Integrated Immunophenotypic and ProMisE Molecular Profiling as Predictors of Immune Checkpoint Inhibitor Response in Endometrial Cancer

Integrated Immunophenotypic and ProMisE Molecular Profiling as Predictors of Immune Checkpoint Inhibitor Response in Endometrial 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 Research Article Integrated Immunophenotypic and ProMisE Molecular Profiling as Predictors of Immune Checkpoint Inhibitor Response in Endometrial Cancer Komei Katayama, Nobuhisa Yoshikawa, Wenting Liu, Kae Nakamura, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8733104/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: Immune checkpoint inhibitors (ICIs) are an important treatment option for recurrent endometrial cancer (EC); however, responses vary widely. Although the ProMisE molecular classification provides prognostic and predictive insights, it does not fully explain the heterogeneity of ICI outcomes. Further characterization of the tumor immune microenvironment (TIME) is required to refine biomarker development. Methods: We retrospectively analyzed 72 patients with recurrent EC treated with ICIs. Immune markers, including CD8, CD68, CD163, CD47, CD276, PD-L1, and HLA class I, were quantified using digital pathology. Associations between immune markers, ICI response, and progression-free survival (PFS) were evaluated overall and within ProMisE molecular subtypes. Results: Responders exhibited a more immunologically active TIME, characterized by increased CD8⁺ T-cell infiltration in the central tumor (CT), whereas non-responders showed enriched CD68⁺ tumor-associated macrophages (TAMs) at the invasive margin (IM) and higher CD47 expression. Distinct immune landscapes were observed across ProMisE subtypes. Mismatch repair-deficient (MMRd) tumors showed high CD8 + T cell infiltration, no specific molecular profile (NSMP) tumors exhibited intermediate immune activation but variable CD47 expression, and p53-abnormal (p53abn) tumors demonstrated the most immunosuppressive profile with high CD47 expression and TAM density. Subtype-specific analyses revealed that PFS was predicted by different immune determinants, including CD8 + T cell infiltration in MMRd, CD47 expression level in NSMP, and CD68IM in p53abn. Conclusions: Tumor immune phenotypes provide predictive information beyond molecular classification alone. Integrating ProMisE classification with immunological profiling enables more precise stratification of ICI benefits. Distinct TIME features across subtypes underscore the need for subtype-tailored immunotherapeutic strategies. Endometrial cancer Immune checkpoint inhibitors Tumor immune microenvironment ProMisE molecular classification CD8⁺ T cells Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Endometrial cancer (EC) is one of the most common gynecological malignancies worldwide, and its incidence continues to rise, particularly in developed countries with increasing rates of obesity and metabolic disorders [ 1 , 2 ]. While early stage EC can often be cured with surgery alone, patients with recurrent or advanced disease have limited treatment options and a poor prognosis [ 3 ]. In recent years, immune checkpoint inhibitors (ICIs) targeting the programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) pathway have demonstrated promising efficacy in a subset of patients with recurrent EC [ 4 , 5 ]. Nevertheless, a significant number of patients fail to respond, highlighting the urgent need for reliable biomarkers to predict treatment efficacy. One of the key determinants of ICI response in EC is the tumor molecular subtype. The Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) molecular classification stratifies tumors into four molecular subtypes: POLE-mutated (POLEmut), mismatch repair-deficient (MMRd), p53-abnormal (p53abn), and no specific molecular profile (NSMP), which differ in their genomic features, immune microenvironment, and clinical outcomes [ 6 , 7 ]. MMRd and POLEmut tumors are characterized by a high tumor mutational burden and significant lymphocytic infiltration, while NSMP and p53abn tumors exhibit a relatively “cold” immune phenotype and poor ICI responsiveness [ 8 – 10 ]. However, even within each subtype, considerable heterogeneity in ICI response exists, suggesting that additional immunological or microenvironmental factors influence therapeutic outcomes [ 11 , 12 ]. Growing evidence highlights the pivotal role of TIME in shaping ICI responsiveness. Beyond molecular subtypes, the spatial organization of tumor-infiltrating lymphocytes (TILs) has emerged as a critical determinant of the tumor immune landscape. A recent study demonstrated that the spatial distribution of CD8 + T-cells could stratify EC into three distinct immunophenotypes: inflamed, excluded, and desert, which were closely associated with both ProMisE subtypes and patient survival [ 13 ]. Specifically, inflamed tumors with abundant intratumoral CD8 + TILs showed favorable outcomes, whereas excluded and desert phenotypes were correlated with poor survival, particularly within the NSMP and p53abn groups. These findings highlight that spatial immunophenotyping provides complementary prognostic information beyond molecular classification and may serve as a practical predictor of ICI response. In addition to lymphocyte distribution, other components of the TIME, including tumor-associated macrophages (TAMs) and immune checkpoint molecules such as CD47 and CD276 (B7-H3), modulate antitumor immunity and contribute to ICI resistance [ 14 – 23 ]. However, the integrated impact of immune phenotypes, macrophage infiltration, and checkpoint molecule expression on clinical outcomes across EC molecular subtypes remains unclear. Recent spatial and quantitative profiling studies have further demonstrated that immune marker expression patterns and their correlations differ across EC molecular subtypes, influencing both the prognosis and immunotherapy efficacy [ 13 , 24 ]. Although several studies have evaluated TIME in EC, few have systematically compared the immunologic features of ICI responders and non-responders, and even fewer have examined how these predictive immune parameters differ across ProMisE subtypes. Therefore, integrating immunophenotypic profiling with molecular classification may offer a more refined biomarker strategy for patient selection and therapeutic decision-making. Therefore, we retrospectively analyzed 72 patients with recurrent EC treated with ICIs to clarify how distinct immune landscapes interact with molecular subtypes to influence the clinical outcomes. By integrating the ProMisE molecular classification with quantitative immunophenotyping via digital image analysis, this study aimed to identify subtype-specific immune determinants that distinguish ICI responders from non-responders. Our findings provide new insights into the coordinated regulation of the tumor immune microenvironment and aim to inform more precise, subtype-tailored immunotherapeutic strategies for EC. Patients and Methods Study design and patient cohort This retrospective, exploratory biomarker discovery study aimed to investigate the association between immune phenotypes, molecular classification, and survival outcomes in patients with recurrent EC treated with ICIs. A total of 72 patients who received ICI therapy between May 2019 and December 2024 at Nagoya University Hospital and its affiliated institutions (Fujita Health University Hospital, Ogaki Municipal Hospital, Fujita Health University Bantane Hospital, and Aichi Cancer Center) were included in this study. All patients had histologically confirmed EC and underwent surgical intervention after a definitive diagnosis. This study was approved by the Institutional Review Board of Nagoya University (approval number: 2018 − 0400). Frozen biopsy samples and formalin-fixed paraffin-embedded (FFPE) tumor tissues were collected for analysis. Case selection and clinical data collection Clinical and pathological characteristics, treatment history, and outcomes were retrieved from electronic medical records. Treatment response was evaluated according to the Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1). Patients were classified into two groups: ICI responders (ICI-R), defined as complete response (CR), partial response (PR), or stable disease (SD) lasting ≥ 12 months, and non-responders (ICI-NR), defined as progressive disease (PD) or SD lasting < 12 months. All 72 patients underwent simple or modified radical hysterectomy with bilateral salpingo-oophorectomy (BSO). Pelvic and/or para-aortic lymphadenectomy was performed in accordance with the Japanese Society of Gynecologic Oncology (JSGO) guidelines, with adjustments based on patient age and general condition. Surgical specimens were staged according to FIGO 2018 staging system. Adjuvant therapy was administered according to the JSGO guidelines: no adjuvant therapy for low-risk patients and platinum-based chemotherapy or radiotherapy for intermediate- to high-risk patients. ICI regimens consisted of pembrolizumab monotherapy or pembrolizumab plus lenvatinib. POLE exonuclease domain sequencing Hotspot mutations in exons 9, 13, and 14 of the POLE gene were identified using Sanger sequencing. Genomic DNA was extracted from fresh-frozen tissues using the NucleoSpin® DNA Rapidlyse kit (Macherey–Nagel, Germany) or from FFPE samples using the QIAamp DNA FFPE Advanced UNG Kit (Qiagen, Germany), according to the manufacturer’s instructions. PCR amplification was performed using Blend Taq Plus (Toyobo, Japan) under the conditions previously described by Yamazaki et al. [ 25 ]. PCR products were visualized on 2% agarose gels in 1×TAE buffer and purified using the QIAquick Gel Extraction Kit (Qiagen). DNA concentrations were measured using a NanoDrop One spectrophotometer (Thermo Fisher Scientific, USA). Sequencing was outsourced to Eurofins Genomics (Tokyo, Japan). Sequence data were analyzed using SnapGene Viewer (GSL Biotech, USA), and mutations were confirmed using BLAST alignment (NCBI). POLE pathogenic variants were defined as one of the nine single-nucleotide substitutions on exons 9, 13, and 14: c.857C > G (P286R), c.884T > G (M295R), c.890C > T (S297F), c.1231G > T/C (V411L), c.1270C > A (L424I), c.1307C > G (P436R), c.1331T > A (M444K), c.1366G > C (A456P), and c.1376C > T (S459F) [ 26 ]. Immunohistochemistry (IHC) IHC was conducted on 4-µm sections of FFPE tumor sections, including both the central tumor (CT) and invasive margin (IM) areas. Primary antibodies included CD8 (clone C8/144b, Dako, 1:100), PMS2 (A16-4, Biocare Medical, 1:100), MSH6 (BC/44, Biocare Medical, 1:100), p53 (DO-7, Dako, 1:100), CD68 (KP1, Abcam, 1:3000), CD163 (EPR19518, Abcam, 1:1000), CD47 (D307P, CST, 1:200), CD276 (EPR20115, Abcam, 1:4000), HLA-1 (EPR22172, Abcam, 1:4000), and PD-L1 (E1L3N®, CST, 1:100). Antigen retrieval was performed using 10 mM sodium citrate buffer (pH 6.0) or 1×Immunoactive buffer (pH 9.0, Matsunami, Osaka, Japan) at 95°C for 20 min. Endogenous peroxidase activity was blocked with 0.3% hydrogen peroxide in methanol for 20 min. The sections were incubated overnight with primary antibodies at 4°C, followed by incubation with biotin-labeled secondary antibodies and peroxidase-conjugated streptavidin using the Histofine SAB-PO Kit (Nichirei, Tokyo). DAB was used as the chromogen, and the slides were counterstained with hematoxylin, dehydrated, and mounted. MMR status was determined based on the absence of nuclear staining for PMS2 or MSH6 with intact internal positive controls, as previously described [ 27 ]. p53 abnormality was defined as either diffuse strong nuclear staining in ≥ 80% of tumor cells or complete lack of staining, using adjacent non-neoplastic cells as internal controls. Wild-type tumor cells exhibited weak and heterogeneous staining patterns in the present study. Quantification of immune markers Slides were scanned using a VS120-S5 digital slide scanner (Evident, Tokyo, Japan) and analyzed with QuPath software (v0.3.0). For CD8 + T cells, five distinct regions (0.25 × 0.25 mm each) were selected in both the CT and IM regions, and the mean CD8 + cell density (cells/mm²) was calculated. Quantification was