Crosstalk between the Programmed Death Pathway and TIGIT/CD155/DNAM-1 Axis in NK Cell-Mediated Immunosuppression in Ovarian Cancer

Crosstalk between the Programmed Death Pathway and TIGIT/CD155/DNAM-1 Axis in NK Cell-Mediated Immunosuppression in Ovarian Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Crosstalk between the Programmed Death Pathway and TIGIT/CD155/DNAM-1 Axis in NK Cell-Mediated Immunosuppression in Ovarian Cancer Anna Pawłowska-Łachut, Wiktoria Skiba, Paulina Pieniądz-Feculak, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9051409/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Ovarian cancer (OC) is the gynecological malignancy with the highest mortality rate. Preclinical studies have shown that dual blockade of immune checkpoints (ICPs), such as PD-1 and TIGIT, may be beneficial for OC patients. Thus, the aim of this study was to evaluate the expression of PD-1, TIGIT, and DNAM-1 on NK cells in three OC environments, i.e., peripheral blood (PB), peritoneal fluid (PF), and tumor tissue (TT), and their soluble forms in plasma and PF, to explore their clinical significance. The expression of ICPs on NK cells was analyzed via flow cytometry. The concentrations of soluble forms of these ICPs were measured in plasma and PF via ELISA. The obtained results were correlated with the clinicopathological features of OC patients. We detected an accumulation of PD-1 + NK cells in TT and TIGIT + or DNAM-1 + NK cells in the PF. We demonstrated elevated concentrations of sPD-1 in PF and sTIGIT in OC patient plasma compared to healthy plasma, and alterations in the NK cell immunophenotype were associated with clinicopathological features of OC. This study revealed that PD-1, TIGIT, and DNAM-1 act together as an immunosuppressive network rather than as separate pathways. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Immunology Health sciences/Oncology ovarian cancer TIGIT PD-1 tumor microenvironment dual blockade DNAM-1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Ovarian cancer (OC) is considered the most lethal gynecological malignancy. This is related to its diagnosis in advanced International Federation of Gynecology and Obstetrics (FIGO) stages of the disease as a result of disease heterogeneity, lack of screening tools, and an asymptomatic course of the disease, especially in the early stages 1 – 3 . Notably, OC in the early FIGO stages (stages I and II) is curable. Nonetheless, the majority of cases (approximately 85%) are diagnosed in the III and IV FIGO stages when the five-year survival rate decreases to 29.2%. By way of comparison, for local diseases (I and II FIGO stages), the five-year survival rate is 92% 4,5, and the total five-year survival rate is as low as 47% 6 . Despite the implementation of standard treatment, including cytoreductive surgery, chemotherapy, and biological drugs, e.g., vascular endothelial growth factor inhibitors (VEGFis), e.g., bevacizumab, and poly-ADP ribose polymerase (PARP) inhibitors (PARPis), e.g., olaparib, rucaparib, and niraparib, the prognosis for OC patients is poor 7 , 8 . Thus, there is a need to develop diagnostic/prognostic biomarkers and novel treatment strategies for OC patients. The dynamic and complex interplay within the tumor microenvironment (TME) is a potential target for OC treatment 5 . Immune cells are influenced by various signals from the TME that shape their immunophenotype and modulate their functions. The immunogenicity of cancer directly affects immune evasion through multiple mechanisms, such as immune checkpoints (ICPs), and their expression is considered a crucial factor in predicting the response to immune checkpoint inhibitor (ICI) therapy 9 . In the previous decade, immunotherapies based on immune checkpoint inhibitors, including programmed death pathway inhibitors, revolutionized the treatment of solid malignancies such as melanoma and lung and renal cancer 10 – 12 . This type of treatment has also been approved for the treatment of gynecological malignancies such as uterine and cervical cancer 13 . Nevertheless, inhibitors of programmed death receptor 1 (PD-1), i.e., nivolumab and pembrolizumab, as well as programmed death ligand 1 (PD-L1), i.e., atezolizumab, avelumab, and durvalumab, have shown limited efficacy in OC treatment 5 , 14 . The response rate of OC patients to these drugs is low to intermediate 15 , 16 , with a total response rate of 10–15%. Notably, the resistance of OC to ICIs is related to a number of factors, such as a decreased density of TILs, OC heterogeneity, low microsatellite instability, tumor mutation burden, regulation of ICPs via cytokine and miRNA networks, and interplay within the TME. The complexity of the molecular, genetic, and immunological background of OC patients resistant to ICIs was presented in detail in our previous paper 14 . The Food and Drug Administration (FDA) has not approved any ICIs for the treatment of OC monotherapy 8 . On February 10, 2026, the FDA approved pembrolizumab in combination with paclitaxel, with or without bevacizumab, for adults with PD-L1 - positive platinum-resistant ovarian cancer after one or two prior therapies. This finding represents a key advance in the introduction of biomarker-selected immunotherapy into the earlier management of platinum-resistant OC 17 . Moreover, recent studies have shown that focusing only on the PD-1/PD-L1/PD-L2 pathway, its inhibitors, and T-cell activity with respect to immunosuppression in the ovarian TME is insufficient 5 , 18 – 22 . To fill this gap, more comprehensive studies are needed on the complexity of interactions in the TME, including an understanding of the activity of other effector cells and coexpressed ICPs in terms of clinical implications. Although the PD-1 receptor is considered a main regulator of T-cell activity, other coexpressed ICPs, such as costimulatory DNAX accessory molecule-1 (CD226; DNAM-1) and coinhibitory T-cell immunoglobulin and the ITIM domain (TIGIT), influence their functions 23 – 25 . Both TIGIT and PD-1 receptors are exhaustion markers of cytotoxic T cells (CD8 + ) 26 . The interplay between ligands and receptors in the TIGIT/CD155/DNAM-1 axis is far more intricate than that in the PD-1/PD-L1/PD-L2 pathway. Among the ligands for TIGIT are CD155, CD112, CD113, and nectin-4. The complexity of the axis is emphasized by the fact that the ligands CD155 and CD112 are shared with the costimulatory receptor DNAM-1 and the inhibitory receptor TACTILE (CD96). The detailed information concerning the interaction between the PD-1/PD-L1/PD-L2 pathway and the TIGIT/CD155/DNAM-1 axis and the effects of dual blockade in OC patients was provided in our previous study 27 . The complex network of interactions between the TIGIT/CD155/DNAM-1 and PD-1/PD-L1/PD-L2 pathways is presented in Fig. 1 . Recent studies have shown that the implementation of dual blockade of ICPs, especially the combination of PD-1/PD-L1 and TIGIT inhibitors, may be beneficial for OC patients. The binding of TIGIT to its ligands results in the inhibition of NK cell and T-cell activity, proliferation, and cytokine secretion 27 . Unlike TIGIT, DNAM-1 is a costimulatory receptor, and its binding to CD115 or CD112 stimulates the lysis activity of NK cells targeted against cancer cells. Nonetheless, the ligand CD155 has a lower affinity for DNAM-1 than for TIGIT 23 – 25 , 28 – 30 . Importantly, both pathways work synergistically. Mechanistically, both pathways share a common factor, the costimulatory receptor DNAM-1. Both PD-1 and TIGIT inhibit the activity of DNAM-1 23–26 . Therefore, both pathways need to be simultaneously blocked to fully restore DNAM-1 costimulatory signaling. These findings highlight the reasons why dual blockade has more promising clinical outcomes 26 , 31 – 33 . The crosstalk between PD-1, TIGIT, and DNAM-1 on NK cells in OC patients, considering their impact on immune regulation and immunosuppression within the tumor microenvironment, is a potential target for precision medicine. These ICPs modulate the activity of NK cells, influencing their ability to recognize and eliminate tumor cells, thus offering potential therapeutic targets for enhancing antitumor immunity. Here, we comprehensively evaluated the expression of the main receptors involved in the programmed death pathway and the TIGIT/CD155/DNAM-1 axis, i.e., PD-1, DNAM-1, and TIGIT, on NK cells in peripheral blood (PB), peritoneal fluid (PF), and tumor tissue (TT) with respect to their clinical implications in OC patients. We also evaluated the concentrations of soluble forms of these receptors, i.e., soluble PD-1 (sPD-1), sDNAM-1, and sTIGIT, in the plasma and peritoneal fluid of OC patients compared with those in the plasma of healthy blood donors. The obtained results were analyzed in relation to the clinicopathological data of patients in comparison to those of healthy blood donors to investigate their clinical implications. Materials and methods Patient enrollment and ethics statement The research involved 68 patients, including 56 OC patients and 12 healthy blood donors. OC patients had undergone radical treatment at the II Department of Gynecological Oncology (St. John's Center of Oncology of the Lublin Region, Lublin). The histopathological reports confirmed the diagnosis of OC. The tumors were staged in accordance with the FIGO classification system 34 . Two independent gynecological pathologists classified and graded the tumors on the basis of the Silverberg grading system 35 , and the tumor type was established in accordance with the Kurman and Shih classification 36,37 . The study cohort excluded individuals with a history of previous malignancies; prior chemotherapy or radiation therapy; and allergic, infectious, or autoimmune diseases. The clinicopathological characteristics of the OC patients are shown in Table 1 . All patients and healthy blood donors provided written informed consent. Approval for the research was granted by the Bioethics Committee at the Medical University of Lublin (KE-0254/13/01/2022; KE-0254/102/06/2024; KB-0024/178/12/2024; KB-0024/114/01/2026). The study's control group consisted of 12 healthy blood donors, and the samples were collected from the Regional Centre of Blood Donation and Blood Treatment in Lublin. Each participant provided written informed consent, and all experiments were carried out in compliance with the Declaration of Helsinki. Table 1. Clinicopathological characteristics of OC patients. The clinical features OC patients ( n = 56) FIGO stage, n (%) Early (I–II FIGO stages) 11 (19.64%) I FIGO stage 5 (8.93%) II FIGO stage 6 (10.71%) Advanced (III–IV FIGO stages) 45 (80.36%) III FIGO stage 40 (71.43%) IV FIGO stage 5 (8.93%) Grading (histological differentiation), n (%) Intermediate grade (G2) 17 (30.36%) Low grade (G3) 39 (69.64%) The OC classification according to Kurman and Shih, n (%) Type I 21 (37.5%) low-grade serous ovarian cancer (LGSOC) 7 (12.5%) endometrioid 10 (17.86%) mucinous 3 (5.36%) clear cell 1 (1.78%) Type II 35 (62.5%) high-grade serous ovarian cancer (HGSOC) 34 (60.72%) carcinosarcoma 1 (1.78%) Menopausal Status Before menopause 10 (17.86%) After menopause 46 (82.14%) Ca125 median, range (U/mL) 491.00 (14.96-8766) HE4 median, range (pmol/L) 583 (45.3-12114) Age (median), years (range) 61 (29-85) Body mass index (BMI) range, median (kg/m 2 ) 27.73 (17.94-44.92) Mononuclear cells (MNCs) isolation procedure All PB samples were drawn into EDTA-coated tubes (Sarstedt, Germany) prior to surgery and processed immediately. MNCs were isolated via density gradient centrifugation at 2800 × rpm for 20 minutes at room temperature via Lymphoprep (StemCell Technologies, Canada). The cells from the interphase layer were then collected. After two washes with phosphate-buffered saline (PBS; Capricorn Scientific GmbH, Germany), the cells were resuspended in PBS. During surgery, PF and TT were collected aseptically. MNCs from PF were also isolated via centrifugation in a density gradient (2800 × rpm for 20 minutes at room temperature) via Lymphoprep (StemCell Technologies, Canada). The following procedure was the same as that used for MNCs isolated from PB. Freshly resected OC tissue was chopped and placed in a gentleMACS C tube and then processed via the Tumor Dissociation Kit (Miltenyi Biotec) to isolate tumor-infiltrating MNCs. The suspensions of cells were subsequently filtered with a mesh filter (70 mm; BD Biosciences), and the subsequent procedure, including density gradient centrifugation and washing, was carried out in the same manner as described for the PB and PF samples. NK cell immunophenotyping via flow cytometry Previously isolated MNCs (1 × 10 6 cells) from PB, PF, and TT were stained with fluorochrome-labeled monoclonal antibodies (mAbs) directed against specific surface markers for 20 mins. The panel of fluorochrome-conjugated monoclonal antibodies included the following: anti-CD3 FITC/(CD16+CD56) PE Cocktail (BioLegend), anti-PD-1 PE/Cyanine7 (BioLegend), anti-TIGIT PE/Cyanine7 (BioLegend), and anti-DNAM-1 PerCP/Cyanine5.5 (BioLegend). The stained cells were subsequently washed twice with PBS. Flow cytometric analysis (FACSCanto I, Becton Dickinson, USA) was performed to determine the percentage of NK cells expressing PD-1, TIGIT, or DNAM-1. The frequencies of these cells are expressed as percentages of the total MNCs population. For each sample, 50,000 events were recorded