CD28 co-stimulatory domain enhances efficacy of CER T cell therapy compared to 4-1BB in an ovarian cancer mouse model

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Beatty, Min Ma, Payal Goala, Shannon McSain, Sae Bom Lee, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7179778/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Ovarian cancer remains a significant cause of cancer-related mortality, with epithelial ovarian cancer (EOC) being the most common subtype. Despite advances in treatment, the 5-year survival rate for late-stage EOC remains low due to factors such as tumor heterogeneity and an immunosuppressive tumor microenvironment (TME). This study investigates the therapeutic potential of chimeric endocrine receptor (CER) T cells engineered to express follicle stimulating hormone (FSH) in a syngeneic mouse model of EOC expressing follicle stimulating hormone receptor (ID8-FSHR). We compared two different co-stimulatory domains—CD28ζ and 4-1BBζ—in FSH-CER T cells and found that FSH-CD28ζ CER T cells exhibited enhanced cytotoxicity, proliferation, and cytokine secretion in vitro and in vivo. In the ID8-FSHR mouse model, FSH-28ζ CER T cells significantly reduced tumor burden and extended survival compared to FSH-hBBζ and control CER T cells. However, the therapeutic efficacy was compromised by T cell exhaustion, with all FSH-CER T cells expressing high levels of exhaustion markers after 7 days. In summary, incorporating a CD28ζ costimulatory domain enhances the efficacy of FSH-CER T cells, highlighting their therapeutic potential in ovarian cancer and supporting the development of strategies to mitigate immune exhaustion. Biological sciences/Cancer Biological sciences/Immunology Health sciences/Oncology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Ovarian cancer is responsible for 2.3% of all female cancer deaths in the United States with over 12,000 women dying each year from the disease 1,2 . Due to the lack of symptoms, ovarian cancer is usually diagnosed at a late stage when the cancer has spread throughout the abdominal cavity 3–5 . The 5-year survival rate is approximately 30% for patients diagnosed with late-stage disease 6–8 . The majority of ovarian cancers are EOC 3,9 . Attempts to target solid tumors with CAR T cells have yielded mixed results, largely due to inadequate T cell trafficking, immunosuppressive signaling, tumor heterogeneity, and a hostile TME. TME factors including hypoxia, acidity, dense stroma, cancer-associated fibroblasts (CAFs), inflammatory cytokines, and suppressive immune cells such as regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), and tumor-associated macrophages (TAMs) limit CAR T cell efficacy 10–17 . EOC generally metastasizes to the peritoneal tissue and the omentum causing blockage of the lymphatic system which leads to ascites 18 . The aggressive tumor growth, immunosuppressive TME, and metastasis observed in human disease are recapitulated in the ID8 syngeneic mouse model transduced with vascular endothelial growth factor alpha ( Vegf-a ) and beta-defensin 129 ( Defb29 ) which increases tumor burden, vascular growth and ascites and shortens survival 19 . We used C57BL/6 mice challenged with ID8 cells for in vivo studies to test FSH-CER T cells, providing a platform for investigating the disease's pathogenesis and therapeutic interventions. Recently, FSHR was found to be expressed in nearly all ovarian cancer subtypes and targeting FSHR⁺ tumors with newly developed CER T cells significantly reduced tumor burden and prolonged survival in both patient-derived xenograft (PDX) and solid tumor models 20 . Unlike traditional chimeric antigen receptors (CARs), which rely on synthetic single-chain variable fragments (scFvs) derived from monoclonal antibodies to recognize tumor antigens, CERs utilize naturally occurring peptide hormones, such as follicle stimulating hormone (FSH), as their extracellular binding domain. This approach capitalizes on native ligand-receptor specificity, potentially improving selectivity and reducing the risk of off-tumor toxicity often associated with scFv-based targeting. A clinical trial using FSH-CER T cells began in January of 2020 using 4-1BB as the intracellular domain (ICD). However, mechanisms of inhibition of FSH-CER in the EOC TME are poorly understood. There is no consensus on the most effective ICD when targeting solid tumors and previous studies during the development of FSH-CERs did not include comparison of different ICDs. Here, we developed FSH-CERs with either CD28ζ or 4-1BBζ ICDs to compare their efficacy in an immunocompetent mouse model of EOC. We found that CD28ζ conferred enhanced CER T cell efficacy, as measured by increased cytokine secretion, proliferation, and tumor cell killing in vitro and improved ascites formation, tumor burden (bioluminescence reading; BLI), and survival in vivo . CER T cells with a CD28ζ ICD hold promise as a strategy to improve outcomes for patients with EOC and warrant clinical investigation. Results FSHR + ID8 Cells Alter Gene Expression and Increase EOC Aggression To study the therapeutic efficacy of a FSHR-targeting CER T cell, we used a retroviral vector to sequentially transduce ID8 ( Defb29/Vegf-a ) cells with firefly luciferase and a construct encoding FSHR linked to GFP via a flexible glycine-serine linker (Fig. 1 A). Throughout this report these cells are referred to as ID8-FSHR cells while those not transduced with FSHR are referred to as ID8 cells. FSHR expression was observed using confocal fluorescent microscopy and flow cytometry (Supplementary Fig. 1a). ID8-FSHR cells caused extensive ascites and metastatic lesions on the peritoneum (Supplementary Fig. 1b). To confirm FSHR expression in the patient population, we collected ascites from eight patients with EOC and isolated cells to assess FSHR expression using immunohistochemistry (IHC). All patients expressed FSHR (Supplementary Fig. 1c). We performed bulk RNA-seq on peritoneal lavages of mice seven days post-ID8 and ID8-FSHR tumor implantation. We found significant increases in RNA expression of oncogenes ( Nos1, Ehf, Gpa33 ) that increase migration, proliferation, and metastasis and significant decreases of tumor suppressor genes ( Ctnna2, Prex2, Ank3 ) (Fig. 1 b). In the ID8 mouse model, weight is used as a marker of disease progression due to the development of ascites 21 . Notably, mice bearing ID8-FSHR tumors exhibited significantly greater weight gain by day 12 than those with ID8 tumors (Fig. 1 c). Weekly bioluminescence imaging (BLI), initiated 7 days after intraperitoneal (IP) injection of ID8-FSHR and ID8 cells, revealed a significant increase in BLI starting at day 14, which became more pronounced by day 21 (Fig. 1 d). Mice injected with ID8-FSHR cells exhibited significantly reduced survival compared to those injected with ID8 cells (Fig. 1 e). These data suggest that expression of FSHR makes ID8 cells grow more aggressively in vivo . We hypothesized the TME was altered by the addition of FSHR into ID8 cells. To test this hypothesis, we measured cytokines present in ascites (Supplementary Fig. 1d). We found significantly decreased G-CSF, IL-6 and VEGF within ascites from ID8-FSHR tumors compared to ID8 (Fig. 2 a). Liquid chromatography mass spectrometry (LC-MS) metabolomic analysis of ascites from ID8 and ID8-FSHR tumor-bearing mice revealed significant metabolic shifts, including downregulation of phosphocreatine, phosphocholine, and citrate/isocitrate, suggesting altered energy metabolism in ID8-FSHR tumors. Conversely, ascites from mice bearing ID8-FSHR exhibited increased levels of methyl beta-D-galactoside, D-galactonic acid gamma-lactone, and trigonelline, indicating a shift toward alternative metabolic pathways (Fig. 2 b). These findings suggest that ID8-FSHR cells reprogram their metabolic landscape, potentially enhancing their proliferative and metastatic capabilities. Proteomic analysis of ascites from ID8-FSHR and ID8 bearing mice revealed significant differences in protein expression (Fig. 2 c). Notably, several proteins were significantly downregulated in ID8-FSHR ascites, including SHBG, PLOD2, and PTX3, which play key roles in hormone regulation, extracellular matrix remodeling, and immune response, respectively. A2MG and EGFLA, factors involved in protease inhibition and cell signaling, were also decreased. Conversely, ID8-FSHR ascites exhibited increased levels of proteins such as AAMDC, REG3G, CK054, ACTZ, and HSPB1, which are associated with metabolic regulation, stress responses, and tumor progression (Fig. 2 c). These findings suggest that ID8-FSHR tumors create a distinct ascitic microenvironment characterized by reduced extra cellular matrix stability and immune modulation, while upregulating proteins that may contribute to tumor survival and aggressiveness. FSH-CER T cells exhibit cytotoxicity in vitro against ID8-FSHR To target tumors expressing FSHR, we developed FSH-CERs expressing either a CD28z or 4-1BBz co-stimulatory domain or a truncated version of CD3ζ (called FSH-28ζ, FSH-hBBζ and FSH-Δζ, respectively) 22,23 (Fig. 3 a). We characterized their cytotoxic function, expression dynamics, and receptor stability following target cell interaction. In real-time cytotoxicity assays (RTCA), FSH-28ζ CER T cells had significantly increased cytotoxicity compared to FSH-hBBζ CER T cells when co-cultured with ID8-FSHR target cells but not ID8 cells (Fig. 3 b). After CER T cell generation, FSH-28ζ CER T cells exhibited a significantly higher rate of gene transfer than FSH-hBBζ CER T cells, but they had similar levels of CER expression as measured by mean fluorescence intensity (MFI; Fig. 3 c). FSH-CERs were down regulated after 48 hours of stimulation, suggesting endocytosis of CERs upon activation (Fig. 3 d). To better understand the mechanisms underlying the disparity in anti-tumor efficacy of CERs with different ICDs, we next characterized their proliferative capacity, memory phenotype, cytokine secretion, and activation profile in vitro . FSH-CER T cells were co-cultured with ID8-FSHR for 48 hours at a 9:1 effector-to-target ratio and collected for analysis. In response to stimulation with tumor cells, FSH-28ζ CER T cells exhibited enhanced proliferation and increased T central memory (T CM ) and T effector memory (T EM ) phenotype compared to either FSH-Δζ and FSH-hBBζ (Fig. 4 a–b). Additionally, the CD4/CD8 ratio favored CD8 T cells significantly more in FSH-28ζ CER T cells (Fig. 4 c). Cytokine secretion analysis revealed that FSH-28ζ CER T cells released higher levels of IFN-γ, IL-6, and TNF-α (Fig. 4 d). We next generated CERs using T cells from C57BL/6J Nur77 GFP reporter mice, which express GFP under control of the Nr4a1 ( Nur77 ) promoter—a well-established surrogate marker for T cell receptor (TCR) signaling strength 24 . In this model, GFP fluorescence serves as a quantitative readout of T cell activation in response to ligand binding. This system allows for direct, real-time measurement of intracellular signaling downstream of CER stimulation. Upon stimulation with tumor cells, FSH-28ζ CER T cells exhibited significantly greater Nur77 expression compared to their FSH-hBBζ counterparts, indicating enhanced receptor signaling (Fig. 4 e). To validate this finding, we assessed the expression of activation and inhibitory markers 24 hours after co-culture with tumor cells. FSH-28ζ CER T cells exhibited significantly higher levels of Glut1, a metabolic marker necessary for glycolysis—the primary metabolic pathway supporting activated T cells—along with increased expression of CD69, an early activation marker, and the inhibitory receptors PD-1 and LAG-3, which are commonly upregulated following T cell activation and can indicate functional exhaustion (Fig. 4 f). Taken together, these data demonstrate that a CD28 ICD enhances the activation and anti-tumor efficacy of FSH-CER T cells compared to a 4-1BB ICD in vitro . FSH-28z CERs confer significant survival advantage in ID8-FSHR mouse model To determine whether the in vitro differences observed between FSH-CER constructs translated to therapeutic benefit in a physiologically relevant setting, we evaluated their efficacy in a syngeneic, immunocompetent mouse model of ovarian cancer. This model allowed us to assess not only tumor control and survival, but also the immune landscape within the peritoneal cavity following CER T cell therapy. 4x10 6 ID8-FSHR cells were injected IP on day 0 and mice were sorted into groups according to BLI on day 6. 