independently performed by two gynecologists, and the mean value was calculated. Similarly, CD68 + and CD163 + macrophages were quantified in three distinct CT and IM regions. CD47, CD276, and HLA class I expression were scored semiquantitatively using an H-score by two observers. PD-L1 staining was evaluated on the cell membranes of tumor cells, and the tumor proportion score (TPS) was used as an assessment index. Regardless of the staining intensity or whether the membrane staining was partial or circumferential, any discernible membrane staining was regarded as positive. Spatial immunophenotypes based on CD8 TIL distribution The spatial pattern of CD8 + TILs was assessed according to the International Immuno-Oncology Biomarker Working Group guidelines [ 28 ]. Based on prior studies [ 13 ], tumors were allocated to one of three spatial immunophenotypes (inflamed, excluded, and desert) by comparing the CD8 + T-cell densities in the CT and IM. Lesions with CD8 + TIL density ≥ 1000 cells/mm² in both CT and IM were categorized as 'inflamed', reflecting abundant intratumoral infiltration. Tumors showing < 1000 cells/mm² in CT but ≥ 1000 cells/mm² in IM were considered 'excluded', indicating peripheral accumulation without effective intratumoral entry. When CD8 + densities were < 1000 cells/mm² in both CT and IM, tumors were assigned to the 'desert' phenotype, consistent with the paucity of T-cell infiltration across compartments. Correlation analysis of immune markers To investigate the coordinated regulation within the tumor immune microenvironment, pairwise correlations among all immune markers (CD8, CD68, CD163, PD-L1 TPS, HLA class I, CD276, and CD47) were evaluated using Pearson’s correlation coefficients. The results were visualized in a correlation matrix heatmap, enabling the assessment of shared immunologic patterns distinguishing ICI-R from ICI-NR. ProMisE molecular classification Tumors were classified into molecular subtypes according to the WHO-recommended ProMisE workflow [ 6 ]. 'POLEmut' tumors were identified by exon sequencing, as described above. Tumors showing complete loss of PMS2 or MSH6 nuclear staining were categorized as 'MMRd.’ Cases with abnormal p53 IHC patterns were classified as 'p53abn.’ The remaining tumors without these alterations were defined as 'NSMP.’ Statistical analysis Comparisons between ICI-R and ICI-NR groups were performed using the Mann–Whitney U test or chi-square test, as appropriate. Kaplan–Meier curves were used for survival analysis, and differences were compared using the log-rank test. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards regression, including subtype-specific forest plots, to evaluate prognostic determinants within each ProMisE group. PFS was defined as the interval from ICI initiation to disease progression or the last follow-up. OS was defined as the time from ICI initiation to death from any cause or the last known survival. Statistical significance was set at P < 0.05. All analyses were performed using GraphPad Prism v10.4.2. Results Patient characteristics Among the 72 patients included in this study, 37 (51%) were classified as ICI-R and 35 (49%) as ICI-NR according to the RECIST v1.1 criteria. Tumor samples obtained at the time of the initial surgery were used for molecular and immunophenotypic evaluations. The clinicopathological and treatment characteristics of the entire cohort are summarized in Table 1 . The majority of patients (67%) had endometrioid histology, and 71% were diagnosed with advanced disease (FIGO stage III–IV). While the ICI-R group showed a significantly higher body mass index (BMI) than the ICI-NR group, no significant differences were observed in age, histological subtype, disease stage, lymphovascular space invasion (LVSI), or the extent of lymphadenectomy. Previous treatments and ICI regimens were well balanced between the two groups. Table 1 Clinicopathological and treatment characteristics of the study cohort Variables Total (n = 72) ICI-R (n = 37) ICI-NR (n = 35) p-value Age (years), mean ± SD 63.69 ± 9.73 62.62 ± 10.33 64.83 ± 9.06 0.34 BMI (kg/m 2 ), mean ± SD 23.06 ± 4.01 24.01 ± 4.16 22.05 ± 3.65 0.04 Histological type, n (%) 0.17 Endometrioid G1/2 28 (39%) 18 (49%) 10 (29%) Endometrioid G3 20 (28%) 11 (30%) 9 (26%) Serous 6 (8%) 2 (5%) 4 (11%) Others 18 (25%) 6 (16%) 12 (34%) Stage (FIGO 2018), n (%) 0.3 Ⅰ 18 (25%) 8 (22%) 10 (29%) Ⅱ 3 (4%) 3 (8%) 0 (0%) Ⅲ 29 (40%) 16 (43%) 13 (37%) Ⅳ 22 (31%) 10 (27%) 12 (34%) LVSI, n (%) 0.38 (-) 21 (29%) 13 (35%) 8 (23%) (+) 51 (71%) 24 (65%) 27 (77%) Surgical completeness, n (%) 0.13 complete 48 (67%) 28 (76%) 20 (57%) Incomplete 24 (33%) 9 (24%) 15 (43%) Systematic lymphadenectomy, n (%) 0.24 No 33 (46%) 14 (38%) 19 (54%) Yes 39 (54%) 23 (62%) 16 (46%) TFI, n (%) 0.24 6m 39 (54%) 23 (62%) 16 (46%) ICI regimen, n (%) 0.71 Pem 8 (11%) 5 (14%) 3 (9%) Len + Pem 64 (89%) 32 (86%) 32 (91%) Prior chemotherapy regimens, n (%) 0.45 1 49 (68%) 27 (73%) 22 (63%) > 2 23 (32%) 10 (27%) 13 (37%) BMI, body mass index; Others, carcinosarcoma/ clear cell carcinoma/ mixed carcinoma/ dedifferentiated carcinoma/ undifferentiated carcinoma; FIGO, International Federation of Gynecology and Obstetrics staging system (2018); LVSI, lymphovascular space invasion; TFI, treatment-free interval; 6m, 6 months; ICI, immune checkpoint inhibitor; Pem, pembrolizumab; Len + Pem, lenvatinib plus pembrolizumab Comparison of immune markers between ICI-R and ICI-NR groups The CT and IM regions on the hematoxylin and eosin stain are shown in Fig. 1 A. CD8⁺, CD68⁺, and CD163⁺ cells were automatically quantified in multiple distinct areas of both the CT and IM regions using QuPath software (Fig. 1 B and Supplementary Figure S1 ). PD-L1 expression was evaluated using the TPS, based on the membranous staining of tumor cells. Representative images of CD8⁺, CD68⁺, CD163⁺, and PD-L1–positive cells are presented in Fig. 1 C. Other immune markers were evaluated within the tumor regions, and representative immunohistochemical images of each marker are shown in Fig. 1 D. Quantitative comparisons of individual immune markers between the ICI-R and ICI-NR groups are shown in Figs. 2 A–J. The density of CD8⁺ T cells in the tumor center (CD8.CT) was significantly higher in the ICI-R group than in the ICI-NR group (1,107 vs. 420 cells/mm², p < 0.001) (Fig. 2 A). Conversely, the CD68⁺ macrophage density at the invasive margin (CD68.IM) was significantly higher in the ICI-NR group (542 cells/mm² vs. 935 cells/mm², p = 0.007) (Fig. 2 D). Similarly, the expression of CD47, assessed using the H-score, was markedly elevated in the ICI-NR group (77 vs. 126, p = 0.002) (Fig. 2 I). No significant differences were observed between the groups for CD8.IM, CD68.CT, CD163.CT, CD163.IM, HLA-I, and CD276 expression levels. PD-L1 TPS was generally low in this cohort and did not differ significantly between the ICI-R and ICI-NR groups. Prognostic impact of response-associated immune markers To further clarify the prognostic relevance of response-associated immune markers, we assessed their impact on PFS. Higher CD8.CT and CD8.IM was significantly associated with prolonged PFS. In contrast, although CD68.IM and CD47 expression were enriched in ICI-NR, and neither marker showed a statistically significant association with PFS when analyzed individually (Fig. 3 ). These findings suggest that while macrophage-related and innate immune suppressive markers characterize non-response, cytotoxic T-cell infiltration is the primary determinant of survival benefit in this cohort. These findings suggest that while cytotoxic T-cell infiltration has a clear prognostic impact in ICI-treated patients, macrophage-associated and innate immuneevasion markers may also influence outcomes, albeit more modestly in this cohort. Immunophenotypes based on CD8⁺ T-cell infiltration patterns Given the consistent prognostic impact of CD8⁺ T-cell density, we further explored the spatial distribution of CD8⁺ T cells. As a complementary analysis, we evaluated spatial immunophenotypes based on CD8⁺ T-cell distribution, classifying tumors into inflamed, excluded, and desert phenotypes according to intratumoral and peritumoral CD8⁺ TIL density (Supplementary Figure S2 ). In the inflamed phenotype, abundant CD8⁺ TILs were observed in both CT and IM. In contrast, the excluded phenotype showed dense CD8⁺ T-cell accumulation in the IM but sparse infiltration within the CT. The desert phenotype was characterized by a paucity of CD8⁺ TILs in both the tumor and stroma regions (Fig. S2 A). Patients were classified into three immunophenotypes, and PFS was compared using the Kaplan–Meier method. Among the total cohort, 20 patients were classified as inflamed, 28 as excluded, and 24 as deserts (Fig. S2 B). The inflamed group demonstrated the highest ICI responsiveness and the most favorable PFS. Conversely, the desert group exhibited the poorest response and shortest PFS (Fig. S2 C, p < 0.01). When the excluded and desert phenotypes were combined as a “non-inflamed” category, the inflamed phenotype exhibited a significantly higher response rate than non-inflamed tumors (Fig. S2 D, p < 0.01). These results suggest that the spatial distribution of intratumoral CD8⁺ T-cells serves as a useful predictor of ICI efficacy in EC. Correlation analysis of immune markers We performed a correlation analysis among all immune markers to explore the coordinated regulation within the TIME. Figure 2 K shows a heatmap of Pearson’s correlation of the analyzed immune parameters. CD47 was positively correlated with macrophage-associated markers, including CD68 and CD163, suggesting the coordinated upregulation of immunosuppressive components. In contrast, CD8-related parameters showed weak or inverse correlations with CD47 and TAM markers, indicating divergent patterns of immune activation. PD-L1 exhibited minimal correlation with other immune markers, suggesting relative independence from the broader immune landscape in this cohort. Overall, these correlation patterns indicate that ICI-NR is characterized by a coordinated macrophage- and CD47-associated immunosuppressive profile, whereas ICI-R tends to exhibit a cytotoxic T-cell–dominant immune signature. Association between ProMisE molecular classification and ICI response The distribution of the ProMisE molecular subtypes within the cohort is summarized in Table 2 . Among the 72 patients, 21 (29%) were classified as MMRd, 38 (53%) as NSMP, and 13 (18%) as p53abn. No POLEmut subtype was identified. Regarding clinicopathological background, patients in the p53abn group tended to be older, with fewer endometrioid tumors and a higher proportion of non-endometrioid histologies. Other clinical characteristics did not differ significantly between the subtypes. The choice of ICI regimen also differed across subtypes: pembrolizumab monotherapy was included only in the MMRd group, whereas all patients in the NSMP and p53abn groups received pembrolizumab plus lenvatinib combination therapy. ICI responsiveness varied substantially according to the ProMisE classification (Fig. 4 A). Among the ICI-R (n = 37), 15 patients (41%) were classified as MMRd, 20 (54%) as NSMP, and 2 (5%) as p53abn. In contrast, among the ICI-NR (n = 35), 6 patients (17%) were MMRd, 18 (51%) were NSMP, and 11 (31%) were p53abn. Accordingly, the MMRd subtype demonstrated the highest proportion of ICI-R, whereas the p53abn subtype was enriched for ICI-NR. The NSMP group showed an intermediate distribution with relatively balanced proportions of ICI-R and ICI-NR. Furthermore, survival analysis revealed that the p53abn subtype also had the poorest prognosis, exhibiting significantly shorter PFS and OS compared with the MMRd and NSMP groups (Fig. 4 B–C), findings are consistent with previously reported outcomes. Table 2 ProMisE molecular classification in the cohort Variables MMRd (n = 21) NSMP (n = 38) p53abn (n = 13) p-value Age (years), mean ± SD 60.00 ± 9.12 63.76 ± 9.92 69.46 ± 7.62 0.04 BMI (kg/m 2 ), mean ± SD 23.43 ± 4.13 23.38 ± 4.35 21.50 ± 2.30 0.45 Histological type, n (%) 0.03 Endometrioid G1/2 11 (52%) 16 (42%) 1 (8%) Endometrioid G3 7 (33%) 10 (26%) 3 (23%) Serous 1 (5%) 2 (6%) 3 (23%) Others 2 (10%) 10 (26%) 6 (46%) Stage (FIGO 2018), n (%) 0.62 Ⅰ 7 (33%) 7 (18%) 4 (31%) Ⅱ 0 (0%) 3 (8%) 0 (0%) Ⅲ 9 (43%) 14 (37%) 6 (46%) Ⅳ 5 (24%) 14 (37%) 3 (23%) LVSI, n (%) 0.73 (-) 6 (29%) 10 (26%) 5 (38%) (+) 15 (71%) 28 (74%) 8 (62%) Systematic lymphadenectomy, n (%) 0.37 No 7 (33%) 19 (50%) 7 (54%) Yes 14 (67%) 19 (50%) 6 (46%) TFI, n (%) 0.28 6m 9 (43%) 24 (63%) 6 (46%) ICI regimen, n (%) 2 7 (33%) 12 (32%) 4 (31%) Continuous variables are presented as mean ± standard deviation, and categorical variables are presented as number (%). P values were calculated using one-way ANOVA for continuous variables and Fisher’s exact test for categorical variables. ProMisE, Proactive Molecular Risk Classifier for Endometrial Cancer; MMRd, mismatch repair deficiency; NSMP, no specific molecular profile; p53abn, p53 abnormality; BMI, body mass index; Others, carcinosarcoma/ clear cell carcinoma/ mixed carcinoma/ dedifferentiated carcinoma/ undifferentiated carcinoma; FIGO, International Federation of Gynecology and Obstetrics staging system (2018); LVSI, lymphovascular space invasion; TFI, treatment-free interval; 6m, 6 months; ICI, immune checkpoint inhibitor; Pem, pembrolizumab; Len + Pem, lenvatinib plus pembrolizumab Immune marker profiles across ProMisE molecular subtypes To further characterize the subtype-specific immune landscapes, we compared the expression levels of individual immune markers across the ProMisE molecular subtypes (Fig. 4 D–M). Quantitative analysis revealed that CD8.CT was significantly higher in the MMRd subtype than in the NSMP and p53abn subtypes (Fig. 4 D). In contrast, CD8.IM did not differ significantly among the subtypes (Fig. 4 E). Macrophage-related markers exhibited distinct subtype-associated patterns. While CD68.CT and CD68.IM did not differ significantly across the subtypes (Fig. 4 F–G), CD163.CT was significantly higher in the p53abn subtype than in MMRd and NSMP tumors (Fig. 4 H). No significant differences were observed in CD163 expression. IM (Fig. 4 I). The expression of immune regulatory molecules also varied according to the molecular subtype. CD47 expression was markedly elevated in p53abn tumors compared to MMRd and NSMP tumors (Fig. 4 L), whereas HLA class I and CD276 expression did not show significant subtype-dependent differences (Fig. 4 J–K). PD-L1 expression, assessed by TPS, was low overall and did not differ significantly among the ProMisE subtypes (Fig. 4 M). Collectively, these findings demonstrate that distinct immune profiles are associated with the ProMisE molecular subtypes. In particular, the MMRd subtype is characterized by increased intratumoral CD8⁺ T-cell infiltration, whereas the p53abn subtype exhibits features of an immunosuppressive microenvironment, including an elevated CD163⁺ macrophage density and increased CD47 expression. These subtype-specific immune characteristics may contribute to the observed heterogeneity in ICI response and clinical outcomes. Subtype-specific associations between immune markers and PFS Next, we evaluated the association between immune markers and PFS within each ProMisE molecular subtype. Figure 5 A–C show forest plots summarizing the HRs and 95% CIs for PFS associated with individual immune markers in the MMRd, NSMP, and p53abn cohorts. In addition, for representative immune markers that showed a significant or suggestive association with PFS, patients within each ProMisE molecular subtype were stratified into high and low expression groups based on the median value, and PFS was compared using the Kaplan–Meier method (Fig. 5 D–F). In the MMRd cohort, higher CD8.CT was significantly associated with prolonged PFS (HR 0.32, 95% CI: 0.11–0.94). No significant associations were observed for macrophage-related markers, immune checkpoint molecules, or PD-L1 expression (Fig. 5 A). Kaplan–Meier analysis further confirmed that patients with high CD8.CT exhibited a significantly longer PFS than those with low CD8 expression.CT (Fig. 5 D). In the NSMP cohort, none of the immune markers reached statistical significance in the forest plot analysis, although CD47 expression showed a trend toward an increased risk of disease progression (Fig. 5 B). Consistent with this observation, Kaplan–Meier analysis demonstrated that patients with high CD47 expression tended to have shorter PFS than those with low CD47 expression (Fig. 5 E). In the p53abn cohort, increased CD68 expression was observed. IM was significantly associated with shorter PFS (HR 2.19, 95% CI 1.07–4.50), whereas CD276 expression also showed a trend toward adverse prognosis in the forest plot analysis (Fig. 5 C). Kaplan–Meier analysis confirmed that high CD68.IM was significantly associated with inferior PFS compared to low CD68 expression.IM in this subtype (Fig. 5 F). Collectively, these analyses indicate that the prognostic relevance of immune microenvironmental factors differs substantially among the ProMisE molecular subtypes. CD8⁺ T-cell infiltration appears to play a dominant prognostic role in MMRd tumors, whereas macrophage-related immunosuppressive features are more strongly associated with disease progression in p53abn tumors, with intermediate and less pronounced effects observed in NSMP tumors. Discussion ICIs have emerged as an important therapeutic option for recurrent EC; however, their durable clinical benefits are limited to a subset of patients. Although molecular classification using the ProMisE system provides critical biological and prognostic information, it does not fully explain the heterogeneity of ICI responsiveness. In this study, we comprehensively evaluated the immune microenvironmental features quantified by IHC and digital pathology and examined their associations with ICI response and survival across and within ProMisE molecular subtypes. Our findings highlight that immune determinants differ substantially between ProMisE subtypes and that the prognostic relevance of individual immune markers is subtype-dependent. Consistent with prior reports, intratumoral CD8⁺ T-cell infiltration emerged as a key determinant of favorable ICI outcomes. High CD8⁺ T-cell density was associated with improved response and prolonged PFS, reinforcing the central role of cytotoxic T-cell-mediated antitumor immunity in EC treated with ICIs. While spatial immune phenotypes based on CD8⁺ T-cell distribution further stratified prognosis, these analyses were exploratory and are presented as supplementary data only. Importantly, our results extend previous observations by demonstrating that the prognostic impact of CD8⁺ T-cell infiltration is not uniform across molecular subtypes but is most pronounced in the MMRd subtype, consistent with its hypermutated and immunogenic nature. Beyond lymphocytic infiltration, our analyses underscore the importance of innate immune and myeloid-related pathways, particularly in non–MMRd tumors. TAM–related markers exhibited distinct subtype-specific patterns. Across the entire cohort, a higher CD68⁺ macrophage density in the IM tended to be associated with poorer outcomes, consistent with previous reports demonstrating the negative prognostic impact of TAM density in EC treated with ICIs [ 16 ]. Notably, in our study, the prognostic relevance of CD68.IM was particularly pronounced in the p53abn subtype, in which increased macrophage infiltration was significantly associated with shortened PFS. This finding suggests that macrophage-driven immunosuppression may play a dominant role in shaping treatment resistance in p53abn tumors, which are known to harbor aggressive biological features and poor clinical outcomes. CD47 expression further highlights the heterogeneity of immune regulation across the ProMisE subtypes. Although high CD47 expression did not show a statistically significant association with PFS in the overall cohort, it was enriched in ICI non-responders and demonstrated subtype-specific prognostic trends. While high CD47 expression tended to be associated with shorter PFS in the NSMP subtype, its expression levels were highest overall in p53abn tumors, indicating that innate immune evasion via the CD47–SIRPα axis may be a defining feature of this subtype. CD47 is recognized as a “don’t-eat-me” signal that inhibits macrophage-mediated phagocytosis and facilitates immune evasion [ 19 ]. Elevated CD47 expression has been associated with ICI resistance in multiple malignancies. Our findings further support CD47 as a key immunosuppressive marker with both prognostic and therapeutic significance in EC. Importantly, recent evidence suggests that CD47 may be a promising therapeutic target for EC [ 20 ]. In contrast, PD-L1 expression showed limited associations with immune contexture, molecular subtype, or clinical outcome in the present cohort. The PD-L1 positivity rate in our cohort was lower than that reported in previous studies [ 29 ]. Prior reports have indicated that PD-L1 expression in EC is variable but generally modest, with higher expression typically observed in MMRd or POLEmut tumors [ 30 ]. The low positivity observed in this study may reflect the predominance of NSMP and p53abn tumors, both of which are historically associated with lower PD-L1 expression. Moreover, PD-L1 did not emerge as a strong predictor of ICI responsiveness in our analysis, further underscoring its limitations as a standalone biomarker, consistent with previous findings. Interestingly, no POLEmut subtype was identified in this cohort. POLEmut subtypes are known to exhibit pronounced immune activation and exceptionally favorable prognoses, with rare recurrence [ 31 ]. Although ICI efficacy is also anticipated in this subtype, the absence of POLEmut tumors in our cohort, which consisted exclusively of recurrent cases, likely reflects their intrinsic biological behavior, particularly their strong resistance to recurrence. Taken together, our subtype-specific analyses revealed that both the immune composition and its prognostic significance varied substantially across the ProMisE molecular classes. In MMRd tumors, cytotoxic T-cell infiltration is the dominant favorable determinant, whereas in p53abn tumors, macrophage-related and innate immune suppressive pathways appear to exert a stronger influence on the disease progression. NSMP tumors exhibit an intermediate phenotype with less pronounced but potentially targetable immunosuppressive features. These findings suggest that differences in immune regulation between ProMisE subtypes, rather than immune markers in isolation, are critical determinants of ICI outcomes. Accordingly, integrating ProMisE classification with immune profiling provides a more refined framework for identifying patients who are most likely to benefit from ICIs and supports the development of subtype-tailored immunotherapeutic strategies. Limitations This study had several limitations. First, the sample size was modest, especially within individual molecular subtypes, which may have limited the statistical power and generalizability of the results. Larger multi-institutional cohorts are needed to validate these findings. Second, the analysis was based primarily on tumor samples obtained at the initial surgery rather than at recurrence or at the time of ICI initiation. Tumor evolution and treatment-induced changes may alter the immune microenvironment over time, potentially affecting the performance of biomarkers. Third, although we assessed multiple immune markers using IHC and digital pathology, functional validation and spatial transcriptomic analysis were not performed. Future studies incorporating spatial transcriptomics or single-cell approaches may provide deeper mechanistic insights into tumor-immune interactions. Conclusion In conclusion, our study demonstrates that immune microenvironmental features interact with ProMisE molecular subtypes in a complex and subtype-specific manner, influencing ICI outcomes in recurrent EC. While the ProMisE classification provides essential molecular context, integrating immune marker profiling—particularly markers of cytotoxic T-cell infiltration and innate immune suppression—enables a more refined stratification of patients. These findings support the development of subtype-tailored immunotherapeutic strategies and highlight the importance of molecular–immunologic integration for precision immunotherapy in EC. Future studies should build on these findings to develop integrated predictive models and to explore therapeutic approaches targeting immunosuppressive pathways, with the goal of achieving more precise and effective immunotherapy. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Nagoya University (approval number: 2018 − 0400). Consent to participate Informed consent was obtained from all individual participants included in the study. Consent to publish The authors affirm that human research participants provided informed consent for publication of the images in all Figures. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contribution K.K. conceived and designed the study, collected and curated the clinical data, performed the immunohistochemical and statistical analyses, and drafted the manuscript. N.Y. and S.H. contributed to study design, data interpretation, and critical revision of the manuscript. Y.S., Y.K., F.U., K.S., E.W. and S.S. contributed to pathological evaluation and interpretation of immunohistochemical findings. W.L., K.N. and M.K. contributed to data acquisition and clinical data management. N.Y. supervised the overall study, provided conceptual guidance, and critically revised the manuscript for important intellectual content. All authors read and approved the final version of the manuscript. Acknowledgement This study was supported by JSPS KAKENHI (grant number 25K12631). Data Availability The datasets analyzed during the current study are available from the corresponding author on reasonable request. 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Cancers (Basel) 14:3911. https://doi.org/10.3390/cancers14163911 Zhang Y, Ju B, Cheng R, Ding T, Wu J (2024) PD-L1 expression and immune infiltration across molecular subtypes of endometrial cancer: an integrative-analysis of molecular classification and immune subtypes. Hum Pathol 154:105704. https://doi.org/10.1016/j.humpath.2024.105704 Fanale D, Corsini LR, Piraino P et al (2025) POLE-mutated endometrial cancer: new perspectives on the horizon? Front Oncol 15:1633260. https://doi.org/10.3389/fonc.2025.1633260 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigureS1.tif Supplementary Figure S1. Workflow for automated quantification of immune cell markers using QuPath software. Representative screenshots illustrate region-of-interest (ROI) selection, cell detection, and classification of positive and negative cells. Nuclear detection was performed based on hematoxylin optical density, followed by identification of marker-positive cells using predefined intensity thresholds. Cell density was calculated as the number of positive cells per square millimeter within each ROI. SupplementaryFigureS2.tif Supplementary Figure S2. Spatial immunophenotypes based on CD8⁺ T-cell infiltration patterns. (A) Representative HE and CD8 immunohistochemical images illustrating the three spatial immunophenotypes: inflamed, excluded, and desert. Inflamed tumors are characterized by abundant CD8⁺ TILs distributed throughout both the CT and the IM. Excluded tumors show dense CD8⁺ TIL accumulation at the IM with limited infiltration into the CT, whereas desert tumors exhibit minimal CD8⁺ TILs in both regions. (B)Distribution of the three immunophenotypes within the entire cohort (n = 72). (C) Kaplan–Meier analysis of PFS according to the three spatial immunophenotypes. (D) Kaplan–Meier curves comparing inflamed versus non-inflamed (excluded + desert) phenotypes. HE, Hematoxylin and eosin; IHC, Immunohistochemistry; CT, tumor center; IM, invasive margin; PFS, Progression-free survival. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Feb, 2026 Reviews received at journal 20 Feb, 2026 Reviews received at journal 08 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers agreed at journal 01 Feb, 2026 Reviewers agreed at journal 30 Jan, 2026 Reviewers invited by journal 30 Jan, 2026 Editor assigned by journal 30 Jan, 2026 Submission checks completed at journal 30 Jan, 2026 First submitted to journal 29 Jan, 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. 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15:28:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8733104/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8733104/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101790111,"identity":"dee3ea03-f331-4b9a-982e-7a3f35d496e2","added_by":"auto","created_at":"2026-02-03 16:03:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":651141,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative immunohistochemical staining of immune markers.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eH\u0026amp;E staining showing the CT and IM. \u003cstrong\u003e(B) \u003c/strong\u003eSchematic overview of automated quantification of CD8⁺, CD68⁺, and CD163⁺ cells performed in multiple distinct regions within both the CT and IM. \u003cstrong\u003e(C) \u003c/strong\u003eRepresentative immunohistochemical images showing positive cells for CD8, CD68, CD163, and PD-L1. PD-L1 expression was evaluated on tumor cell membranes using the TPS.\u003cstrong\u003e(D) \u003c/strong\u003eRepresentative staining patterns of HLA class I, CD276, and CD47, evaluated within tumor regions using the H-score method. Scale bars are indicated in each panel.\u003c/p\u003e\n\u003cp\u003eCT, tumor center; IM, invasive margin.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8733104/v1/16cd7d5115dcdc91a894ea30.jpg"},{"id":101790112,"identity":"1c8c0ca6-b7c6-4004-9a7d-794488d5e44d","added_by":"auto","created_at":"2026-02-03 16:03:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":272179,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuantitative comparison and interrelationships of immune markers between ICI responders (R) and non-responders (NR).\u003c/strong\u003e\u003cbr\u003e\nPanels A–J show quantitative differences in individual immune markers between the R and NR groups:\u003cstrong\u003e (A)\u003c/strong\u003e CD8⁺ T cells in the central tumor (CD8/CT), \u003cstrong\u003e(B)\u003c/strong\u003eCD8⁺ T cells in the invasive margin (CD8/IM), \u003cstrong\u003e(C)\u003c/strong\u003e CD68⁺ macrophages in the CT (CD68/CT), \u003cstrong\u003e(D)\u003c/strong\u003e CD68⁺ macrophages in the IM (CD68/IM), \u003cstrong\u003e(E)\u003c/strong\u003e CD163⁺ macrophages in the CT (CD163/CT), \u003cstrong\u003e(F)\u003c/strong\u003e CD163⁺ macrophages in the IM (CD163/IM), \u003cstrong\u003e(G) \u003c/strong\u003eHLA class I expression (H-score), \u003cstrong\u003e(H)\u003c/strong\u003e CD276 expression (H-score), \u003cstrong\u003e(I)\u003c/strong\u003e CD47 expression (H-score), and \u003cstrong\u003e(J)\u003c/strong\u003e PD-L1 expression evaluated by TPS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(K)\u003c/strong\u003ePearson correlation heatmap illustrating interrelationships among immune markers across the entire cohort, with hierarchical clustering applied to both rows and columns.\u003c/p\u003e\n\u003cp\u003eData are presented as mean ± standard error.\u003cbr\u003e\nStatistical significance is indicated as follows: *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001; ns, not significant.\u003c/p\u003e\n\u003cp\u003eCT, tumor center; IM, invasive margin; R, ICI responders; NR, ICI non-responders; TPS, tumor proportion score.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8733104/v1/bc07aa4aa3d54a7669a578cc.jpg"},{"id":101880690,"identity":"11da8653-6884-4032-8d9b-d25cf05e2888","added_by":"auto","created_at":"2026-02-04 15:05:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":149106,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plots showing HRs and 95% CIs for the association between immune markers and PFS within the study cohort.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHRs were estimated using univariable Cox proportional hazards models.\u003c/p\u003e\n\u003cp\u003eCT, tumor center; IM, invasive margin; HR, hazard ratios; 95%CI, 95% confidence intervals.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8733104/v1/b82d1d4787b2f348acb49267.jpg"},{"id":101880572,"identity":"64ca2c43-47a2-471f-948a-c8f7e2f46369","added_by":"auto","created_at":"2026-02-04 15:03:39","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":320944,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between ProMisE molecular classification, survival outcomes, and immune markers.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003eDistribution of ProMisE molecular subtypes in ICI-R and ICI-NR. \u003cstrong\u003e(B)\u003c/strong\u003eKaplan–Meier curves for PFS according to ProMisE molecular subtypes. \u003cstrong\u003e(C)\u003c/strong\u003eKaplan–Meier curves for OS stratified by ProMisE classification. \u003cstrong\u003e(D–M)\u003c/strong\u003e Comparison of immune markers across ProMisE molecular subtypes: \u003cstrong\u003e(D)\u003c/strong\u003e CD8/CT, \u003cstrong\u003e(E)\u003c/strong\u003e CD8/IM, \u003cstrong\u003e(F)\u003c/strong\u003e CD68/CT, \u003cstrong\u003e(G)\u003c/strong\u003eCD68/IM, \u003cstrong\u003e(H)\u003c/strong\u003e CD163/CT, \u003cstrong\u003e(I)\u003c/strong\u003e CD163/IM, \u003cstrong\u003e(J)\u003c/strong\u003e HLA class I (H-score), \u003cstrong\u003e(K)\u003c/strong\u003e CD276 (H-score), \u003cstrong\u003e(L)\u003c/strong\u003e CD47 (H-score), and \u003cstrong\u003e(M)\u003c/strong\u003ePD-L1 (TPS score).\u003c/p\u003e\n\u003cp\u003eStatistical significance is indicated as follows: *P \u0026lt; 0.05, **P \u0026lt; 0.01; ns, not significant.\u003c/p\u003e\n\u003cp\u003eICI-R, ICI responders; ICI-NR, ICI non-responders; PFS, Progression-free survival; OS, overall survival; CT, tumor center; IM, invasive margin; MMRd, mismatch repair deficiency; NSMP, no specific molecular profile; P53abn, p53 abnormality\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8733104/v1/ba968094f2eac497cecca27b.jpg"},{"id":101790116,"identity":"32e4d607-1615-4302-b502-0580e83a0559","added_by":"auto","created_at":"2026-02-03 16:03:53","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":424896,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubtype-specific associations between immune markers and PFS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A–C)\u003c/strong\u003eForest plots showing HRs and 95% CIs for the association between immune markers and PFS within each ProMisE molecular subtype: \u003cstrong\u003e(A)\u003c/strong\u003eMMRd cohort, \u003cstrong\u003e(B)\u003c/strong\u003eNSMP cohort, and \u003cstrong\u003e(C) \u003c/strong\u003ep53abn cohort. HRs were estimated using univariable Cox proportional hazards models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(D–F)\u003c/strong\u003eKaplan–Meier curves for PFS stratified by high versus low expression of representative immune markers showing prognostic relevance within each subtype: \u003cstrong\u003e(D)\u003c/strong\u003eCD8⁺ T-cell density in the CT (CD8.CT) in the MMRd cohort, \u003cstrong\u003e(E)\u003c/strong\u003e CD47 expression in the NSMP cohort, and \u003cstrong\u003e(F)\u003c/strong\u003e CD68⁺ macrophage density in the IM (CD68.IM) in the p53abn cohort. P values were calculated using the log-rank test.\u003c/p\u003e\n\u003cp\u003eMMRd, mismatch repair deficiency; NSMP, no specific molecular profile; p53abn, p53 abnormality; CT, tumor center; IM, invasive margin; HR, hazard ratios; 95%CI, 95% confidence intervals.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8733104/v1/dd5589d449974ec93a4c4c5e.jpg"},{"id":102745580,"identity":"e686ec95-eb64-43da-8b2a-a8ef55c6d7e9","added_by":"auto","created_at":"2026-02-16 08:52:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3453197,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8733104/v1/1f24475f-0e92-4a04-bf57-5638488afb3d.pdf"},{"id":101790117,"identity":"5fe7f43b-e62b-4822-86f3-aed4d2788103","added_by":"auto","created_at":"2026-02-03 16:03:54","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":578444,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S1. Workflow for automated quantification of immune cell markers using QuPath software.