and analyzed via FacsDiva software. The percentages of NK cells with ICPs expression were reported as percentages relative to each specific cell subset (i.e., PD-1 + CD56 + CD16 + CD3 - , TIGIT + CD56 + CD16 + CD3 - , or DNAM + CD56 + CD16 + CD3 - populations). Enzyme-Linked Immunosorbent Assay procedure The concentrations of soluble forms of selected ICPs, i.e., PD-1, TIGIT, and DNAM-1, in the plasma and PF of OC patients and the plasma of healthy blood donors were established via ELISA tests according to the manufacturer's instructions (sPD-1, MA, USA; sTIGIT, ELK Co. Ltd. Biotechnology, China; sDNAM-1, Biorbyt, UK). The absorbance of the plate was measured via an ELX800 plate reader (BioTek Instruments, Inc., USA). The obtained results were analyzed with Gen5 software (BioTek Instruments, Inc.). The concentrations of sPD-1, sTIGIT, and sDNAM-1 were determined via interpolation from a standard curve. Statistical analysis of the obtained results The obtained results were statistically analyzed via GraphPad Prism 9.1.2. and Statistica 13.0PL. To compare the results from PB, PF, and TT within the same OC patients, the Wilcoxon paired test was used. For comparisons between groups (e.g., study group vs control group), the Mann‒Whitney U test was applied. To assess the correlation between two variables, the Spearman rank correlation test was utilized. The results are presented as the median, with minimum and maximum values. Statistical significance was defined as a p value of less than 0.05 ( p <0.05). Results The percentages of NK cells in the peripheral blood, peritoneal fluid, and among tumor-infiltrating cells from OC patients The analysis revealed significant differences in the percentages of CD56 + CD16 + CD3 - cells between the ovarian cancer environments and the peripheral blood of healthy donors. We observed the highest frequency of CD56 + CD16 + CD3 - cells in the PB of OC patients than in those of PF patients ( p <0.01; median 10.05% vs. 4.50%), in the PB of the control group ( p 0.05; median 10.05% vs. 4.57%; Figure 2. A). The frequency of NK cells with PD-1, TIGIT, and DNAM-1 expression in PB, PF, and TT from OC patients. The obtained data also revealed a disparity between CD56 + CD16 + CD3 - cells with PD-1 and TIGIT expression in different environments of OC patients. We detected the highest percentage of PD-1 + CD56 + CD16 + CD3 - cells among the tumor-infiltrating cells, which was significantly higher than that in PB (p<0.05; median 47.22% vs. 26.57%) and higher than that in PF (median 47.22% vs. 32.50%) and PB of control group (median 47.22% vs. 42.92%); nevertheless, the differences were not statistically significant ( p >0.05; Figure 2). B). We did not observe a statistically significant difference in the percentage of this cell subpopulation in the PB of patients and the control group ( p >0.05). We established the highest frequency of TIGIT + CD56 + CD16 + CD3 - cells in the PF of OC patients, and the value was significantly higher than that in the PB ( p 0.05). We also detected a significantly greater percentage of this cells subpopulation in TT than in the PB of healthy donors ( p 0.05). Moreover, we found a significantly higher percentage of DNAM-1 + CD56 + CD16 + CD3 - cells in the PF than in the PB of OC patients (median 70.34% vs. 58.29%; p 0.05; Figure 2). D). We did not observe a statistically significant difference in the percentage of this cell subpopulation in the PB between patients and the control group ( p >0.05). Concentration of soluble forms of PD-1, TIGIT, and DNAM-1 receptors in ovarian cancer environments Our analysis revealed differences in the concentrations of sPD-1 and sTIGIT in the plasma and PF of OC patients and in the plasma of healthy donors. We observed the highest level of sPD-1 in PF, which was significantly higher than that in the plasma of OC patients ( p 0.001; median 43.80 pg/mL vs. 24.97 pg/mL; Figure 3). A). Moreover, we detected the highest concentration of sTIGIT in the plasma of the OC, which was significantly higher than that in the plasma of the control group ( p 0.05; median 4.58 pg/mL vs. 1.56 pg/mL) (Figure 3). B). The results revealed the highest concentration of sDNAM-1 in the plasma of healthy blood donors, which was higher than that in the plasma (median 215.65 pg/mL vs. 177.74 pg/mL) and PF (median 215.65 pg/mL vs. 61.29 pg/mL) of OC patients; nevertheless, we did not observe statistically significant differences ( p >0.05; Figure 3). C). Notably, the differences in the number of patients included in the sTIGIT concentration analysis resulted from the difficulty of meeting the predefined measurement range, as the values were highly dispersed. Some samples were excluded because their sTIGIT levels in plasma or PF were too low to be detected within the assay range, whereas others exceeded the upper limit of quantification. Percentages of NK cells with PD-1, TIGIT or DNAM-1 expression in patients with different clinical manifestations of OC We subsequently evaluated the clinical relevance of NK cells with PD-1, TIGIT, or DNAM-1 expression in OC patients. The percentage of these cell subpopulations was correlated with particular OC clinicopathological features, i.e., FIGO stage (I-II vs. III-IV), grade (II vs. III), and Kurman and Shih tumor type (type I vs. type II), in comparison to their distribution in the PB of healthy donors. We found that the percentage of CD56 + CD16 + CD3 - cells in the PB of OC patients was significantly higher in advanced FIGO stages (III-IV) than in PB of control group ( p 0.05; Figure 4. A). There was a significantly higher percentage of CD56 + CD16 + CD3 - cells in the PB of patients with grade II disease than in those with grade III disease, as did the PB of the control group (both p <0.05; Figure 4. B). We detected the highest percentage of CD56 + CD16 + CD3 - cells in the PB of patients with type II tumors according to Kurman and Shih, and it was significantly higher than that in the PB of patients with type I tumors ( p <0.05; Figure 4. C), as well as the control group ( p <0.01; Figure 4. C). We also showed that the percentage of PD-1 + CD56 + CD16 + CD3 - cells was significantly higher in the PF of patients with grade II OC than in those with grade III ( p 0.05). A lower percentage of PD-1 + CD56 + CD16 + CD3 - cells was found in the PF of patients with type II tumors than in patients with type I tumors ( p 0.05). There was no other significant disparity in the percentages of NK cells and PD-1, TIGIT, or DNAM-1 positive NK cells in PB, PF, or TT in the different clinicopathological features of patients with OC ( p >0.05). 2.5. Relationships between the clinicopathological characteristics of OC patients and the percentages of NK cells with PD-1, TIGIT, or DNAM-1 expression in OC environments In the next step, we assessed the relationships between the clinicopathological characteristics of patients with OC and the percentages of CD56 + CD16 + CD3 - cells and CD56 + CD16 + CD3 - cells with PD-1, TIGIT, or DNAM-1 expression in three different OC environments. We detected a positive relationship between the percentage of CD56 + CD16 + CD3 - cells in the PF and the age of OC patients (R Spearman: 0.471; t(N-2) 2.777, p <0.01; Figure 5). A). Conversely, there was a negative association between the percentage of CD56 + CD16 + CD3 - cells in the PF and BMI in OC patients (R Spearman: -0.437; t(N-2) -2,569, p <0.05; There were no other significant relationships between the percentages of NK cells and NK cells with PD-1, TIGIT or DNAM-1 expression in PB or PF and among tumor-infiltrating cells and the clinicopathological characteristics of OC patients, i.e., FIGO stage, grade, age, type of tumor according to Kurman and Shih, age, menopausal status, or HE4 and Ca125 concentrations ( p >0.05). Discussion Despite advancements in ovarian cancer treatment, the five-year survival of OC patients is still poor. Recently, published studies, including both fundamental studies and clinical trials, have focused on the application of ICIs, mainly anti-PD-1/PD-L1, in OC treatment 19 , 38 , 39 . Notably, the role and clinical implications of coexpressed ICPs, both coinhibitory (TIGIT) and costimulatory (DNAM-1), on effector immune cells in OC are still unexplored. Thus, investigating the role of not only the PD-1/PD-L1/PD-L2 axis but also other coexpressed ICPs is necessary to develop effective tools to treat OC patients. NK cells play a predominant role in immune surveillance. Their activity is controlled by a balance between activating and inhibitory receptors, which allows them to selectively eliminate tumor cells that display activating ligands and evade those with strong inhibitory signals. The effectiveness of NK cells in OC patients is inhibited by immunosuppressive factors within the TME 40 , 41 . Our study demonstrated that the TIGIT/CD156/DNAM-1 axis significantly influences the NK cells immunophenotype and function, both systemically and locally, revealing new insights into how ovarian cancer cells may evade NK cell-mediated immunity. We aimed to determine how different environments of OC influence the immunophenotype and functional potential of NK cells, specifically the balance between inhibitory (PD-1, TIGIT) and activating (DNAM-1) receptors. Thus, we evaluated the expression of PD-1, TIGIT, and DNAM-1 on NK cells in three different environments (PB, PF, TT) of OC patients compared with healthy blood donors. Our findings revealed altered percentages of NK cells with PD-1 and TIGIT expression across different environments in OC patients. Similarly to Maas et al., we observed an immunophenotype shift of NK cells in favor of an inhibitory shift 40 . The increased expression of inhibitory receptors, i.e., PD-1 among tumor-infiltrating cells and TIGIT in PF, suggests local suppression of NK cell functions and potential tumor immune evasion. These results align with previous reports showing that the TME can modulate the balance of activating and inhibitory receptors on NK cells, which may have implications for immunotherapy strategies 25 , 40 , 42 . Moreover, the upregulation of TIGIT and DNAM-1 expression on NK cells in PF suggests that an increased percentage of TIGIT + NK cells can competitively inhibit DNAM-1 signaling through shared ligands (i.e., CD155), establishing a locally reinforced immunosuppressive environment that limits effective antitumor responses even when DNAM-1 is expressed. Moreover, according to Pounds et al., there is a relationship between the proportion of increased percentage of NK cells with DNAM-1 expression and poor survival of OC patients 43 . The present study revealed altered concentrations of soluble ICPs in OC patients. Specifically, the sPD-1 concentration was the highest in PF, suggesting its local accumulation in the TME. Moreover, a significantly higher concentration of sPD-1 was detected in the plasma of OC patients than in that of healthy controls, indicating systemic immune modulation. Similarly, the sTIGIT concentration was elevated in the plasma of OC patients compared with that of healthy donors. Although the differences in the concentrations of sDNAM-1 in different OC environments and the control group did not reach statistical significance, we observed a consistent trend toward its highest concentration in the plasma of healthy donors. This pattern suggests potential environment-specific immunomodulation of NK cells in OC, which may reflect local immunosuppression. OC may promote immune evasion through increased concentrations of inhibitory signals (sPD-1, sTIGIT) and decreased activating signals (sDNAM-1), which is consistent with previous reports on the immunosuppressive role of soluble ICPs in the TME 42 , 44 , 45 . Changes in NK cells immunophenotypes are also related to the clinical features of OC. Thus, we assessed the clinical relevance of NK cells expressing PD-1, TIGIT, and DNAM-1 in OC patients. A higher percentage of NK cells was observed in the PB of OC patients with advanced stages of disease (FIGO III/IV), both grade II and III, and tumor types I and II according to Kurman and Shih than in the control group. These findings suggest that the percentage of circulating NK cells increases with disease progression and tumor aggressiveness. Notably, their antitumor activity may be limited by the immunosuppressive TME (i.e., the inhibitory receptors PD-1 and TIGIT, as well as TGF-β and IL-10) 42 , 46 , 47 . The increased percentage of PD-1⁺NK cells in grade II of OC compared with that in the control group and their higher percentage in type I compared with type II of OC may be related to recruitment via a chemokine gradient (i.e. CXCR3-CXCL9/CXCL10/CXCL11 and CX3CR1-CX3CL1) and their cytotoxic function may be restrained by inhibitory signals within the ascites 48 , 49 . Similar to our findings, Greppi et al. reported an increased percentage of PD-1 + NK cells in the peritoneal fluid of high-grade serous ovarian cancer patients, while this cell subpopulation is rarely detected in healthy blood donors. Moreover, the increased percentage of these cells was related to poor overall survival, which highlights the clinical and prognostic significance of PD-1⁺NK cells 50 . We further investigated the relationships between the clinicopathological features of OC patients and the percentages of NK cells or NK cells with PD-1, TIGIT, or DNAM-1 expression in different OC environments. Similar