4x10 6 FSH-CERs were injected IP on day 7. Due to luciferase dilution in the presence of ascites, BLI was measured weekly exclusively in mice lacking ascites. Mice were also weighed 3 times weekly to track the progression of disease (Fig. 5 a). Mice treated with FSH-28ζ showed a trend towards less weight gain than those treated with FSH-hBBζ or FSH-Δζ (Fig. 5 b). Mice treated with either FSH-28ζ or FSH-hBBζ CER T cells showed significantly reduced BLI over time, with FSH-28ζ treatment additionally resulting in a significant extension of survival compared to both FSH-Δζ and FSH-hBBζ groups (Fig. 5 c–d). To investigate how FSH-CER therapy modulates the TME, we analyzed cellular populations within the peritoneal cavity seven days after T cell injection by performing a peritoneal lavage (Fig. 5 e). Notably, ID8-FSHR tumor cells were undetectable in mice treated with FSH-28ζ CER T cells and significantly reduced in those treated with FSH-hBBζ (Fig. 5 f). In the collected cell populations, we observed an increase in the frequency of polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) in both the FSH-28ζ and FSH-hBBζ treatment groups compared to FSH-Δζ. Interestingly, FSH-hBBz treatment led to increased frequencies of CD4⁺ T cells, CD8⁺T cells compared to FSH-Δζ, and increased NK cells compared to either FSH-Δζ, or FSH-28ζ, an unexpected outcome given its reduced cytotoxic performance (Fig. 5 d). Phenotypic Characterization of FSH-CER T Cells in the Tumor Microenvironment Building on our analysis of the immune landscape, we assessed the presence, differentiation state, and exhaustion marker expression of FSH-CER T cells within the TME. FSH-CER T cells comprised a small percentage of the cells isolated from peritoneal lavage, with FSH-hBBζ CER T cells being modestly more frequent than FSH-28ζ or FSH-Δζ (Fig. 6 a). Using CD62L vs. CD44 staining to assess T cell differentiation, we found that both FSH-28ζ and FSH-hBBζ CER T cells exhibited significantly fewer T CM and a higher proportion of T EM compared to FSH-Δζ CER T cells (Fig. 6 b). Of those CERs isolated from the TME, all expressed high levels of Lag-3 and PD1 (Fig. 6 c). All FSH-CERs expressed similar levels of CTLA-4, TIGIT, CD95 and KLRG1. Nearly all FSH-CER T cells isolated from the TME were CD8-positive (Fig. 6 c). Ascites from ID8-FSHR Tumors Inhibit FSH-CER T Cell Activity To investigate the effect of the TME on FSH-CER T cells, we co-cultured them with 10% ascites from either mice bearing ID8 or ID8-FSHR tumors 25 . In cytotoxicity assays, ascites from ID8-FSHR but not ID8 tumor-bearing mice inhibited the function of both FSH-hBBζ and FSH-28ζ CER T cells, with a more pronounced suppression observed in the hBBζ group—supporting the interpretation that CD28ζ confers greater resilience to the ovarian tumor microenvironment (Fig. 7 a–b). Likewise, secretion of cytokines indicative of CER T cell activation (IFNγ and TNFα) was drastically inhibited by ID8-FSHR but not ID8 ascites (Fig. 7 c). We found FSH-CER T cells co-cultured with ID8-FSHR ascites were limited in proliferation, expression of Nur77 , and upregulation of the activation surface marker Glut1. (Supplementary Fig. 2a). To test if this inhibition was specific to FSH-CERs, we investigated whether mouse CD19 CAR T cells were inhibited by ID8 or ID8-FSHR ascites. We transduced ID8-FSHR cells with mouse CD19 (ID8-FSHR-mCD19) so they could be targeted by CD19 CAR T cells. When co-cultured with ascites from mice bearing ID8-FSHR tumors—which previously inhibited FSH-CER T cells—CD19 CAR T cells were not inhibited by ascites in RTCA or cytokine released into the supernatant (Fig. 7 d–e). Mouse CD19 CAR T cells were also able to proliferate, and while Nur77 expression was significantly reduced, the decrease was less pronounced than that observed in FSH-CER T cells (Supplementary Fig. 2b). Taken together, these data suggest that inhibition was specific to FSH-CER T cells. We first hypothesized that a tumor-derived factor could be inhibiting FSH-CER T cells. It has been previously reported that a tumor associated factor found in ID8 ascites causes endoplasmic reticulum (ER) stress of tumor infiltrating lymphocytes (TILs) through alternative splicing of XBP1 26,27 . However, in ascites-derived FSH-CERs we did not find upregulation of XBP1s with FSH-CER treatment, despite being able to identify alternative splicing of XBP1s using tunicamycin to induce ER stress (Supplementary Fig. 2c). Next, we hypothesized that FSHR was being shed from tumor cells into the TME and preventing activation of the FSH-CER by binding to the CER. However, FSHR was not detected in ascites by using an orbitrap mass analyzer with multiple strategies to enhance the sensitivity. Alternatively, we hypothesized that C57BL/6J mice were generating antibodies against the overexpressed FSHR on tumor cells, which were retained in the acellular ascites and interfered with FSH-CER activation either through binding to the FSH-CER or binding to FSHR, preventing binding of FSH between FSHR. To test this, we injected B6.129S7-Rag1-/-1tm1Mom/J ( Rag1-/-) mice, which lack B and T cells, with either ID8 and ID8-FSHR cells and collected ascites (Fig. 7 g). In coculture assay with either ascites, FSH-CD28ζ CER T cells were more effective than FSH-hBBζ, as measured by RTCA or release of cytokines (Fig. 7 g-i), consistent with the idea that FSH-CD28ζ CER T cells have greater intrinsic anti-tumor activity. In contrast to significant inhibition seen with ascites from wildtype mice (Fig. 7 a–c), FSH-CER T cells were not inhibited by FSHR ascites from Rag1-/- mice (Fig. 7 g–i), demonstrating the mechanism that antibodies or other factors produced by endogenous B or T cells in the presence of FSHR + tumor cells inhibit FSH-CER T cells. Discussion Solid tumors pose unique challenges to T cell immunotherapy, largely due to the immunosuppressive nature of the TME, which is shaped by chronic inflammation, suppressive cytokines, and inhibitory immune cell populations 28 . Here, we demonstrate that targeting FSHR + tumor cells with FSH-CER T cells engineered with a CD28ζ ICD enhances their efficacy compared to a 4-1BBζ ICD. However, both ICDs are susceptible to exhaustion and inhibition by ascites from tumors overexpressing FSHR. Studies investigating the role of FSHR in promoting tumor growth have yielded mixed results, with some suggesting its involvement in promoting proliferation, enhancing survival, and contributing to cell migration and invasion, while others report less definitive impacts 29–32 . This variability may depend on factors such as tumor type, experimental model, and the cellular context in which FSHR signaling is examined. Our findings demonstrate that FSHR expression is consistently present in EOC patient-derived ascites samples, as all eight patients tested expressed FSHR. FSHR is also normally expressed in mice, primarily in the granulosa cells of the ovary and the Sertoli cells of the testis, reflecting its canonical role in reproductive physiology 33 . In the context of EOC, normal ovarian tissue is removed 34 , limiting the potential of on-target, off-tumor effects of FSH-CERs. While low-level expression has been reported in other tissues such as the adrenal glands and bone marrow, its functional relevance outside the reproductive axis remains unclear. ID8-FSHR cells in vivo upregulated oncogenes and down regulated tumor suppressor genes. In particular, genes associated with the PI3K/MEK/ERK signaling pathway were upregulated in ID8-FSHR tumors, suggesting a possible potential therapeutic vulnerability in targeting strategy for FSHR + tumors. The molecular changes observed in ID8-FSHR tumors suggest a shift toward a more aggressive tumor phenotype, potentially driven by activation of pro-survival and proliferative pathways. Interestingly, observed lower levels of IL-6, G-CSF, and VEGF in the ID8-FSHR ascites suggest a blunted inflammatory response to tumor growth. These cytokines are typically elevated in response to tumor-driven inflammation and are involved in promoting immune cell recruitment, angiogenesis, and acute-phase signaling 35 . Their reduced expression in ID8-FSHR tumors may reflect a failure of the innate immune system to recognize and respond to tumor-associated cues, or it may indicate the presence of a highly suppressive TME. FSHR overexpression may contribute to immune evasion by dampening the inflammatory signals that typically accompany tumor progression. These findings raise important questions about the mechanistic links between FSHR signaling and immune escape. Prior studies have shown that CD28ζ tends to promote rapid T cell activation and cytokine release, whereas 4-1BBζ is thought to enhance persistence and memory formation 36 . Interestingly, our findings suggest that persistence and memory formation of CAR T cells may not fully translate to the TME, where CD28ζ-containing CERs appeared more effective. This raises important questions about how different co-stimulatory domains interact with context-specific immune suppression and whether certain ICDs may be better suited for solid tumors with high immune regulatory pressure. The discrepancy between these FSH-CERs may be due to differences in binding affinity, receptor clustering, or downstream signaling, highlighting the importance of co-stimulatory domain selection in CER design 37,38 . Further mechanistic studies are needed to understand why 4-1BBζ performed poorly in this setting and how co-stimulatory signaling might be optimized in CER design. The immune landscape of the peritoneal cavity showed that mice treated with FSH-CER T cells had increased frequencies of PMN-MDSCs compared to controls, perhaps due to decreased numbers of tumor cells. In FSH-28ζ-treated mice, ID8-FSHR cells were nearly undetectable in the peritoneal lavage samples. This observation supports the conclusion that FSH-28ζ CER T cells exhibit greater cytotoxicity in this mouse model. Further in vivo analysis of FSH-CER T cells revealed signs of immune exhaustion across all groups, including the inactive FSH-Δζ control, suggesting a uniform response in the TME regardless of the differences in activation driven by the ICDs. In sum, the TME appears broadly suppressive of FSH-CER T cells regardless of activation status, with CD28ζ emerging as the most effective ICD for tumor clearance; however, activated FSH-CER T cells (FSH-28ζ, FSH-hBBζ) may also drive suppression by increasing the MDSC population within TME 39 . The functional suppression of FSH-CER T cells observed in vivo suggested a role for soluble immunosuppressive factors within the TME. We hypothesized that ascites might contain inhibitory elements that directly impair CER T cell activity. Ascites from mice bearing ID8-FSHR tumors, but not ID8 tumors, suppressed FSH-CER function across multiple assays, including RTCA, cytokine secretion, and activation marker expression. Notably, this effect was specific to FSH-CER T cells and did not extend to CD19 CAR T cells, suggesting a unique vulnerability related to the FSH–FSHR axis. These findings led us to hypothesize that FSHR is shed into the TME, where it interferes with the activation clustering of FSH-CER T cells by binding to their membrane-bound receptors. Although other GPCRs have been shown to shed into the TME 40 , we were unable to detect shed FSHR in ascites samples. The absence of FSHR shedding observed in our proteomic analyses suggests that soluble FSHR is not responsible for the observed inhibition of FSH-CER T cells in ascites. These results prompted us to test an alternative hypothesis that inhibition might instead be mediated by antibodies generated against overexpressed FSHR. These antibodies could block FSH-CER T cells from binding to membrane-bound FSHR, thereby impairing their function. These antibodies could block FSH-CER T cells from binding to membrane-bound FSHR, thereby impairing their function. In support of this hypothesis, ID8-FSHR ascites from Rag1-/- mice—which lack B cells to produce antibodies—did not inhibit anti-tumor activity of FSH-CER T cells. These findings demonstrate that endogenous T or B cells contribute to the inhibition of FSH-CER T cells, though further studies are needed to explore this mechanism fully. The CD28ζ-based FSH-CER T cells consistently outperformed their 4-1BBζ counterparts in these conditions, suggesting that this ICD may confer partial resistance to the immune suppressive effects of ascites. This could reflect differences in early activation kinetics, resilience to checkpoint engagement, or distinct downstream signaling thresholds between ICDs. Altogether, these findings indicate both the therapeutic promise and the complexity of targeting FSHR in ovarian cancer, underscoring the need for further investigation into the immune-mediated mechanisms that regulate CER T cell function within the TME. Materials And Methods All methods were conducted in accordance with institutional guidelines and regulations, and this study is reported in compliance with the ARRIVE guidelines. Mice C56BL/6J and Rag-/- mice were purchased from Jackson Laboratories in accordance with approved protocols by H. Lee Moffitt Cancer Center and Research Institute, The University of South Florida Institutional Animal Care Use Committee and Roswell Park Cancer Center Comparative Oncology Shared Research (COSR). Nur77 GFP + mice were bred in-house at H. Lee Moffitt Cancer Center and Research Institute and Roswell Park Comprehensive Cancer Center. All mice used for experiments were between the ages of 6–12 weeks. Tumor burden was monitored using BLI and body weight gain as a surrogate for ascites development. Due to the intraperitoneal and disseminated nature of the ID8-FSHR model, discrete