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRepresentative screenshots illustrate region-of-interest (ROI) selection, cell detection, and classification of positive and negative cells. Nuclear detection was performed based on hematoxylin optical density, followed by identification of marker-positive cells using predefined intensity thresholds. Cell density was calculated as the number of positive cells per square millimeter within each ROI.\u003c/p\u003e","description":"","filename":"SupplementaryFigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8733104/v1/70a1f1e12be49d43988b6f9c.tif"},{"id":101790115,"identity":"6dab703c-d460-445a-ac05-4ceede023c51","added_by":"auto","created_at":"2026-02-03 16:03:53","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":466034,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S2. Spatial immunophenotypes based on CD8⁺ T-cell infiltration patterns.\u003cbr\u003e\n(A)\u003c/strong\u003e Representative HE and CD8 immunohistochemical images illustrating the three spatial immunophenotypes: inflamed, excluded, and desert. Inflamed tumors are characterized by abundant CD8⁺ TILs distributed throughout both the CT and the IM. Excluded tumors show dense CD8⁺ TIL accumulation at the IM with limited infiltration into the CT, whereas desert tumors exhibit minimal CD8⁺ TILs in both regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003eDistribution of the three immunophenotypes within the entire cohort (n = 72).\u003cbr\u003e\n \u003cstrong\u003e(C)\u003c/strong\u003e Kaplan–Meier analysis of PFS according to the three spatial immunophenotypes.\u003cbr\u003e\n \u003cstrong\u003e(D)\u003c/strong\u003e Kaplan–Meier curves comparing inflamed versus non-inflamed (excluded + desert) phenotypes.\u003c/p\u003e\n\u003cp\u003eHE, Hematoxylin and eosin; IHC, Immunohistochemistry; CT, tumor center; IM, invasive margin; PFS, Progression-free survival.\u003c/p\u003e","description":"","filename":"SupplementaryFigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8733104/v1/f9d14b7a5dc520e647b3b114.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated Immunophenotypic and ProMisE Molecular Profiling as Predictors of Immune Checkpoint Inhibitor Response in Endometrial Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometrial cancer (EC) is one of the most common gynecological malignancies worldwide, and its incidence continues to rise, particularly in developed countries with increasing rates of obesity and metabolic disorders [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While early stage EC can often be cured with surgery alone, patients with recurrent or advanced disease have limited treatment options and a poor prognosis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In recent years, immune checkpoint inhibitors (ICIs) targeting the programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) pathway have demonstrated promising efficacy in a subset of patients with recurrent EC [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Nevertheless, a significant number of patients fail to respond, highlighting the urgent need for reliable biomarkers to predict treatment efficacy.\u003c/p\u003e \u003cp\u003eOne of the key determinants of ICI response in EC is the tumor molecular subtype. The Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) molecular classification stratifies tumors into four molecular subtypes: POLE-mutated (POLEmut), mismatch repair-deficient (MMRd), p53-abnormal (p53abn), and no specific molecular profile (NSMP), which differ in their genomic features, immune microenvironment, and clinical outcomes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. MMRd and POLEmut tumors are characterized by a high tumor mutational burden and significant lymphocytic infiltration, while NSMP and p53abn tumors exhibit a relatively \u0026ldquo;cold\u0026rdquo; immune phenotype and poor ICI responsiveness [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, even within each subtype, considerable heterogeneity in ICI response exists, suggesting that additional immunological or microenvironmental factors influence therapeutic outcomes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGrowing evidence highlights the pivotal role of TIME in shaping ICI responsiveness. Beyond molecular subtypes, the spatial organization of tumor-infiltrating lymphocytes (TILs) has emerged as a critical determinant of the tumor immune landscape. A recent study demonstrated that the spatial distribution of CD8\u003csup\u003e+\u003c/sup\u003e T-cells could stratify EC into three distinct immunophenotypes: inflamed, excluded, and desert, which were closely associated with both ProMisE subtypes and patient survival [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Specifically, inflamed tumors with abundant intratumoral CD8\u003csup\u003e+\u003c/sup\u003e TILs showed favorable outcomes, whereas excluded and desert phenotypes were correlated with poor survival, particularly within the NSMP and p53abn groups. These findings highlight that spatial immunophenotyping provides complementary prognostic information beyond molecular classification and may serve as a practical predictor of ICI response.\u003c/p\u003e \u003cp\u003eIn addition to lymphocyte distribution, other components of the TIME, including tumor-associated macrophages (TAMs) and immune checkpoint molecules such as CD47 and CD276 (B7-H3), modulate antitumor immunity and contribute to ICI resistance [\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, the integrated impact of immune phenotypes, macrophage infiltration, and checkpoint molecule expression on clinical outcomes across EC molecular subtypes remains unclear.\u003c/p\u003e \u003cp\u003eRecent spatial and quantitative profiling studies have further demonstrated that immune marker expression patterns and their correlations differ across EC molecular subtypes, influencing both the prognosis and immunotherapy efficacy [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Although several studies have evaluated TIME in EC, few have systematically compared the immunologic features of ICI responders and non-responders, and even fewer have examined how these predictive immune parameters differ across ProMisE subtypes. Therefore, integrating immunophenotypic profiling with molecular classification may offer a more refined biomarker strategy for patient selection and therapeutic decision-making.\u003c/p\u003e \u003cp\u003eTherefore, we retrospectively analyzed 72 patients with recurrent EC treated with ICIs to clarify how distinct immune landscapes interact with molecular subtypes to influence the clinical outcomes. By integrating the ProMisE molecular classification with quantitative immunophenotyping via digital image analysis, this study aimed to identify subtype-specific immune determinants that distinguish ICI responders from non-responders. Our findings provide new insights into the coordinated regulation of the tumor immune microenvironment and aim to inform more precise, subtype-tailored immunotherapeutic strategies for EC.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and patient cohort\u003c/h2\u003e \u003cp\u003eThis retrospective, exploratory biomarker discovery study aimed to investigate the association between immune phenotypes, molecular classification, and survival outcomes in patients with recurrent EC treated with ICIs. A total of 72 patients who received ICI therapy between May 2019 and December 2024 at Nagoya University Hospital and its affiliated institutions (Fujita Health University Hospital, Ogaki Municipal Hospital, Fujita Health University Bantane Hospital, and Aichi Cancer Center) were included in this study. All patients had histologically confirmed EC and underwent surgical intervention after a definitive diagnosis. This study was approved by the Institutional Review Board of Nagoya University (approval number: 2018\u0026thinsp;\u0026minus;\u0026thinsp;0400). Frozen biopsy samples and formalin-fixed paraffin-embedded (FFPE) tumor tissues were collected for analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCase selection and clinical data collection\u003c/h3\u003e\n\u003cp\u003eClinical and pathological characteristics, treatment history, and outcomes were retrieved from electronic medical records. Treatment response was evaluated according to the Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1). Patients were classified into two groups: ICI responders (ICI-R), defined as complete response (CR), partial response (PR), or stable disease (SD) lasting\u0026thinsp;\u0026ge;\u0026thinsp;12 months, and non-responders (ICI-NR), defined as progressive disease (PD) or SD lasting\u0026thinsp;\u0026lt;\u0026thinsp;12 months. All 72 patients underwent simple or modified radical hysterectomy with bilateral salpingo-oophorectomy (BSO). Pelvic and/or para-aortic lymphadenectomy was performed in accordance with the Japanese Society of Gynecologic Oncology (JSGO) guidelines, with adjustments based on patient age and general condition. Surgical specimens were staged according to FIGO 2018 staging system. Adjuvant therapy was administered according to the JSGO guidelines: no adjuvant therapy for low-risk patients and platinum-based chemotherapy or radiotherapy for intermediate- to high-risk patients. ICI regimens consisted of pembrolizumab monotherapy or pembrolizumab plus lenvatinib.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePOLE\u003c/b\u003e \u003cb\u003eexonuclease domain sequencing\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHotspot mutations in exons 9, 13, and 14 of the \u003cem\u003ePOLE\u003c/em\u003e gene were identified using Sanger sequencing. Genomic DNA was extracted from fresh-frozen tissues using the \u003cem\u003eNucleoSpin\u0026reg; DNA Rapidlyse\u003c/em\u003e kit (Macherey\u0026ndash;Nagel, Germany) or from FFPE samples using the \u003cem\u003eQIAamp DNA FFPE Advanced UNG Kit\u003c/em\u003e (Qiagen, Germany), according to the manufacturer\u0026rsquo;s instructions. PCR amplification was performed using Blend Taq Plus (Toyobo, Japan) under the conditions previously described by Yamazaki et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. PCR products were visualized on 2% agarose gels in 1\u0026times;TAE buffer and purified using the \u003cem\u003eQIAquick Gel Extraction Kit\u003c/em\u003e (Qiagen). DNA concentrations were measured using a \u003cem\u003eNanoDrop One\u003c/em\u003e spectrophotometer (Thermo Fisher Scientific, USA). Sequencing was outsourced to Eurofins Genomics (Tokyo, Japan). Sequence data were analyzed using \u003cem\u003eSnapGene Viewer\u003c/em\u003e (GSL Biotech, USA), and mutations were confirmed using BLAST alignment (NCBI). POLE pathogenic variants were defined as one of the nine single-nucleotide substitutions on exons 9, 13, and 14: c.857C\u0026thinsp;\u0026gt;\u0026thinsp;G (P286R), c.884T\u0026thinsp;\u0026gt;\u0026thinsp;G (M295R), c.890C\u0026thinsp;\u0026gt;\u0026thinsp;T (S297F), c.1231G\u0026thinsp;\u0026gt;\u0026thinsp;T/C (V411L), c.1270C\u0026thinsp;\u0026gt;\u0026thinsp;A (L424I), c.1307C\u0026thinsp;\u0026gt;\u0026thinsp;G (P436R), c.1331T\u0026thinsp;\u0026gt;\u0026thinsp;A (M444K), c.1366G\u0026thinsp;\u0026gt;\u0026thinsp;C (A456P), and c.1376C\u0026thinsp;\u0026gt;\u0026thinsp;T (S459F) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eImmunohistochemistry (IHC)\u003c/h3\u003e\n\u003cp\u003eIHC was conducted on 4-\u0026micro;m sections of FFPE tumor sections, including both the central tumor (CT) and invasive margin (IM) areas. Primary antibodies included CD8 (clone C8/144b, Dako, 1:100), PMS2 (A16-4, Biocare Medical, 1:100), MSH6 (BC/44, Biocare Medical, 1:100), p53 (DO-7, Dako, 1:100), CD68 (KP1, Abcam, 1:3000), CD163 (EPR19518, Abcam, 1:1000), CD47 (D307P, CST, 1:200), CD276 (EPR20115, Abcam, 1:4000), HLA-1 (EPR22172, Abcam, 1:4000), and PD-L1 (E1L3N\u0026reg;, CST, 1:100). Antigen retrieval was performed using 10 mM sodium citrate buffer (pH 6.0) or 1\u0026times;Immunoactive buffer (pH 9.0, Matsunami, Osaka, Japan) at 95\u0026deg;C for 20 min. Endogenous peroxidase activity was blocked with 0.3% hydrogen peroxide in methanol for 20 min. The sections were incubated overnight with primary antibodies at 4\u0026deg;C, followed by incubation with biotin-labeled secondary antibodies and peroxidase-conjugated streptavidin using the \u003cem\u003eHistofine SAB-PO Kit\u003c/em\u003e (Nichirei, Tokyo). DAB was used as the chromogen, and the slides were counterstained with hematoxylin, dehydrated, and mounted. MMR status was determined based on the absence of nuclear staining for PMS2 or MSH6 with intact internal positive controls, as previously described [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. p53 abnormality was defined as either diffuse strong nuclear staining in \u0026ge;\u0026thinsp;80% of tumor cells or complete lack of staining, using adjacent non-neoplastic cells as internal controls. Wild-type tumor cells exhibited weak and heterogeneous staining patterns in the present study.