to other authors 51 – 54 , we reported a negative correlation between the percentage of CD56⁺CD16 + CD3⁻ cells in ascites and the age of OC patients. This phenomenon may be explained by immunosenescence, as aging is associated with increased NK cell numbers but reduced cytotoxic function 52 , 54 , 54 . Moreover, Fraser et al. reported that the antitumor activity of NK cells in peritoneal fluid is inhibited via CA125 55 . Additionally, the percentage of CD56⁺CD16 + CD3⁻ cells in the PF was negatively correlated with the BMI of OC patients. Many studies 56 , 57 , 57 – 59 have shown that obesity is related to altered NK cells number and their impaired cytotoxic functions, including cytokines expression, which leads to weakened immunosurveillance. In summary, this study advances the current understanding of ICPs regulation in OC by identifying PD-1, TIGIT, and DNAM-1 as functionally interconnected components of a coordinated immunosuppressive network rather than independent pathways. The success of dual TIGIT and PD-1/PD-L1 blockade mostly depends on the selection of the group of patients in which this type of treatment is most beneficial. Moreover, the development of predictive biomarkers could help optimize combination therapies and enhance their efficacy. The intricate interplay between the TIGIT/CD155/DNAM-1 axis and the PD-1/PD-L1/PD-L2 pathway underscores the complexity of restoring NK cells effector functions. Conclusion Ovarian cancer is associated with altered NK cells distribution and increased inhibitory signaling, reflecting both local and systemic immunosuppression. A major challenge for effective immunotherapy in OC is overcoming the immunosuppressive TME. We showed an accumulation of PD-1 + NK cells among tumor-infiltrating cells and TIGIT + NK cells in PF, which may contribute to impaired antitumor immunity. We also observed an increased percentage of DNAM-1 + NK cells in the PF, suggesting an imbalance between inhibitory and activating signaling pathways within these two axes. Additionally, we demonstrated elevated concentrations of sPD-1 in the PF and sTIGIT in the plasma of OC patients compared with healthy controls. These findings support the reinforcement of inhibitory signaling both systemically and within the ovarian cancer microenvironment. Furthermore, alterations in the NK cell immunophenotype are associated with the clinicopathological features of OC, suggesting an association between the distribution of NK cells and PD-1-positive NK cells and disease progression. We also demonstrated that the proportion of NK cells in PF was positively correlated with patient age and negatively correlated with BMI, suggesting that factors such as aging and obesity may influence the NK cell distribution in OC environments. Declarations Acknowledgments Manuscript preparation was supported during Harvard Medical School’s Polish Clinical Scholars Research Training Program, organised by the Agencja Badań Medycznych (ABM, English: Medical Research Agency, Warsaw, Poland). We sincerely thank the staff of the Second Department of Gynecological Oncology, St. John's Center of Oncology of Lublin, for their invaluable assistance in collecting samples. We are also deeply grateful to all the blood donors and patients for their participation in the study. Disclosure The author(s) report no conflicts of interest in this work. Funding This research was funded in whole by the National Science Centre, Poland Preludium grant. number 2021/41/N/NZ6/01727. Author contributions Conceptualization, A.P.Ł.; methodology, A.P.Ł. and I.W.; validation, E.B.-B. and I.W.; formal analysis, A.P.Ł., K.W.-C. and D.S.; investigation, A.P.Ł., W.S. and P.P.-F.; resources, J.Ł., M.B., M.K., J.K. and J.T.; writing-original draft preparation, A.P.Ł. and I.W.; writing-review and editing, A.P.Ł., I.W..; visualization, A.P.Ł.; supervision, J.T. and I.W.; project administration, A.P.Ł.; funding acquisition, A.P.Ł. All authors have read and agreed to the published version of the manuscript. Data availability statement The datasets generated and/or analysed during the current study are available in the RepOD Repository for Open Data repository, https://repod.icm.edu.pl/dataset.xhtml?token=7bf6968a-bd64-4f72-8676-5e90b8a5be95. References Webb, P. M. & Jordan, S. J. Global epidemiology of epithelial ovarian cancer. Nat Rev Clin Oncol 21 , 389–400 (2024). Liberto, J. M. et al. Current and Emerging Methods for Ovarian Cancer Screening and Diagnostics: A Comprehensive Review. Cancers (Basel) 14 , 2885 (2022). Cai, G. et al. Artificial intelligence-based models enabling accurate diagnosis of ovarian cancer using laboratory tests in China: a multicentre, retrospective cohort study. The Lancet Digital Health 6 , e176–e186 (2024). Gutic, B. et al. Preliminary outcomes of five-year survival for ovarian malignancies in profiled Serbian Oncology Centre. Clinics (Sao Paulo) 78 , 100204 (2023). Blanc-Durand, F., Clemence Wei Xian, L. & Tan, D. S. P. Targeting the immune microenvironment for ovarian cancer therapy. Front Immunol 14 , 1328651 (2023). Lin, H. et al. A prognosis‑predictive nomogram of ovarian cancer with two immune‑related genes: CDC20B and PNPLA5. Oncology Letters 20 , 1–1 (2020). Ovarian Epithelial, Fallopian, & Peritoneal Cancer Treatment (PDQ®) - NCI. https://www.cancer.gov/types/ovarian/hp/ovarian-epithelial-treatment-pdq (2025). Drugs Approved for Ovarian, Fallopian Tube, or Primary Peritoneal Cancer - NCI. https://www.cancer.gov/about-cancer/treatment/drugs/ovarian (2025). Pawłowska, A. et al. Immunotherapies based on PD-1/PD-L1 pathway inhibitors in ovarian cancer treatment. Clin Exp Immunol 195 , 334–344 (2019). Mahoney, K. M., Freeman, G. J. & McDermott, D. F. The Next Immune-Checkpoint Inhibitors: PD-1/PD-L1 Blockade in Melanoma. Clin Ther 37 , 764–782 (2015). Kaur, J., Patil, G., Geynisman, D. M. & Ghatalia, P. Role of perioperative immunotherapy in localized renal cell carcinoma. Ther Adv Med Oncol 15 , 17588359231181497 (2023). Lahiri, A. et al. Lung cancer immunotherapy: progress, pitfalls, and promises. Molecular Cancer 22 , 40 (2023). Peng, H., He, X. & Wang, Q. Immune checkpoint blockades in gynecological cancers: A review of clinical trials. Acta Obstetricia et Gynecologica Scandinavica 101 , 941–951 (2022). Pawłowska, A. et al. Current Understanding on Why Ovarian Cancer Is Resistant to Immune Checkpoint Inhibitors. International Journal of Molecular Sciences 24 , 10859 (2023). Mathieu Luyckx, M. D., Jean-Luc Squifflet, M. D., Annika M Bruger, P. H. D. & Jean-Francois Baurain, M. D. Recurrent High Grade Serous Ovarian Cancer Management. Exon Publications 87–103 (2022) doi:10.36255/exon-publications-ovarian-cancer-management. Cascio, S. et al. Cancer-associated MSC drive tumor immune exclusion and resistance to immunotherapy, which can be overcome by Hedgehog inhibition. Science Advances 7 , eabi5790 (2021). Research, C. for D. E. and. FDA approves pembrolizumab with paclitaxel for platinum-resistant epithelial ovarian, fallopian tube, or primary peritoneal carcinoma. FDA https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-pembrolizumab-paclitaxel-platinum-resistant-epithelial-ovarian-fallopian-tube-or (2026). Tassi, E. et al. Epithelial ovarian cancer is infiltrated by activated effector T cells co-expressing CD39, PD-1, TIM-3, CD137 and interacting with cancer cells and myeloid cells. Front Immunol 14 , 1212444 (2023). Pawłowska, A. et al. Clinical and Prognostic Value of Antigen-Presenting Cells with PD-L1/PD-L2 Expression in Ovarian Cancer Patients. International Journal of Molecular Sciences 22 , 11563 (2021). Pawłowska, A. et al. Programmed Death-1 Receptor (PD-1) as a Potential Prognosis Biomarker for Ovarian Cancer Patients. CMAR Volume 12 , 9691–9709 (2020). Weimer, P. et al. Tissue-Specific Expression of TIGIT, PD-1, TIM-3, and CD39 by γδ T Cells in Ovarian Cancer. Cells 11 , 964 (2022). Chen, F., Xu, Y., Liu, X., Dong, N. & Tian, L. TIGIT+ CD4+ regulatory T cells enhance PD-1 expression on CD8+ T cells and promote tumor growth in a murine ovarian cancer model. J Ovarian Res 17 , 252 (2024). Ge, Z., Peppelenbosch, M. P., Sprengers, D. & Kwekkeboom, J. TIGIT, the Next Step Towards Successful Combination Immune Checkpoint Therapy in Cancer. Front Immunol 12 , 699895 (2021). Chauvin, J.-M. & Zarour, H. M. TIGIT in cancer immunotherapy. J Immunother Cancer 8 , (2020). Sanchez-Correa, B. et al. DNAM-1 and the TIGIT/PVRIG/TACTILE Axis: Novel Immune Checkpoints for Natural Killer Cell-Based Cancer Immunotherapy. Cancers 11 , 877 (2019). Banta, K. L. et al. Mechanistic convergence of the TIGIT and PD-1 inhibitory pathways necessitates co-blockade to optimize anti-tumor CD8+ T cell responses. Immunity 55 , 512-526.e9 (2022). Pawłowska, A. et al. The Dual Blockade of the TIGIT and PD-1/PD-L1 Pathway as a New Hope for Ovarian Cancer Patients. Cancers (Basel) 14 , 5757 (2022). Yu, X. et al. The surface protein TIGIT suppresses T cell activation by promoting the generation of mature immunoregulatory dendritic cells. Nat Immunol 10 , 48–57 (2009). Okumura, G. et al. Tumor-derived soluble CD155 inhibits DNAM-1-mediated antitumor activity of natural killer cells. J Exp Med 217 , e20191290 (2020). Stanietsky, N. et al. Mouse TIGIT inhibits NK-cell cytotoxicity upon interaction with PVR. Eur J Immunol 43 , 2138–2150 (2013). Chiang, E. Y. & Mellman, I. TIGIT-CD226-PVR axis: advancing immune checkpoint blockade for cancer immunotherapy. J Immunother Cancer 10 , e004711 (2022). Hui, E. et al. T cell costimulatory receptor CD28 is a primary target for PD-1-mediated inhibition. Science 355 , 1428–1433 (2017). Wang, B. et al. Combination cancer immunotherapy targeting PD-1 and GITR can rescue CD8+ T cell dysfunction and maintain memory phenotype. Science Immunology 3 , eaat7061 (2018). Prat, J. & FIGO Committee on Gynecologic Oncology. Staging classification for cancer of the ovary, fallopian tube, and peritoneum. Int J Gynaecol Obstet 124 , 1–5 (2014). Silverberg, S. G. Histopathologic grading of ovarian carcinoma: a review and proposal. Int J Gynecol Pathol 19 , 7–15 (2000). Kurman, R. J. & Shih, I.-M. The Dualistic Model of Ovarian Carcinogenesis: Revisited, Revised, and Expanded. Am J Pathol 186 , 733–747 (2016). Shih, I.-M. & Kurman, R. J. Ovarian tumorigenesis: a proposed model based on morphological and molecular genetic analysis. Am J Pathol 164 , 1511–1518 (2004). Michalczyk, K. & Chudecka-Głaz, A. M. Safety of combination therapy with olaparib and bevacizumab in the maintenance treatment of a patients with newly diagnosed ovarian cancer. Oncology in Clinical Practice 0 , (2025). Campos, S. M., DiSilvestro, J. & Blank, S. V. Maintenance therapy after first‐line therapy for ovarian cancer: Quantitative effectiveness. CA Cancer J Clin 76 , e70057 (2026). Maas, R. J. et al. TIGIT blockade enhances functionality of peritoneal NK cells with altered expression of DNAM-1/TIGIT/CD96 checkpoint molecules in ovarian cancer. OncoImmunology 9 , 1843247 (2020). Uppendahl, L. D., Dahl, C. M., Miller, J. S., Felices, M. & Geller, M. A. Natural Killer Cell-Based Immunotherapy in Gynecologic Malignancy: A Review. Front. Immunol. 8 , (2018). Ning, J. & Yao, L. Natural killer cell exhaustion in ovarian cancer: from molecular suppression to therapeutic revival. Front. Immunol. 16 , (2026). Pounds, R. et al. The emergence of DNAM-1 as the facilitator of NK cell-mediated killing in ovarian cancer. Front Immunol 15 , 1477781 (2024). Grottoli, M. et al. Immune Checkpoint Blockade: A Strategy to Unleash the Potential of Natural Killer Cells in the Anti-Cancer Therapy. Cancers 14 , (2022). Cao, Y. et al. Immune checkpoint molecules in natural killer cells as potential targets for cancer immunotherapy. Sig Transduct Target Ther 5 , 250 (2020). Qi, R., Yang, J., Shen, S., Yu, Y. & Yang, Q. Role of the tumor microenvironment in chemotherapy resistance in ovarian cancer and targeted therapy. J Ovarian Res 19 , 14 (2025). Tonetti, C. R. et al. Ovarian Cancer-Associated Ascites Have High Proportions of Cytokine-Responsive CD56bright NK Cells. Cells 10 , (2021). Ozga, A. J., Chow, M. T. & Luster, A. D. Chemokines and the immune response to cancer. Immunity 54 , 859–874 (2021). Rainczuk, A., Rao, J., Gathercole, J. & Stephens, A. N. The emerging role of CXC chemokines in epithelial ovarian cancer. Reproduction 144 , 303–317 (2012). Greppi, M. et al. PD-1+ NK cell subsets in high grade serous ovarian cancer: an indicator of disease severity and a target for combined immune-checkpoint blockade. J Exp Clin Cancer Res 44 , 258 (2025). Gayoso, I. et al. Immunosenescence of Human Natural Killer Cells. J Innate Immun 3 , 337–343 (2011). Camous, X., Pera, A., Solana, R. & Larbi, A. NK Cells in Healthy Aging and Age-Associated Diseases. J Biomed Biotechnol 2012 , 195956 (2012). Gergues, M. et al. Senescence, NK cells, and cancer: navigating the crossroads of aging and disease. Front. Immunol. 16 , (2025). Brauning, A. et al. Aging of the Immune System: Focus on Natural Killer Cells Phenotype and Functions. Cells 11 , 1017 (2022). Fraser, C. C. et al. Ovarian Cancer Ascites Inhibits Transcriptional Activation of NK Cells Partly through CA125. J Immunol 208 , 2227–2238 (2022). Michelet, X. et al. Metabolic reprogramming of natural killer cells in obesity limits antitumor responses. Nat Immunol 19 , 1330–1340 (2018). Bähr, I., Spielmann, J., Quandt, D. & Kielstein, H. Obesity-Associated Alterations of Natural Killer Cells and Immunosurveillance of Cancer. Front Immunol 11 , 245 (2020). Viel, S. et al. Alteration of Natural Killer cell phenotype and function in obese individuals. Clin Immunol 177 , 12–17 (2017). O’Shea, D., Cawood, T. J., O’Farrelly, C. & Lynch, L. Natural killer cells in obesity: impaired function and increased susceptibility to the effects of cigarette smoke. PLoS One 5 , e8660 (2010). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 May, 2026 Reviews received at journal 12 May, 2026 Reviews received at journal 06 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 17 Apr, 2026 Editor invited by journal 19 Mar, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 11 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9051409","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":628941288,"identity":"fc2491e3-70c9-4177-a330-c5158bfe72f9","order_by":0,"name":"Anna Pawłowska-Łachut","email":"data:image/png;base64,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","orcid":"","institution":"Medical University of Lublin","correspondingAuthor":true,"prefix":"","firstName":"Anna","middleName":"","lastName":"Pawłowska-Łachut","suffix":""},{"id":628941289,"identity":"7fe5c6ec-ab8f-471c-baf1-bab4f3674081","order_by":1,"name":"Wiktoria Skiba","email":"","orcid":"","institution":"Medical University of Lublin","correspondingAuthor":false,"prefix":"","firstName":"Wiktoria","middleName":"","lastName":"Skiba","suffix":""},{"id":628941290,"identity":"494ac006-5311-4557-9722-494d3ae93c69","order_by":2,"name":"Paulina Pieniądz-Feculak","email":"","orcid":"","institution":"Medical University of Lublin","correspondingAuthor":false,"prefix":"","firstName":"Paulina","middleName":"","lastName":"Pieniądz-Feculak","suffix":""},{"id":628941291,"identity":"cf7f5d5d-1d9f-4099-b3a2-9a13cb3fae7e","order_by":3,"name":"Karolina Włodarczyk-Ciekańska","email":"","orcid":"","institution":"Medical University of Lublin","correspondingAuthor":false,"prefix":"","firstName":"Karolina","middleName":"","lastName":"Włodarczyk-Ciekańska","suffix":""},{"id":628941292,"identity":"cac27003-65a6-4cbd-ac6e-294712f749c8","order_by":4,"name":"Dorota Suszczyk","email":"","orcid":"","institution":"Medical University of Lublin","correspondingAuthor":false,"prefix":"","firstName":"Dorota","middleName":"","lastName":"Suszczyk","suffix":""},{"id":628941293,"identity":"5c60aaac-1ba9-4675-9a84-4ba599bf51fb","order_by":5,"name":"Jakub Łyczba","email":"","orcid":"","institution":"St. John's Center of Oncology of the Lublin Region","correspondingAuthor":false,"prefix":"","firstName":"Jakub","middleName":"","lastName":"Łyczba","suffix":""},{"id":628941294,"identity":"8df6cdab-c309-47df-a5c5-bd727682bfa9","order_by":6,"name":"Monika Bilska","email":"","orcid":"","institution":"St. John's Center of Oncology of the Lublin Region","correspondingAuthor":false,"prefix":"","firstName":"Monika","middleName":"","lastName":"Bilska","suffix":""},{"id":628941295,"identity":"968e8426-53c0-4d74-b845-8053da88e282","order_by":7,"name":"Michalina Kazimierczuk","email":"","orcid":"","institution":"St. John's Center of Oncology of the Lublin Region","correspondingAuthor":false,"prefix":"","firstName":"Michalina","middleName":"","lastName":"Kazimierczuk","suffix":""},{"id":628941296,"identity":"39e2e1bf-9c6a-4030-97c4-3bb418f9f654","order_by":8,"name":"Józef Kotarski","email":"","orcid":"","institution":"St. John's Center of Oncology of the Lublin Region","correspondingAuthor":false,"prefix":"","firstName":"Józef","middleName":"","lastName":"Kotarski","suffix":""},{"id":628941297,"identity":"2898db63-ecf3-4c37-a375-845f3df71194","order_by":9,"name":"Ewa Boniewska-Bernacka","email":"","orcid":"","institution":"University of Opole","correspondingAuthor":false,"prefix":"","firstName":"Ewa","middleName":"","lastName":"Boniewska-Bernacka","suffix":""},{"id":628941298,"identity":"386a27d5-8ec0-4b77-9bab-2b45f0107dc2","order_by":10,"name":"Jacek Tomaszewski","email":"","orcid":"","institution":"St. John's Center of Oncology of the Lublin Region","correspondingAuthor":false,"prefix":"","firstName":"Jacek","middleName":"","lastName":"Tomaszewski","suffix":""},{"id":628941300,"identity":"4078065c-f954-4186-aee9-4af2439d57d1","order_by":11,"name":"Iwona Wertel","email":"","orcid":"","institution":"Medical University of Lublin","correspondingAuthor":false,"prefix":"","firstName":"Iwona","middleName":"","lastName":"Wertel","suffix":""}],"badges":[],"createdAt":"2026-03-06 14:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9051409/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9051409/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108072177,"identity":"936fa16a-1765-4c26-8c03-386de896bf6b","added_by":"auto","created_at":"2026-04-29 06:11:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":464775,"visible":true,"origin":"","legend":"\u003cp\u003eInteractions between the TIGIT/CD155/DNAM-1 and PD-1/PD-L1/PD-L2 pathways. Created in BioRender.com.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9051409/v1/44c2c58b2ddd9d56c3386d7d.png"},{"id":108181558,"identity":"c926455c-93dc-4a9a-83a7-a0b653fd0f4a","added_by":"auto","created_at":"2026-04-30 08:58:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103365,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of NK cells (A) and NK cells expressing PD-1 (B), TIGIT (C), and DNAM-1 (D) in the PB, PF, and TT of OC patients compared with the PB of healthy donors. The median value with the following marks indicates that differences reached statistical significance: * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, ** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, *** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9051409/v1/de1682e10271a5eb827870c0.png"},{"id":108072179,"identity":"6ca27f09-2c0e-4140-9884-99ed64168648","added_by":"auto","created_at":"2026-04-29 06:11:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83893,"visible":true,"origin":"","legend":"\u003cp\u003eThe concentrations of sPD-1 (A), sTIGIT (B), and sDNAM-1 (C) in the serum and PF of OC patients compared with those in healthy blood donors. The median value with the following marks indicates that the difference reached statistical significance: * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, *** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001, **** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9051409/v1/d590e6b541f5e613278d4e61.png"},{"id":108072180,"identity":"76d92207-bfc7-446d-ba61-fa394b61c8cf","added_by":"auto","created_at":"2026-04-29 06:11:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":140615,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of NK cells across different FIGO stages (A), grading (B), and OC types according to Kurman and Shih (C) in the PB of OC patients and NK cells with different PD-1 expression in different grading (D) and OC types according to Kurman and Shih (E) in the PF of OC patients in comparison with the PB of healthy donors. The median value with the following marks indicates that the difference reached statistical significance: * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, ** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9051409/v1/97f54149fca6b57a9ef742c3.png"},{"id":108072181,"identity":"a91e0c77-c3cf-49c5-83a7-59407a35c24b","added_by":"auto","created_at":"2026-04-29 06:11:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":71433,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between the percentage of NK cells in the PF and the age (A) and BMI (B) of OC patients.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9051409/v1/8ae246758b3f53d92461b03f.png"},{"id":108183841,"identity":"d1168188-7700-4cad-a78f-2532d6e3c628","added_by":"auto","created_at":"2026-04-30 09:02:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1191223,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9051409/v1/46197547-07ef-4a7a-8f45-083bbe7c77a9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Crosstalk between the Programmed Death Pathway and TIGIT/CD155/DNAM-1 Axis in NK Cell-Mediated Immunosuppression in Ovarian Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOvarian cancer (OC) is considered the most lethal gynecological malignancy. This is related to its diagnosis in advanced International Federation of Gynecology and Obstetrics (FIGO) stages of the disease as a result of disease heterogeneity, lack of screening tools, and an asymptomatic course of the disease, especially in the early stages\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Notably, OC in the early FIGO stages (stages I and II) is curable. Nonetheless, the majority of cases (approximately 85%) are diagnosed in the III and IV FIGO stages when the five-year survival rate decreases to 29.2%. By way of comparison, for local diseases (I and II FIGO stages), the five-year survival rate is 92%\u003csup\u003e4,5,\u003c/sup\u003e and the total five-year survival rate is as low as 47%\u003csup\u003e6\u003c/sup\u003e. Despite the implementation of standard treatment, including cytoreductive surgery, chemotherapy, and biological drugs, e.g., vascular endothelial growth factor inhibitors (VEGFis), e.g., bevacizumab, and poly-ADP ribose polymerase (PARP) inhibitors (PARPis), e.g., olaparib, rucaparib, and niraparib, the prognosis for OC patients is poor\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Thus, there is a need to develop diagnostic/prognostic biomarkers and novel treatment strategies for OC patients.\u003c/p\u003e \u003cp\u003eThe dynamic and complex interplay within the tumor microenvironment (TME) is a potential target for OC treatment\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Immune cells are influenced by various signals from the TME that shape their immunophenotype and modulate their functions. The immunogenicity of cancer directly affects immune evasion through multiple mechanisms, such as immune checkpoints (ICPs), and their expression is considered a crucial factor in predicting the response to immune checkpoint inhibitor (ICI) therapy\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the previous decade, immunotherapies based on immune checkpoint inhibitors, including programmed death pathway inhibitors, revolutionized the treatment of solid malignancies such as melanoma and lung and renal cancer\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. This type of treatment has also been approved for the treatment of gynecological malignancies such as uterine and cervical cancer\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Nevertheless, inhibitors of programmed death receptor 1 (PD-1), i.e., nivolumab and pembrolizumab, as well as programmed death ligand 1 (PD-L1), i.e., atezolizumab, avelumab, and durvalumab, have shown limited efficacy in OC treatment\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The response rate of OC patients to these drugs is low to intermediate\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, with a total response rate of 10\u0026ndash;15%. Notably, the resistance of OC to ICIs is related to a number of factors, such as a decreased density of TILs, OC heterogeneity, low microsatellite instability, tumor mutation burden, regulation of ICPs via cytokine and miRNA networks, and interplay within the TME. The complexity of the molecular, genetic, and immunological background of OC patients resistant to ICIs was presented in detail in our previous paper\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Food and Drug Administration (FDA) has not approved any ICIs for the treatment of OC monotherapy\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. On February 10, 2026, the FDA approved pembrolizumab in combination with paclitaxel, with or without bevacizumab, for adults with PD-L1\u003cb\u003e-\u003c/b\u003epositive platinum-resistant ovarian cancer after one or two prior therapies. This finding represents a key advance in the introduction of biomarker-selected immunotherapy into the earlier management of platinum-resistant OC\u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMoreover, recent studies have shown that focusing only on the PD-1/PD-L1/PD-L2 pathway, its inhibitors, and T-cell activity with respect to immunosuppression in the ovarian TME is insufficient\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. To fill this gap, more comprehensive studies are needed on the complexity of interactions in the TME, including an understanding of the activity of other effector cells and coexpressed ICPs in terms of clinical implications.