tumor size measurements were not feasible. During BLI mice were under general anesthesia using isoflurane (2–3% for induction, 1–2% for maintenance) delivered in oxygen via a precision vaporizer and nose cone. Mice were euthanized using carbon dioxide (CO₂) inhalation followed by cervical dislocation, in accordance with institutional protocols. All efforts were made to minimize animal discomfort. Cell lines and generation of retroviral/lentiviral constructs ID8 ( Defb-29/Vegf-a ) cells (referred to ID8 within this manuscript) were a generous gift from Jose Conejo-Garcia at Duke University Medical Center. ID8 cells are epithelial ovarian cancer cells derived from C57BL/6J mice. ID8 cells were transduced with a firefly luciferase-expressing lentiviral vector (Addgene; 108542) according to the manufacturer’s instructions. Luciferase expression was confirmed by luminescence using an EnVision multilabel plate reader following incubation with ONE-Glo™ Luciferase Assay System according to manufacturer’s instructions (Promega; E6110). ID8 luciferase cells were then retrovirally transduced with FSHR H29 virus tagged with GFP and sorted into ID8 vs. ID8-FSHR populations using a FACSAria SORP Cell sorter gated on GFP positivity. Both ID8 and ID8-FSHR cells were maintained using DMEM supplemented with 10% heat-inactivated fetal bovine serum (HI-FBS), 2mM L-glutamine, and 100U/mL penicillin/streptomycin. To ensure cell line integrity, we routinely screened for mycoplasma contamination using the Universal Mycoplasma Detection Kit (ATCC; 30-1012K) and the MycoAlert™ PLUS Detection Kit (Lonza; LT07-710). Sequences for FSH-CERs were generously provided by Dr. Jose Conejo-Garcia at Duke University Medical Center. FSH-Δζ, FSH-28ζ and FSH-hBBζ constructs were packaged in SFG gamma-retroviral vectors as previously described 41 . FSHR GFP vector was packaged into SFG plasmid utilizing the mouse wild-type sequence with GFP or mCherry included in the cytoplasmic portion of the sequence with a glycine-serine linker. Mouse T cell isolation and CER T cell generation C57BL/6J (B6;000664), B6.PL-Thy1 a /CyJ (Thy1.1;000406 ) and B6J(B6N)-Tg(Nr4a1-EGFP/cre)820Khog/PalcaJ (Nur77 GFP ; 016617) FSH-CER generation follows a previously published protocol 41 . In short, mouse spleens were excised and mashed through a 40µm cell strainer. Using the EasySep Mouse T Cell Isolation Kit (STEMCELL technologies; 19851) CD3 + T cells were isolated. Anti-CD3/CD28 Dynabeads and 100IU recombinant human IL-2 were used to expand CD3 + isolated T cells. T cells were spinoculated with Phoenix-ECO viral supernatants at 2000g, for 1hr at 32C at 24 hours and again at 48 hours post isolation. CD3/CD28 Dynabeads were removed by placing the cell suspension on a magnetic separation rack. After allowing the beads to migrate to the tube wall, the bead-free cell-containing supernatant was carefully collected as the flow-through for downstream applications. CER gene transfer was confirmed by flow cytometry using the rabbit monoclonal antibody (mAb) G4S-PE as a positivity marker. During this process mouse CER T cells were cultured in a 37C incubator, 5% CO2 using RPMI-1640 supplemented with 10% HI-FBS, 2uM L-glutamine, 100 U/mL Penicillin/Streptomycin, 1x nonessential amino acids, 1mM sodium pyruvate. 10mM HEPES buffer, 55uM 2-mercaptoethanol and 100IU rhIL-2. ID8-FSHR mouse model For mouse experiments, 4x10 6 ID8-FSHR or ID8 cells were injected IP into either C57BL/6J or B6.129S7-Rag1-/-1tm1Mom/J mice at day 0. At day 6, BLI was measured using IVIS spectrum (Perkin Elmer), and mice were then assigned to groups to ensure that each group had the same average BLI value. At day 7, 4x10 6 FSH-CER T cells were injected IP. Mice underwent triweekly weight measurements, and weekly BLI was performed until the onset of ascites. Upon reaching 25% weight gain relative to the average of 4 control mice, mice were euthanized. Ascites was collected from the IP cavity using a 22-guage needle and was centrifuged at 2000g for 10 minutes to form a cell pellet and serum. Serum was filtered using a .22uM filter from Millipore (Cat. # SLGSR33SS) to confirm acellularity. Samples were stored at -80C avoiding freeze/thaw cycles. For co-culture experiments, acellular ascites was added directly to complete RPMI media at a final concentration of 10%. This ascites-supplemented media was used in both RTCA and cytokine secretion assays. Cytokine Secretion Assay FSH-CER T cells were co-cultured with target cells at a 9:1 ratio of effector-to-target cells for 24 hours. Supernatants were collected from co-culture assays with and without 10% ascites. Supernatants were diluted at a 1:3 ratio (sample to diluent) and cytokines evaluated using the automated ELISA platform (ELLA; Bio-Techne) according to the manufacturer’s instruction. Real-Time Cytotoxic Assay (RTCA) 1x10 4 ID8 and ID8-FSHR cells were seeded on an xCELLigence plate (Agilent; 300601010) and impedance was read for 4-24hrs. 100uL of supernatant was removed and FSH-CERs were added at 9x10 4 in 100uL of media. Impedance was measured for an additional 72 hours. Bulk RNA-seq Mice were injected with 4x10 6 ID8 or ID8-FSHR cells at day 0 and euthanized on day 7. A peritoneal lavage was performed using 8 mL of PBS injected IP. Mice were palpated and subsequently the PBS was extracted using a 22-gage needle and 10mL syringe. Cells in ascites were counted using Cellometer™ Auto T4 automated cell counter (Nexcelom: CMT-AT4P). 1x10 6 cells were isolated and was pelleted at 2000g for 10 minutes at room temperature. Using liquid nitrogen, cells were snap frozen and stored at -80C. The Genomics Shared Resource (GSR) at Roswell Park Cancer Center extracted RNA using miRNeasy kit (Qiagen; 217004) and tested RNA concentrations using High Sensitivity RNA ScreenTape® (Agilent; 5067–5579). Bulk RNA-seq was performed using KAPA RNA HyperPrep Kit with RiboErase (HMR) for ribosomal depletion. Sequencing was performed on an Illumina NovaSeq 6000 to generate paired-end 100 bp reads. Raw reads were quality-checked using FastQC to examine base-quality distribution patterns. The reads were mapped to the GRCm38 mouse reference genome and GENCODE (v25) annotation database using STAR (v2.7.9a). Alignment files were indexed using samtools (v1.14). Gene-level quantification was performed using featureCounts (Subread v1.6.4) using the frOv = 0.95 parameter. Genes with fewer than maximum 10 reads across samples were excluded from further analysis. Differential expression analysis was carried out using the DESeq2 package (v1.26.0) in R, applying an adjusted p-value cutoff of 0.05 (Benjamini-Hochberg correction) and log₂ fold change threshold of ± 1. Gene set enrichment analysis (GSEA) was performed using the GSEA desktop application from the Broad Institute, with enrichment considered significant at FDR < 25%. Visualization of differentially expressed genes was conducted using ggplot2 in R to generate volcano plots. Proteomics / Metabolomics Proteomics was performed by the Jun Qu laboratory at the University at Buffalo using acellular ascites derived from mice bearing ID8 or ID8-FSHR tumors. To evaluate the presence of shed FSHR protein, a pooled positive sample was subjected to in-depth data-independent acquisition (DIA) using both DIA-NN (an open-source neural network-based tool) and Spectronaut (a commercial DIA platform). Despite high proteomic depth—quantifying 1,546 proteins with DIA-NN and 1,055 with Spectronaut—FSHR was not detected. For quantitative analysis of the ascites proteome, samples were processed using surfactant-cocktail assisted protein extraction, precipitation, and on-pellet digestion (SEPOD), followed by extensive liquid chromatography separation and mass spectrometry using the Orbitrap Astral MS platform. Label-free quantification was performed using DIA-NN to assess differences in protein abundance between experimental groups. Untargeted metabolomics on acellular ascites derived from ID8 and ID8-FSHR bearing mice was performed by the metabolomics core at H. Lee Moffitt Cancer Center using LC-MS/MS. Metabolite identification and quantification were performed on a Thermo-Scientific Q Exactive Orbitrap mass spectrometer, employing established workflows for comprehensive small molecule profiling and post-translational modification (PTM) analysis Patient Samples and immunofluorescent staining Ascites samples were collected from patients with epithelial ovarian cancer (EOC) under an IRB-approved protocol (STUDY00002327; Roswell Park Cancer Center). Samples were centrifuged at 2,000g for 10 minutes at room temperature to pellet the cells. The pellet was fixed in 1:10 formalin (Fisher chemical; SF1004) for 24 hours, followed by an additional 24-hour incubation in 70% ethanol. Slides were prepared by the Pharmacology and Therapeutics Core at Roswell Park Cancer Center. Samples were stained with DAPI and FSHR antibodies listed in Table 1 . Slides were imaged using Cytation™ 5 Cell Imaging Multi-Mode Reader by Brian Buckley from the Drug Discovery Core Shared Resource at Roswell Park Cancer Center. Flow Cytometry Cells were first stained with a fixable Live/Dead viability dye (see Table 1 ) for 20 minutes at 4°C in autoMACS rinsing solution supplemented with 50 mL FBS (Miltenyi Biotec; 130-091-222), protected from light. Following a wash, surface staining was performed by incubating cells with fluorochrome-conjugated antibodies (listed in Table 1 ) for 30 minutes at 4°C in the dark. All antibodies were titrated prior to use to determine the optimal staining concentration. When required, fluorophore-conjugated secondary antibodies (listed in Table 1 ) were added following primary staining and incubated for 20 minutes at 4°C. After staining, cells were washed twice with MACS buffer and resuspended for analysis. For intracellular staining—including Glut1—cells were fixed and permeabilized using the eBioscience™ Foxp3 / Transcription Factor Staining Buffer Set (Thermofisher; 00‑5523‑00) according to the manufacturer’s protocol. Samples were acquired on a Cytek Aurora spectral flow cytometer and analyzed using FlowJo v10.8 (BD Biosciences). Compensation was done using single-stained controls, and gating strategies were aided by fluorescence-minus-one (FMO) controls where applicable. Statistical Analysis Statistical analyses were performed using GraphPad Prism v9.5.1 (GraphPad Software, San Diego, CA), unless otherwise stated in the figure legends. Data are presented as mean ± standard deviation (SD). Statistical tests included unpaired two-tailed Student’s t-tests, one-way or two-way ANOVA with appropriate post-hoc corrections, and log-rank [Mantel–Cox] tests for survival analysis, as specified in each figure legend. A p -value < 0.05 was considered statistically significant. Significance thresholds are denoted as follows: p < 0.05 (* ), p < 0.002 (** ) , p < 0.0002 ( ***), p < 0.0001 (****). For clarity, figures or panels not annotated with statistical values were not statistically significant. Table 1 Key Resources Table Marker Fluorophore Clone Species Dilution Application CD11b BB700 M1/70 Rat 1:50 Flow Cytometry CD11c BV650 N418 Ar. Hamster 1:20 Flow Cytometry CD19 PE Dazzle-594 6D5 Rat 1:100 Flow Cytometry CD3 BUV395 17A2 Rat 1:50 Flow Cytometry CD4 PE-Cy7 GK1.5 Rat 1:200 Flow Cytometry CD4 BV785 GK1.5 Rat 1:200 Flow Cytometry CD44 APC-780 IM7 Rat 1:100 Flow Cytometry CD45 BUV395 17A2 Rat 1:67 Flow Cytometry CD62L BV605 MEL-14 Rat 1:100 Flow Cytometry CD69 AF-700 H1.2F3 Ar. Hamster 1:50 Flow Cytometry CD80 APC 16-10A1 Ar. Hamster 1:150 Flow Cytometry CD86 APC-eF780 GL1 Rat 1:80 Flow Cytometry CD8a BUV737 53 − 6.7 Rat 1:150 Flow Cytometry CD90.1 PE-Cy5 HIS51 Mouse 1:167 Flow Cytometry CD95 APC 15A7 Mouse 1:50 Flow Cytometry CTLA-4 PE-eFluor 610 UC10-4B9 Ar. Hamster 1:100 Flow Cytometry F4/80 BUV563 BM8 Rat 1:100 Flow Cytometry FSHR Unconjugated 3D5G9 Rabbit 1:100 Immune Fluorescence G4S PE E702V Rabbit 1:67 Flow Cytometry Glut1 Unconjugated E4S6I Rabbit 1:100 Flow Cytometry KLRG1 BUV737 2F1 Rabbit 1:167 Flow Cytometry Lag-3 PerCP-eFluor 710 C9B7W Rat 1:100 Flow Cytometry Live/Dead Zombie NIR 1:200 Flow Cytometry Ly6C BV510 HK1.4 Rat 1:100 Flow Cytometry Ly6G BV785 1A8 Rat 1:100 Flow Cytometry MHC II AF-700 M5/114.15.2 Rat 1:150 Flow Cytometry NK1.1 BUV805 PK136 Mouse 1:10 Flow Cytometry PD1 PE-Cy7 J43 Ar. Hamster 1:50 Flow Cytometry Secondary FITC AB_228426 Rabbit 1:200 Immune Fluorescence TIGIT BV421 GIGD7 Rat 1:50 Flow Cytometry The datasets generated and/or analyzed during the current study are available in the GEO repository under accession number GSE303863 and in the PRIDE repository under accession number PXD066774. Declarations Data availability The datasets generated and/or analyzed during the current study are available in the GEO repository under accession number GSE303863 and in the PRIDE repository under accession number PXD066774. Funding The authors received no external funding for this work. Acknowledgements This work was supported by a Developmental Research Program (DRP) award from the Roswell Park–University of Chicago Ovarian Cancer SPORE. Metabolomics analysis was performed with support from the metabolomics core at Moffitt Cancer Center. Genomic and bulk RNA sequencing analyses were conducted using the Genomics Shared Resource at Moffitt Cancer Center and Roswell Park Comprehensive Cancer Center. Flow cytometry was performed using the Flow & Immune Analysis Shared Resource, and animal studies were conducted with the support of the Comparative Oncology Shared Resource, both at Roswell Park. In vivo mouse imaging was performed by the Translational Imaging Shared Resource. Immunofluorescence staining of patient samples was performed by Ellen Karasik in the Experimental Tumor Model Shared Resource at Roswell Park. We thank all core personnel for their expert assistance. Author contributions N.B. and M.L.D. conceived and designed the study. N.B. performed all experiments and wrote the manuscript. M.M. conducted and analyzed the proteomics and metabolomics experiments. P.G., S.M., S.B.L., and J.C.B. provided experimental assistance. Y.P.K. contributed to the analysis of metabolomics data. 