\u003c/p\u003e\n\u003ch3\u003eQuantification of immune markers\u003c/h3\u003e\n\u003cp\u003eSlides were scanned using a \u003cem\u003eVS120-S5\u003c/em\u003e digital slide scanner (Evident, Tokyo, Japan) and analyzed with QuPath software (v0.3.0). For CD8\u003csup\u003e+\u003c/sup\u003e T cells, five distinct regions (0.25 \u0026times; 0.25 mm each) were selected in both the CT and IM regions, and the mean CD8\u0026thinsp;+\u0026thinsp;cell density (cells/mm\u0026sup2;) was calculated. Quantification was independently performed by two gynecologists, and the mean value was calculated. Similarly, CD68\u003csup\u003e+\u003c/sup\u003e and CD163\u003csup\u003e+\u003c/sup\u003e macrophages were quantified in three distinct CT and IM regions. CD47, CD276, and HLA class I expression were scored semiquantitatively using an H-score by two observers. PD-L1 staining was evaluated on the cell membranes of tumor cells, and the tumor proportion score (TPS) was used as an assessment index. Regardless of the staining intensity or whether the membrane staining was partial or circumferential, any discernible membrane staining was regarded as positive.\u003c/p\u003e\n\u003ch3\u003eSpatial immunophenotypes based on CD8 TIL distribution\u003c/h3\u003e\n\u003cp\u003eThe spatial pattern of CD8\u003csup\u003e+\u003c/sup\u003e TILs was assessed according to the International Immuno-Oncology Biomarker Working Group guidelines [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Based on prior studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], tumors were allocated to one of three spatial immunophenotypes (inflamed, excluded, and desert) by comparing the CD8\u003csup\u003e+\u003c/sup\u003e T-cell densities in the CT and IM. Lesions with CD8\u0026thinsp;+\u0026thinsp;TIL density\u0026thinsp;\u0026ge;\u0026thinsp;1000 cells/mm\u0026sup2; in both CT and IM were categorized as 'inflamed', reflecting abundant intratumoral infiltration. Tumors showing\u0026thinsp;\u0026lt;\u0026thinsp;1000 cells/mm\u0026sup2; in CT but \u0026ge;\u0026thinsp;1000 cells/mm\u0026sup2; in IM were considered 'excluded', indicating peripheral accumulation without effective intratumoral entry. When CD8\u0026thinsp;+\u0026thinsp;densities were \u0026lt;\u0026thinsp;1000 cells/mm\u0026sup2; in both CT and IM, tumors were assigned to the 'desert' phenotype, consistent with the paucity of T-cell infiltration across compartments.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis of immune markers\u003c/h2\u003e \u003cp\u003eTo investigate the coordinated regulation within the tumor immune microenvironment, pairwise correlations among all immune markers (CD8, CD68, CD163, PD-L1 TPS, HLA class I, CD276, and CD47) were evaluated using Pearson\u0026rsquo;s correlation coefficients. The results were visualized in a correlation matrix heatmap, enabling the assessment of shared immunologic patterns distinguishing ICI-R from ICI-NR.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProMisE molecular classification\u003c/h3\u003e\n\u003cp\u003eTumors were classified into molecular subtypes according to the WHO-recommended ProMisE workflow [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. 'POLEmut' tumors were identified by exon sequencing, as described above. Tumors showing complete loss of PMS2 or MSH6 nuclear staining were categorized as 'MMRd.\u0026rsquo; Cases with abnormal p53 IHC patterns were classified as 'p53abn.\u0026rsquo; The remaining tumors without these alterations were defined as 'NSMP.\u0026rsquo;\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eComparisons between ICI-R and ICI-NR groups were performed using the Mann\u0026ndash;Whitney U test or chi-square test, as appropriate. Kaplan\u0026ndash;Meier curves were used for survival analysis, and differences were compared using the log-rank test. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards regression, including subtype-specific forest plots, to evaluate prognostic determinants within each ProMisE group. PFS was defined as the interval from ICI initiation to disease progression or the last follow-up. OS was defined as the time from ICI initiation to death from any cause or the last known survival. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were performed using GraphPad Prism v10.4.2.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eAmong the 72 patients included in this study, 37 (51%) were classified as ICI-R and 35 (49%) as ICI-NR according to the RECIST v1.1 criteria. Tumor samples obtained at the time of the initial surgery were used for molecular and immunophenotypic evaluations. The clinicopathological and treatment characteristics of the entire cohort are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The majority of patients (67%) had endometrioid histology, and 71% were diagnosed with advanced disease (FIGO stage III\u0026ndash;IV). While the ICI-R group showed a significantly higher body mass index (BMI) than the ICI-NR group, no significant differences were observed in age, histological subtype, disease stage, lymphovascular space invasion (LVSI), or the extent of lymphadenectomy. Previous treatments and ICI regimens were well balanced between the two groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological and treatment characteristics of the study cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;72)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICI-R (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICI-NR (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.69\u0026thinsp;\u0026plusmn;\u0026thinsp;9.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.62\u0026thinsp;\u0026plusmn;\u0026thinsp;10.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.83\u0026thinsp;\u0026plusmn;\u0026thinsp;9.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.06\u0026thinsp;\u0026plusmn;\u0026thinsp;4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.01\u0026thinsp;\u0026plusmn;\u0026thinsp;4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.05\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistological type, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrioid G1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrioid G3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage (FIGO 2018), n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLVSI, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgical completeness, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecomplete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncomplete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSystematic lymphadenectomy, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTFI, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 6m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 6m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICI regimen, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLen\u0026thinsp;+\u0026thinsp;Pem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrior chemotherapy regimens, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eBMI, body mass index; Others, carcinosarcoma/ clear cell carcinoma/ mixed carcinoma/ dedifferentiated carcinoma/ undifferentiated carcinoma; FIGO, International Federation of Gynecology and Obstetrics staging system (2018); LVSI, lymphovascular space invasion; TFI, treatment-free interval; 6m, 6 months; ICI, immune checkpoint inhibitor; Pem, pembrolizumab; Len\u0026thinsp;+\u0026thinsp;Pem, lenvatinib plus pembrolizumab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComparison of immune markers between ICI-R and ICI-NR groups\u003c/h2\u003e \u003cp\u003eThe CT and IM regions on the hematoxylin and eosin stain are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. CD8⁺, CD68⁺, and CD163⁺ cells were automatically quantified in multiple distinct areas of both the CT and IM regions using QuPath software (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). PD-L1 expression was evaluated using the TPS, based on the membranous staining of tumor cells. Representative images of CD8⁺, CD68⁺, CD163⁺, and PD-L1\u0026ndash;positive cells are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC. Other immune markers were evaluated within the tumor regions, and representative immunohistochemical images of each marker are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD. Quantitative comparisons of individual immune markers between the ICI-R and ICI-NR groups are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;J. The density of CD8⁺ T cells in the tumor center (CD8.CT) was significantly higher in the ICI-R group than in the ICI-NR group (1,107 vs. 420 cells/mm\u0026sup2;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Conversely, the CD68⁺ macrophage density at the invasive margin (CD68.IM) was significantly higher in the ICI-NR group (542 cells/mm\u0026sup2; vs. 935 cells/mm\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.007) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Similarly, the expression of CD47, assessed using the H-score, was markedly elevated in the ICI-NR group (77 vs. 126, p\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI). No significant differences were observed between the groups for CD8.IM, CD68.CT, CD163.CT, CD163.IM, HLA-I, and CD276 expression levels. PD-L1 TPS was generally low in this cohort and did not differ significantly between the ICI-R and ICI-NR groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic impact of response-associated immune markers\u003c/h2\u003e \u003cp\u003eTo further clarify the prognostic relevance of response-associated immune markers, we assessed their impact on PFS. Higher CD8.CT and CD8.IM was significantly associated with prolonged PFS. In contrast, although CD68.IM and CD47 expression were enriched in ICI-NR, and neither marker showed a statistically significant association with PFS when analyzed individually (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These findings suggest that while macrophage-related and innate immune suppressive markers characterize non-response, cytotoxic T-cell infiltration is the primary determinant of survival benefit in this cohort. These findings suggest that while cytotoxic T-cell infiltration has a clear prognostic impact in ICI-treated patients, macrophage-associated and innate immuneevasion markers may also influence outcomes, albeit more modestly in this cohort.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImmunophenotypes based on CD8⁺ T-cell infiltration patterns\u003c/h2\u003e \u003cp\u003eGiven the consistent prognostic impact of CD8⁺ T-cell density, we further explored the spatial distribution of CD8⁺ T cells. As a complementary analysis, we evaluated spatial immunophenotypes based on CD8⁺ T-cell distribution, classifying tumors into inflamed, excluded, and desert phenotypes according to intratumoral and peritumoral CD8⁺ TIL density (Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). In the inflamed phenotype, abundant CD8⁺ TILs were observed in both CT and IM. In contrast, the excluded phenotype showed dense CD8⁺ T-cell accumulation in the IM but sparse infiltration within the CT. The desert phenotype was characterized by a paucity of CD8⁺ TILs in both the tumor and stroma regions (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA). Patients were classified into three immunophenotypes, and PFS was compared using the Kaplan\u0026ndash;Meier method. Among the total cohort, 20 patients were classified as inflamed, 28 as excluded, and 24 as deserts (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB). The inflamed group demonstrated the highest ICI responsiveness and the most favorable PFS. Conversely, the desert group exhibited the poorest response and shortest PFS (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). When the excluded and desert phenotypes were combined as a \u0026ldquo;non-inflamed\u0026rdquo; category, the inflamed phenotype exhibited a significantly higher response rate than non-inflamed tumors (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eD, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These results suggest that the spatial distribution of intratumoral CD8⁺ T-cells serves as a useful predictor of ICI efficacy in EC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis of immune markers\u003c/h2\u003e \u003cp\u003eWe performed a correlation analysis among all immune markers to explore the coordinated regulation within the TIME. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eK shows a heatmap of Pearson\u0026rsquo;s correlation of the analyzed immune parameters. CD47 was positively correlated with macrophage-associated markers, including CD68 and CD163, suggesting the coordinated upregulation of immunosuppressive components. In contrast, CD8-related parameters showed weak or inverse correlations with CD47 and TAM markers, indicating divergent patterns of immune activation. PD-L1 exhibited minimal correlation with other immune markers, suggesting relative independence from the broader immune landscape in this cohort. Overall, these correlation patterns indicate that ICI-NR is characterized by a coordinated macrophage- and CD47-associated immunosuppressive profile, whereas ICI-R tends to exhibit a cytotoxic T-cell\u0026ndash;dominant immune signature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between ProMisE molecular classification and ICI response\u003c/h2\u003e \u003cp\u003eThe distribution of the ProMisE molecular subtypes within the cohort is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Among the 72 patients, 21 (29%) were classified as MMRd, 38 (53%) as NSMP, and 13 (18%) as p53abn. No POLEmut subtype was identified. Regarding clinicopathological background, patients in the p53abn group tended to be older, with fewer endometrioid tumors and a higher proportion of non-endometrioid histologies. Other clinical characteristics did not differ significantly between the subtypes. The choice of ICI regimen also differed across subtypes: pembrolizumab monotherapy was included only in the MMRd group, whereas all patients in the NSMP and p53abn groups received pembrolizumab plus lenvatinib combination therapy. ICI responsiveness varied substantially according to the ProMisE classification (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Among the ICI-R (n\u0026thinsp;=\u0026thinsp;37), 15 patients (41%) were classified as MMRd, 20 (54%) as NSMP, and 2 (5%) as p53abn. In contrast, among the ICI-NR (n\u0026thinsp;=\u0026thinsp;35), 6 patients (17%) were MMRd, 18 (51%) were NSMP, and 11 (31%) were p53abn. Accordingly, the MMRd subtype demonstrated the highest proportion of ICI-R, whereas the p53abn subtype was enriched for ICI-NR. The NSMP group showed an intermediate distribution with relatively balanced proportions of ICI-R and ICI-NR. Furthermore, survival analysis revealed that the p53abn subtype also had the poorest prognosis, exhibiting significantly shorter PFS and OS compared with the MMRd and NSMP groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u0026ndash;C), findings are consistent with previously reported outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e ProMisE molecular classification in the cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMRd (n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNSMP (n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep53abn (n\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.00\u0026thinsp;\u0026plusmn;\u0026thinsp;9.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.76\u0026thinsp;\u0026plusmn;\u0026thinsp;9.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.46\u0026thinsp;\u0026plusmn;\u0026thinsp;7.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.43\u0026thinsp;\u0026plusmn;\u0026thinsp;4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.38\u0026thinsp;\u0026plusmn;\u0026thinsp;4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistological type, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrioid G1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrioid G3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage (FIGO 2018), n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLVSI, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSystematic lymphadenectomy, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTFI, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 6m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 6m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICI regimen, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLen\u0026thinsp;+\u0026thinsp;Pem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrior chemotherapy regimens, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical variables are presented as number (%). P values were calculated using one-way ANOVA for continuous variables and Fisher\u0026rsquo;s exact test for categorical variables.\u003c/p\u003e \u003cp\u003eProMisE, Proactive Molecular Risk Classifier for Endometrial Cancer; MMRd, mismatch repair deficiency; NSMP, no specific molecular profile; p53abn, p53 abnormality; BMI, body mass index; Others, carcinosarcoma/ clear cell carcinoma/ mixed carcinoma/ dedifferentiated carcinoma/ undifferentiated carcinoma; FIGO, International Federation of Gynecology and Obstetrics staging system (2018); LVSI, lymphovascular space invasion; TFI, treatment-free interval; 6m, 6 months; ICI, immune checkpoint inhibitor; Pem, pembrolizumab; Len\u0026thinsp;+\u0026thinsp;Pem, lenvatinib plus pembrolizumab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eImmune marker profiles across ProMisE molecular subtypes\u003c/h2\u003e \u003cp\u003eTo further characterize the subtype-specific immune landscapes, we compared the expression levels of individual immune markers across the ProMisE molecular subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u0026ndash;M). Quantitative analysis revealed that CD8.CT was significantly higher in the MMRd subtype than in the NSMP and p53abn subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). In contrast, CD8.IM did not differ significantly among the subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Macrophage-related markers exhibited distinct subtype-associated patterns. While CD68.CT and CD68.IM did not differ significantly across the subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF\u0026ndash;G), CD163.CT was significantly higher in the p53abn subtype than in MMRd and NSMP tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). No significant differences were observed in CD163 expression. IM (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI). The expression of immune regulatory molecules also varied according to the molecular subtype. CD47 expression was markedly elevated in p53abn tumors compared to MMRd and NSMP tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eL), whereas HLA class I and CD276 expression did not show significant subtype-dependent differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ\u0026ndash;K). PD-L1 expression, assessed by TPS, was low overall and did not differ significantly among the ProMisE subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eM).\u003c/p\u003e \u003cp\u003eCollectively, these findings demonstrate that distinct immune profiles are associated with the ProMisE molecular subtypes. In particular, the MMRd subtype is characterized by increased intratumoral CD8⁺ T-cell infiltration, whereas the p53abn subtype exhibits features of an immunosuppressive microenvironment, including an elevated CD163⁺ macrophage density and increased CD47 expression. These subtype-specific immune characteristics may contribute to the observed heterogeneity in ICI response and clinical outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSubtype-specific associations between immune markers and PFS\u003c/h2\u003e \u003cp\u003eNext, we evaluated the association between immune markers and PFS within each ProMisE molecular subtype. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026ndash;C show forest plots summarizing the HRs and 95% CIs for PFS associated with individual immune markers in the MMRd, NSMP, and p53abn cohorts. In addition, for representative immune markers that showed a significant or suggestive association with PFS, patients within each ProMisE molecular subtype were stratified into high and low expression groups based on the median value, and PFS was compared using the Kaplan\u0026ndash;Meier method (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD\u0026ndash;F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the MMRd cohort, higher CD8.CT was significantly associated with prolonged PFS (HR 0.32, 95% CI: 0.11\u0026ndash;0.94). No significant associations were observed for macrophage-related markers, immune checkpoint molecules, or PD-L1 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Kaplan\u0026ndash;Meier analysis further confirmed that patients with high CD8.CT exhibited a significantly longer PFS than those with low CD8 expression.CT (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eIn the NSMP cohort, none of the immune markers reached statistical significance in the forest plot analysis, although CD47 expression showed a trend toward an increased risk of disease progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Consistent with this observation, Kaplan\u0026ndash;Meier analysis demonstrated that patients with high CD47 expression tended to have shorter PFS than those with low CD47 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eIn the p53abn cohort, increased CD68 expression was observed. IM was significantly associated with shorter PFS (HR 2.19, 95% CI 1.07\u0026ndash;4.50), whereas CD276 expression also showed a trend toward adverse prognosis in the forest plot analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Kaplan\u0026ndash;Meier analysis confirmed that high CD68.IM was significantly associated with inferior PFS compared to low CD68 expression.IM in this subtype (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eCollectively, these analyses indicate that the prognostic relevance of immune microenvironmental factors differs substantially among the ProMisE molecular subtypes. CD8⁺ T-cell infiltration appears to play a dominant prognostic role in MMRd tumors, whereas macrophage-related immunosuppressive features are more strongly associated with disease progression in p53abn tumors, with intermediate and less pronounced effects observed in NSMP tumors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eICIs have emerged as an important therapeutic option for recurrent EC; however, their durable clinical benefits are limited to a subset of patients. Although molecular classification using the ProMisE system provides critical biological and prognostic information, it does not fully explain the heterogeneity of ICI responsiveness. In this study, we comprehensively evaluated the immune microenvironmental features quantified by IHC and digital pathology and examined their associations with ICI response and survival across and within ProMisE molecular subtypes. Our findings highlight that immune determinants differ substantially between ProMisE subtypes and that the prognostic relevance of individual immune markers is subtype-dependent.