\u003c/p\u003e \u003cp\u003eAlthough the PD-1 receptor is considered a main regulator of T-cell activity, other coexpressed ICPs, such as costimulatory DNAX accessory molecule-1 (CD226; DNAM-1) and coinhibitory T-cell immunoglobulin and the ITIM domain (TIGIT), influence their functions\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Both TIGIT and PD-1 receptors are exhaustion markers of cytotoxic T cells (CD8\u003csup\u003e+\u003c/sup\u003e)\u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe interplay between ligands and receptors in the TIGIT/CD155/DNAM-1 axis is far more intricate than that in the PD-1/PD-L1/PD-L2 pathway. Among the ligands for TIGIT are CD155, CD112, CD113, and nectin-4. The complexity of the axis is emphasized by the fact that the ligands CD155 and CD112 are shared with the costimulatory receptor DNAM-1 and the inhibitory receptor TACTILE (CD96). The detailed information concerning the interaction between the PD-1/PD-L1/PD-L2 pathway and the TIGIT/CD155/DNAM-1 axis and the effects of dual blockade in OC patients was provided in our previous study\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The complex network of interactions between the TIGIT/CD155/DNAM-1 and PD-1/PD-L1/PD-L2 pathways is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRecent studies have shown that the implementation of dual blockade of ICPs, especially the combination of PD-1/PD-L1 and TIGIT inhibitors, may be beneficial for OC patients. The binding of TIGIT to its ligands results in the inhibition of NK cell and T-cell activity, proliferation, and cytokine secretion\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Unlike TIGIT, DNAM-1 is a costimulatory receptor, and its binding to CD115 or CD112 stimulates the lysis activity of NK cells targeted against cancer cells. Nonetheless, the ligand CD155 has a lower affinity for DNAM-1 than for TIGIT\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eImportantly, both pathways work synergistically. Mechanistically, both pathways share a common factor, the costimulatory receptor DNAM-1. Both PD-1 and TIGIT inhibit the activity of DNAM-1\u003csup\u003e23\u0026ndash;26\u003c/sup\u003e. Therefore, both pathways need to be simultaneously blocked to fully restore DNAM-1 costimulatory signaling. These findings highlight the reasons why dual blockade has more promising clinical outcomes\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe crosstalk between PD-1, TIGIT, and DNAM-1 on NK cells in OC patients, considering their impact on immune regulation and immunosuppression within the tumor microenvironment, is a potential target for precision medicine. These ICPs modulate the activity of NK cells, influencing their ability to recognize and eliminate tumor cells, thus offering potential therapeutic targets for enhancing antitumor immunity.\u003c/p\u003e \u003cp\u003eHere, we comprehensively evaluated the expression of the main receptors involved in the programmed death pathway and the TIGIT/CD155/DNAM-1 axis, i.e., PD-1, DNAM-1, and TIGIT, on NK cells in peripheral blood (PB), peritoneal fluid (PF), and tumor tissue (TT) with respect to their clinical implications in OC patients. We also evaluated the concentrations of soluble forms of these receptors, i.e., soluble PD-1 (sPD-1), sDNAM-1, and sTIGIT, in the plasma and peritoneal fluid of OC patients compared with those in the plasma of healthy blood donors. The obtained results were analyzed in relation to the clinicopathological data of patients in comparison to those of healthy blood donors to investigate their clinical implications.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003ch2\u003ePatient enrollment and ethics statement\u003c/h2\u003e\n\u003cp\u003eThe research involved 68 patients, including 56 OC patients and 12 healthy blood donors. OC patients\u0026nbsp;had undergone radical treatment at\u0026nbsp;the II Department of Gynecological Oncology (St. John\u0026apos;s Center of Oncology of the Lublin Region, Lublin). The histopathological reports confirmed the diagnosis of OC. The tumors were staged in accordance with the FIGO classification system\u003csup\u003e34\u003c/sup\u003e. Two independent gynecological pathologists classified and graded the tumors on the basis of the Silverberg grading system\u003csup\u003e35\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e and the tumor type was established in accordance with the\u0026nbsp;Kurman and Shih classification\u003csup\u003e36,37\u003c/sup\u003e. The study cohort excluded individuals with a history of previous malignancies; prior chemotherapy or radiation therapy; and allergic, infectious, or autoimmune diseases. The clinicopathological characteristics of the OC patients are shown in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eAll patients and healthy blood donors provided written informed consent.\u0026nbsp;Approval for the research was granted by the Bioethics Committee\u0026nbsp;at the Medical University of Lublin (KE-0254/13/01/2022; KE-0254/102/06/2024; KB-0024/178/12/2024; KB-0024/114/01/2026).\u003c/p\u003e\n\u003cp\u003eThe study\u0026apos;s control group consisted of 12 healthy blood donors, and the samples were collected from the Regional Centre of Blood Donation and Blood Treatment in Lublin. Each participant provided written informed consent, and all experiments were carried out in compliance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eClinicopathological characteristics of OC patients.\u003c/p\u003e\n\u003ctable style=\"width: 4.3e+2pt;border: none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eThe clinical features\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOC patients (\u003cem\u003en\u003c/em\u003e = 56)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eFIGO stage, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEarly (I\u0026ndash;II FIGO stages)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11 (19.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eI FIGO stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (8.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eII FIGO stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (10.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdvanced (III\u0026ndash;IV\u0026nbsp;FIGO stages)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45 (80.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIII FIGO stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40 (71.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIV FIGO stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (8.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eGrading (histological differentiation), \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIntermediate grade (G2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17 (30.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow grade (G3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39 (69.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eThe OC classification according to Kurman and Shih, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eType I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elow-grade serous ovarian cancer (LGSOC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eendometrioid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10 (17.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003emucinous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (5.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eclear cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (1.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eType II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ehigh-grade serous ovarian cancer (HGSOC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34 (60.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ecarcinosarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (1.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eMenopausal Status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBefore menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10 (17.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAfter menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46 (82.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCa125 median, range (U/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e491.00 (14.96-8766)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHE4 median, range (pmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e583 (45.3-12114)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (median), years (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61 (29-85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBody mass index (BMI) range, median (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.73 (17.94-44.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMononuclear cells (MNCs) isolation procedure\u003c/h2\u003e\n\u003cp\u003eAll PB samples were drawn into EDTA-coated tubes (Sarstedt, Germany) prior to surgery and processed immediately. MNCs were isolated via density gradient centrifugation at 2800 \u0026times; rpm for 20 minutes at room temperature via Lymphoprep (StemCell Technologies, Canada). The cells from the interphase layer were then collected. After two washes with phosphate-buffered saline (PBS; Capricorn Scientific GmbH, Germany), the cells were resuspended in PBS.\u003c/p\u003e\n\u003cp\u003eDuring surgery, PF and TT were collected aseptically. MNCs from PF were also isolated via centrifugation in a density gradient (2800 \u0026times; rpm for 20 minutes at room temperature) via Lymphoprep (StemCell Technologies, Canada). The following procedure was the same as that used for MNCs isolated from PB.\u003c/p\u003e\n\u003cp\u003eFreshly resected OC tissue was chopped and placed in a gentleMACS C tube and then processed via the Tumor Dissociation Kit (Miltenyi Biotec) to isolate tumor-infiltrating MNCs. The suspensions of cells were subsequently filtered with a mesh filter (70 mm; BD Biosciences), and\u0026nbsp;the subsequent procedure, including density gradient centrifugation and washing, was carried out in the same manner as described for the PB and PF samples.\u003c/p\u003e\n\u003ch2\u003eNK cell immunophenotyping via flow cytometry\u003c/h2\u003e\n\u003cp\u003ePreviously isolated MNCs (1\u0026nbsp;\u0026times; 10\u003csup\u003e6\u003c/sup\u003e cells) from PB, PF, and TT were stained with fluorochrome-labeled monoclonal antibodies (mAbs) directed against specific surface markers for 20 mins. The panel of fluorochrome-conjugated monoclonal antibodies included the following: anti-CD3 FITC/(CD16+CD56) PE Cocktail (BioLegend), anti-PD-1 PE/Cyanine7 (BioLegend), anti-TIGIT PE/Cyanine7 (BioLegend), and anti-DNAM-1 PerCP/Cyanine5.5 (BioLegend).\u003c/p\u003e\n\u003cp\u003eThe stained cells were subsequently washed twice with PBS. Flow cytometric analysis (FACSCanto I, Becton Dickinson, USA) was performed to determine the percentage of NK cells expressing PD-1, TIGIT, or DNAM-1. The frequencies of these cells are expressed as percentages of the total MNCs population. For each sample, 50,000 events were recorded and analyzed via FacsDiva software. The percentages of NK cells with ICPs expression were reported as percentages relative to each specific cell subset (i.e., PD-1\u003csup\u003e+\u003c/sup\u003eCD56\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u003c/sup\u003e, TIGIT\u003csup\u003e+\u003c/sup\u003eCD56\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u003c/sup\u003e, or DNAM\u003csup\u003e+\u003c/sup\u003eCD56\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u003c/sup\u003e populations).\u003c/p\u003e\n\u003ch2\u003eEnzyme-Linked Immunosorbent Assay procedure\u003c/h2\u003e\n\u003cp\u003eThe concentrations of soluble forms of selected ICPs, i.e., PD-1, TIGIT, and DNAM-1, in the plasma and PF of OC patients and the plasma of healthy blood donors were established via ELISA tests according to the manufacturer\u0026apos;s instructions (sPD-1, MA, USA; sTIGIT, ELK Co. Ltd. Biotechnology, China; sDNAM-1, Biorbyt, UK).\u003c/p\u003e\n\u003cp\u003eThe absorbance of the plate was measured via an ELX800 plate reader (BioTek Instruments, Inc., USA). The obtained results were analyzed with Gen5 software (BioTek Instruments, Inc.). The concentrations of sPD-1, sTIGIT, and sDNAM-1 were determined via interpolation from a standard curve.\u003c/p\u003e\n\u003ch2\u003eStatistical analysis of the obtained results\u003c/h2\u003e\n\u003cp\u003eThe obtained results were statistically analyzed via GraphPad Prism 9.1.2. and Statistica 13.0PL. To compare the results from PB, PF, and TT within the same OC patients, the Wilcoxon paired test was used. For comparisons between groups (e.g., study group vs control group), the Mann‒Whitney U test was applied. To assess the correlation between two variables, the Spearman rank correlation test was utilized. The results are presented as the median, with minimum and maximum values. Statistical significance was defined as a \u003cem\u003ep\u003c/em\u003e value of less than 0.05 (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eThe percentages of NK cells in the peripheral blood, peritoneal fluid, and among tumor-infiltrating cells from OC patients\u003c/h2\u003e\n\u003cp\u003eThe analysis revealed significant differences in the percentages of CD56\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u0026nbsp;\u003c/sup\u003ecells between the ovarian cancer environments and the peripheral blood of healthy donors. We observed the highest frequency of CD56\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u003c/sup\u003e cells in the PB of OC patients than in those of PF patients (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; median 10.05% vs. 4.50%), in the PB of the control group (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; median 10.05% vs. 4.79%), and higher than among OC infiltrating cells; however, the difference did not reach statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05; median 10.05% vs. 4.57%; Figure 2. A).\u003c/p\u003e\n\u003ch2\u003eThe frequency of NK cells with PD-1, TIGIT, and DNAM-1 expression in PB, PF, and TT from OC patients.