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Supplementary Files SupplementaryInformation.pdf Cite Share Download PDF Status: Published Journal Publication published 11 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 20 Oct, 2025 Reviews received at journal 19 Oct, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviews received at journal 08 Sep, 2025 Reviews received at journal 02 Sep, 2025 Reviewers agreed at journal 15 Aug, 2025 Reviewers agreed at journal 09 Aug, 2025 Reviewers invited by journal 06 Aug, 2025 Editor assigned by journal 06 Aug, 2025 Editor invited by journal 01 Aug, 2025 Submission checks completed at journal 30 Jul, 2025 First submitted to journal 30 Jul, 2025 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. 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(A) Diagram showing FSHR.GFP viral vector transduced into ID8 cells. (B) Heat map showing the 40 most differentially expressed genes according to bulk-RNAseq of tumors at day 7 post-implantation. Expression values are represented as Log\u003csub\u003e2 \u003c/sub\u003efold change, where the red indicates upregulation and blue indicates downregulation relative to mean expression across samples. (C) Weights of C57BL6 mice implanted IP with 5x10\u003csup\u003e6\u003c/sup\u003e ID8 or ID8-FSHR cells or that received no tumor. Asterisks indicate statistically significant differences between the ID8-Fshr group and both ID8 and no tumor controls at each timepoint. (statistical analysis by two-way ANOVA) n=30 mice per group.\u0026nbsp; Data shown are from one biological replicate (D-E) BLI and survival of mice injected with ID8 or ID8-FSHR. (two-way ANOVA; Log-rank [Mantel-Cox] test)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7179778/v1/d2d86d50e6f1e2323eb7f9e4.png"},{"id":88888156,"identity":"bfc3b864-ccf9-484c-8945-fdd12bf855b5","added_by":"auto","created_at":"2025-08-12 12:19:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49630,"visible":true,"origin":"","legend":"\u003cp\u003eFSHR+ ID8 tumors remodel the ascites microenvironment through cytokine alterations and metabolic and proteomic reprogramming (A) Cytokine levels of ascites derived from C57BL/6J mice bearing ID8 or ID8-FSHR tumors (\u003cem\u003en\u003c/em\u003e=3/group, one biological replicate). (statistical analysis by unpaired t-test) (B) Volcano plot showing differentially abundant metabolites in ascites from ID8-FSHR versus ID8 tumor-bearing mice. Metabolites highlighted in red are upregulated in ID8-FSHR ascites, while those in blue are downregulated in ID8-FSHR ascites (i.e., enriched in ID8 ascites). P value determined by unpaired t-test. (C) Volcano plot showing differentially expressed proteins in ascites from ID8-FSHR versus ID8 tumors. Proteins highlighted in red are upregulated in ID8-FSHR ascites; blue indicates proteins downregulated in ID8-FSHR ascites (relative upregulation in ID8). P value determined by unpaired t-test. For B and C, n=X mice per group. Data are from one biological replicate. Fold change cutoff: \u0026gt; 1.3-fold or \u0026lt; 0.77-fold with a statistical cutoff of p \u0026lt;0.05.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7179778/v1/c407d050e9b89214be98f16e.png"},{"id":88889229,"identity":"72280f40-3467-4faf-9d9e-406e197f3140","added_by":"auto","created_at":"2025-08-12 12:35:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60726,"visible":true,"origin":"","legend":"\u003cp\u003eFSH-CERs exhibit cytotoxicity \u003cem\u003ein vitro\u003c/em\u003e against ID8-FSHR cancer cells. (A) Schematic of the three FSH-CER constructs: FSH-Δζ (truncated ICD), FSH-28ζ (CD28ζ), and FSH-4-1BBζ (hBBζ) (B) Real-time cytotoxicity assay (RTCA) of FSH-CER T cells co-cultured with ID8 or ID8-FSHR target cells at a 9:1 effector-to-target ratio. Cytotoxicity was measured using the xCELLigence system. Data shown are from one representative experiment performed in quadruplicate. Statistical comparison was performed by two-way ANOVA; a representative timepoint is displayed. (C) Transduction efficiency and expression levels of FSH-CERs, shown as percentage of cells with CER expression and mean fluorescence intensity (MFI) of GFP or mCherry reporters. (one-way ANOVA)\u003cstrong\u003e \u003c/strong\u003e(D) CER expression by GFP positivity of FSH-CER T cells after 48 hours of co-culture with ID8-FSHR.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7179778/v1/cbcd2867dbd6cd537e1d00e8.png"},{"id":88888977,"identity":"555ac776-f419-49e9-be4e-f540081c5b94","added_by":"auto","created_at":"2025-08-12 12:27:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":36045,"visible":true,"origin":"","legend":"\u003cp\u003eFSH-28z CER T cells exhibit increased cytokine release, proliferation and activation compared to FSH-hBBζ CER T cells after co-culture with ID8-FSHR cells \u003cem\u003ein vitro\u003c/em\u003e.\u003cstrong\u003e \u0026nbsp;\u003c/strong\u003eIn all experiments, analysis was performed after FSH-CER T cells were co-cultured with ID8-FSHR for 48 hours at a 9:1 effector-to-target ratio unless otherwise specified.\u003cstrong\u003e \u003c/strong\u003e(A) Proliferation of FSH-CERs as measured by dilution of CellTrace Far Red (CTFR). (ordinary one-way ANOVA) (B) Memory phenotype of FSH-CERs based on CD62L and CD44 expression. Naïve T cells were identified as CD62L+CD44−, T\u003csub\u003eCM\u003c/sub\u003e as CD62L+ CD44+, and T\u003csub\u003eEM\u003c/sub\u003e as CD62L-CD44+. Cells were gated on singlets, live, CD3⁺ T cells prior to subset analysis. (C) CD4/CD8 ratio of FSH-CER T cells. (D) FSH-CERs were co-cultured with ID8-FSHR cells for 24 hours and cytokines were measured in supernatant. (E) \u003cem\u003eNur77\u003c/em\u003e\u003csup\u003eGFP\u003c/sup\u003e expression in FSH-CER T cells following stimulation with ID8-FSHR, indicating TCR signaling strength. (F) Expression of activation and exhaustion markers on FSH-CER T cells after 24-hour stimulation. All statistical analysis done by two-way ANOVA unless otherwise specified.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7179778/v1/5805e298894cfdea88e578fe.png"},{"id":88890171,"identity":"9ac96b1e-9040-4548-abdf-904f8a3b1d5b","added_by":"auto","created_at":"2025-08-12 12:43:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":64018,"visible":true,"origin":"","legend":"\u003cp\u003eFSH-28z CERs exhibit significant survival advantage in ID8-FSHR mouse model\u003c/p\u003e\n\u003cp\u003e(A)\u003cstrong\u003e \u003c/strong\u003eSchematic illustrating the experimental timeline for tumor implantation and FSH-CER T cell treatment corresponding to panels B–D.\u003cstrong\u003e \u003c/strong\u003eB-C\u003cstrong\u003e \u003c/strong\u003e(\u003cem\u003en\u003c/em\u003e=8/group)\u003cstrong\u003e \u003c/strong\u003e(B) Mice were weighed as a surrogate for ascites development and disease progression. (C)\u003cstrong\u003e \u003c/strong\u003eBLI measurements indicative of tumor burden over three weeks post-treatment; BLI measurements were only taken from mice without visible ascites. Asterisks indicate statistically significant differences between FSH-Δζ and both FSH-28ζ and FSH-hBBζ (statistical analysis by two-way ANOVA) (D) Kaplan–Meier survival curve pooled from four independent experiments (Δζ: \u003cem\u003en\u003c/em\u003e = 37, 28ζ: \u003cem\u003en\u003c/em\u003e = 44, hBBζ: \u003cem\u003en\u003c/em\u003e = 19), with standardized protocols across studies (Log-rank [Mantel–Cox] test). (E)\u003cstrong\u003e \u003c/strong\u003eExperimental schematic for immune profiling corresponding to panel F. (F) Immune landscape of peritoneal lavage samples collected seven days after T cell injection. Cell populations were quantified via flow cytometry to assess myeloid and lymphoid composition. See methods for analysis parameters. (two-way ANOVA)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7179778/v1/2fdef800fa4ab62f9fcaca69.png"},{"id":88888163,"identity":"9870f6f9-8079-4f91-acaa-32d5155b2ba9","added_by":"auto","created_at":"2025-08-12 12:19:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":43034,"visible":true,"origin":"","legend":"\u003cp\u003ePeritoneal lavage reveals that FSH-CER T cells express high levels of exhaustion markers \u003cem\u003ein vivo\u003c/em\u003e. (A) Transduced FSH-CER T cells were identified by flow cytometry from peritoneal lavage (see Figure 5e for schematic) samples using G4S expression (see gating strategy indicated by arrows). Analyses in panels B–D were performed on the G4S⁺ population.\u003cstrong\u003e \u003c/strong\u003e(B)\u003cstrong\u003e \u003c/strong\u003ePhenotypic characterization of CER T cells determined by CD62L and CD44 expression: naïve (CD62L⁺CD44⁻), T\u003csub\u003eCM\u003c/sub\u003e (CD62L⁺CD44⁺), and T\u003csub\u003eEM\u003c/sub\u003e (CD62L⁻CD44⁺). Cells were gated on singlets, live, CD3⁺ T cells prior to subset analysis. (statistical analysis by two-way ANOVA)\u003cstrong\u003e \u003c/strong\u003e(C) Expression of exhaustion markers LAG-3, PD-1, and CTLA-4, as well as additional exhaustion and activation markers including TIGIT, CD95, KLRG1, and CD69, was assessed among tumor-infiltrating FSH-CER T cells.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7179778/v1/8cda56b299705dcefd6854cf.png"},{"id":88888166,"identity":"7871f383-222c-46fb-b37b-d89061ba3cc0","added_by":"auto","created_at":"2025-08-12 12:19:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":74866,"visible":true,"origin":"","legend":"\u003cp\u003eAscites from ID8-FSHR tumors in \u003cem\u003eC57Bl/6J\u003c/em\u003e mice selectively suppresses FSH-CER T cell activity. (A-I)\u003cstrong\u003e \u003c/strong\u003eFSH-CER or CD19 CAR T cells were cocultured ID8-FSHR or ID8-FSHR-CD19r target cells in the presence of 10%\u003cstrong\u003e \u003c/strong\u003eascites from \u003cem\u003eC57BL/6J\u003c/em\u003eor \u003cem\u003eRag-/-\u003c/em\u003e mice as indicated. N= normal conditions, (-) = coculture with ascites from mice bearing ID8 tumors, (+) = co-culture with ascites from mice bearing ID8-FSHR tumors. (A, C, E) Real-time cytotoxicity assay (RTCA) Data is from one representative experiment run in quadruplicate. (B, D, F) Cytokine levels (IFN-γ and TNF-α) were measured in supernatants 24 hours post-stimulation. Data is from one representative experiment run in quadruplicate All statistical analyses were performed using two-way ANOVA; for RTCA data, statistical significance is shown at a single representative timepoint. Statistical comparison: \u0026lt;0.12 (ns), \u0026lt;0.033 (*), \u0026lt;0.002 (**), \u0026lt;0.0002 (***), \u0026lt;0.0001 (****).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7179778/v1/756c05f79127afe760eaba12.png"},{"id":104739369,"identity":"d10b7d03-4bde-4a46-9775-9d5c1b9da816","added_by":"auto","created_at":"2026-03-16 16:04:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1816381,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7179778/v1/cc9ed1d8-bea2-4114-a3f9-66b54cc52b75.pdf"},{"id":88888160,"identity":"6f688f1b-8eb0-4ddd-8390-e4fcffad8734","added_by":"auto","created_at":"2025-08-12 12:19:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":742940,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7179778/v1/8b9153cefe6599aa6f9622fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CD28 co-stimulatory domain enhances efficacy of CER T cell therapy compared to 4-1BB in an ovarian cancer mouse model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOvarian cancer is responsible for 2.3% of all female cancer deaths in the United States with over 12,000 women dying each year from the disease \u003csup\u003e1,2\u003c/sup\u003e. Due to the lack of symptoms, ovarian cancer is usually diagnosed at a late stage when the cancer has spread throughout the abdominal cavity \u003csup\u003e3\u0026ndash;5\u003c/sup\u003e. The 5-year survival rate is approximately 30% for patients diagnosed with late-stage disease \u003csup\u003e6\u0026ndash;8\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe majority of ovarian cancers are EOC \u003csup\u003e3,9\u003c/sup\u003e. Attempts to target solid tumors with CAR T cells have yielded mixed results, largely due to inadequate T cell trafficking, immunosuppressive signaling, tumor heterogeneity, and a hostile TME. TME factors including hypoxia, acidity, dense stroma, cancer-associated fibroblasts (CAFs), inflammatory cytokines, and suppressive immune cells such as regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), and tumor-associated macrophages (TAMs) limit CAR T cell efficacy \u003csup\u003e10\u0026ndash;17\u003c/sup\u003e. EOC generally metastasizes to the peritoneal tissue and the omentum causing blockage of the lymphatic system which leads to ascites \u003csup\u003e18\u003c/sup\u003e. The aggressive tumor growth, immunosuppressive TME, and metastasis observed in human disease are recapitulated in the ID8 syngeneic mouse model transduced with vascular endothelial growth factor alpha (\u003cem\u003eVegf-a\u003c/em\u003e) and beta-defensin 129 (\u003cem\u003eDefb29\u003c/em\u003e) which increases tumor burden, vascular growth and ascites and shortens survival \u003csup\u003e19\u003c/sup\u003e. We used C57BL/6 mice challenged with ID8 cells for \u003cem\u003ein vivo\u003c/em\u003e studies to test FSH-CER T cells, providing a platform for investigating the disease's pathogenesis and therapeutic interventions.