression also had significantly prolonged overall survival, emphasizing the protective role of ICOS in LUAD. Immune infiltration analysis confirmed positive correlations between ICOS expression and anti-tumor cell subsets. These subsets include CD8⁺ effector T cells and M1 macrophages. ICOS expression was also negatively correlated with pro-tumor populations, such as regulatory T cells and M2 macrophages. All these findings indicate that ICOS was an ideal imaging biomarker and a protective factor in cancer immunotherapies.\u003c/p\u003e \u003cp\u003eIn our previous study, the \u003csup\u003e89\u003c/sup\u003eZr-labeled anti-ICOS monoclonal antibody are limited in clinical translation. The large size and molecular weight of antibodies often leads to poor tissue penetration and prolonged blood circulation, which may result in more radiation exposure. In contrast, the nanobody ICOS-53 developed in this study has multiple distinct advantages. The small size and low molecular weight confer strong tissue penetration and fast \u003cem\u003ein vivo\u003c/em\u003e clearance, which were usually considered as great advantages for PET imaging probes. For nanobody screening, alpaca immunization with recombinant protein of certain targets could easily generate plenty of nanobody candidates. Under the help of phage or yeast display platforms, we may gain targeting nanobodies with high binding affinity, and this has streamlined the protocol of binder selection. Following this protocol, ICOS-53 with high binding affinity at picomole level was generated. PET imaging with [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 could distinguish ICOS\u003csup\u003e+\u003c/sup\u003e tumors from ICOS\u003csup\u003e\u0026minus;\u003c/sup\u003e tumors, validating the high specificity of this probe to ICOS proteins. Further ex vivo biodistribution and IF staining had confirmed this viewpoint. Despite the advantages listed above, there are still limitations in this study. To assess the capacity of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 in detecting human ICOS, we employed ICOS stable expression cell line CHO-ICOS and subcutaneous xenograft tumor models in immunodeficient NSG mice. These models lack a functionally intact immune system. They cannot recapitulate the complexity of the tumor microenvironment. Further work should be assessed in a human ICOS gene transgenic or human hematopoietic stem cells reconstruction mouse model.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this proof-of-concept work, we successfully developed and validated the first-in-class nanobody-based PET tracer targeting human ICOS\u0026ndash;[\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53. [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 could noninvasively detect human ICOS+ cells and tumors with high specificity and affinity, further PET imaging studies in assessing immune responses are warranted.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eICOS\u0026emsp;Inducible T-cell co-stimulator receptor\u003c/p\u003e\n\u003cp\u003ePET\u0026emsp;Positron emission tomography\u003c/p\u003e\n\u003cp\u003eCT\u0026emsp;Computed tomography\u003c/p\u003e\n\u003cp\u003eNb\u0026emsp;Nanobody\u003c/p\u003e\n\u003cp\u003eSPR\u0026emsp;Surface plasmon resonance\u003c/p\u003e\n\u003cp\u003eROI\u0026emsp;Region of interest\u003c/p\u003e\n\u003cp\u003eIF\u0026emsp;Immunofluorescence\u003c/p\u003e\n\u003cp\u003eCHO\u0026emsp;Chinese hamster ovary\u003c/p\u003e\n\u003cp\u003eNSG\u0026emsp;NOD scid gamma\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Liang Yong for his technical assistance during the experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJYL and ZYX conceived and supervised the study. JML and SD performed nanobody screening, radiotracer synthesis, in vitro experiments, and conducted PET/CT imaging and animal studies. WY, YZ, and WRL contributed to immunofluorescence staining and biodistribution experiments. JML carried out bioinformatics and statistical analyses. JML and SD drafted the manuscript. ZYX and JYL critically revised the manuscript. All authors reviewed and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China(82272056)(2023.01\u0026mdash;2026.12)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJoint Fund of Zhejiang Provincial Natural Science Foundation of China (ZCLKLZ25H1602)(2025.01\u0026mdash;2027.12)\u003c/p\u003e\n\u003cp\u003eThe Ningbo Major Research and Development Plan Project(2024Z214)(2024.06\u0026mdash;2027.05)\u003c/p\u003e\n\u003cp\u003eNingbo Natural Science Foundation(2024J043)(2024.06\u0026mdash;2027.05)\u003c/p\u003e\n\u003cp\u003eNingbo Yongjiang Talent Programme(2023)(2024.01\u0026mdash;2028.12)\u003c/p\u003e\n\u003cp\u003eHarbin Medical University Research Project（SYDW2025-003）（2025.01--2027.12）\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll research involving animal subjects was approved by the Local Ethical Committee of Harbin Medical University Animal Care and Use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorresponding Authors\u003c/p\u003e\n\u003cp\u003eJianyu Liu- Department of Orthopedics, the Second Affiliated Hospital of Harbin Medical University, Harbin, China\u003c/p\u003e\n\u003cp\u003eZunyu Xiao- Department of