\u003c/h2\u003e\n\u003cp\u003eThe obtained data also revealed a disparity between CD56\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u0026nbsp;\u003c/sup\u003ecells with PD-1 and TIGIT expression in different environments of OC patients. We detected the highest percentage of PD-1\u003csup\u003e+\u003c/sup\u003eCD56\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u003c/sup\u003e cells among the tumor-infiltrating cells, which was significantly higher than that in PB (p\u0026lt;0.05; median 47.22% vs. 26.57%) and higher than that in PF (median 47.22% vs. 32.50%) and PB of control group (median 47.22% vs. 42.92%); nevertheless, the differences were not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05; Figure 2). B). We did not observe a statistically significant difference in the percentage of this cell subpopulation in the PB of patients and the control group (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eWe established the highest frequency of TIGIT\u003csup\u003e+\u003c/sup\u003eCD56\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u003c/sup\u003e cells in the PF of OC patients, and the value was significantly higher than that in the PB (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001; median 89.18% vs. 75.86%) and higher in comparison to TT (median 89.18% vs. 64.19%) and control group (median 89.18% vs. 77.92%); nonetheless, the differences were not significant (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05). We also detected a significantly greater percentage of this cells subpopulation in TT than in the PB of healthy donors (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001; Figure 2. C). We did not observe a statistically significant difference in the percentage of this cell subpopulation in the PB of patients and the control group (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eMoreover, we found a significantly higher percentage of DNAM-1\u003csup\u003e+\u003c/sup\u003eCD56\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u003c/sup\u003e cells in the PF than in the PB of OC patients (median 70.34% vs. 58.29%; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05) and higher than in the TT (median PF vs. TT totals 70.34% vs. 54.62%) and the PB of healthy donors (median 70.34% vs. 63.89%); however, the differences were not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05; Figure 2). D). We did not observe a statistically significant difference in the percentage of this cell subpopulation in the PB between patients and the control group (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05).\u003c/p\u003e\n\u003ch2\u003eConcentration of soluble forms of PD-1, TIGIT, and DNAM-1 receptors in ovarian cancer environments\u003c/h2\u003e\n\u003cp\u003eOur analysis revealed differences in the concentrations of sPD-1 and sTIGIT in the plasma and PF of OC patients and in the plasma of healthy donors. We observed the highest level of sPD-1 in PF, which was significantly higher than that in the plasma of OC patients (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001; median 109.54 pg/mL vs. 43.80 pg/mL). We also detected a significantly higher level of sPD-1 in the plasma of OC patients than in that of control group (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.001; median 43.80 pg/mL vs. 24.97 pg/mL; Figure 3). A).\u003c/p\u003e\n\u003cp\u003eMoreover, we detected the highest concentration of sTIGIT in the plasma of the OC, which was significantly higher than that in the plasma of the control group (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; median 4.58 pg/mL vs. 1.44 pg/mL) and higher than that in the PF; however, the difference did not reach statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05; median 4.58 pg/mL vs. 1.56 pg/mL) (Figure 3). B).\u003c/p\u003e\n\u003cp\u003eThe results revealed the highest concentration of sDNAM-1 in the plasma of healthy blood donors, which was higher than that in the plasma (median 215.65 pg/mL vs. 177.74 pg/mL) and PF (median 215.65 pg/mL vs. 61.29 pg/mL) of OC patients; nevertheless, we did not observe statistically significant differences (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05; Figure 3). C). Notably, the differences in the number of patients included in the sTIGIT concentration analysis resulted from the difficulty of meeting the predefined measurement range, as the values were highly dispersed. Some samples were excluded because their sTIGIT levels in plasma or PF were too low to be detected within the assay range, whereas others exceeded the upper limit of quantification.\u003c/p\u003e\n\u003ch2\u003ePercentages of NK cells with PD-1, TIGIT or DNAM-1 expression in patients with different clinical manifestations of OC\u003c/h2\u003e\n\u003cp\u003eWe subsequently evaluated the clinical relevance of NK cells with PD-1, TIGIT, or DNAM-1 expression in OC patients. The percentage of these cell subpopulations was correlated with particular OC clinicopathological features, i.e., FIGO stage (I-II vs. III-IV), grade (II vs. III), and Kurman and Shih tumor type (type I vs. type II), in comparison to their distribution in the PB of healthy donors.\u003c/p\u003e\n\u003cp\u003eWe found that the percentage of CD56\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u003c/sup\u003e cells in the PB of OC patients was significantly higher in advanced FIGO stages (III-IV) than in PB of control group (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; Figure 4. A) and lower in advanced (III/IV) than in early (I/II) FIGO stages; nevertheless, the difference was not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05; Figure 4. A).\u003c/p\u003e\n\u003cp\u003eThere was a significantly higher percentage of CD56\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u003c/sup\u003e cells in the PB of patients with grade II disease than in those with grade III disease, as did the PB of the control group (both \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; Figure 4. B).\u003c/p\u003e\n\u003cp\u003eWe detected the highest percentage of CD56\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u003c/sup\u003e cells in the PB of patients with type II tumors according to Kurman and Shih, and it was significantly higher than that in the PB of patients with type I tumors (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; Figure 4. C), as well as the control group (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; Figure 4. C).\u003c/p\u003e\n\u003cp\u003eWe also showed that the percentage of PD-1\u003csup\u003e+\u003c/sup\u003eCD56\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u003c/sup\u003e cells was significantly higher in the PF of patients with grade II OC than in those with grade III (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; Figure 4). D) and higher in grade II and grade III patients than in the control group; nonetheless, the differences did not reach statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eA lower percentage of PD-1\u003csup\u003e+\u003c/sup\u003eCD56\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u003c/sup\u003e cells was found in the PF of patients with type II tumors than in patients with type I tumors (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; Figure 4. E), which was lower than that in the control group; nevertheless, the difference did not reach statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eThere was no other significant disparity in the percentages of NK cells and PD-1, TIGIT, or DNAM-1 positive NK cells in PB, PF, or TT in the different clinicopathological features of patients with OC (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05).\u003c/p\u003e\n\u003ch2\u003e2.5. Relationships between the clinicopathological characteristics of OC patients and the percentages of NK cells with PD-1, TIGIT, or DNAM-1 expression in OC environments\u003c/h2\u003e\n\u003cp\u003eIn the next step, we assessed the relationships between the clinicopathological characteristics of patients with OC and the percentages of CD56\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u003c/sup\u003e cells and CD56\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u003c/sup\u003e cells with PD-1, TIGIT, or DNAM-1 expression in three different OC environments. We detected a positive relationship between the percentage of CD56\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u003c/sup\u003e cells in the PF and the age of OC patients (R Spearman: 0.471; t(N-2) 2.777, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; Figure 5). A). Conversely, there was a negative association between the percentage of CD56\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u0026nbsp;\u003c/sup\u003ecells in the PF and BMI in OC patients (R Spearman: -0.437; t(N-2) -2,569, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; There were no other significant relationships between the percentages of NK cells and NK cells with PD-1, TIGIT or DNAM-1 expression in PB or PF and among tumor-infiltrating cells and the clinicopathological characteristics of OC patients, i.e., FIGO stage, grade, age, type of tumor according to Kurman and Shih, age, menopausal status, or HE4 and Ca125 concentrations (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite advancements in ovarian cancer treatment, the five-year survival of OC patients is still poor. Recently, published studies, including both fundamental studies and clinical trials, have focused on the application of ICIs, mainly anti-PD-1/PD-L1, in OC treatment\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Notably, the role and clinical implications of coexpressed ICPs, both coinhibitory (TIGIT) and costimulatory (DNAM-1), on effector immune cells in OC are still unexplored. Thus, investigating the role of not only the PD-1/PD-L1/PD-L2 axis but also other coexpressed ICPs is necessary to develop effective tools to treat OC patients.\u003c/p\u003e \u003cp\u003eNK cells play a predominant role in immune surveillance. Their activity is controlled by a balance between activating and inhibitory receptors, which allows them to selectively eliminate tumor cells that display activating ligands and evade those with strong inhibitory signals. The effectiveness of NK cells in OC patients is inhibited by immunosuppressive factors within the TME\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Our study demonstrated that the TIGIT/CD156/DNAM-1 axis significantly influences the NK cells immunophenotype and function, both systemically and locally, revealing new insights into how ovarian cancer cells may evade NK cell-mediated immunity. We aimed to determine how different environments of OC influence the immunophenotype and functional potential of NK cells, specifically the balance between inhibitory (PD-1, TIGIT) and activating (DNAM-1) receptors. Thus, we evaluated the expression of PD-1, TIGIT, and DNAM-1 on NK cells in three different environments (PB, PF, TT) of OC patients compared with healthy blood donors.\u003c/p\u003e \u003cp\u003eOur findings revealed altered percentages of NK cells with PD-1 and TIGIT expression across different environments in OC patients. Similarly to Maas et al., we observed an immunophenotype shift of NK cells in favor of an inhibitory shift\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The increased expression of inhibitory receptors, i.e., PD-1 among tumor-infiltrating cells and TIGIT in PF, suggests local suppression of NK cell functions and potential tumor immune evasion. These results align with previous reports showing that the TME can modulate the balance of activating and inhibitory receptors on NK cells, which may have implications for immunotherapy strategies\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Moreover, the upregulation of TIGIT and DNAM-1 expression on NK cells in PF suggests that an increased percentage of TIGIT\u003csup\u003e+\u003c/sup\u003eNK cells can competitively inhibit DNAM-1 signaling through shared ligands (i.e., CD155), establishing a locally reinforced immunosuppressive environment that limits effective antitumor responses even when DNAM-1 is expressed. Moreover, according to Pounds et al., there is a relationship between the proportion of increased percentage of NK cells with DNAM-1 expression and poor survival of OC patients\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe present study revealed altered concentrations of soluble ICPs in OC patients. Specifically, the sPD-1 concentration was the highest in PF, suggesting its local accumulation in the TME. Moreover, a significantly higher concentration of sPD-1 was detected in the plasma of OC patients than in that of healthy controls, indicating systemic immune modulation. Similarly, the sTIGIT concentration was elevated in the plasma of OC patients compared with that of healthy donors. Although the differences in the concentrations of sDNAM-1 in different OC environments and the control group did not reach statistical significance, we observed a consistent trend toward its highest concentration in the plasma of healthy donors. This pattern suggests potential environment-specific immunomodulation of NK cells in OC, which may reflect local immunosuppression. OC may promote immune evasion through increased concentrations of inhibitory signals (sPD-1, sTIGIT) and decreased activating signals (sDNAM-1), which is consistent with previous reports on the immunosuppressive role of soluble ICPs in the