\u003c/p\u003e\u003cp\u003eRecently, FSHR was found to be expressed in nearly all ovarian cancer subtypes and targeting FSHR⁺ tumors with newly developed CER T cells significantly reduced tumor burden and prolonged survival in both patient-derived xenograft (PDX) and solid tumor models \u003csup\u003e20\u003c/sup\u003e. Unlike traditional chimeric antigen receptors (CARs), which rely on synthetic single-chain variable fragments (scFvs) derived from monoclonal antibodies to recognize tumor antigens, CERs utilize naturally occurring peptide hormones, such as follicle stimulating hormone (FSH), as their extracellular binding domain. This approach capitalizes on native ligand-receptor specificity, potentially improving selectivity and reducing the risk of off-tumor toxicity often associated with scFv-based targeting. A clinical trial using FSH-CER T cells began in January of 2020 using 4-1BB as the intracellular domain (ICD). However, mechanisms of inhibition of FSH-CER in the EOC TME are poorly understood. There is no consensus on the most effective ICD when targeting solid tumors and previous studies during the development of FSH-CERs did not include comparison of different ICDs. Here, we developed FSH-CERs with either CD28ζ or 4-1BBζ ICDs to compare their efficacy in an immunocompetent mouse model of EOC. We found that CD28ζ conferred enhanced CER T cell efficacy, as measured by increased cytokine secretion, proliferation, and tumor cell killing \u003cem\u003ein vitro\u003c/em\u003e and improved ascites formation, tumor burden (bioluminescence reading; BLI), and survival \u003cem\u003ein vivo\u003c/em\u003e. CER T cells with a CD28ζ ICD hold promise as a strategy to improve outcomes for patients with EOC and warrant clinical investigation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eFSHR\u0026thinsp;+\u0026thinsp;ID8 Cells Alter Gene Expression and Increase EOC Aggression\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo study the therapeutic efficacy of a FSHR-targeting CER T cell, we used a retroviral vector to sequentially transduce ID8 (\u003cem\u003eDefb29/Vegf-a\u003c/em\u003e) cells with firefly luciferase and a construct encoding FSHR linked to GFP via a flexible glycine-serine linker (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Throughout this report these cells are referred to as ID8-FSHR cells while those not transduced with FSHR are referred to as ID8 cells. FSHR expression was observed using confocal fluorescent microscopy and flow cytometry (Supplementary Fig.\u0026nbsp;1a). ID8-FSHR cells caused extensive ascites and metastatic lesions on the peritoneum (Supplementary Fig.\u0026nbsp;1b). To confirm FSHR expression in the patient population, we collected ascites from eight patients with EOC and isolated cells to assess FSHR expression using immunohistochemistry (IHC). All patients expressed FSHR (Supplementary Fig.\u0026nbsp;1c). We performed bulk RNA-seq on peritoneal lavages of mice seven days post-ID8 and ID8-FSHR tumor implantation. We found significant increases in RNA expression of oncogenes (\u003cem\u003eNos1, Ehf, Gpa33\u003c/em\u003e) that increase migration, proliferation, and metastasis and significant decreases of tumor suppressor genes (\u003cem\u003eCtnna2, Prex2, Ank3\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). In the ID8 mouse model, weight is used as a marker of disease progression due to the development of ascites \u003csup\u003e21\u003c/sup\u003e. Notably, mice bearing ID8-FSHR tumors exhibited significantly greater weight gain by day 12 than those with ID8 tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Weekly bioluminescence imaging (BLI), initiated 7 days after intraperitoneal (IP) injection of ID8-FSHR and ID8 cells, revealed a significant increase in BLI starting at day 14, which became more pronounced by day 21 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Mice injected with ID8-FSHR cells exhibited significantly reduced survival compared to those injected with ID8 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). These data suggest that expression of FSHR makes ID8 cells grow more aggressively \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe hypothesized the TME was altered by the addition of FSHR into ID8 cells. To test this hypothesis, we measured cytokines present in ascites (Supplementary Fig.\u0026nbsp;1d). We found significantly decreased G-CSF, IL-6 and VEGF within ascites from ID8-FSHR tumors compared to ID8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Liquid chromatography mass spectrometry (LC-MS) metabolomic analysis of ascites from ID8 and ID8-FSHR tumor-bearing mice revealed significant metabolic shifts, including downregulation of phosphocreatine, phosphocholine, and citrate/isocitrate, suggesting altered energy metabolism in ID8-FSHR tumors. Conversely, ascites from mice bearing ID8-FSHR exhibited increased levels of methyl beta-D-galactoside, D-galactonic acid gamma-lactone, and trigonelline, indicating a shift toward alternative metabolic pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). These findings suggest that ID8-FSHR cells reprogram their metabolic landscape, potentially enhancing their proliferative and metastatic capabilities. Proteomic analysis of ascites from ID8-FSHR and ID8 bearing mice revealed significant differences in protein expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Notably, several proteins were significantly downregulated in ID8-FSHR ascites, including SHBG, PLOD2, and PTX3, which play key roles in hormone regulation, extracellular matrix remodeling, and immune response, respectively. A2MG and EGFLA, factors involved in protease inhibition and cell signaling, were also decreased. Conversely, ID8-FSHR ascites exhibited increased levels of proteins such as AAMDC, REG3G, CK054, ACTZ, and HSPB1, which are associated with metabolic regulation, stress responses, and tumor progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). These findings suggest that ID8-FSHR tumors create a distinct ascitic microenvironment characterized by reduced extra cellular matrix stability and immune modulation, while upregulating proteins that may contribute to tumor survival and aggressiveness.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFSH-CER T cells exhibit cytotoxicity\u003c/b\u003e \u003cb\u003ein vitro\u003c/b\u003e \u003cb\u003eagainst ID8-FSHR\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo target tumors expressing FSHR, we developed FSH-CERs expressing either a CD28z or 4-1BBz co-stimulatory domain or a truncated version of CD3ζ (called FSH-28ζ, FSH-hBBζ and FSH-Δζ, respectively)\u003csup\u003e22,23\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). We characterized their cytotoxic function, expression dynamics, and receptor stability following target cell interaction. In real-time cytotoxicity assays (RTCA), FSH-28ζ CER T cells had significantly increased cytotoxicity compared to FSH-hBBζ CER T cells when co-cultured with ID8-FSHR target cells but not ID8 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). After CER T cell generation, FSH-28ζ CER T cells exhibited a significantly higher rate of gene transfer than FSH-hBBζ CER T cells, but they had similar levels of CER expression as measured by mean fluorescence intensity (MFI; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). FSH-CERs were down regulated after 48 hours of stimulation, suggesting endocytosis of CERs upon activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). To better understand the mechanisms underlying the disparity in anti-tumor efficacy of CERs with different ICDs, we next characterized their proliferative capacity, memory phenotype, cytokine secretion, and activation profile \u003cem\u003ein vitro\u003c/em\u003e. FSH-CER T cells were co-cultured with ID8-FSHR for 48 hours at a 9:1 effector-to-target ratio and collected for analysis. In response to stimulation with tumor cells, FSH-28ζ CER T cells exhibited enhanced proliferation and increased T central memory (T\u003csub\u003eCM\u003c/sub\u003e) and T effector memory (T\u003csub\u003eEM\u003c/sub\u003e) phenotype compared to either FSH-Δζ and FSH-hBBζ (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u0026ndash;b). Additionally, the CD4/CD8 ratio favored CD8 T cells significantly more in FSH-28ζ CER T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Cytokine secretion analysis revealed that FSH-28ζ CER T cells released higher levels of IFN-γ, IL-6, and TNF-α (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). We next generated CERs using T cells from C57BL/6J Nur77\u003csup\u003eGFP\u003c/sup\u003e reporter mice, which express GFP under control of the \u003cem\u003eNr4a1\u003c/em\u003e (\u003cem\u003eNur77\u003c/em\u003e) promoter\u0026mdash;a well-established surrogate marker for T cell receptor (TCR) signaling strength \u003csup\u003e24\u003c/sup\u003e. In this model, GFP fluorescence serves as a quantitative readout of T cell activation in response to ligand binding. This system allows for direct, real-time measurement of intracellular signaling downstream of CER stimulation. Upon stimulation with tumor cells, FSH-28ζ CER T cells exhibited significantly greater \u003cem\u003eNur77\u003c/em\u003e expression compared to their FSH-hBBζ counterparts, indicating enhanced receptor signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). To validate this finding, we assessed the expression of activation and inhibitory markers 24 hours after co-culture with tumor cells. FSH-28ζ CER T cells exhibited significantly higher levels of Glut1, a metabolic marker necessary for glycolysis\u0026mdash;the primary metabolic pathway supporting activated T cells\u0026mdash;along with increased expression of CD69, an early activation marker, and the inhibitory receptors PD-1 and LAG-3, which are commonly upregulated following T cell activation and can indicate functional exhaustion (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef). Taken together, these data demonstrate that a CD28 ICD enhances the activation and anti-tumor efficacy of FSH-CER T cells compared to a 4-1BB ICD \u003cem\u003ein vitro\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFSH-28z CERs confer significant survival advantage in ID8-FSHR mouse model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo determine whether the \u003cem\u003ein vitro\u003c/em\u003e differences observed between FSH-CER constructs translated to therapeutic benefit in a physiologically relevant setting, we evaluated their efficacy in a syngeneic, immunocompetent mouse model of ovarian cancer. This model allowed us to assess not only tumor control and survival, but also the immune landscape within the peritoneal cavity following CER T cell therapy. 4x10\u003csup\u003e6\u003c/sup\u003e ID8-FSHR cells were injected IP on day 0 and mice were sorted into groups according to BLI on day 6. 