\u003c/p\u003e \u003cp\u003eConsistent with prior reports, intratumoral CD8⁺ T-cell infiltration emerged as a key determinant of favorable ICI outcomes. High CD8⁺ T-cell density was associated with improved response and prolonged PFS, reinforcing the central role of cytotoxic T-cell-mediated antitumor immunity in EC treated with ICIs. While spatial immune phenotypes based on CD8⁺ T-cell distribution further stratified prognosis, these analyses were exploratory and are presented as supplementary data only. Importantly, our results extend previous observations by demonstrating that the prognostic impact of CD8⁺ T-cell infiltration is not uniform across molecular subtypes but is most pronounced in the MMRd subtype, consistent with its hypermutated and immunogenic nature.\u003c/p\u003e \u003cp\u003eBeyond lymphocytic infiltration, our analyses underscore the importance of innate immune and myeloid-related pathways, particularly in non\u0026ndash;MMRd tumors. TAM\u0026ndash;related markers exhibited distinct subtype-specific patterns. Across the entire cohort, a higher CD68⁺ macrophage density in the IM tended to be associated with poorer outcomes, consistent with previous reports demonstrating the negative prognostic impact of TAM density in EC treated with ICIs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Notably, in our study, the prognostic relevance of CD68.IM was particularly pronounced in the p53abn subtype, in which increased macrophage infiltration was significantly associated with shortened PFS. This finding suggests that macrophage-driven immunosuppression may play a dominant role in shaping treatment resistance in p53abn tumors, which are known to harbor aggressive biological features and poor clinical outcomes.\u003c/p\u003e \u003cp\u003eCD47 expression further highlights the heterogeneity of immune regulation across the ProMisE subtypes. Although high CD47 expression did not show a statistically significant association with PFS in the overall cohort, it was enriched in ICI non-responders and demonstrated subtype-specific prognostic trends. While high CD47 expression tended to be associated with shorter PFS in the NSMP subtype, its expression levels were highest overall in p53abn tumors, indicating that innate immune evasion via the CD47\u0026ndash;SIRPα axis may be a defining feature of this subtype. CD47 is recognized as a \u0026ldquo;don\u0026rsquo;t-eat-me\u0026rdquo; signal that inhibits macrophage-mediated phagocytosis and facilitates immune evasion [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Elevated CD47 expression has been associated with ICI resistance in multiple malignancies. Our findings further support CD47 as a key immunosuppressive marker with both prognostic and therapeutic significance in EC. Importantly, recent evidence suggests that CD47 may be a promising therapeutic target for EC [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast, PD-L1 expression showed limited associations with immune contexture, molecular subtype, or clinical outcome in the present cohort. The PD-L1 positivity rate in our cohort was lower than that reported in previous studies [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Prior reports have indicated that PD-L1 expression in EC is variable but generally modest, with higher expression typically observed in MMRd or POLEmut tumors [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The low positivity observed in this study may reflect the predominance of NSMP and p53abn tumors, both of which are historically associated with lower PD-L1 expression. Moreover, PD-L1 did not emerge as a strong predictor of ICI responsiveness in our analysis, further underscoring its limitations as a standalone biomarker, consistent with previous findings.\u003c/p\u003e \u003cp\u003eInterestingly, no POLEmut subtype was identified in this cohort. POLEmut subtypes are known to exhibit pronounced immune activation and exceptionally favorable prognoses, with rare recurrence [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Although ICI efficacy is also anticipated in this subtype, the absence of POLEmut tumors in our cohort, which consisted exclusively of recurrent cases, likely reflects their intrinsic biological behavior, particularly their strong resistance to recurrence.\u003c/p\u003e \u003cp\u003eTaken together, our subtype-specific analyses revealed that both the immune composition and its prognostic significance varied substantially across the ProMisE molecular classes. In MMRd tumors, cytotoxic T-cell infiltration is the dominant favorable determinant, whereas in p53abn tumors, macrophage-related and innate immune suppressive pathways appear to exert a stronger influence on the disease progression. NSMP tumors exhibit an intermediate phenotype with less pronounced but potentially targetable immunosuppressive features. These findings suggest that differences in immune regulation between ProMisE subtypes, rather than immune markers in isolation, are critical determinants of ICI outcomes. Accordingly, integrating ProMisE classification with immune profiling provides a more refined framework for identifying patients who are most likely to benefit from ICIs and supports the development of subtype-tailored immunotherapeutic strategies.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study had several limitations. First, the sample size was modest, especially within individual molecular subtypes, which may have limited the statistical power and generalizability of the results. Larger multi-institutional cohorts are needed to validate these findings. Second, the analysis was based primarily on tumor samples obtained at the initial surgery rather than at recurrence or at the time of ICI initiation. Tumor evolution and treatment-induced changes may alter the immune microenvironment over time, potentially affecting the performance of biomarkers. Third, although we assessed multiple immune markers using IHC and digital pathology, functional validation and spatial transcriptomic analysis were not performed. Future studies incorporating spatial transcriptomics or single-cell approaches may provide deeper mechanistic insights into tumor-immune interactions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study demonstrates that immune microenvironmental features interact with ProMisE molecular subtypes in a complex and subtype-specific manner, influencing ICI outcomes in recurrent EC. While the ProMisE classification provides essential molecular context, integrating immune marker profiling\u0026mdash;particularly markers of cytotoxic T-cell infiltration and innate immune suppression\u0026mdash;enables a more refined stratification of patients. These findings support the development of subtype-tailored immunotherapeutic strategies and highlight the importance of molecular\u0026ndash;immunologic integration for precision immunotherapy in EC. Future studies should build on these findings to develop integrated predictive models and to explore therapeutic approaches targeting immunosuppressive pathways, with the goal of achieving more precise and effective immunotherapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003e This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Nagoya University (approval number: 2018\u0026thinsp;\u0026minus;\u0026thinsp;0400).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003e Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publish\u003c/strong\u003e \u003cp\u003e The authors affirm that human research participants provided informed consent for publication of the images in all Figures.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK.K. conceived and designed the study, collected and curated the clinical data, performed the immunohistochemical and statistical analyses, and drafted the manuscript. N.Y. and S.H. contributed to study design, data interpretation, and critical revision of the manuscript. Y.S., Y.K., F.U., K.S., E.W. and S.S. contributed to pathological evaluation and interpretation of immunohistochemical findings. W.L., K.N. and M.K. contributed to data acquisition and clinical data management. N.Y. supervised the overall study, provided conceptual guidance, and critically revised the manuscript for important intellectual content. All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study was supported by JSPS KAKENHI (grant number 25K12631).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Front Oncol 15:1633260. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fonc.2025.1633260\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2025.1633260\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Endometrial cancer, Immune checkpoint inhibitors, Tumor immune microenvironment, ProMisE molecular classification, CD8⁺ T cells","lastPublishedDoi":"10.21203/rs.3.rs-8733104/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8733104/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eImmune checkpoint inhibitors (ICIs) are an important treatment option for recurrent endometrial cancer (EC); however, responses vary widely. Although the ProMisE molecular classification provides prognostic and predictive insights, it does not fully explain the heterogeneity of ICI outcomes. Further characterization of the tumor immune microenvironment (TIME) is required to refine biomarker development.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe retrospectively analyzed 72 patients with recurrent EC treated with ICIs. Immune markers, including CD8, CD68, CD163, CD47, CD276, PD-L1, and HLA class I, were quantified using digital pathology. Associations between immune markers, ICI response, and progression-free survival (PFS) were evaluated overall and within ProMisE molecular subtypes.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eResponders exhibited a more immunologically active TIME, characterized by increased CD8⁺ T-cell infiltration in the central tumor (CT), whereas non-responders showed enriched CD68⁺ tumor-associated macrophages (TAMs) at the invasive margin (IM) and higher CD47 expression. Distinct immune landscapes were observed across ProMisE subtypes. Mismatch repair-deficient (MMRd) tumors showed high CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration, no specific molecular profile (NSMP) tumors exhibited intermediate immune activation but variable CD47 expression, and p53-abnormal (p53abn) tumors demonstrated the most immunosuppressive profile with high CD47 expression and TAM density. Subtype-specific analyses revealed that PFS was predicted by different immune determinants, including CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration in MMRd, CD47 expression level in NSMP, and CD68IM in p53abn.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eTumor immune phenotypes provide predictive information beyond molecular classification alone. Integrating ProMisE classification with immunological profiling enables more precise stratification of ICI benefits. Distinct TIME features across subtypes underscore the need for subtype-tailored immunotherapeutic strategies.\u003c/p\u003e","manuscriptTitle":"Integrated Immunophenotypic and ProMisE Molecular Profiling as Predictors of Immune Checkpoint Inhibitor Response in Endometrial Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 16:03:49","doi":"10.21203/rs.3.rs-8733104/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-23T21:29:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-20T08:59:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-08T22:42:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17735652910607351612567927755772349766","date":"2026-02-05T17:35:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144323337497027005905266255032758586069","date":"2026-02-01T18:17:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205714360789087593212747186783299611635","date":"2026-01-30T23:22:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-30T17:04:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-30T15:21:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-30T15:19:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Immunology, Immunotherapy","date":"2026-01-29T14:53:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"962e45a0-d967-4398-8b9f-f5bc1c0feaf5","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T01:53:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 16:03:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8733104","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8733104","identity":"rs-8733104","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}