TME\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eChanges in NK cells immunophenotypes are also related to the clinical features of OC. Thus, we assessed the clinical relevance of NK cells expressing PD-1, TIGIT, and DNAM-1 in OC patients. A higher percentage of NK cells was observed in the PB of OC patients with advanced stages of disease (FIGO III/IV), both grade II and III, and tumor types I and II according to Kurman and Shih than in the control group. These findings suggest that the percentage of circulating NK cells increases with disease progression and tumor aggressiveness. Notably, their antitumor activity may be limited by the immunosuppressive TME (i.e., the inhibitory receptors PD-1 and TIGIT, as well as TGF-β and IL-10)\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The increased percentage of PD-1⁺NK cells in grade II of OC compared with that in the control group and their higher percentage in type I compared with type II of OC may be related to recruitment via a chemokine gradient (i.e. CXCR3-CXCL9/CXCL10/CXCL11 and CX3CR1-CX3CL1) and their cytotoxic function may be restrained by inhibitory signals within the ascites\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Similar to our findings, Greppi et al. reported an increased percentage of PD-1\u003csup\u003e+\u003c/sup\u003eNK cells in the peritoneal fluid of high-grade serous ovarian cancer patients, while this cell subpopulation is rarely detected in healthy blood donors. Moreover, the increased percentage of these cells was related to poor overall survival, which highlights the clinical and prognostic significance of PD-1⁺NK cells\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe further investigated the relationships between the clinicopathological features of OC patients and the percentages of NK cells or NK cells with PD-1, TIGIT, or DNAM-1 expression in different OC environments. Similar to other authors\u003csup\u003e\u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, we reported a negative correlation between the percentage of CD56⁺CD16\u003csup\u003e+\u003c/sup\u003eCD3⁻ cells in ascites and the age of OC patients. This phenomenon may be explained by immunosenescence, as aging is associated with increased NK cell numbers but reduced cytotoxic function\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Moreover, Fraser et al. reported that the antitumor activity of NK cells in peritoneal fluid is inhibited via CA125\u003csup\u003e55\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, the percentage of CD56⁺CD16\u003csup\u003e+\u003c/sup\u003eCD3⁻ cells in the PF was negatively correlated with the BMI of OC patients. Many studies\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e have shown that obesity is related to altered NK cells number and their impaired cytotoxic functions, including cytokines expression, which leads to weakened immunosurveillance.\u003c/p\u003e \u003cp\u003eIn summary, this study advances the current understanding of ICPs regulation in OC by identifying PD-1, TIGIT, and DNAM-1 as functionally interconnected components of a coordinated immunosuppressive network rather than independent pathways. The success of dual TIGIT and PD-1/PD-L1 blockade mostly depends on the selection of the group of patients in which this type of treatment is most beneficial. Moreover, the development of predictive biomarkers could help optimize combination therapies and enhance their efficacy. The intricate interplay between the TIGIT/CD155/DNAM-1 axis and the PD-1/PD-L1/PD-L2 pathway underscores the complexity of restoring NK cells effector functions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOvarian cancer is associated with altered NK cells distribution and increased inhibitory signaling, reflecting both local and systemic immunosuppression. A major challenge for effective immunotherapy in OC is overcoming the immunosuppressive TME. We showed an accumulation of PD-1\u003csup\u003e+\u003c/sup\u003eNK cells among tumor-infiltrating cells and TIGIT\u003csup\u003e+\u003c/sup\u003eNK cells in PF, which may contribute to impaired antitumor immunity. We also observed an increased percentage of DNAM-1\u003csup\u003e+\u003c/sup\u003eNK cells in the PF, suggesting an imbalance between inhibitory and activating signaling pathways within these two axes.\u003c/p\u003e \u003cp\u003eAdditionally, we demonstrated elevated concentrations of sPD-1 in the PF and sTIGIT in the plasma of OC patients compared with healthy controls. These findings support the reinforcement of inhibitory signaling both systemically and within the ovarian cancer microenvironment.\u003c/p\u003e \u003cp\u003eFurthermore, alterations in the NK cell immunophenotype are associated with the clinicopathological features of OC, suggesting an association between the distribution of NK cells and PD-1-positive NK cells and disease progression. We also demonstrated that the proportion of NK cells in PF was positively correlated with patient age and negatively correlated with BMI, suggesting that factors such as aging and obesity may influence the NK cell distribution in OC environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch1\u003eAcknowledgments\u003c/h1\u003e\n\u003cp\u003eManuscript preparation was supported during Harvard Medical School\u0026rsquo;s Polish Clinical Scholars Research Training Program, organised by the Agencja Badań Medycznych (ABM, English: Medical Research Agency, Warsaw, Poland).\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the staff of the Second Department of Gynecological Oncology, St. John\u0026apos;s Center of Oncology of Lublin, for their invaluable assistance in collecting samples. We are also deeply grateful to all the blood donors and patients for their participation in the study.\u003c/p\u003e\n\u003ch1\u003eDisclosure\u003c/h1\u003e\n\u003cp\u003eThe author(s) report no conflicts of interest in this work.\u003c/p\u003e\n\u003ch1\u003eFunding\u003c/h1\u003e\n\u003cp\u003eThis research was funded in whole by the National Science Centre, Poland Preludium grant.\u003c/p\u003e\n\u003cp\u003enumber 2021/41/N/NZ6/01727.\u003c/p\u003e\n\u003ch1\u003eAuthor contributions\u003c/h1\u003e\n\u003cp\u003eConceptualization, A.P.Ł.; methodology, A.P.Ł. and I.W.; validation, E.B.-B. and I.W.; formal analysis, A.P.Ł., K.W.-C. and D.S.; investigation, A.P.Ł., W.S. and P.P.-F.; resources, J.Ł., M.B., M.K., J.K. and J.T.; writing-original draft preparation, A.P.Ł. and I.W.; writing-review and editing, A.P.Ł., I.W..; visualization, A.P.Ł.; supervision, J.T. and I.W.; project administration, A.P.Ł.; funding acquisition, A.P.Ł. All\u0026nbsp;authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch1\u003eData availability statement\u003c/h1\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the RepOD Repository for Open Data repository, https://repod.icm.edu.pl/dataset.xhtml?token=7bf6968a-bd64-4f72-8676-5e90b8a5be95.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWebb, P. M. \u0026amp; Jordan, S. J. Global epidemiology of epithelial ovarian cancer. \u003cem\u003eNat Rev Clin Oncol\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 389\u0026ndash;400 (2024).\u003c/li\u003e\n\u003cli\u003eLiberto, J. M. \u003cem\u003eet al.\u003c/em\u003e Current and Emerging Methods for Ovarian Cancer Screening and Diagnostics: A Comprehensive Review. \u003cem\u003eCancers (Basel)\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 2885 (2022).\u003c/li\u003e\n\u003cli\u003eCai, G. \u003cem\u003eet al.\u003c/em\u003e Artificial intelligence-based models enabling accurate diagnosis of ovarian cancer using laboratory tests in China: a multicentre, retrospective cohort study. \u003cem\u003eThe Lancet Digital Health\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, e176\u0026ndash;e186 (2024).\u003c/li\u003e\n\u003cli\u003eGutic, B. \u003cem\u003eet al.\u003c/em\u003e Preliminary outcomes of five-year survival for ovarian malignancies in profiled Serbian Oncology Centre. \u003cem\u003eClinics (Sao Paulo)\u003c/em\u003e \u003cstrong\u003e78\u003c/strong\u003e, 100204 (2023).\u003c/li\u003e\n\u003cli\u003eBlanc-Durand, F., Clemence Wei Xian, L. \u0026amp; Tan, D. S. P. Targeting the immune microenvironment for ovarian cancer therapy. \u003cem\u003eFront Immunol\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1328651 (2023).\u003c/li\u003e\n\u003cli\u003eLin, H. \u003cem\u003eet al.\u003c/em\u003e A prognosis‑predictive nomogram of ovarian cancer with two immune‑related genes: \u0026lt;em\u0026gt;CDC20B\u0026lt;/em\u0026gt; and \u0026lt;em\u0026gt;PNPLA5\u0026lt;/em\u0026gt;. \u003cem\u003eOncology Letters\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 1\u0026ndash;1 (2020).\u003c/li\u003e\n\u003cli\u003eOvarian Epithelial, Fallopian, \u0026amp; Peritoneal Cancer Treatment (PDQ\u0026reg;) - NCI. https://www.cancer.gov/types/ovarian/hp/ovarian-epithelial-treatment-pdq (2025).\u003c/li\u003e\n\u003cli\u003eDrugs Approved for Ovarian, Fallopian Tube, or Primary Peritoneal Cancer - NCI. https://www.cancer.gov/about-cancer/treatment/drugs/ovarian (2025).\u003c/li\u003e\n\u003cli\u003ePawłowska, A. \u003cem\u003eet al.\u003c/em\u003e Immunotherapies based on PD-1/PD-L1 pathway inhibitors in ovarian cancer treatment. \u003cem\u003eClin Exp Immunol\u003c/em\u003e \u003cstrong\u003e195\u003c/strong\u003e, 334\u0026ndash;344 (2019).\u003c/li\u003e\n\u003cli\u003eMahoney, K. M., Freeman, G. J. \u0026amp; McDermott, D. F. The Next Immune-Checkpoint Inhibitors: PD-1/PD-L1 Blockade in Melanoma. \u003cem\u003eClin Ther\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 764\u0026ndash;782 (2015).\u003c/li\u003e\n\u003cli\u003eKaur, J., Patil, G., Geynisman, D. M. \u0026amp; Ghatalia, P. Role of perioperative immunotherapy in localized renal cell carcinoma. \u003cem\u003eTher Adv Med Oncol\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 17588359231181497 (2023).\u003c/li\u003e\n\u003cli\u003eLahiri, A. \u003cem\u003eet al.\u003c/em\u003e Lung cancer immunotherapy: progress, pitfalls, and promises. \u003cem\u003eMolecular Cancer\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 40 (2023).\u003c/li\u003e\n\u003cli\u003ePeng, H., He, X. \u0026amp; Wang, Q. Immune checkpoint blockades in gynecological cancers: A review of clinical trials. \u003cem\u003eActa Obstetricia et Gynecologica Scandinavica\u003c/em\u003e \u003cstrong\u003e101\u003c/strong\u003e, 941\u0026ndash;951 (2022).\u003c/li\u003e\n\u003cli\u003ePawłowska, A. \u003cem\u003eet al.\u003c/em\u003e Current Understanding on Why Ovarian Cancer Is Resistant to Immune Checkpoint Inhibitors. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 10859 (2023).\u003c/li\u003e\n\u003cli\u003eMathieu Luyckx, M. D., Jean-Luc Squifflet, M. D., Annika M Bruger, P. H. D. \u0026amp; Jean-Francois Baurain, M. D. Recurrent High Grade Serous Ovarian Cancer Management. \u003cem\u003eExon Publications\u003c/em\u003e 87\u0026ndash;103 (2022) doi:10.36255/exon-publications-ovarian-cancer-management.\u003c/li\u003e\n\u003cli\u003eCascio, S. \u003cem\u003eet al.\u003c/em\u003e Cancer-associated MSC drive tumor immune exclusion and resistance to immunotherapy, which can be overcome by Hedgehog inhibition. \u003cem\u003eScience Advances\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, eabi5790 (2021).\u003c/li\u003e\n\u003cli\u003eResearch, C. for D. E. and. FDA approves pembrolizumab with paclitaxel for platinum-resistant epithelial ovarian, fallopian tube, or primary peritoneal carcinoma. \u003cem\u003eFDA\u003c/em\u003e https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-pembrolizumab-paclitaxel-platinum-resistant-epithelial-ovarian-fallopian-tube-or (2026).\u003c/li\u003e\n\u003cli\u003eTassi, E. \u003cem\u003eet al.\u003c/em\u003e Epithelial ovarian cancer is infiltrated by activated effector T cells co-expressing CD39, PD-1, TIM-3, CD137 and interacting with cancer cells and myeloid cells. \u003cem\u003eFront Immunol\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1212444 (2023).\u003c/li\u003e\n\u003cli\u003ePawłowska, A. \u003cem\u003eet al.\u003c/em\u003e Clinical and Prognostic Value of Antigen-Presenting Cells with PD-L1/PD-L2 Expression in Ovarian Cancer Patients. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 11563 (2021).\u003c/li\u003e\n\u003cli\u003ePawłowska, A. \u003cem\u003eet al.\u003c/em\u003e Programmed Death-1 Receptor (PD-1) as a Potential Prognosis Biomarker for Ovarian Cancer Patients. \u003cem\u003eCMAR\u003c/em\u003e \u003cstrong\u003eVolume 12\u003c/strong\u003e, 9691\u0026ndash;9709 (2020).\u003c/li\u003e\n\u003cli\u003eWeimer, P. \u003cem\u003eet al.\u003c/em\u003e Tissue-Specific Expression of TIGIT, PD-1, TIM-3, and CD39 by \u0026gamma;\u0026delta; T Cells in Ovarian Cancer. \u003cem\u003eCells\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 964 (2022).\u003c/li\u003e\n\u003cli\u003eChen, F., Xu, Y., Liu, X., Dong, N. \u0026amp; Tian, L. TIGIT+ CD4+ regulatory T cells enhance PD-1 expression on CD8+ T cells and promote tumor growth in a murine ovarian cancer model. \u003cem\u003eJ Ovarian Res\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 252 (2024).\u003c/li\u003e\n\u003cli\u003eGe, Z., Peppelenbosch, M. P., Sprengers, D. \u0026amp; Kwekkeboom, J. TIGIT, the Next Step Towards Successful Combination Immune Checkpoint Therapy in Cancer. \u003cem\u003eFront Immunol\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 699895 (2021).