4x10\u003csup\u003e6\u003c/sup\u003e FSH-CERs were injected IP on day 7. Due to luciferase dilution in the presence of ascites, BLI was measured weekly exclusively in mice lacking ascites. Mice were also weighed 3 times weekly to track the progression of disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Mice treated with FSH-28ζ showed a trend towards less weight gain than those treated with FSH-hBBζ or FSH-Δζ (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Mice treated with either FSH-28ζ or FSH-hBBζ CER T cells showed significantly reduced BLI over time, with FSH-28ζ treatment additionally resulting in a significant extension of survival compared to both FSH-Δζ and FSH-hBBζ groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec\u0026ndash;d). To investigate how FSH-CER therapy modulates the TME, we analyzed cellular populations within the peritoneal cavity seven days after T cell injection by performing a peritoneal lavage (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). Notably, ID8-FSHR tumor cells were undetectable in mice treated with FSH-28ζ CER T cells and significantly reduced in those treated with FSH-hBBζ (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). In the collected cell populations, we observed an increase in the frequency of polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) in both the FSH-28ζ and FSH-hBBζ treatment groups compared to FSH-Δζ. Interestingly, FSH-hBBz treatment led to increased frequencies of CD4⁺ T cells, CD8⁺T cells compared to FSH-Δζ, and increased NK cells compared to either FSH-Δζ, or FSH-28ζ, an unexpected outcome given its reduced cytotoxic performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhenotypic Characterization of FSH-CER T Cells in the Tumor Microenvironment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBuilding on our analysis of the immune landscape, we assessed the presence, differentiation state, and exhaustion marker expression of FSH-CER T cells within the TME. FSH-CER T cells comprised a small percentage of the cells isolated from peritoneal lavage, with FSH-hBBζ CER T cells being modestly more frequent than FSH-28ζ or FSH-Δζ (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Using CD62L vs. CD44 staining to assess T cell differentiation, we found that both FSH-28ζ and FSH-hBBζ CER T cells exhibited significantly fewer T\u003csub\u003eCM\u003c/sub\u003e and a higher proportion of T\u003csub\u003eEM\u003c/sub\u003e compared to FSH-Δζ CER T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Of those CERs isolated from the TME, all expressed high levels of Lag-3 and PD1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). All FSH-CERs expressed similar levels of CTLA-4, TIGIT, CD95 and KLRG1. Nearly all FSH-CER T cells isolated from the TME were CD8-positive (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAscites from ID8-FSHR Tumors Inhibit FSH-CER T Cell Activity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate the effect of the TME on FSH-CER T cells, we co-cultured them with 10% ascites from either mice bearing ID8 or ID8-FSHR tumors\u003csup\u003e25\u003c/sup\u003e. In cytotoxicity assays, ascites from ID8-FSHR but not ID8 tumor-bearing mice inhibited the function of both FSH-hBBζ and FSH-28ζ CER T cells, with a more pronounced suppression observed in the hBBζ group\u0026mdash;supporting the interpretation that CD28ζ confers greater resilience to the ovarian tumor microenvironment (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea\u0026ndash;b). Likewise, secretion of cytokines indicative of CER T cell activation (IFNγ and TNFα) was drastically inhibited by ID8-FSHR but not ID8 ascites (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). We found FSH-CER T cells co-cultured with ID8-FSHR ascites were limited in proliferation, expression of \u003cem\u003eNur77\u003c/em\u003e, and upregulation of the activation surface marker Glut1. (Supplementary Fig.\u0026nbsp;2a). To test if this inhibition was specific to FSH-CERs, we investigated whether mouse CD19 CAR T cells were inhibited by ID8 or ID8-FSHR ascites. We transduced ID8-FSHR cells with mouse CD19 (ID8-FSHR-mCD19) so they could be targeted by CD19 CAR T cells. When co-cultured with ascites from mice bearing ID8-FSHR tumors\u0026mdash;which previously inhibited FSH-CER T cells\u0026mdash;CD19 CAR T cells were not inhibited by ascites in RTCA or cytokine released into the supernatant (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed\u0026ndash;e). Mouse CD19 CAR T cells were also able to proliferate, and while Nur77 expression was significantly reduced, the decrease was less pronounced than that observed in FSH-CER T cells (Supplementary Fig.\u0026nbsp;2b). Taken together, these data suggest that inhibition was specific to FSH-CER T cells. We first hypothesized that a tumor-derived factor could be inhibiting FSH-CER T cells. It has been previously reported that a tumor associated factor found in ID8 ascites causes endoplasmic reticulum (ER) stress of tumor infiltrating lymphocytes (TILs) through alternative splicing of XBP1 \u003csup\u003e26,27\u003c/sup\u003e. However, in ascites-derived FSH-CERs we did not find upregulation of XBP1s with FSH-CER treatment, despite being able to identify alternative splicing of XBP1s using tunicamycin to induce ER stress (Supplementary Fig.\u0026nbsp;2c). Next, we hypothesized that FSHR was being shed from tumor cells into the TME and preventing activation of the FSH-CER by binding to the CER. However, FSHR was not detected in ascites by using an orbitrap mass analyzer with multiple strategies to enhance the sensitivity. Alternatively, we hypothesized that C57BL/6J mice were generating antibodies against the overexpressed FSHR on tumor cells, which were retained in the acellular ascites and interfered with FSH-CER activation either through binding to the FSH-CER or binding to FSHR, preventing binding of FSH between FSHR. To test this, we injected \u003cem\u003eB6.129S7-Rag1-/-1tm1Mom/J\u003c/em\u003e (\u003cem\u003eRag1-/-)\u003c/em\u003e mice, which lack B and T cells, with either ID8 and ID8-FSHR cells and collected ascites (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eg). In coculture assay with either ascites, FSH-CD28ζ CER T cells were more effective than FSH-hBBζ, as measured by RTCA or release of cytokines (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eg-i), consistent with the idea that FSH-CD28ζ CER T cells have greater intrinsic anti-tumor activity. In contrast to significant inhibition seen with ascites from wildtype mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea\u0026ndash;c), FSH-CER T cells were not inhibited by FSHR ascites from \u003cem\u003eRag1-/-\u003c/em\u003e mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eg\u0026ndash;i), demonstrating the mechanism that antibodies or other factors produced by endogenous B or T cells in the presence of FSHR\u0026thinsp;+\u0026thinsp;tumor cells inhibit FSH-CER T cells.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSolid tumors pose unique challenges to T cell immunotherapy, largely due to the immunosuppressive nature of the TME, which is shaped by chronic inflammation, suppressive cytokines, and inhibitory immune cell populations \u003csup\u003e28\u003c/sup\u003e. Here, we demonstrate that targeting FSHR\u0026thinsp;+\u0026thinsp;tumor cells with FSH-CER T cells engineered with a CD28ζ ICD enhances their efficacy compared to a 4-1BBζ ICD. However, both ICDs are susceptible to exhaustion and inhibition by ascites from tumors overexpressing FSHR. Studies investigating the role of FSHR in promoting tumor growth have yielded mixed results, with some suggesting its involvement in promoting proliferation, enhancing survival, and contributing to cell migration and invasion, while others report less definitive impacts \u003csup\u003e29\u0026ndash;32\u003c/sup\u003e. This variability may depend on factors such as tumor type, experimental model, and the cellular context in which FSHR signaling is examined. Our findings demonstrate that FSHR expression is consistently present in EOC patient-derived ascites samples, as all eight patients tested expressed FSHR. FSHR is also normally expressed in mice, primarily in the granulosa cells of the ovary and the Sertoli cells of the testis, reflecting its canonical role in reproductive physiology \u003csup\u003e33\u003c/sup\u003e. In the context of EOC, normal ovarian tissue is removed \u003csup\u003e34\u003c/sup\u003e, limiting the potential of on-target, off-tumor effects of FSH-CERs. While low-level expression has been reported in other tissues such as the adrenal glands and bone marrow, its functional relevance outside the reproductive axis remains unclear. ID8-FSHR cells \u003cem\u003ein vivo\u003c/em\u003e upregulated oncogenes and down regulated tumor suppressor genes. In particular, genes associated with the PI3K/MEK/ERK signaling pathway were upregulated in ID8-FSHR tumors, suggesting a possible potential therapeutic vulnerability in targeting strategy for FSHR\u0026thinsp;+\u0026thinsp;tumors. The molecular changes observed in ID8-FSHR tumors suggest a shift toward a more aggressive tumor phenotype, potentially driven by activation of pro-survival and proliferative pathways. Interestingly, observed lower levels of IL-6, G-CSF, and VEGF in the ID8-FSHR ascites suggest a blunted inflammatory response to tumor growth. These cytokines are typically elevated in response to tumor-driven inflammation and are involved in promoting immune cell recruitment, angiogenesis, and acute-phase signaling\u003csup\u003e35\u003c/sup\u003e. Their reduced expression in ID8-FSHR tumors may reflect a failure of the innate immune system to recognize and respond to tumor-associated cues, or it may indicate the presence of a highly suppressive TME. FSHR overexpression may contribute to immune evasion by dampening the inflammatory signals that typically accompany tumor progression. These findings raise important questions about the mechanistic links between FSHR signaling and immune escape.\u003c/p\u003e\u003cp\u003ePrior studies have shown that CD28ζ tends to promote rapid T cell activation and cytokine release, whereas 4-1BBζ is thought to enhance persistence and memory formation \u003csup\u003e36\u003c/sup\u003e. Interestingly, our findings suggest that persistence and memory formation of CAR T cells may not fully translate to the TME, where CD28ζ-containing CERs appeared more effective. This raises important questions about how different co-stimulatory domains interact with context-specific immune suppression and whether certain ICDs may be better suited for solid tumors with high immune regulatory pressure. The discrepancy between these FSH-CERs may be due to differences in binding affinity, receptor clustering, or downstream signaling, highlighting the importance of co-stimulatory domain selection in CER design\u003csup\u003e37,38\u003c/sup\u003e. Further mechanistic studies are needed to understand why 4-1BBζ performed poorly in this setting and how co-stimulatory signaling might be optimized in CER design.\u003c/p\u003e\u003cp\u003eThe immune landscape of the peritoneal cavity showed that mice treated with FSH-CER T cells had increased frequencies of PMN-MDSCs compared to controls, perhaps due to decreased numbers of tumor cells. In FSH-28ζ-treated mice, ID8-FSHR cells were nearly undetectable in the peritoneal lavage samples. This observation supports the conclusion that FSH-28ζ CER T cells exhibit greater cytotoxicity in this mouse model. Further \u003cem\u003ein vivo\u003c/em\u003e analysis of FSH-CER T cells revealed signs of immune exhaustion across all groups, including the inactive FSH-Δζ control, suggesting a uniform response in the TME regardless of the differences in activation driven by the ICDs. In sum, the TME appears broadly suppressive of FSH-CER T cells regardless of activation status, with CD28ζ emerging as the most effective ICD for tumor clearance; however, activated FSH-CER T cells (FSH-28ζ, FSH-hBBζ) may also drive suppression by increasing the MDSC population within TME \u003csup\u003e39\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe functional suppression of FSH-CER T cells observed \u003cem\u003ein vivo\u003c/em\u003e suggested a role for soluble immunosuppressive factors within the TME. We hypothesized that ascites might contain inhibitory elements that directly impair CER T cell activity. Ascites from mice bearing ID8-FSHR tumors, but not ID8 tumors, suppressed FSH-CER function across multiple assays, including RTCA, cytokine secretion, and activation marker expression. Notably, this effect was specific to FSH-CER T cells and did not extend to CD19 CAR T cells, suggesting a unique vulnerability related to the FSH\u0026ndash;FSHR axis. These findings led us to hypothesize that FSHR is shed into the TME, where it interferes with the activation clustering of FSH-CER T cells by binding to their membrane-bound receptors. Although other GPCRs have been shown to shed into the TME \u003csup\u003e40\u003c/sup\u003e, we were unable to detect shed FSHR in ascites samples. The absence of FSHR shedding observed in our proteomic analyses suggests that soluble FSHR is not responsible for the observed inhibition of FSH-CER T cells in ascites. These results prompted us to test an alternative hypothesis that inhibition might instead be mediated by antibodies generated against overexpressed FSHR. These antibodies could block FSH-CER T cells from binding to membrane-bound FSHR, thereby impairing their function. These antibodies could block FSH-CER T cells from binding to membrane-bound FSHR, thereby impairing their function. In support of this hypothesis, ID8-FSHR ascites from \u003cem\u003eRag1-/-\u003c/em\u003e mice\u0026mdash;which lack B cells to produce antibodies\u0026mdash;did not inhibit anti-tumor activity of FSH-CER T cells. These findings demonstrate that endogenous T or B cells contribute to the inhibition of FSH-CER T cells, though further studies are needed to explore this mechanism fully. The CD28ζ-based FSH-CER T cells consistently outperformed their 4-1BBζ counterparts in these conditions, suggesting that this ICD may confer partial resistance to the immune suppressive effects of ascites. This could reflect differences in early activation kinetics, resilience to checkpoint engagement, or distinct downstream signaling thresholds between ICDs. Altogether, these findings indicate both the therapeutic promise and the complexity of targeting FSHR in ovarian cancer, underscoring the need for further investigation into the immune-mediated mechanisms that regulate CER T cell function within the TME.