Nuclear Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, China\u003c/p\u003e\n\u003cp\u003eAuthors\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJianming Li- Department of Orthopedics, the Second Affiliated Hospital of Harbin Medical University, Harbin, China; Department of Nuclear Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, China\u003c/p\u003e\n\u003cp\u003eShao Duan- Department of Orthopedics, the Second Affiliated Hospital of Harbin Medical University, Harbin, China; Department of Nuclear Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, China\u003c/p\u003e\n\u003cp\u003eWei You- Department of Nuclear Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, China\u003c/p\u003e\n\u003cp\u003eYao Zhao- Department of Orthopedics, the Second Affiliated Hospital of Harbin Medical University, Harbin, China; Department of Nuclear Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, China\u003c/p\u003e\n\u003cp\u003eWeiran Li- Department of Orthopedics, the Second Affiliated Hospital of Harbin Medical University, Harbin, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWaldman, A. 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A.; Wistuba, II; Kadara, H.; Zhang, J.; Moran, C.; Zhang, J., Integrative genomic and transcriptomic profiling of pulmonary sarcomatoid carcinoma identifies molecular subtypes associated with distinct immune features and clinical outcomes. \u003cem\u003eCancer innovation \u003c/em\u003e\u003cstrong\u003e2024,\u003c/strong\u003e \u003cem\u003e3\u003c/em\u003e (3), e112.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"ejnmmi-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejre","sideBox":"Learn more about [EJNMMI Research](http://ejnmmires.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejre/default.aspx","title":"EJNMMI Research","twitterHandle":"@officialEANM","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ICOS, Nanobody, Cancer Immunotherapy, Molecular Imaging, T Cells","lastPublishedDoi":"10.21203/rs.3.rs-8725887/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8725887/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInducible T-cell co-stimulator receptor (ICOS) is a conserved biomarker mainly expressed on activated T cells. It had been proved that PET imaging targeting ICOS was a promising strategy for assessment of immune responses in cancer immunotherapy. For further translating this strategy into clinic, in the present study, we had developed the first-in-class ICOS targeting nanobody ICOS-53 via alpaca immunization and yeast display screening.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe binding affinity of ICOS-53 to recombinant ICOS protein was validated by surface plasmon resonance, and the KD value was 85.3 pM. ICOS-53 was then conjugated to NOTA and radiolabeled with [\u003csup\u003e68\u003c/sup\u003eGa]GaCl\u003csub\u003e3\u003c/sub\u003e. In PET imaging study, higher accumulation of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 could be observed in CHO-ICOS (CHO transfected by human ICOS) tumors, compared to CHO cohort from all-time points examined. Good correlation between PET imaging quantitative results and biodistribution could be observed (R\u0026sup2;=0.627, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Immunofluorescence staining also confirmed the high ICOS expression in CHO-ICOS tumors.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur data demonstrated that we had developed a novel nanobody based PET tracer targeting human ICOS, [\u003csup\u003e68\u003c/sup\u003eGa]Ga-NOTA-ICOS-53 PET imaging was a promising strategy for tracking ICOS+ cells both in vitro and in vivo.\u003c/p\u003e","manuscriptTitle":"Development and evaluation of the first-in-class nanobody based PET tracer targeting human inducible T-cell co-stimulator receptor","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 11:41:35","doi":"10.21203/rs.3.rs-8725887/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-02-16T15:23:35+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-13T13:33:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-13T06:57:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"EJNMMI Research","date":"2026-02-12T06:56:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"ejnmmi-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejre","sideBox":"Learn more about [EJNMMI Research](http://ejnmmires.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejre/default.aspx","title":"EJNMMI Research","twitterHandle":"@officialEANM","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"27908579-bd04-4ee1-b57d-79d7d607a1b3","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:02:00+00:00","versionOfRecord":{"articleIdentity":"rs-8725887","link":"https://doi.org/10.1186/s13550-026-01432-w","journal":{"identity":"ejnmmi-research","isVorOnly":false,"title":"EJNMMI Research"},"publishedOn":"2026-04-19 15:58:50","publishedOnDateReadable":"April 19th, 2026"},"versionCreatedAt":"2026-02-19 11:41:35","video":"","vorDoi":"10.1186/s13550-026-01432-w","vorDoiUrl":"https://doi.org/10.1186/s13550-026-01432-w","workflowStages":[]},"version":"v1","identity":"rs-8725887","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8725887","identity":"rs-8725887","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}