\u003c/li\u003e\n\u003cli\u003eChauvin, J.-M. \u0026amp; Zarour, H. M. TIGIT in cancer immunotherapy. \u003cem\u003eJ Immunother Cancer\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, (2020).\u003c/li\u003e\n\u003cli\u003eSanchez-Correa, B. \u003cem\u003eet al.\u003c/em\u003e DNAM-1 and the TIGIT/PVRIG/TACTILE Axis: Novel Immune Checkpoints for Natural Killer Cell-Based Cancer Immunotherapy. \u003cem\u003eCancers\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 877 (2019).\u003c/li\u003e\n\u003cli\u003eBanta, K. L. \u003cem\u003eet al.\u003c/em\u003e Mechanistic convergence of the TIGIT and PD-1 inhibitory pathways necessitates co-blockade to optimize anti-tumor CD8+ T cell responses. \u003cem\u003eImmunity\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 512-526.e9 (2022).\u003c/li\u003e\n\u003cli\u003ePawłowska, A. \u003cem\u003eet al.\u003c/em\u003e The Dual Blockade of the TIGIT and PD-1/PD-L1 Pathway as a New Hope for Ovarian Cancer Patients. \u003cem\u003eCancers (Basel)\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 5757 (2022).\u003c/li\u003e\n\u003cli\u003eYu, X. \u003cem\u003eet al.\u003c/em\u003e The surface protein TIGIT suppresses T cell activation by promoting the generation of mature immunoregulatory dendritic cells. \u003cem\u003eNat Immunol\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 48\u0026ndash;57 (2009).\u003c/li\u003e\n\u003cli\u003eOkumura, G. \u003cem\u003eet al.\u003c/em\u003e Tumor-derived soluble CD155 inhibits DNAM-1-mediated antitumor activity of natural killer cells. \u003cem\u003eJ Exp Med\u003c/em\u003e \u003cstrong\u003e217\u003c/strong\u003e, e20191290 (2020).\u003c/li\u003e\n\u003cli\u003eStanietsky, N. \u003cem\u003eet al.\u003c/em\u003e Mouse TIGIT inhibits NK-cell cytotoxicity upon interaction with PVR. \u003cem\u003eEur J Immunol\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 2138\u0026ndash;2150 (2013).\u003c/li\u003e\n\u003cli\u003eChiang, E. Y. \u0026amp; Mellman, I. TIGIT-CD226-PVR axis: advancing immune checkpoint blockade for cancer immunotherapy. \u003cem\u003eJ Immunother Cancer\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, e004711 (2022).\u003c/li\u003e\n\u003cli\u003eHui, E. \u003cem\u003eet al.\u003c/em\u003e T cell costimulatory receptor CD28 is a primary target for PD-1-mediated inhibition. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e355\u003c/strong\u003e, 1428\u0026ndash;1433 (2017).\u003c/li\u003e\n\u003cli\u003eWang, B. \u003cem\u003eet al.\u003c/em\u003e Combination cancer immunotherapy targeting PD-1 and GITR can rescue CD8+ T cell dysfunction and maintain memory phenotype. \u003cem\u003eScience Immunology\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, eaat7061 (2018).\u003c/li\u003e\n\u003cli\u003ePrat, J. \u0026amp; FIGO Committee on Gynecologic Oncology. Staging classification for cancer of the ovary, fallopian tube, and peritoneum. \u003cem\u003eInt J Gynaecol Obstet\u003c/em\u003e \u003cstrong\u003e124\u003c/strong\u003e, 1\u0026ndash;5 (2014).\u003c/li\u003e\n\u003cli\u003eSilverberg, S. G. Histopathologic grading of ovarian carcinoma: a review and proposal. \u003cem\u003eInt J Gynecol Pathol\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 7\u0026ndash;15 (2000).\u003c/li\u003e\n\u003cli\u003eKurman, R. J. \u0026amp; Shih, I.-M. The Dualistic Model of Ovarian Carcinogenesis: Revisited, Revised, and Expanded. \u003cem\u003eAm J Pathol\u003c/em\u003e \u003cstrong\u003e186\u003c/strong\u003e, 733\u0026ndash;747 (2016).\u003c/li\u003e\n\u003cli\u003eShih, I.-M. \u0026amp; Kurman, R. J. Ovarian tumorigenesis: a proposed model based on morphological and molecular genetic analysis. \u003cem\u003eAm J Pathol\u003c/em\u003e \u003cstrong\u003e164\u003c/strong\u003e, 1511\u0026ndash;1518 (2004).\u003c/li\u003e\n\u003cli\u003eMichalczyk, K. \u0026amp; Chudecka-Głaz, A. M. Safety of combination therapy with olaparib and bevacizumab in the maintenance treatment of a patients with newly diagnosed ovarian cancer. \u003cem\u003eOncology in Clinical Practice\u003c/em\u003e \u003cstrong\u003e0\u003c/strong\u003e, (2025).\u003c/li\u003e\n\u003cli\u003eCampos, S. M., DiSilvestro, J. \u0026amp; Blank, S. V. Maintenance therapy after first‐line therapy for ovarian cancer: Quantitative effectiveness. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e \u003cstrong\u003e76\u003c/strong\u003e, e70057 (2026).\u003c/li\u003e\n\u003cli\u003eMaas, R. J. \u003cem\u003eet al.\u003c/em\u003e TIGIT blockade enhances functionality of peritoneal NK cells with altered expression of DNAM-1/TIGIT/CD96 checkpoint molecules in ovarian cancer. \u003cem\u003eOncoImmunology\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 1843247 (2020).\u003c/li\u003e\n\u003cli\u003eUppendahl, L. D., Dahl, C. M., Miller, J. S., Felices, M. \u0026amp; Geller, M. A. Natural Killer Cell-Based Immunotherapy in Gynecologic Malignancy: A Review. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, (2018).\u003c/li\u003e\n\u003cli\u003eNing, J. \u0026amp; Yao, L. Natural killer cell exhaustion in ovarian cancer: from molecular suppression to therapeutic revival. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, (2026).\u003c/li\u003e\n\u003cli\u003ePounds, R. \u003cem\u003eet al.\u003c/em\u003e The emergence of DNAM-1 as the facilitator of NK cell-mediated killing in ovarian cancer. \u003cem\u003eFront Immunol\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1477781 (2024).\u003c/li\u003e\n\u003cli\u003eGrottoli, M. \u003cem\u003eet al.\u003c/em\u003e Immune Checkpoint Blockade: A Strategy to Unleash the Potential of Natural Killer Cells in the Anti-Cancer Therapy. \u003cem\u003eCancers\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, (2022).\u003c/li\u003e\n\u003cli\u003eCao, Y. \u003cem\u003eet al.\u003c/em\u003e Immune checkpoint molecules in natural killer cells as potential targets for cancer immunotherapy. \u003cem\u003eSig Transduct Target Ther\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 250 (2020).\u003c/li\u003e\n\u003cli\u003eQi, R., Yang, J., Shen, S., Yu, Y. \u0026amp; Yang, Q. Role of the tumor microenvironment in chemotherapy resistance in ovarian cancer and targeted therapy. \u003cem\u003eJ Ovarian Res\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 14 (2025).\u003c/li\u003e\n\u003cli\u003eTonetti, C. R. \u003cem\u003eet al.\u003c/em\u003e Ovarian Cancer-Associated Ascites Have High Proportions of Cytokine-Responsive CD56bright NK Cells. \u003cem\u003eCells\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, (2021).\u003c/li\u003e\n\u003cli\u003eOzga, A. J., Chow, M. T. \u0026amp; Luster, A. D. Chemokines and the immune response to cancer. \u003cem\u003eImmunity\u003c/em\u003e \u003cstrong\u003e54\u003c/strong\u003e, 859\u0026ndash;874 (2021).\u003c/li\u003e\n\u003cli\u003eRainczuk, A., Rao, J., Gathercole, J. \u0026amp; Stephens, A. N. The emerging role of CXC chemokines in epithelial ovarian cancer. \u003cem\u003eReproduction\u003c/em\u003e \u003cstrong\u003e144\u003c/strong\u003e, 303\u0026ndash;317 (2012).\u003c/li\u003e\n\u003cli\u003eGreppi, M. \u003cem\u003eet al.\u003c/em\u003e PD-1+ NK cell subsets in high grade serous ovarian cancer: an indicator of disease severity and a target for combined immune-checkpoint blockade. \u003cem\u003eJ Exp Clin Cancer Res\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 258 (2025).\u003c/li\u003e\n\u003cli\u003eGayoso, I. \u003cem\u003eet al.\u003c/em\u003e Immunosenescence of Human Natural Killer Cells. \u003cem\u003eJ Innate Immun\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 337\u0026ndash;343 (2011).\u003c/li\u003e\n\u003cli\u003eCamous, X., Pera, A., Solana, R. \u0026amp; Larbi, A. NK Cells in Healthy Aging and Age-Associated Diseases. \u003cem\u003eJ Biomed Biotechnol\u003c/em\u003e \u003cstrong\u003e2012\u003c/strong\u003e, 195956 (2012).\u003c/li\u003e\n\u003cli\u003eGergues, M. \u003cem\u003eet al.\u003c/em\u003e Senescence, NK cells, and cancer: navigating the crossroads of aging and disease. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, (2025).\u003c/li\u003e\n\u003cli\u003eBrauning, A. \u003cem\u003eet al.\u003c/em\u003e Aging of the Immune System: Focus on Natural Killer Cells Phenotype and Functions. \u003cem\u003eCells\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1017 (2022).\u003c/li\u003e\n\u003cli\u003eFraser, C. C. \u003cem\u003eet al.\u003c/em\u003e Ovarian Cancer Ascites Inhibits Transcriptional Activation of NK Cells Partly through CA125. \u003cem\u003eJ Immunol\u003c/em\u003e \u003cstrong\u003e208\u003c/strong\u003e, 2227\u0026ndash;2238 (2022).\u003c/li\u003e\n\u003cli\u003eMichelet, X. \u003cem\u003eet al.\u003c/em\u003e Metabolic reprogramming of natural killer cells in obesity limits antitumor responses. \u003cem\u003eNat Immunol\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 1330\u0026ndash;1340 (2018).\u003c/li\u003e\n\u003cli\u003eB\u0026auml;hr, I., Spielmann, J., Quandt, D. \u0026amp; Kielstein, H. Obesity-Associated Alterations of Natural Killer Cells and Immunosurveillance of Cancer. \u003cem\u003eFront Immunol\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 245 (2020).\u003c/li\u003e\n\u003cli\u003eViel, S. \u003cem\u003eet al.\u003c/em\u003e Alteration of Natural Killer cell phenotype and function in obese individuals. \u003cem\u003eClin Immunol\u003c/em\u003e \u003cstrong\u003e177\u003c/strong\u003e, 12\u0026ndash;17 (2017).\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Shea, D., Cawood, T. J., O\u0026rsquo;Farrelly, C. \u0026amp; Lynch, L. Natural killer cells in obesity: impaired function and increased susceptibility to the effects of cigarette smoke. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, e8660 (2010).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"ovarian cancer, TIGIT, PD-1, tumor microenvironment, dual blockade, DNAM-1","lastPublishedDoi":"10.21203/rs.3.rs-9051409/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9051409/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOvarian cancer (OC) is the gynecological malignancy with the highest mortality rate. Preclinical studies have shown that dual blockade of immune checkpoints (ICPs), such as PD-1 and TIGIT, may be beneficial for OC patients. Thus, the aim of this study was to evaluate the expression of PD-1, TIGIT, and DNAM-1 on NK cells in three OC environments, i.e., peripheral blood (PB), peritoneal fluid (PF), and tumor tissue (TT), and their soluble forms in plasma and PF, to explore their clinical significance. The expression of ICPs on NK cells was analyzed via flow cytometry. The concentrations of soluble forms of these ICPs were measured in plasma and PF via ELISA. The obtained results were correlated with the clinicopathological features of OC patients. We detected an accumulation of PD-1\u003csup\u003e+\u003c/sup\u003eNK cells in TT and TIGIT\u003csup\u003e+\u003c/sup\u003e or DNAM-1\u003csup\u003e+\u003c/sup\u003eNK cells in the PF. We demonstrated elevated concentrations of sPD-1 in PF and sTIGIT in OC patient plasma compared to healthy plasma, and alterations in the NK cell immunophenotype were associated with clinicopathological features of OC. This study revealed that PD-1, TIGIT, and DNAM-1 act together as an immunosuppressive network rather than as separate pathways.\u003c/p\u003e","manuscriptTitle":"Crosstalk between the Programmed Death Pathway and TIGIT/CD155/DNAM-1 Axis in NK Cell-Mediated Immunosuppression in Ovarian Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 06:11:27","doi":"10.21203/rs.3.rs-9051409/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-18T06:39:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T08:43:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T14:31:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T11:42:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145291115036030421900897628822552512202","date":"2026-04-23T16:50:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257817997214277712510602031308763586172","date":"2026-04-21T10:08:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42852081698266430662275309991046882657","date":"2026-04-21T06:00:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T08:53:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-17T17:37:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-19T14:30:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-11T14:21:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-11T09:38:51+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":"dfe8cefc-d76f-4673-90ca-a577a79d758e","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-18T06:39:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T08:43:13+00:00","index":73,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T14:31:38+00:00","index":72,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T11:42:56+00:00","index":71,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":66943357,"name":"Health sciences/Biomarkers"},{"id":66943358,"name":"Biological sciences/Cancer"},{"id":66943359,"name":"Biological sciences/Immunology"},{"id":66943360,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-05-18T06:54:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 06:11:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9051409","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9051409","identity":"rs-9051409","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}