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003eAll methods were conducted in accordance with institutional guidelines and regulations, and this study is reported in compliance with the ARRIVE guidelines.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMice\u003c/b\u003e\u003c/p\u003e\u003cp\u003eC56BL/6J and \u003cem\u003eRag-/-\u003c/em\u003e mice were purchased from Jackson Laboratories in accordance with approved protocols by H. Lee Moffitt Cancer Center and Research Institute, The University of South Florida Institutional Animal Care Use Committee and Roswell Park Cancer Center Comparative Oncology Shared Research (COSR). \u003cem\u003eNur77\u003c/em\u003e\u003csup\u003eGFP\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;mice were bred in-house at H. Lee Moffitt Cancer Center and Research Institute and Roswell Park Comprehensive Cancer Center. All mice used for experiments were between the ages of 6\u0026ndash;12 weeks. Tumor burden was monitored using BLI and body weight gain as a surrogate for ascites development. Due to the intraperitoneal and disseminated nature of the ID8-FSHR model, discrete tumor size measurements were not feasible. During BLI mice were under general anesthesia using isoflurane (2\u0026ndash;3% for induction, 1\u0026ndash;2% for maintenance) delivered in oxygen via a precision vaporizer and nose cone. Mice were euthanized using carbon dioxide (CO₂) inhalation followed by cervical dislocation, in accordance with institutional protocols. All efforts were made to minimize animal discomfort.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCell lines and generation of retroviral/lentiviral constructs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eID8 (\u003cem\u003eDefb-29/Vegf-a\u003c/em\u003e) cells (referred to ID8 within this manuscript) were a generous gift from Jose Conejo-Garcia at Duke University Medical Center. ID8 cells are epithelial ovarian cancer cells derived from C57BL/6J mice. ID8 cells were transduced with a firefly luciferase-expressing lentiviral vector (Addgene; 108542) according to the manufacturer\u0026rsquo;s instructions. Luciferase expression was confirmed by luminescence using an EnVision multilabel plate reader following incubation with ONE-Glo\u0026trade; Luciferase Assay System according to manufacturer\u0026rsquo;s instructions (Promega; E6110). ID8 luciferase cells were then retrovirally transduced with FSHR H29 virus tagged with GFP and sorted into ID8 vs. ID8-FSHR populations using a FACSAria SORP Cell sorter gated on GFP positivity. Both ID8 and ID8-FSHR cells were maintained using DMEM supplemented with 10% heat-inactivated fetal bovine serum (HI-FBS), 2mM L-glutamine, and 100U/mL penicillin/streptomycin. To ensure cell line integrity, we routinely screened for mycoplasma contamination using the Universal Mycoplasma Detection Kit (ATCC; 30-1012K) and the MycoAlert\u0026trade; PLUS Detection Kit (Lonza; LT07-710). Sequences for FSH-CERs were generously provided by Dr. Jose Conejo-Garcia at Duke University Medical Center. FSH-Δζ, FSH-28ζ and FSH-hBBζ constructs were packaged in SFG gamma-retroviral vectors as previously described \u003csup\u003e41\u003c/sup\u003e. FSHR GFP vector was packaged into SFG plasmid utilizing the mouse wild-type sequence with GFP or mCherry included in the cytoplasmic portion of the sequence with a glycine-serine linker.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMouse T cell isolation and CER T cell generation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eC57BL/6J (B6;000664), \u003cem\u003eB6.PL-Thy1\u003c/em\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/CyJ\u003c/em\u003e (Thy1.1;000406\u003cem\u003e)\u003c/em\u003e and \u003cem\u003eB6J(B6N)-Tg(Nr4a1-EGFP/cre)820Khog/PalcaJ\u003c/em\u003e (Nur77\u003csup\u003eGFP\u003c/sup\u003e; 016617) FSH-CER generation follows a previously published protocol \u003csup\u003e41\u003c/sup\u003e. In short, mouse spleens were excised and mashed through a 40\u0026micro;m cell strainer. Using the EasySep Mouse T Cell Isolation Kit (STEMCELL technologies; 19851) CD3\u0026thinsp;+\u0026thinsp;T cells were isolated. Anti-CD3/CD28 Dynabeads and 100IU recombinant human IL-2 were used to expand CD3\u0026thinsp;+\u0026thinsp;isolated T cells. T cells were spinoculated with Phoenix-ECO viral supernatants at 2000g, for 1hr at 32C at 24 hours and again at 48 hours post isolation. CD3/CD28 Dynabeads were removed by placing the cell suspension on a magnetic separation rack. After allowing the beads to migrate to the tube wall, the bead-free cell-containing supernatant was carefully collected as the flow-through for downstream applications. CER gene transfer was confirmed by flow cytometry using the rabbit monoclonal antibody (mAb) G4S-PE as a positivity marker. During this process mouse CER T cells were cultured in a 37C incubator, 5% CO2 using RPMI-1640 supplemented with 10% HI-FBS, 2uM L-glutamine, 100 U/mL Penicillin/Streptomycin, 1x nonessential amino acids, 1mM sodium pyruvate. 10mM HEPES buffer, 55uM 2-mercaptoethanol and 100IU rhIL-2.\u003c/p\u003e\u003cp\u003e\u003cb\u003eID8-FSHR mouse model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor mouse experiments, 4x10\u003csup\u003e6\u003c/sup\u003e ID8-FSHR or ID8 cells were injected IP into either C57BL/6J or \u003cem\u003eB6.129S7-Rag1-/-1tm1Mom/J\u003c/em\u003e mice at day 0. At day 6, BLI was measured using IVIS spectrum (Perkin Elmer), and mice were then assigned to groups to ensure that each group had the same average BLI value. At day 7, 4x10\u003csup\u003e6\u003c/sup\u003e FSH-CER T cells were injected IP. Mice underwent triweekly weight measurements, and weekly BLI was performed until the onset of ascites. Upon reaching 25% weight gain relative to the average of 4 control mice, mice were euthanized. Ascites was collected from the IP cavity using a 22-guage needle and was centrifuged at 2000g for 10 minutes to form a cell pellet and serum. Serum was filtered using a .22uM filter from Millipore (Cat. # SLGSR33SS) to confirm acellularity. Samples were stored at -80C avoiding freeze/thaw cycles. For co-culture experiments, acellular ascites was added directly to complete RPMI media at a final concentration of 10%. This ascites-supplemented media was used in both RTCA and cytokine secretion assays.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCytokine Secretion Assay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFSH-CER T cells were co-cultured with target cells at a 9:1 ratio of effector-to-target cells for 24 hours. Supernatants were collected from co-culture assays with and without 10% ascites. Supernatants were diluted at a 1:3 ratio (sample to diluent) and cytokines evaluated using the automated ELISA platform (ELLA; Bio-Techne) according to the manufacturer\u0026rsquo;s instruction.\u003c/p\u003e\u003cp\u003e\u003cb\u003eReal-Time Cytotoxic Assay (RTCA)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1x10\u003csup\u003e4\u003c/sup\u003e ID8 and ID8-FSHR cells were seeded on an xCELLigence plate (Agilent; 300601010) and impedance was read for 4-24hrs. 100uL of supernatant was removed and FSH-CERs were added at 9x10\u003csup\u003e4\u003c/sup\u003e in 100uL of media. Impedance was measured for an additional 72 hours.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBulk RNA-seq\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMice were injected with 4x10\u003csup\u003e6\u003c/sup\u003e ID8 or ID8-FSHR cells at day 0 and euthanized on day 7. A peritoneal lavage was performed using 8 mL of PBS injected IP. Mice were palpated and subsequently the PBS was extracted using a 22-gage needle and 10mL syringe. Cells in ascites were counted using Cellometer\u0026trade; Auto T4 automated cell counter (Nexcelom: CMT-AT4P). 1x10\u003csup\u003e6\u003c/sup\u003e cells were isolated and was pelleted at 2000g for 10 minutes at room temperature. Using liquid nitrogen, cells were snap frozen and stored at -80C. The Genomics Shared Resource (GSR) at Roswell Park Cancer Center extracted RNA using miRNeasy kit (Qiagen; 217004) and tested RNA concentrations using High Sensitivity RNA ScreenTape\u0026reg; (Agilent; 5067\u0026ndash;5579). Bulk RNA-seq was performed using KAPA RNA HyperPrep Kit with RiboErase (HMR) for ribosomal depletion. Sequencing was performed on an Illumina NovaSeq 6000 to generate paired-end 100 bp reads. Raw reads were quality-checked using FastQC to examine base-quality distribution patterns. The reads were mapped to the GRCm38 mouse reference genome and GENCODE (v25) annotation database using STAR (v2.7.9a). Alignment files were indexed using samtools (v1.14). Gene-level quantification was performed using featureCounts (Subread v1.6.4) using the frOv\u0026thinsp;=\u0026thinsp;0.95 parameter. Genes with fewer than maximum 10 reads across samples were excluded from further analysis. Differential expression analysis was carried out using the DESeq2 package (v1.26.0) in R, applying an adjusted p-value cutoff of 0.05 (Benjamini-Hochberg correction) and log₂ fold change threshold of \u0026plusmn;\u0026thinsp;1. Gene set enrichment analysis (GSEA) was performed using the GSEA desktop application from the Broad Institute, with enrichment considered significant at FDR\u0026thinsp;\u0026lt;\u0026thinsp;25%. Visualization of differentially expressed genes was conducted using ggplot2 in R to generate volcano plots.\u003c/p\u003e\u003cp\u003e\u003cb\u003eProteomics / Metabolomics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eProteomics was performed by the Jun Qu laboratory at the University at Buffalo using acellular ascites derived from mice bearing ID8 or ID8-FSHR tumors. To evaluate the presence of shed FSHR protein, a pooled positive sample was subjected to in-depth data-independent acquisition (DIA) using both DIA-NN (an open-source neural network-based tool) and Spectronaut (a commercial DIA platform). Despite high proteomic depth\u0026mdash;quantifying 1,546 proteins with DIA-NN and 1,055 with Spectronaut\u0026mdash;FSHR was not detected. For quantitative analysis of the ascites proteome, samples were processed using surfactant-cocktail assisted protein extraction, precipitation, and on-pellet digestion (SEPOD), followed by extensive liquid chromatography separation and mass spectrometry using the Orbitrap Astral MS platform. Label-free quantification was performed using DIA-NN to assess differences in protein abundance between experimental groups. Untargeted metabolomics on acellular ascites derived from ID8 and ID8-FSHR bearing mice was performed by the metabolomics core at H. Lee Moffitt Cancer Center using LC-MS/MS. Metabolite identification and quantification were performed on a Thermo-Scientific Q Exactive Orbitrap mass spectrometer, employing established workflows for comprehensive small molecule profiling and post-translational modification (PTM) analysis\u003c/p\u003e\u003cp\u003e\u003cb\u003ePatient Samples and immunofluorescent staining\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAscites samples were collected from patients with epithelial ovarian cancer (EOC) under an IRB-approved protocol (STUDY00002327; Roswell Park Cancer Center). Samples were centrifuged at 2,000g for 10 minutes at room temperature to pellet the cells. The pellet was fixed in 1:10 formalin (Fisher chemical; SF1004) for 24 hours, followed by an additional 24-hour incubation in 70% ethanol. Slides were prepared by the Pharmacology and Therapeutics Core at Roswell Park Cancer Center. Samples were stained with DAPI and FSHR antibodies listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Slides were imaged using Cytation\u0026trade; 5 Cell Imaging Multi-Mode Reader by Brian Buckley from the Drug Discovery Core Shared Resource at Roswell Park Cancer Center.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFlow Cytometry\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCells were first stained with a fixable Live/Dead viability dye (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) for 20 minutes at 4\u0026deg;C in autoMACS rinsing solution supplemented with 50 mL FBS (Miltenyi Biotec; 130-091-222), protected from light. Following a wash, surface staining was performed by incubating cells with fluorochrome-conjugated antibodies (listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) for 30 minutes at 4\u0026deg;C in the dark. All antibodies were titrated prior to use to determine the optimal staining concentration. When required, fluorophore-conjugated secondary antibodies (listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were added following primary staining and incubated for 20 minutes at 4\u0026deg;C. After staining, cells were washed twice with MACS buffer and resuspended for analysis. For intracellular staining\u0026mdash;including Glut1\u0026mdash;cells were fixed and permeabilized using the eBioscience\u0026trade; Foxp3 / Transcription Factor Staining Buffer Set (Thermofisher; 00‑5523‑00) according to the manufacturer\u0026rsquo;s protocol. Samples were acquired on a Cytek Aurora spectral flow cytometer and analyzed using FlowJo v10.8 (BD Biosciences). Compensation was done using single-stained controls, and gating strategies were aided by fluorescence-minus-one (FMO) controls where applicable.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using GraphPad Prism v9.5.1 (GraphPad Software, San Diego, CA), unless otherwise stated in the figure legends. Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Statistical tests included unpaired two-tailed Student\u0026rsquo;s t-tests, one-way or two-way ANOVA with appropriate post-hoc corrections, and log-rank [Mantel\u0026ndash;Cox] tests for survival analysis, as specified in each figure legend. A \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Significance thresholds are denoted as follows: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (*\u003cem\u003e), p\u0026thinsp;\u0026lt;\u0026thinsp;0.002 (**\u003c/em\u003e\u003cb\u003e)\u003c/b\u003e, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0002 (\u003c/em\u003e***), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 (****). For clarity, figures or panels not annotated with statistical values were not statistically significant.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eKey Resources Table\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFluorophore\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClone\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDilution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eApplication\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD11b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBB700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM1/70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD11c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBV650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAr. Hamster\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePE Dazzle-594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6D5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBUV395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17A2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePE-Cy7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGK1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBV785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGK1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAPC-780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIM7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBUV395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17A2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD62L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBV605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMEL-14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAF-700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eH1.2F3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAr. Hamster\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16-10A1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAr. Hamster\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAPC-eF780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGL1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD8a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBUV737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53\u0026thinsp;\u0026minus;\u0026thinsp;6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD90.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePE-Cy5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHIS51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15A7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCTLA-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePE-eFluor 610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUC10-4B9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAr. Hamster\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF4/80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBUV563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBM8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFSHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnconjugated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3D5G9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRabbit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eImmune Fluorescence\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG4S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eE702V\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRabbit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlut1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnconjugated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eE4S6I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRabbit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKLRG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBUV737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2F1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRabbit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLag-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePerCP-eFluor 710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC9B7W\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLive/Dead\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZombie NIR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLy6C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBV510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHK1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLy6G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBV785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1A8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMHC II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAF-700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM5/114.15.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNK1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBUV805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePK136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePD1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePE-Cy7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJ43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAr. Hamster\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFITC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAB_228426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRabbit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eImmune Fluorescence\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIGIT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBV421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGIGD7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlow Cytometry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the GEO repository under accession number GSE303863 and in the PRIDE repository under accession number PXD066774.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the GEO repository under accession number GSE303863 and in the PRIDE repository under accession number PXD066774.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no external funding for this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a Developmental Research Program (DRP) award from the Roswell Park\u0026ndash;University of Chicago Ovarian Cancer SPORE. Metabolomics analysis was performed with support from the metabolomics core at Moffitt Cancer Center. Genomic and bulk RNA sequencing analyses were conducted using the Genomics Shared Resource at Moffitt Cancer Center and Roswell Park Comprehensive Cancer Center. Flow cytometry was performed using the Flow \u0026amp; Immune Analysis Shared Resource, and animal studies were conducted with the support of the Comparative Oncology Shared Resource, both at Roswell Park. \u003cem\u003eIn vivo\u003c/em\u003e mouse imaging was performed by the Translational Imaging Shared Resource. Immunofluorescence staining of patient samples was performed by Ellen Karasik in the Experimental Tumor Model Shared Resource at Roswell Park. We thank all core personnel for their expert assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN.B. and M.L.D. conceived and designed the study. N.B. performed all experiments and wrote the manuscript. M.M. conducted and analyzed the proteomics and metabolomics experiments. P.G., S.M., S.B.L., and J.C.B. provided experimental assistance. Y.P.K. contributed to the analysis of metabolomics data. E.C. and J.W. performed the analysis of bulk RNA sequencing data. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary information is available for this paper. Correspondence and requests for materials should be addressed to Marco L. Davila.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel, R. L., Giaquinto, A. N. \u0026amp; Jemal, A. 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Gammaretroviral Production and T Cell Transduction to Genetically Retarget Primary T Cells Against Cancer. \u003cem\u003eMethods Mol Biol\u003c/em\u003e \u003cstrong\u003e1514\u003c/strong\u003e, 111-118, doi:10.1007/978-1-4939-6548-9_9 (2017).\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7179778/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7179778/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOvarian cancer remains a significant cause of cancer-related mortality, with epithelial ovarian cancer (EOC) being the most common subtype. Despite advances in treatment, the 5-year survival rate for late-stage EOC remains low due to factors such as tumor heterogeneity and an immunosuppressive tumor microenvironment (TME). This study investigates the therapeutic potential of chimeric endocrine receptor (CER) T cells engineered to express follicle stimulating hormone (FSH) in a syngeneic mouse model of EOC expressing follicle stimulating hormone receptor (ID8-FSHR). We compared two different co-stimulatory domains\u0026mdash;CD28ζ and 4-1BBζ\u0026mdash;in FSH-CER T cells and found that FSH-CD28ζ CER T cells exhibited enhanced cytotoxicity, proliferation, and cytokine secretion \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo.\u003c/em\u003e In the ID8-FSHR mouse model, FSH-28ζ CER T cells significantly reduced tumor burden and extended survival compared to FSH-hBBζ and control CER T cells. However, the therapeutic efficacy was compromised by T cell exhaustion, with all FSH-CER T cells expressing high levels of exhaustion markers after 7 days. In summary, incorporating a CD28ζ costimulatory domain enhances the efficacy of FSH-CER T cells, highlighting their therapeutic potential in ovarian cancer and supporting the development of strategies to mitigate immune exhaustion.\u003c/p\u003e","manuscriptTitle":"CD28 co-stimulatory domain enhances efficacy of CER T cell therapy compared to 4-1BB in an ovarian cancer mouse model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 12:19:23","doi":"10.21203/rs.3.rs-7179778/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-20T08:30:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-19T19:58:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240818320071788874005234567047685285729","date":"2025-09-30T10:37:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-08T18:59:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-03T00:34:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223357396933962628099541890133593798482","date":"2025-08-15T22:24:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300674841929755039573990580346691075369","date":"2025-08-09T23:10:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-07T01:00:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-06T15:25:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-01T05:44:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-30T20:14:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-30T20:10:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a91c4d69-66e5-44b3-98fa-924de08449d1","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52918139,"name":"Biological sciences/Cancer"},{"id":52918140,"name":"Biological sciences/Immunology"},{"id":52918141,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-03-16T16:02:02+00:00","versionOfRecord":{"articleIdentity":"rs-7179778","link":"https://doi.org/10.1038/s41598-026-43225-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-11 15:58:11","publishedOnDateReadable":"March 11th, 2026"},"versionCreatedAt":"2025-08-12 12:19:23","video":"","vorDoi":"10.1038/s41598-026-43225-0","vorDoiUrl":"https://doi.org/10.1038/s41598-026-43225-0","workflowStages":[]},"version":"v1","identity":"rs-7179778","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7179778","identity":"rs-7179778","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}